Measure parameter shell geometry in paired Qwen3.5-2B checkpoints

Reviewed-on: #8

.gitattributes

@@ -4,3 +4,6 @@ results/** -text
 results/**/*.csv -whitespace
 results/**/*.svg -whitespace
 results/**/*.log -whitespace
+
+# Preserve captured scientific stdout byte-for-byte, including blank final lines.
+results/head-dominance/*-output.txt -whitespace

README.md

@@ -1,5 +1,9 @@
 # Model parameter similarity experiments
 
+**[研究图册与固定在线副本](docs/FIGURE_GALLERY.md)**：连续分布、联合消融及原始PDF/SVG。
+
+[两个实际2B检查点的参数球壳结论](docs/MODEL_SHELL_FINDINGS.md) · [完整数值报告](results/model-shell/REPORT.md) · [固定分组和均匀参照协议](model_shell/PROTOCOL.md)。
+
 Research on repeated learned functions in **Qwen3.5-2B**: a completed static weight baseline
 and a training-free CPU functional pilot. The CPU study found similar existing RMSNorm
 functions at layer indices 12/13, with a uniform analytic error bound and heldout substitution checks.
@@ -38,3 +42,20 @@ block scales are alternative partitions and their savings must not be summed.
 
 Dependencies are pinned in `requirements.txt`; install only prebuilt wheels on the remote host.
 Full resolved versions and run provenance accompany each result. See the SOP for commands.
+
+## CPU block activation measurements
+
+See [measurement definitions](activation/README.md) and [observed activation report](results/block-activation/REPORT.md) for all 24 layers, contiguous output-channel blocks, exact/near-zero rates and output energy concentration. This is an observational SiLU-gated study, not a pruning or reuse quality claim.
+
+## Adjacent-layer head influence concentration
+
+The CPU study measures all 23 adjacent transitions, observation-wise effective
+head counts and Q/K/V influence shares, with equal-size weak/strong/random joint
+removal controls. See [definitions and reproduction](dominance/README.md),
+[fixed protocol](dominance/PROTOCOL.md), and [measured report](results/head-dominance/REPORT.md).
+These are descriptive intervention effects, not additive contribution probabilities
+or validated final-LM pruning gains.
+
+## Existing GSM8K task-finetuning comparison
+
+[Paired study and reproduction](finetune_compare/README.md) · [Fixed protocol](finetune_compare/PROTOCOL.md) · [Results](results/finetune-comparison/REPORT.md). This compares an existing pure-LoRA GRPO checkpoint to exactly the same original release; no training is performed here.

activation/README.md

@@ -0,0 +1,45 @@
+# Observed block activation, CPU only
+
+Measures the retained Qwen3.5-2B checkpoint without training or substitutions.
+This model uses SiLU-gated MLPs and sigmoid attention gates, not ReLU.
+Leaky ReLU also does not generally create exact zero on negative inputs.
+
+The probe records every linear projection in all 24 layers, plus MLP SiLU
+and gate-times-up intermediates and full-attention sigmoid gates. It does not
+record every internal recurrent state, convolution output or attention probability.
+A block means contiguous **output channels** (64 or 128), hence contiguous
+rows of a projection matrix; it is not a Transformer layer or arbitrary 2D tile.
+MLP gated-product channels correspond to columns consumed by down_proj.
+It is incorrect to call an individual weight active/inactive based only on its value.
+
+For every sample, module and block, metrics.csv reports:
+- exact zero and negative element fractions;
+- near-zero fractions at |x| <= 0.01 RMS and <= 0.1 RMS;
+- fraction of channels ever above 0.01 RMS over the 32 tokens;
+- minimum fraction of channels carrying 90% squared-output energy.
+
+RMS is computed within each observed sample/module/block; width=0 records the
+whole module. Thus thresholds are explicitly relative and are not shared absolute
+cutoffs across layers or blocks. Activation rate at a threshold is 1-near-zero rate.
+A small value or an energy-concentrated output does not prove that a block can
+be skipped, removed or shared without affecting LM quality. Exact zero is measured
+in this FP32 streaming implementation of BF16 source weights; other execution dtypes
+can have different underflow rates.
+
+Four prior authored Chinese/English texts, 32 tokens each, descriptive measurements
+only. No training, fresh-generalization claim, GPU, weight download or cleanup.
+The existing streaming implementation was checked against the official implementation
+in the earlier CPU study; this experiment changes only observation, not model equations.
+
+Remote commands (authorized server 8.211.154.134):
+```
+OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 venv/bin/python -B -m activation.run --source runs/cpu-qwen35-2b --out runs/NEW_ACTIVATION_DIR
+OPENBLAS_NUM_THREADS=1 venv/bin/python -B -m unittest tests.test_activation -v
+venv/bin/python -B activation/report.py runs/NEW_ACTIVATION_DIR
+```
+
+run.py measures; report.py plots saved rows and derives aggregates;
+__init__.py marks the package; tests/test_activation.py checks exact sparsity,
+ever-active behavior, all-zero inputs and scale invariance. Raw activations are
+not saved or needed for these descriptive statistics. Existing caches and weights
+are retained untouched. Full sample token IDs and CPU time/RSS appear in summary.json.

activation/audit.py

@@ -0,0 +1,28 @@
+"""Audit recorded scope, metrics and reproducibility files; no model execution."""
+import csv,json,hashlib
+from pathlib import Path
+import sys
+p=Path(sys.argv[1]);r=list(csv.DictReader((p/'metrics.csv').open()));s=json.loads((p/'summary.json').read_text())
+assert s['completed'] and len(r)==s['rows']==4*20904
+assert len({x['sample'] for x in r})==4
+assert {int(x['module'].split('.')[1]) for x in r}==set(range(24))
+for x in r:
+    for k in ['zero_rate','negative_rate','near_001_rms','near_01_rms','ever_active_001','energy90_channel_fraction']:assert 0<=float(x[k])<=1,(x,k)
+    assert float(x['zero_rate'])<=float(x['near_001_rms'])<=float(x['near_01_rms'])
+    assert int(x['channels'])==int(x['stop'])-int(x['start'])
+a=json.loads((p/'aggregates.json').read_text());lines=['# 实测激活率：SiLU 门控模型','', '覆盖24层、4条中英文文本，每条32 token；所有投影输出及 MLP 门控中间量，按64/128输出通道分块。','', '| 输出 | 精确零比例均值 | 0.01 RMS以下均值 | 0.1 RMS以下均值 | 32 token内曾活跃通道比例均值 | 90%能量所需通道比例均值 |','|---|---:|---:|---:|---:|---:|']
+for k,v in a.items():
+    cols=[v[m]['mean']*100 for m in ['zero_rate','near_001_rms','near_01_rms','ever_active_001','energy90_channel_fraction']]
+    lines.append('| '+k+' | '+' | '.join(f'{z:.4f}%' for z in cols)+' |')
+blocks=[x for x in r if x['width']=='128']; dead=sum(float(x['zero_rate'])==1 for x in blocks)
+lines+=['',f'128通道块×文本观测共{len(blocks)}条，整个32 token窗口全为精确零的块观测数为{dead}。', '', '表中是整模块逐文本统计再取均值；每个分块的独立统计见 metrics.csv。阈值是各自RMS的比例；阈值下激活率=1−近零比例。能量集中不等于可删除或可替换。', '', '仅测该模型、四条短文本；不推断所有语言/长上下文。SiLU不把负值整体截零，负数不等于静默。未执行删块/跳算的质量验证。权重和既有缓存全部保留。', '',f"CPU测量耗时{s['elapsed_seconds']:.1f}秒，峰值RSS{s['peak_rss_kib']/1024:.1f} MiB。"]
+(p/'REPORT.md').write_text('\n'.join(lines)+'\n')
+lines_extra=[]
+for suffix in ['mlp.gated_product','self_attn.q_proj.output','self_attn.k_proj.output','self_attn.v_proj.output']:
+    rr=[x for x in blocks if x['module'].endswith(suffix)]
+    act=sorted(1-float(x['near_01_rms']) for x in rr)
+    lines_extra.append(f'{suffix}: 128通道块在0.1 RMS阈值上的激活率范围 {100*act[0]:.2f}%–{100*act[-1]:.2f}%。')
+with (p/'REPORT.md').open('a') as f:f.write('\n'+'\n'.join(lines_extra)+'\n')
+(p/'audit.json').write_text(json.dumps({'passed':True,'rows':len(r),'layers':24,'samples':4,'all_zero_128_blocks':dead},indent=2))
+files=[x for x in p.rglob('*') if x.is_file() and x.name!='checksums.json'];(p/'checksums.json').write_text(json.dumps({str(x.relative_to(p)):hashlib.sha256(x.read_bytes()).hexdigest() for x in sorted(files)},indent=2))
+print((p/'REPORT.md').read_text())

activation/report.py

@@ -0,0 +1,38 @@
+"""Render scientific figures from recorded CPU measurements."""
+import csv,json
+from pathlib import Path
+import numpy as np
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+import sys
+root=Path(sys.argv[1]); rows=list(csv.DictReader((root/'metrics.csv').open()))
+full=[r for r in rows if r['width']=='0']
+fig,axs=plt.subplots(1,3,figsize=(15,4.5),layout='constrained')
+for suffix,label in [('gate_proj.output','gate preactivation'),('silu_gate','SiLU gate'),('gated_product','gated product')]:
+    selected=[r for r in full if r['module'].endswith('mlp.'+suffix)]
+    for ax,key in zip(axs,['zero_rate','near_001_rms','near_01_rms']):
+        xs=[];ys=[]
+        for layer in range(24):
+            z=[float(r[key]) for r in selected if r['module'].startswith(f'layers.{layer}.')];xs.append(layer);ys.append(np.mean(z))
+        ax.plot(xs,ys,label=label)
+for ax,title in zip(axs,['Exact zero fraction','Near zero: |x| <= 0.01 RMS','Near zero: |x| <= 0.1 RMS']):
+    ax.set(title=title,xlabel='Layer index',ylabel='Fraction');ax.set_ylim(bottom=0);ax.grid(alpha=.2)
+axs[-1].legend();fig.suptitle('Observed MLP activation sparsity: four texts, 32 tokens each; no training')
+root.joinpath('figures').mkdir(exist_ok=True)
+for ext in ('png','pdf','svg'):fig.savefig(root/'figures'/('mlp_activation.'+ext),dpi=180)
+plt.close(fig)
+blocks=[r for r in rows if r['width']=='128' and r['module'].endswith('mlp.gated_product')]
+fig,axs=plt.subplots(1,2,figsize=(12,4.5),layout='constrained')
+for ax,key,title in zip(axs,['near_01_rms','energy90_channel_fraction'],['Near-zero fraction at 0.1 block RMS','Fraction of channels carrying 90% energy']):
+    matrix=[]
+    for layer in range(24):
+        rr=[r for r in blocks if r['module'].startswith(f'layers.{layer}.')]; starts=sorted(set(int(r['start']) for r in rr));matrix.append([np.mean([float(r[key]) for r in rr if int(r['start'])==s]) for s in starts])
+    im=ax.imshow(matrix,aspect='auto',vmin=0,vmax=1);ax.set(title=title,xlabel='Contiguous 128-channel block',ylabel='Layer index');fig.colorbar(im,ax=ax)
+fig.suptitle('MLP gated-product blocks: average over four observed texts')
+for ext in ('png','pdf','svg'):fig.savefig(root/'figures'/('block_activity.'+ext),dpi=180)
+agg={}
+for suffix in ['mlp.gate_proj.output','mlp.silu_gate','mlp.gated_product','self_attn.q_proj.output','self_attn.k_proj.output','self_attn.v_proj.output']:
+    rr=[r for r in full if r['module'].endswith(suffix)]
+    agg[suffix]={'observations':len(rr),**{key:{'min':min(float(r[key]) for r in rr),'mean':float(np.mean([float(r[key]) for r in rr])),'max':max(float(r[key]) for r in rr)} for key in ['zero_rate','negative_rate','near_001_rms','near_01_rms','ever_active_001','energy90_channel_fraction']}}
+(root/'aggregates.json').write_text(json.dumps(agg,indent=2))

activation/run.py

@@ -0,0 +1,48 @@
+"""CPU-only observed activation sparsity; no checkpoint changes or training."""
+import json,csv,time,resource,hashlib
+from pathlib import Path
+import numpy as np
+from tokenizers import Tokenizer
+from functional.model import StreamingLM,silu,sigmoid
+
+
+def metrics(x):
+    x=np.asarray(x,dtype=np.float64).reshape(-1,x.shape[-1]); a=np.abs(x); energy=np.square(x).sum(0); total=energy.sum()
+    rms=float(np.sqrt(np.mean(x*x))); cutoff=.01*rms
+    ordered=np.sort(energy)[::-1]; n90=int(np.searchsorted(np.cumsum(ordered),.9*total)+1) if total else 0
+    return dict(elements=x.size,channels=x.shape[-1],rms=rms,zero_rate=float(np.mean(x==0)),negative_rate=float(np.mean(x<0)),near_001_rms=float(np.mean(a<=cutoff)),near_01_rms=float(np.mean(a<=.1*rms)),ever_active_001=float(np.mean(np.any(a>cutoff,axis=0))),channels_for_90pct_energy=n90,energy90_channel_fraction=n90/x.shape[-1])
+
+class Probe(StreamingLM):
+    def record(self,x,name):
+        for width in (0,64,128):
+            for start in range(0,x.shape[-1],width or x.shape[-1]):
+                stop=min(start+(width or x.shape[-1]),x.shape[-1]); z=x[...,start:stop]
+                self.rows.append(dict(sample=self.sample,module=name,width=width,start=start,stop=stop,**metrics(z)))
+    def linear(self,x,name,rows=256):
+        y=super().linear(x,name,rows); self.record(y,name+'.output')
+        if name.endswith('self_attn.q_proj'):
+            dim=self.config['head_dim']; mixed=y.reshape(*y.shape[:-1],-1,2*dim)
+            self.record(sigmoid(mixed[...,dim:]).reshape(*y.shape[:-1],-1),name+'.sigmoid_gate')
+        return y
+    def mlp(self,h,layer):
+        p=f'layers.{layer}.mlp.'; pre=self.linear(h,p+'gate_proj'); g=silu(pre); u=self.linear(h,p+'up_proj')
+        self.record(g,p+'silu_gate'); self.record(g*u,p+'gated_product')
+        return self.linear(g*u,p+'down_proj')
+
+def main():
+    import argparse
+    p=argparse.ArgumentParser();p.add_argument('--source',type=Path,required=True);p.add_argument('--out',type=Path,required=True);a=p.parse_args();a.out.mkdir(parents=True,exist_ok=False)
+    started=time.monotonic();cfg=json.loads((a.source/'model_config.json').read_text()); lm=Probe(a.source/'weights',cfg,seconds=3600);lm.rows=[]
+    samples=json.loads(Path('crossqkv/fresh_samples.json').read_text())['samples'];tok=Tokenizer.from_file(str(a.source/'tokenizer.json'))
+    for s in samples:
+        lm.sample=s['id']; ids=tok.encode(s['text']).ids[:32];s['token_ids']=ids
+        lm.forward(np.array([ids],dtype=np.int64))
+        first=not (a.out/'metrics.csv').exists()
+        with (a.out/'metrics.csv').open('a') as f:
+            w=csv.DictWriter(f,fieldnames=list(lm.rows[0]))
+            if first:w.writeheader()
+            w.writerows(lm.rows)
+        print('completed',s['id'],len(lm.rows),flush=True);lm.rows.clear()
+    summary={'completed':True,'elapsed_seconds':time.monotonic()-started,'peak_rss_kib':resource.getrusage(resource.RUSAGE_SELF).ru_maxrss,'config_activation':lm.config.get('hidden_act'),'layer_count':len(lm.config['layer_types']),'samples':samples,'rows':sum(1 for _ in (a.out/'metrics.csv').open())-1,'block_widths':[64,128],'threshold_reference':'RMS within each sample/module/block, thresholds 0.01 and 0.1; exact zero measured separately','training':False,'weights_deleted':False,'limits':['4 short texts, 32 tokens each, descriptive only','Block is contiguous output channels, not Transformer block','Small output does not prove removable computation or parameter interchangeability','All samples previously used; no generalization claim','Relative threshold differs between blocks; width=0 is whole module reference']}
+    (a.out/'summary.json').write_text(json.dumps(summary,indent=2))
+if __name__=='__main__':main()

docs/FIGURE_GALLERY.md

@@ -0,0 +1,57 @@
+# 研究图册：Qwen3.5-2B 参数几何与头贡献
+
+这份图册与原始PNG/PDF/SVG一同保存在Git中。头贡献基线图片已随PR #6合并到main；其固定提交链接不依赖聊天附件或本机路径。后续实验分别列于下方，可通过图册所在的Git提交查看对应版本。
+
+原始结果提交：`098ea8650c7d0e06339c66e3a0509e38b85b3d7d`；模型revision：`15852e8c16360a2fea060d615a32b45270f8a8fc`。
+
+| 基线指标（逐观测中位数） | QKV复合变化 | √JS检索距离 |
+|---|---:|---:|
+| 有效头数÷总头数 | 76.0% | 55.8% |
+| 最强25%头的影响份额 | 44.6% | 56.5% |
+| 最弱25%头的影响份额 | 10.6% | 6.8% |
+
+**√JS有效头数：8.931822/16；P10=5.784081，P90=11.672658。** 这是消融影响的集中程度，不是注意力概率质量。原基线为4条文本各32 token，普通注意力接收层的query7–31。
+
+## 连续分布与尺度敏感性
+
+![连续分布与尺度敏感性](../results/head-dominance/figures/retrieval_scale_sensitivity.png)
+
+[PNG固定提交副本](https://gitea.entroia.com/Zayden-Jung/exp-model-parameter-similarity/raw/commit/098ea8650c7d0e06339c66e3a0509e38b85b3d7d/results/head-dominance/figures/retrieval_scale_sensitivity.png)
+[PDF固定提交副本](https://gitea.entroia.com/Zayden-Jung/exp-model-parameter-similarity/raw/commit/098ea8650c7d0e06339c66e3a0509e38b85b3d7d/results/head-dominance/figures/retrieval_scale_sensitivity.pdf)
+[SVG固定提交副本](https://gitea.entroia.com/Zayden-Jung/exp-model-parameter-similarity/raw/commit/098ea8650c7d0e06339c66e3a0509e38b85b3d7d/results/head-dominance/figures/retrieval_scale_sensitivity.svg)
+
+## 联合强弱头消融
+
+![联合强弱头消融](../results/head-dominance/figures/joint_removal_effects.png)
+
+[PNG固定提交副本](https://gitea.entroia.com/Zayden-Jung/exp-model-parameter-similarity/raw/commit/098ea8650c7d0e06339c66e3a0509e38b85b3d7d/results/head-dominance/figures/joint_removal_effects.png)
+[PDF固定提交副本](https://gitea.entroia.com/Zayden-Jung/exp-model-parameter-similarity/raw/commit/098ea8650c7d0e06339c66e3a0509e38b85b3d7d/results/head-dominance/figures/joint_removal_effects.pdf)
+[SVG固定提交副本](https://gitea.entroia.com/Zayden-Jung/exp-model-parameter-similarity/raw/commit/098ea8650c7d0e06339c66e3a0509e38b85b3d7d/results/head-dominance/figures/joint_removal_effects.svg)
+
+[完整基线报告](../results/head-dominance/REPORT.md) · [全部图表目录](../results/head-dominance/figures) · [图册SHA清单](figure-gallery-manifest.json)
+
+使用√JS距离进行微调前后配对比较；保留JS散度作为尺度敏感性参考，避免混淆平方尺度与距离尺度。
+
+## GSM8K微调前后配对图
+
+[配对报告](../results/finetune-comparison/REPORT.md) · [CDF](../results/finetune-comparison/figures/paired_effective_heads_cdf.png) · [逐层分布](../results/finetune-comparison/figures/paired_effective_heads_by_layer.png) · [配对变化](../results/finetune-comparison/figures/paired_effective_heads_delta.png)
+
+同目录保留PDF/SVG。图表随代码、原始数据和SHA清单一同提交，不依赖聊天附件。
+
+## 高维壳与稀疏性对照
+
+![稀疏球壳对照](../results/sparse-shell/sparse_shell_controls.png)
+
+[条件定理、反例与数据说明](../results/sparse-shell/REPORT.md) · [PDF](../results/sparse-shell/sparse_shell_controls.pdf) · [SVG](../results/sparse-shell/sparse_shell_controls.svg)
+
+## 两个实际2B检查点的参数球壳检验
+
+[结论与解释](MODEL_SHELL_FINDINGS.md) · [实测报告、分组数量与限制](../results/model-shell/REPORT.md) · [固定协议](../model_shell/PROTOCOL.md) · [指标源表](../results/model-shell/metrics.csv) · [校验清单](../results/model-shell/audit.json)
+
+![内侧质量对均匀参照的敏感性](../results/model-shell/figures/interior_reference_sensitivity.png)
+
+![径向分布的内外两尾](../results/model-shell/figures/radial_shape_two_tails.png)
+
+![固定角色的径向CDF示例](../results/model-shell/figures/fixed_role_radial_cdfs.png)
+
+[微调前后配对图](../results/model-shell/figures/finetune_paired_radii.png) · [有限样本参与率](../results/model-shell/figures/participation_reference.png)。每幅图同目录均保存PDF/SVG，完整CDF及参照数据保存在`profiles.json.gz`，实测范数保存在`measured_radii.npz`；均随本次成果进入Git。

docs/MODEL_SHELL_FINDINGS.md

@@ -0,0 +1,50 @@
+# 实际2B参数：比均匀参照更靠里吗？
+
+**在明确的“校准最大半径以内”口径下，是；但数据同时显示更宽的径向分布，不能概括为整团参数向内收缩。此次GSM8K微调几乎没有改变这些几何指标。**
+
+这轮直接读取了保留的Qwen3.5-2B权重和同一底座的已有纯LoRA GRPO检查点，在8.211.154.134上使用CPU完成。没有训练、推理或删除权重。
+
+## 一个模型有多少组高维分布？
+
+按事先固定的参数矩阵行／列与语言注意力头视图规则，每个模型有 **1,558组**：554个矩阵视图、1,004个按头视图。它们有重叠，不是模型固有的唯一分布数量。
+
+主要汇总使用1,521个样本充足、未抽取坐标的视图。另有36个小样本视图和1个坐标抽样视图，全部单列并保留数据。整个检查点包括语言、视觉与MTP，共632个参数张量；319个一维张量和18个低维卷积核不满足这次“高维向量云”的规则。
+
+## 有更多内侧质量，但也有更长的外尾
+
+每组最多512个参数向量，一半校准中心及半径，另一半测量。对照也使用同样的校准规则；没有用所有向量的最大范数事后包住全部数据。
+
+以原点为中心，比较归一化半径 `r / R_cal <= 0.9`：
+
+|视图|组数|相对均匀球的内侧质量差中位数|相对匹配均值、逐坐标方差的均匀盒的质量差中位数|
+|---|---:|---:|---:|
+|矩阵行／列|517|+98.0个百分点|+97.7个百分点|
+|按头|1,004|+97.3个百分点|+97.3个百分点|
+
+改用校准均值作为中心，结果仍有约96.9–97.7个百分点的差异。因此，这个现象并非仅来自中心放在原点。
+
+但最大半径很容易受到少数大范数向量影响。换成各自的校准RMS尺度，并比较完整CDF后，**93.8%的矩阵视图和87.8%的头视图同时存在超过10个百分点的内尾、外尾差异**。去均值后分别为94.4%和86.4%。这说明两尾同时增加是普遍现象，比“所有向量整体更靠内”更符合这些数据。
+
+![最大半径与RMS尺度下的固定角色示例](../results/model-shell/figures/fixed_role_radial_cdfs.png)
+
+这三组示例按模块角色固定：GDN的Q头、全注意力的Q头、MLP上投影行。上排用校准最大半径，下排用RMS；它们只是帮助读图，不代替全量统计。
+
+## 是否说明稀疏或低维？
+
+还不能如此推断。采样视图的逐组精确零值率中位数为0；点坐标能量参与率除以维度的中位数约0.31。径向内侧质量、坐标能量集中和样本间的有效维数是不同的测量对象。
+
+本轮同时记录了有限样本Gram参与率，并与同样本数的均匀参照比较。它受校准样本秩上限限制，不能作为真实内在维度的直接估计。盒参照也只匹配边际范围或逐坐标矩，并未匹配相关结构。因此，本轮没有单独识别“稀疏支持”导致的效应。
+
+## 微调前后改变了吗？
+
+LoRA实际改变72个投影矩阵，影响972个视图，其中936个属于主要汇总。只对这些受影响的主要视图配对，以免大量未改动参数稀释差异。
+
+原点中心下，逐组中位 `r/R_cal` 的配对变化中位数为 **6.30×10⁻⁷**，最大绝对变化 **4.03×10⁻⁴**。去均值后的结论相近。90%半径质量差在原点中心口径下未跨越当前经验CDF的分辨率；这不表示两套权重完全相同。
+
+因此，这两个检查点不能支持“微调后、更好的模型更靠内”的结论；本轮也没有测任务质量。参数几何中的宽径向结构可以继续挖掘，但它本身还不是功能重复或可互相替代的证据。
+
+## 数据与核验
+
+[完整数值报告](../results/model-shell/REPORT.md) · [冻结协议](../model_shell/PROTOCOL.md) · [复现方式](../model_shell/README.md) · [指标CSV](../results/model-shell/metrics.csv) · [摘要JSON](../results/model-shell/summary.json) · [审计SHA](../results/model-shell/audit.json)
+
+6,232个模型×中心×视图组合全部核验；CDF从保留的校准／留出范数复算，检查有限样本秩上限，原模型和适配器SHA均与既有记录一致。远程4项测试通过，覆盖索引、LoRA读取、中心与尺度、均匀球校准和稀疏贴壳反例。图表、完整CDF及对照摘要、实测范数一同保存。结论为描述性统计：视图相关且重叠，没有将其当作独立重复实验进行显著性检验。

docs/figure-gallery-manifest.json

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+{
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+    {
+      "path": "results/head-dominance/figures/joint_removal_effects.png",
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+    {
+      "path": "results/head-dominance/figures/joint_removal_effects.pdf",
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+    },
+    {
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+    }
+  ]
+}

dominance/PROTOCOL.md

@@ -0,0 +1,76 @@
+# Head influence concentration: measurement protocol
+
+Target: quantify the statistical distribution of large and small upstream head
+influences on the next layer's Q/K/V; identity of dominant heads may vary at each
+sample, query token and recipient head. No training, GPU, deletion or model edits.
+
+Scope: all 23 adjacent layer transitions of the retained Qwen3.5-2B text model,
+including 16-head linear attention and 8-head full attention. Measure effective
+Q/K/V, and retrieval probability changes where the recipient uses full attention.
+The intervening source-layer residual/MLP and recipient input normalization must
+be recomputed after interventions. Residual stream is preserved: removing an
+attention head is not removing all information reaching the recipient.
+
+For each observation, remove one upstream attention head's projected contribution
+at a time. Record nonnegative influence magnitude d_i and its absolute scale.
+When sum(d_i)>0, p_i=d_i/sum(d_i) is normalized intervention influence, NOT an
+additive causal attribution or a native attention probability over upstream heads.
+All-zero influence has undefined concentration and must be flagged, not assigned
+uniform or concentrated values. Record small total influences explicitly.
+
+Primary continuous measures:
+- Effective head count: N_eff=1/sum(p_i^2), range [1,H].
+- Effective head fraction: N_eff/H, supports different H.
+- Normalized concentration: C=(sum(p_i^2)-1/H)/(1-1/H), range [0,1].
+These are related summaries, not independent evidence. Report distributions over
+texts/tokens/recipient heads/layers, not only concentration of globally averaged
+influences. Primary query positions 7 onward avoid trivial short-prefix attention.
+
+Secondary intuitive measures: top1/top25%/top50% influence share, bottom25%
+share, and number of heads reaching 90% influence. Preserve raw influence tables.
+No RMS activity threshold is used to define influence concentration.
+
+Joint interventions are required: per observation's weak-head set and strong-head
+set must be removed together and measured, because individual ablations are not
+additive and may conceal cancellation or synergy. Rankings may be input-specific;
+these experiments cannot imply a universally removable fixed head set. Distinguish
+local next-layer QKV/probability effects from unmeasured final LM quality.
+
+CPU measurements, scientific plots, tests and audits execute on the previously
+authorized server 8.211.154.134. All weights and existing caches remain retained.
+
+## Fixed execution details (before real-model measurements)
+
+Four texts are reused from crossqkv/fresh_samples.json (two Chinese/two English),
+32 tokens each; no independent generalization claim. All tokens are computed,
+queries7..31 are primary statistical observations. The observer caches source
+residual inputs and exact per-head projected output components; their sum is
+checked against the native branch output. First text's full forward is checked
+against the canonical streaming forward, and every transition has a null-mask
+control. No cached tensor is deleted.
+
+For each query/recipient query-head, individual influence is the L2 change divided
+by that same baseline vector's L2 (floor1e-12). Q,K,V are analyzed separately.
+A declared balanced score sqrt((relative_Q^2+relative_K^2+relative_V^2)/3) is also
+analyzed; it does not treat their different physical scales as directly additive.
+Full attention: Q/K are measured after learned norm/partial RoPE, V before output
+mixing, and actual softmax retrieval JS is measured. Linear attention: Q/K/V are
+after causal convolution+SiLU; Q/K include the delta-rule L2 normalization and Q
+scale. It has no ordinary softmax retrieval distribution. Full-attention GQA K/V
+are repeated to query-head slots for aligned joint tests; these slots are not
+independent KV heads, and statistical observations are not independent samples.
+
+Joint weak-half/strong-half/random-half/all-head removal uses a separate coherent
+sequence for each recipient query-head. At each source token, choose the donor
+half by that recipient query-head's QKV influence ranking at the same token.
+Recompute full recipient sequence; convolution and QK retrieval can therefore
+propagate changes between positions. Only the intended recipient head is scored
+for each scenario. This is an input-adaptive diagnostic, not a computationally
+free head-selection policy. Random uses fixed seed31000+sample_index*100+layer.
+Compare joint effects to sums of corresponding single effects but do not assume
+additivity. For JS use the same joint sets (selected by QKV, not by JS).
+
+No success criterion assumes that dominance exists. Report effective-head and
+absolute-effect distributions even if contributions are diffuse. No final-LM
+NLL or deployable pruning quality claim. Scientific output includes all raw
+single/joint effects and deletion masks; large baseline caches remain external.

dominance/README.md

@@ -0,0 +1,75 @@
+# Statistical head influence dominance (CPU)
+
+This study answers how concentrated the **distribution of measured upstream-head
+influences** is, without assuming that dominant head identities are fixed across
+texts, positions or recipient heads. Read [registered protocol](PROTOCOL.md).
+
+- `core.py`: normalized effective Q/K/V extraction for both attention types,
+  source projected head decomposition, exact nonlinear downstream recomputation,
+  coherent masks, continuous concentration and JS.
+- `run.py`: four fixed prior texts, all23 adjacent connections, single-head and
+  weak/strong/random-half/all-head interventions, per-transition checkpointing,
+  canonical-forward/null controls, preserved baseline caches.
+- `report.py`: full saved-data distributions, per-transition CSV, continuous
+  effective head plots, influence rank curves, joint tests and Chinese report.
+- `audit.py`: independently recomputes concentration/masks from raw effects,
+  validates scope/null/head sums, and hashes retained caches, source and results.
+- `tests/test_dominance.py`: continuous limiting cases, undefined zero-total,
+  token-dependent masks, JS identity, both canonical mixed-layer paths, and joint
+  interventions on a small checkpoint with known arrays.
+
+Influence for Q/K/V is relative L2 change after deleting one whole donor attention
+head (at all sequence positions), preserving the prior residual and recomputing
+source MLP + recipient normalization. Source **output/value heads** are the units:
+16 for linear attention,8 for full attention. These are not arbitrary individual
+Q/K/V weight blocks. For a recipient query-head at one token, normalize donor
+influences to p. Primary continuous summary is N_eff=1/sum(p²), and N_eff/H.
+C=(sum(p²)-1/H)/(1-1/H) is a related0..1 concentration score. These are not
+independent confirmations. No RMS near-zero threshold or R fitting is used.
+
+Q/K/V are separate primary scales; balanced QKV score is RMS of their three
+relative changes. It is a declared summary, not a norm in a single physical space.
+Zero total implies undefined concentration, not a uniform distribution. Single-head relative effects before head-share normalization and true joint
+effects are retained as well as shares. The saved metric named max_absolute is
+the largest unsigned relative-L2 effect, not an unnormalized hidden-space norm.
+The total field sums individual relative effects; it is not the measured joint
+effect of removing the whole attention branch.
+
+Full-attention recipients additionally have real token-key probability JS changes;
+linear attention does not have an ordinary softmax attention probability matrix.
+Full-attention K/V repeat their GQA groups to query-head slots for aligned joint
+experiments, not as extra independent KV observations. Every headline statistic
+is descriptive: query/token/head observations are dependent, and four short prior
+texts do not establish universal dominance or independent generalization.
+
+Joint masks vary per source token and recipient head. Recompute a coherent full
+sequence for each recipient head, scoring that intended head afterward. Rank by
+single QKV influence, remove weakest half/strongest half/a fixed-seed random half,
+or all attention heads. This investigates interactions and cross-token effects;
+it is not a validated free selector, fixed removable head subset or LM pruning.
+
+Only run model work and scientific plotting on the authorized CPU server:
+```
+OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 venv/bin/python -B -m unittest tests.test_dominance -v
+OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 venv/bin/python -B -m dominance.run --source runs/cpu-qwen35-2b --out runs/NEW_DOMINANCE_DIR --seconds 7200
+venv/bin/python -B -m dominance.report runs/NEW_DOMINANCE_DIR
+venv/bin/python -B -m dominance.audit runs/NEW_DOMINANCE_DIR runs/cpu-qwen35-2b
+```
+
+No training, GPU, new model download or automatic deletion. Raw intervention
+arrays and masks are delivered to Git; large baseline caches stay on the server
+with path/size/SHA metadata. Existing checkpoints/caches are never removed.
+
+The saved sum of selected individual effects is only a query-wise mask-weighted
+reference: individual ablations remove a head at all positions, whereas joint
+masks can vary across positions. Their difference cannot isolate pure nonlinear
+interaction, since temporal intervention supports also differ. Use the measured
+weak/strong/random joint effects directly; do not call this reference an additive
+causal decomposition or a Shapley attribution.
+
+Scale sensitivity: after reviewing the first complete summaries, add sqrt(JS)
+(Jensen–Shannon distance) alongside primary JS divergence. Divergence is roughly
+quadratic for small changes, so shares and effective head counts depend on this
+scale. All original primary statistics and raw interventions remain unchanged;
+primary-analysis-summary.json preserves the pre-sensitivity aggregate for exact
+audit comparison. Retrieval-scale plots and source tables show both definitions.

dominance/audit.py

@@ -0,0 +1,90 @@
+"""Recompute saved concentration, masks, scale and scope checks; preserve caches."""
+import json,hashlib,csv,sys
+from pathlib import Path
+import numpy as np
+from .core import concentration
+
+
+def main(root,source):
+    root=Path(root);source=Path(source);status=json.loads((root/'status.json').read_text());assert status['state']=='completed'
+    samples=json.loads((root/'samples.json').read_text());assert len(samples)==4 and all(len(s['token_ids'])==32 for s in samples)
+    expected={(s['id'],i) for s in samples for i in range(23)};actual={(x['sample'],x['source']) for x in status['completed_links']};assert actual==expected and len(status['completed_links'])==92
+    cfg=json.loads((source/'model_config.json').read_text())['text_config'];obs=dict(np.load(root/'observations.npz'));lookup=sorted(s['id'] for s in samples);counts={};undefined={};full_js_links=0;joint_values={}
+    for sample,layer in sorted(actual):
+        f=root/'raw'/f'{sample}-link-{layer:02d}.npz';d=dict(np.load(f));hs=8 if cfg['layer_types'][layer]=='full_attention' else 16;hr=8 if cfg['layer_types'][layer+1]=='full_attention' else 16
+        if hr==8:
+            full_js_links+=1;d['single_sqrtJS']=np.sqrt(d['single_JS'])
+            for label in ('weak_half','strong_half','random_half','all_heads'):
+                d['joint_'+label+'_sqrtJS']=np.sqrt(d['joint_'+label+'_JS'])
+                mask=d.get('mask_'+label,np.ones((hr,32,hs),dtype=np.float32))
+                d['individual_sum_'+label+'_sqrtJS']=np.sum(d['single_sqrtJS'].transpose(2,1,0)*mask,-1).T
+            d['null_sqrtJS']=np.sqrt(d['null_JS'])
+        masks={label:d['mask_'+label] for label in ('weak_half','strong_half','random_half')}
+        for label,m in masks.items():
+            assert m.shape==(hr,32,hs);assert np.isin(m,[0,1]).all();np.testing.assert_array_equal(m.sum(-1),hs//2)
+        np.testing.assert_array_equal(masks['weak_half']+masks['strong_half'],1)
+        ranked=np.argsort(d['single_QKV'].transpose(2,1,0),axis=-1,kind='stable')
+        np.testing.assert_array_equal(np.take_along_axis(masks['weak_half'],ranked[...,:hs//2],-1),1)
+        for key in ('Q','K','V','QKV')+(('JS','sqrtJS') if hr==8 else ()):
+            single=d['single_'+key];assert single.shape==(hs,32,hr) and np.isfinite(single).all() and np.min(single)>=0
+            effect=single[:,7:,:].transpose(1,2,0);stats=concentration(effect);counts[key]=counts.get(key,0)+effect.shape[0]*hr;undefined[key]=undefined.get(key,0)+int((~stats['valid']).sum())
+            choose=(obs[key+'_source']==layer)&(obs[key+'_sample_index']==lookup.index(sample))
+            assert int(choose.sum())==25*hr
+            for metric,values in stats.items():
+                if metric=='valid':continue
+                np.testing.assert_allclose(obs[key+'_'+metric][choose],values.ravel(),rtol=1e-6,atol=1e-7,equal_nan=True)
+            for label in ('weak_half','strong_half','random_half','all_heads'):
+                mask=masks.get(label,np.ones((hr,32,hs),dtype=np.float32));expected_sum=np.sum(single.transpose(2,1,0)*mask,-1).T
+                np.testing.assert_allclose(d['individual_sum_'+label+'_'+key],expected_sum,rtol=1e-6,atol=1e-8)
+                assert d['joint_'+label+'_'+key].shape==(32,hr) and np.isfinite(d['joint_'+label+'_'+key]).all()
+                joint_values.setdefault((key,label),[]).extend(d['joint_'+label+'_'+key][7:].ravel())
+            assert float(np.max(d['null_'+key]))<=2e-5
+    assert full_js_links==24
+    summary=json.loads((root/'analysis-summary.json').read_text())
+    def check_stats(values,record):
+        v=np.asarray(values,dtype=np.float64);v=v[np.isfinite(v)];assert len(v)==record['count']
+        if len(v):
+            for name,x in zip(['p10','median','p90','mean'],[*np.quantile(v,[.1,.5,.9]),v.mean()]):np.testing.assert_allclose(record[name],x,rtol=2e-6,atol=1e-8)
+    for key in counts:
+        for metric,record in summary['concentration'][key].items():check_stats(obs[key+'_'+metric],record)
+    primary=json.loads((root/'primary-analysis-summary.json').read_text())
+    for section in ('concentration','joint','paired_joint','by_recipient_type'):
+        for key,value in primary[section].items():assert summary[section][key]==value,(section,key)
+    full_sources=sorted({x['source'] for x in status['completed_links'] if x['full_attention_recipient']})
+    assert summary['full_attention_source_indices']==full_sources
+    for key,groups in summary['by_recipient_type'].items():
+        src=obs[key+'_source']
+        for label,metrics in groups.items():
+            choose=np.isin(src,full_sources) if label=='full_attention' else ~np.isin(src,full_sources)
+            for metric,record in metrics.items():check_stats(obs[key+'_'+metric][choose],record)
+    for (key,label),values in joint_values.items():check_stats(values,summary['joint'][key][label])
+    for key in counts:
+        weak=np.asarray(joint_values[(key,'weak_half')]);strong=np.asarray(joint_values[(key,'strong_half')]);random=np.asarray(joint_values[(key,'random_half')]);valid=weak>0;record=summary['paired_joint'][key]
+        assert record['zero_weak_effect_count']==int((~valid).sum())
+        np.testing.assert_allclose(record['weak_less_than_strong_fraction'],np.mean(weak<strong))
+        np.testing.assert_allclose(record['weak_less_than_random_fraction'],np.mean(weak<random))
+        check_stats(strong[valid]/weak[valid],record['strong_over_weak']);check_stats(random[valid]/weak[valid],record['random_over_weak'])
+    controls=json.loads((root/'head-sum-controls.json').read_text());assert len(controls)==96 and max(x['relative_l2'] for x in controls)<2e-5
+    assert status['canonical_forward_relative_error']<2e-5
+    caches=[]
+    for f in sorted((root/'cache').rglob('*')):
+        if f.is_file():caches.append({'path':str(f.resolve()),'bytes':f.stat().st_size,'sha256':hashlib.sha256(f.read_bytes()).hexdigest()})
+    weights=list((source/'weights').rglob('*.safetensors'));assert weights and sum(f.stat().st_size for f in weights)==4548221488
+    manifest=json.loads((source/'manifest.json').read_text());weight_records=[]
+    for f in weights:
+        with f.open('rb') as stream:sha=hashlib.file_digest(stream,'sha256').hexdigest()
+        expected=next(x['upstream_sha256'] for x in manifest['files'] if x['name']==f.name);assert sha==expected
+        weight_records.append({'path':str(f.resolve()),'bytes':f.stat().st_size,'sha256':sha,'matches_upstream':True})
+    external={'host':'8.211.154.134','checkpoint_directory':str((source/'weights').resolve()),'checkpoint_bytes':sum(f.stat().st_size for f in weights),'checkpoint_files':weight_records,'revision':'15852e8c16360a2fea060d615a32b45270f8a8fc','baseline_caches':caches,'automatic_deletion':False,'prior_caches_untouched':True}
+    (root/'external-caches.json').write_text(json.dumps(external,indent=2)+'\n')
+    reference=json.loads((source/'reference-check.json').read_text())
+    assert reference['passed'] and all(x['passed'] for x in reference['tests'])
+    assert hashlib.sha256(Path('functional/model.py').read_bytes()).hexdigest()==reference['implementation_sha256']
+    (root/'prior-official-reference-check.json').write_bytes((source/'reference-check.json').read_bytes())
+    files=list(Path('dominance').glob('*.py'))+[Path('dominance/PROTOCOL.md'),Path('dominance/README.md'),Path('tests/test_dominance.py'),Path('functional/model.py'),Path('crossqkv/fresh_samples.json')]
+    (root/'source-hashes.json').write_text(json.dumps({str(f):hashlib.sha256(f.read_bytes()).hexdigest() for f in files if not f.name.startswith('._')},indent=2)+'\n')
+    audit={'passed':True,'transitions':23,'texts':4,'link_observations':92,'primary_observations_per_metric':counts,'undefined_zero_total_per_metric':undefined,'full_attention_js_links':full_js_links,'head_sum_controls':96,'canonical_forward_relative_error':status['canonical_forward_relative_error'],'all_raw_concentration_and_masks_recomputed':True,'pooled_concentration_and_joint_summary_recomputed':True,'original_primary_summaries_unchanged':True,'sqrt_JS_scale_sensitivity_recomputed':True,'retained_baseline_cache_files':len(caches),'checkpoint_sha_matches_upstream':True,'prior_official_reference_check_passed_same_implementation':True,'prior_official_reference_tests':len(reference['tests'])}
+    (root/'audit.json').write_text(json.dumps(audit,indent=2)+'\n');print(json.dumps(audit,indent=2),flush=True)
+    included=[f for f in root.rglob('*') if f.is_file() and 'cache' not in f.relative_to(root).parts and f.name!='checksums.json']
+    (root/'checksums.json').write_text(json.dumps({str(f.relative_to(root)):hashlib.sha256(f.read_bytes()).hexdigest() for f in sorted(included)},indent=2)+'\n')
+if __name__=='__main__':main(sys.argv[1],sys.argv[2])

dominance/core.py

@@ -0,0 +1,85 @@
+"""Frozen-model head interventions and continuous influence concentration."""
+import numpy as np
+from functional.model import StreamingLM,PREFIX,rope,causal_conv,attention
+
+
+def concentration(d):
+    """Last axis donor head; zero total is undefined, represented by NaNs."""
+    d=np.asarray(d,dtype=np.float64)
+    if not np.isfinite(d).all() or np.any(d<0):raise ValueError('Finite nonnegative influences required')
+    h=d.shape[-1];total=d.sum(-1);valid=total>0
+    p=np.divide(d,total[...,None],out=np.zeros_like(d),where=valid[...,None]);square=np.sum(p*p,-1)
+    neff=np.divide(1,square,out=np.full_like(total,np.nan),where=valid)
+    ordered=np.sort(p,axis=-1)[...,::-1]
+    return {'total':total,'max_absolute':d.max(-1),'valid':valid,'effective_heads':neff,'effective_fraction':neff/h,
+            'concentration':np.where(valid,(square-1/h)/(1-1/h),np.nan),
+            'top1':np.where(valid,ordered[...,0],np.nan),'top25':np.where(valid,ordered[...,:max(1,h//4)].sum(-1),np.nan),
+            'top50':np.where(valid,ordered[...,:max(1,h//2)].sum(-1),np.nan),
+            'bottom25':np.where(valid,ordered[...,-max(1,h//4):].sum(-1),np.nan),
+            'heads90':np.where(valid,1+np.sum(np.cumsum(ordered,axis=-1)<.9,axis=-1),np.nan)}
+
+
+def masks_from_influences(d,weak=True):
+    """d [donor,T,recipient]; output masks [recipient,T,donor]."""
+    ranked=np.argsort(d.transpose(2,1,0),axis=-1,kind='stable');n=d.shape[0]//2
+    selected=ranked[...,:n] if weak else ranked[...,-n:]
+    mask=np.zeros_like(ranked,dtype=np.float32);np.put_along_axis(mask,selected,1,axis=-1)
+    return mask
+
+
+def js_rows(a,b):
+    a=np.asarray(a,dtype=np.float64);b=np.asarray(b,dtype=np.float64);m=(a+b)/2
+    def term(x):return np.where(x>0,x*np.log(np.maximum(x,1e-300)/np.maximum(m,1e-300)),0)
+    return np.maximum(0,.5*np.sum(term(a)+term(b),axis=-1))
+
+
+class InfluenceLM(StreamingLM):
+    capturing=False
+    def linear(self,x,name,rows=256):
+        y=super().linear(x,name,rows)
+        if self.capturing and (name.endswith('self_attn.o_proj') or name.endswith('linear_attn.out_proj')):
+            layer=int(name.split('.')[1]);nh=self.config['num_attention_heads'] if 'self_attn' in name else self.config['linear_num_value_heads']
+            xx=x.reshape(*x.shape[:2],nh,-1);dim=xx.shape[-1];t=self.tensors[PREFIX+name+'.weight'];od,idim=t.shape
+            out=np.empty((*x.shape[:2],nh,od),dtype=np.float32)
+            for start in range(0,od,256):
+                self.guard();stop=min(od,start+256);w=t.read(slice(start*idim,stop*idim)).reshape(stop-start,idim)
+                for head in range(nh):out[:,:,head,start:stop]=xx[:,:,head]@w[:,head*dim:(head+1)*dim].T
+                del w;t.evict()
+            err=float(np.linalg.norm(out.sum(2).astype(np.float64)-y)/max(np.linalg.norm(y.astype(np.float64)),1e-30))
+            if err>2e-5:raise AssertionError(('head sum mismatch',layer,err))
+            self.head_outputs=out;self.head_sum_errors.append({'layer':layer,'relative_l2':err})
+        return y
+
+    def baseline(self,ids,root):
+        h=self.embedding(ids);self.head_sum_errors=[]
+        for layer,kind in enumerate(self.config['layer_types']):
+            raw=h.copy();n=self.normalization(h,f'layers.{layer}.input_layernorm');self.capturing=True
+            out,_=self.full_attention(n,layer) if kind=='full_attention' else (self.linear_attention(n,layer),None)
+            self.capturing=False;heads=self.head_outputs;self.head_outputs=None
+            mid=raw+out;h=mid+self.mlp(self.normalization(mid,f'layers.{layer}.post_attention_layernorm'),layer)
+            np.savez(root/f'layer-{layer}.npz',raw=raw,out=out,heads=heads,next_raw=h)
+            print('baseline layer',layer,flush=True)
+        return h
+
+    def recipient(self,raw,layer):
+        h=self.normalization(raw,f'layers.{layer}.input_layernorm');c=self.config;b,t=h.shape[:2]
+        if c['layer_types'][layer]=='full_attention':
+            p=f'layers.{layer}.self_attn.';nh=c['num_attention_heads'];nk=c['num_key_value_heads'];d=c['head_dim']
+            q=self.linear(h,p+'q_proj').reshape(b,t,nh,2*d)[...,:d];k=self.linear(h,p+'k_proj').reshape(b,t,nk,d);v=self.linear(h,p+'v_proj').reshape(b,t,nk,d)
+            q=self.normalization(q,p+'q_norm');k=self.normalization(k,p+'k_norm');r=c['rope_parameters'];q,k=(rope(z,r['rope_theta'],r['partial_rotary_factor']) for z in (q,k))
+            k=np.repeat(k,nh//nk,axis=2);v=np.repeat(v,nh//nk,axis=2)
+            _,prob=attention(q,k,v)
+            return {'Q':q,'K':k,'V':v,'P':prob}
+        p=f'layers.{layer}.linear_attn.';kh=c['linear_num_key_heads'];vh=c['linear_num_value_heads'];kd=c['linear_key_head_dim'];vd=c['linear_value_head_dim']
+        x=causal_conv(self.linear(h,p+'in_proj_qkv'),self.small(p+'conv1d.weight')[:,0,:]);q,k,v=np.split(x,[kh*kd,2*kh*kd],axis=-1)
+        q=q.reshape(b,t,kh,kd);k=k.reshape(b,t,kh,kd);v=v.reshape(b,t,vh,vd)
+        if vh!=kh:q,k=np.repeat(q,vh//kh,axis=2),np.repeat(k,vh//kh,axis=2)
+        q=q/np.sqrt(np.sum(q*q,axis=-1,keepdims=True)+1e-6)/np.float32(np.sqrt(kd));k=k/np.sqrt(np.sum(k*k,axis=-1,keepdims=True)+1e-6)
+        return {'Q':q,'K':k,'V':v}
+
+    def intervene(self,cache,layer,masks):
+        # Each scenario has a full coherent token-dependent deletion mask.
+        removed=np.einsum('bth,thd->btd',masks,cache['heads'][0],optimize=True)
+        mid=cache['raw']+cache['out']-removed
+        nxt=mid+self.mlp(self.normalization(mid,f'layers.{layer}.post_attention_layernorm'),layer)
+        return self.recipient(nxt,layer+1)

dominance/report.py

@@ -0,0 +1,140 @@
+"""Saved-data scientific analysis; no model invocation or fitting."""
+import csv,json
+from pathlib import Path
+import numpy as np
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+from .core import concentration
+
+
+def stats(x):
+    x=np.asarray(x,dtype=np.float64);valid=x[np.isfinite(x)]
+    if not valid.size:return {'count':0,'p10':None,'median':None,'p90':None,'mean':None}
+    q=np.quantile(valid,[.1,.5,.9]);return {'count':int(valid.size),'p10':float(q[0]),'median':float(q[1]),'p90':float(q[2]),'mean':float(valid.mean())}
+
+
+def csvwrite(p,rows):
+    with p.open('w') as f:
+        w=csv.DictWriter(f,fieldnames=list(rows[0]));w.writeheader();w.writerows(rows)
+
+
+def main(root):
+    root=Path(root);figdir=root/'figures';figdir.mkdir(exist_ok=True);status=json.loads((root/'status.json').read_text());assert status['state']=='completed'
+    rows=[];joint_rows=[];pool={};curves={};joint_pool={};obs={};winner=[];joint_by_layer={}
+    for entry in status['completed_links']:
+        sample=entry['sample'];source=entry['source'];data=dict(np.load(root/'raw'/f'{sample}-link-{source:02d}.npz'))
+        if 'single_JS' in data:
+            data['single_sqrtJS']=np.sqrt(data['single_JS'])
+            for label in ('weak_half','strong_half','random_half','all_heads'):
+                data['joint_'+label+'_sqrtJS']=np.sqrt(data['joint_'+label+'_JS'])
+                masks=data.get('mask_'+label,np.ones_like(data['mask_weak_half']))
+                data['individual_sum_'+label+'_sqrtJS']=np.sum(data['single_sqrtJS'].transpose(2,1,0)*masks,-1).T
+        for key in ('Q','K','V','QKV','JS','sqrtJS'):
+            if 'single_'+key not in data:continue
+            d=data['single_'+key][:,7:,:].transpose(1,2,0);c=concentration(d);valid=c['valid'];h=d.shape[-1]
+            metric={k:v.reshape(-1) for k,v in c.items() if k!='valid'}
+            row={'sample':sample,'source':source,'recipient':source+1,'metric':key,'source_heads':h,'recipient_attention_type':'full_attention' if entry['full_attention_recipient'] else 'linear_attention','observations':d.shape[0]*d.shape[1],'undefined_zero_total':int((~valid).sum())}
+            for k,v in metric.items():
+                pool.setdefault(key,{}).setdefault(k,[]).extend(v)
+                for s,z in stats(v).items():row[k+'_'+s]=z
+            rows.append(row)
+            # Raw per-observation statistics for independently reproducing figures.
+            for k,v in metric.items():obs.setdefault(key+'_'+k,[]).append(v.astype(np.float32))
+            n=d.shape[0]*d.shape[1]
+            for k,v in [('source',np.full(n,source)),('sample_index',np.full(n,sorted({e['sample'] for e in status['completed_links']}).index(sample))),('token',np.repeat(np.arange(7,32),d.shape[1])),('recipient_head',np.tile(np.arange(d.shape[1]),25))]:obs.setdefault(key+'_'+k,[]).append(v.astype(np.int16))
+            p=np.divide(d,d.sum(-1,keepdims=True),out=np.zeros_like(d),where=d.sum(-1,keepdims=True)>0);cum=np.concatenate([np.zeros((*d.shape[:2],1)),np.cumsum(np.sort(p,-1)[...,::-1],-1)],-1)
+            grid=np.linspace(0,1,101);cc=np.array([np.interp(grid,np.linspace(0,1,h+1),v) for v in cum[valid]])
+            curves.setdefault(key,[]).extend(cc)
+            win=np.argmax(d,-1);counts=np.bincount(win[valid],minlength=h)
+            winner.append({'sample':sample,'source':source,'metric':key,'most_frequent_winner_fraction':float(counts.max()/max(counts.sum(),1)),'distinct_winning_heads':int((counts>0).sum()),'head_counts':json.dumps(counts.tolist())})
+            for label in ('weak_half','strong_half','random_half','all_heads'):
+                values=data['joint_'+label+'_'+key][7:];summed=data['individual_sum_'+label+'_'+key][7:]
+                jr={'sample':sample,'source':source,'recipient':source+1,'metric':key,'removed':label}
+                for k,x in [('joint_effect',values),('sum_individual',summed)]:
+                    for s,z in stats(x).items():jr[k+'_'+s]=z
+                joint_rows.append(jr);joint_pool.setdefault(key,{}).setdefault(label,[]).extend(values.ravel());joint_by_layer.setdefault((key,label,source),[]).extend(values.ravel())
+    csvwrite(root/'concentration-by-link.csv',rows);csvwrite(root/'joint-by-link.csv',joint_rows);csvwrite(root/'winner-identities.csv',winner)
+    np.savez_compressed(root/'observations.npz',**{k:np.concatenate(v) for k,v in obs.items()})
+    pooled={k:{m:stats(v) for m,v in metrics.items()} for k,metrics in pool.items()};joint_stats={k:{m:stats(v) for m,v in metrics.items()} for k,metrics in joint_pool.items()}
+    paired={}
+    for key,labels in joint_pool.items():
+        weak=np.asarray(labels['weak_half']);strong=np.asarray(labels['strong_half']);random=np.asarray(labels['random_half']);valid=weak>0
+        paired[key]={'weak_less_than_strong_fraction':float(np.mean(weak<strong)),'weak_less_than_random_fraction':float(np.mean(weak<random)),'zero_weak_effect_count':int((~valid).sum()),'strong_over_weak':stats(strong[valid]/weak[valid]),'random_over_weak':stats(random[valid]/weak[valid])}
+    full_sources=sorted({x['source'] for x in status['completed_links'] if x['full_attention_recipient']})
+    by_type={}
+    for key in pool:
+        src=np.concatenate(obs[key+'_source']);by_type[key]={}
+        for label,choose in [('full_attention',np.isin(src,full_sources)),('linear_attention',~np.isin(src,full_sources))]:
+            if not choose.any():continue
+            by_type[key][label]={m:stats(np.concatenate(obs[key+'_'+m])[choose]) for m in pool[key]}
+    summary={'scale_sensitivity':'sqrt(JS) added after reviewing completed primary summaries; primary JS and all raw interventions unchanged; squared divergences cannot be equated to probability mass or linear-scale effects','by_recipient_type':by_type,'full_attention_source_indices':full_sources,'paired_joint':paired,'pooling':'query7..31, per text/token/recipient-query-head; descriptive dependent observations, not independent samples','concentration':pooled,'joint':joint_stats,'counts':{'links':len(status['completed_links']),'texts':len(json.loads((root/'samples.json').read_text())),'layer_transitions':23},'sample_index_key':sorted({e['sample'] for e in status['completed_links']}),'winner_identity_file':'winner-identities.csv'}
+    (root/'analysis-summary.json').write_text(json.dumps(summary,indent=2,allow_nan=False)+'\n')
+    def save(fig,name):
+        for ext in ('png','pdf','svg'):fig.savefig(figdir/(name+'.'+ext),dpi=180)
+        plt.close(fig)
+    fig,axs=plt.subplots(1,2,figsize=(14,4.8),layout='constrained')
+    colors={'Q':'C0','K':'C1','V':'C2','QKV':'C3'}
+    for key in colors:
+        for ax,metric in zip(axs,['effective_fraction','top1']):
+            med=[];lo=[];hi=[]
+            source= np.concatenate(obs[key+'_source']);vals=np.concatenate(obs[key+'_'+metric])
+            for layer in range(23):
+                z=stats(vals[source==layer]);lo.append(z['p10']);med.append(z['median']);hi.append(z['p90'])
+            ax.plot(range(1,24),med,label=key,color=colors[key]);ax.fill_between(range(1,24),lo,hi,color=colors[key],alpha=.10)
+    axs[0].set(title='Continuous effective-head fraction (median, p10–p90)',ylabel='N_eff / number of donor heads')
+    axs[1].set(title='Largest donor-head influence share',ylabel='Top-1 normalized ablation effect')
+    for ax in axs:
+        for j,source in enumerate(full_sources):ax.axvspan(source+.6,source+1.4,color='gray',alpha=.08,label='Full-attention receiver' if j==0 else None)
+        ax.set(xlabel='Recipient layer index',ylim=(0,1.02));ax.legend();ax.grid(alpha=.2)
+    fig.suptitle('All 23 adjacent transitions; four texts, query positions 7–31; no training')
+    save(fig,'continuous_dominance')
+    fig,axs=plt.subplots(1,2,figsize=(13,4.8),layout='constrained');curve_rows=[]
+    for key in colors:
+        a=np.asarray(curves[key]);lo,med,hi=np.quantile(a,[.1,.5,.9],axis=0);x=np.linspace(0,1,101)
+        axs[0].plot(x,med,label=key,color=colors[key]);axs[0].fill_between(x,lo,hi,color=colors[key],alpha=.1)
+        for j,xx in enumerate(x):curve_rows.append({'metric':key,'fraction_ranked_heads':xx,'p10':lo[j],'median':med[j],'p90':hi[j]})
+        v=np.sort(np.asarray(pool[key]['effective_fraction']));axs[1].plot(v,np.arange(1,len(v)+1)/len(v),label=key,color=colors[key])
+    axs[0].plot([0,1],[0,1],':',color='gray',label='Uniform');axs[0].set(title='Influence concentration curve',xlabel='Fraction of donor heads, largest first',ylabel='Cumulative normalized single-head effects')
+    axs[1].set(title='Continuous effective-head fraction: empirical CDF',xlabel='N_eff / donor heads',ylabel='Fraction of observed cases')
+    for ax in axs:ax.set(xlim=(0,1),ylim=(0,1.02));ax.legend();ax.grid(alpha=.2)
+    fig.suptitle('Observation-wise ranking; dominant identities can differ; shaded p10–p90')
+    csvwrite(root/'concentration-curve.csv',curve_rows);save(fig,'ranked_influence_distribution')
+    fig,axs=plt.subplots(1,2,figsize=(14,4.8),layout='constrained')
+    for label,color in zip(['weak_half','strong_half','random_half','all_heads'],['C0','C3','C1','gray']):
+        for ax,key in zip(axs,['QKV','JS']):
+            selected=[r for r in joint_rows if r['metric']==key and r['removed']==label]
+            xs=sorted(set(r['recipient'] for r in selected));ys=[np.median(joint_by_layer[(key,label,x-1)]) for x in xs]
+            ax.plot(xs,np.maximum(ys,1e-12),'o-',label=label,color=color,markersize=4)
+    axs[0].set(title='Joint removal: balanced QKV relative effect',ylabel='Median token/head relative L2 score')
+    axs[1].set(title='Full-attention recipients: retrieval change',ylabel='Median token/head JS divergence (nats)')
+    for ax in axs:ax.set(xlabel='Recipient layer index',yscale='log');ax.legend();ax.grid(alpha=.2)
+    fig.suptitle('Equal-count weak/strong/random removal; token-wise masks; log display floor 1e-12')
+    save(fig,'joint_removal_effects')
+    fig,axs=plt.subplots(1,2,figsize=(13,4.8),layout='constrained')
+    for key,label,color in [('JS','JS divergence (squared scale)','C4'),('sqrtJS','sqrt(JS) distance','C5')]:
+        a=np.asarray(curves[key]);lo,med,hi=np.quantile(a,[.1,.5,.9],axis=0);x=np.linspace(0,1,101)
+        axs[0].plot(x,med,label=label,color=color);axs[0].fill_between(x,lo,hi,color=color,alpha=.12)
+        for j,xx in enumerate(x):curve_rows.append({'metric':key,'fraction_ranked_heads':xx,'p10':lo[j],'median':med[j],'p90':hi[j]})
+        v=np.sort(np.asarray(pool[key]['effective_fraction']));axs[1].plot(v,np.arange(1,len(v)+1)/len(v),label=label,color=color)
+    axs[0].plot([0,1],[0,1],':',color='gray',label='Uniform');axs[0].set(title='Retrieval influence rank curve',xlabel='Fraction of donor heads, largest first',ylabel='Cumulative normalized intervention effects')
+    axs[1].set(title='Sensitivity to divergence versus distance scale',xlabel='Effective donor-head fraction',ylabel='Fraction of observations')
+    for ax in axs:ax.set(xlim=(0,1),ylim=(0,1.02));ax.legend();ax.grid(alpha=.2)
+    fig.suptitle('Full-attention recipients; sqrt(JS) is post-summary scale sensitivity, not a new model run')
+    csvwrite(root/'concentration-curve.csv',curve_rows);save(fig,'retrieval_scale_sensitivity')
+    lines=['# 上游注意力头对下一层 Q/K/V 的影响集中度','', '实测全部23个相邻层连接、4条既有中英文文本各32 token。主统计取query7–31，按文本/token/接收query头分别计算，再汇总。不是独立样本的显著性检验。','', '影响是移除上游一个头后，下一层Q/K/V向量的相对L2变化；重新计算中间MLP/残差/归一化。各头影响归一化仅构成消融影响分布，不是天然可相加的贡献概率。','', '| 对象 | 有效头比例中位数 [P10,P90] | 最大头份额中位数 | 前25%头份额中位数 | 最小25%头份额中位数 | 90%影响所需头数中位数 |','|---|---:|---:|---:|---:|---:|']
+    for k in ('Q','K','V','QKV','JS','sqrtJS'):
+        if k not in pooled:continue
+        z=pooled[k];a=z['effective_fraction'];lines.append(f"| {k} | {a['median']:.3f} [{a['p10']:.3f},{a['p90']:.3f}] | {z['top1']['median']:.1%} | {z['top25']['median']:.1%} | {z['bottom25']['median']:.1%} | {z['heads90']['median']:.1f} |")
+    lines+=['','sqrtJS为JS距离尺度敏感性分析：在初次完整汇总后增加，原始消融和原JS主结果不变。JS散度近似平方尺度，归一化后会放大强弱差距；JS影响份额不是注意力概率质量份额。','', '按接收层类型分层（避免总体分布混合不同机制）：','', '| 接收层类型 | Q有效头比例中位数 | K | V | QKV |','|---|---:|---:|---:|---:|']
+    for label in ('linear_attention','full_attention'):
+        lines.append('| '+label+' | '+' | '.join(format(by_type[k][label]['effective_fraction']['median'],'.3f') for k in ('Q','K','V','QKV'))+' |')
+    lines+=['','QKV是三个相对变化平方的均值再开根号，避免混加不同Q/K/V尺度；JS仅普通注意力接收层有。普通注意力GQA的K/V复制到query头索引用于配对，不是额外独立KV头。','', '| 联合移除 | QKV相对影响中位数 [P10,P90] | 检索JS中位数 [P10,P90] |','|---|---:|---:|']
+    for label in ('weak_half','strong_half','random_half','all_heads'):
+        a=joint_stats['QKV'][label];b=joint_stats['JS'][label];lines.append(f"| {label} | {a['median']:.5f} [{a['p10']:.5f},{a['p90']:.5f}] | {b['median']:.6f} [{b['p10']:.6f},{b['p90']:.6f}] |")
+    lines+=['', '配对观测：QKV弱半数的实际联合影响小于强半数的比例为'+format(paired['QKV']['weak_less_than_strong_fraction'],'.1%')+'；强/弱影响比中位数为'+format(paired['QKV']['strong_over_weak']['median'],'.3f')+'。这是逐观测比值的中位数，不是两个总体中位数之比。弱效应为零的观测另计，不做除法。', '', '联合实验的弱/强/随机半数头，在各token、各接收头上分别选择，形成完整序列再重算。排名依据单头QKV影响，同样用于JS；它是输入自适应诊断，不是免费可用的剪枝策略。线性注意力的卷积和普通注意力的QK可能传播跨token影响。所有上游头移除仍保留残差背景，不能把它称为删除全部上游信息。', '', '连续指标：N_eff=1/Σp²；有效头比例=N_eff/H；C=(Σp²−1/H)/(1−1/H)。全零总影响时集中度未定义，原始数组保留NaN并在统计时排除、另计数量。单头相对L2影响在做头间占比归一化之前的总和及最大值均保留，避免把微小效应中的集中误认作重要。字段max_absolute指最大无符号相对效应，并非隐藏坐标中的未归一化L2；total是单头效应之和，真正全头联合效果另测。', '', '联合实验中保存的单头影响之和仅为按query掩码加权的参考。单头消融移除整段序列的一个头，联合掩码则可跨位置变化；二者的差不能单独解释成纯非线性交互，因为时间支持也不同。应直接比较实际弱/强/随机联合效果。', '', '## 文件与限制','', '`raw/*.npz`保留每次单头影响、联合影响、相应单头影响之和、删除掩码及空操作对照；`observations.npz`保留逐观测连续指标及索引；CSV和JSON为全部图表源表。原模型权重和大基线缓存继续保留在服务器。','', '只测短文本上的局部下一层作用；不代表全LM质量、可直接删头、所有输入普遍集中，或相似头可互相替代。所有头贡献可能是广泛分布的；报告不以发现稀疏为成功条件。','',f"CPU测量耗时{status['elapsed_seconds']:.1f}秒，峰值RSS{status['peak_rss_kib']/1024:.1f} MiB；无训练、GPU或清理。"]
+    (root/'REPORT.md').write_text('\n'.join(lines)+'\n')
+    print((root/'REPORT.md').read_text())
+if __name__=='__main__':
+    import sys
+    main(sys.argv[1])

dominance/run.py

@@ -0,0 +1,84 @@
+"""All adjacent layers, input-dependent influence distributions; CPU only."""
+import argparse,json,time,resource,hashlib,traceback
+from pathlib import Path
+import numpy as np
+from tokenizers import Tokenizer
+from functional.model import StreamingLM
+from .core import InfluenceLM,concentration,masks_from_influences,js_rows
+
+
+def dump(path,data):path.write_text(json.dumps(data,indent=2,allow_nan=False)+'\n')
+
+
+def distances(base,changed):
+    out={}
+    for key in ('Q','K','V'):
+        a=base[key].astype(np.float64);b=changed[key].astype(np.float64)
+        out[key]=np.linalg.norm(b-a,axis=-1)/np.maximum(np.linalg.norm(a,axis=-1),1e-12)
+    out['QKV']=np.sqrt(sum(out[k]**2 for k in ('Q','K','V'))/3)
+    if 'P' in base:out['JS']=js_rows(base['P'],changed['P']).transpose(0,2,1)
+    return out
+
+
+def link(lm,cache,layer,path,seed):
+    started=time.monotonic();base=lm.recipient(cache['next_raw'],layer+1);hs=cache['heads'].shape[2];t=cache['raw'].shape[1];hr=base['Q'].shape[2]
+    # Whole donor head across the full sequence is the single-ablation intervention.
+    singles={k:[] for k in ('Q','K','V','QKV')+ (('JS',) if 'P' in base else ())}
+    for start in range(0,hs,4):
+        index=list(range(start,min(start+4,hs)));masks=np.zeros((len(index),t,hs),dtype=np.float32)
+        for n,h in enumerate(index):masks[n,:,h]=1
+        scores=distances(base,lm.intervene(cache,layer,masks))
+        for k,v in scores.items():singles[k].extend(v)
+    singles={k:np.asarray(v,dtype=np.float32) for k,v in singles.items()}
+    # Identical cardinality weak/strong/random removals, ranking by balanced QKV influence.
+    # One coherent full-sequence mask per recipient head; only that head's output scored.
+    rank=singles['QKV'];rng=np.random.default_rng(seed)
+    weak=masks_from_influences(rank,True);strong=masks_from_influences(rank,False)
+    random=np.zeros_like(weak)
+    for r in range(hr):
+        for q in range(t):random[r,q,rng.choice(hs,hs//2,replace=False)]=1
+    joint={};sum_individual={}
+    for label,masks in [('weak_half',weak),('strong_half',strong),('random_half',random),('all_heads',np.ones_like(weak))]:
+        values={k:[] for k in singles}
+        for start in range(0,hr,4):
+            stop=min(start+4,hr);scores=distances(base,lm.intervene(cache,layer,masks[start:stop]))
+            for k,v in scores.items():
+                for n,r in enumerate(range(start,stop)):values[k].append(v[n,:,r])
+        for k,v in values.items():
+            joint[label+'_'+k]=np.asarray(v,dtype=np.float32).T
+            sum_individual[label+'_'+k]=np.sum(singles[k].transpose(2,1,0)*masks,axis=-1).T
+    null=distances(base,lm.intervene(cache,layer,np.zeros((1,t,hs),dtype=np.float32)))
+    if max(float(np.max(v)) for v in null.values())>2e-5:raise AssertionError('Null intervention changed recipient')
+    payload={'single_'+k:v for k,v in singles.items()};payload.update({'joint_'+k:v for k,v in joint.items()});payload.update({'individual_sum_'+k:v for k,v in sum_individual.items()})
+    payload.update({'mask_weak_half':weak,'mask_strong_half':strong,'mask_random_half':random})
+    payload.update({'null_'+k:v for k,v in null.items()})
+    np.savez_compressed(path,**payload)
+    return {'source':layer,'recipient':layer+1,'source_heads':hs,'recipient_query_heads':hr,'full_attention_recipient':'P' in base,'elapsed_seconds':time.monotonic()-started,'null_max':max(float(np.max(v)) for v in null.values())}
+
+
+def main():
+    p=argparse.ArgumentParser();p.add_argument('--source',type=Path,required=True);p.add_argument('--out',type=Path,required=True);p.add_argument('--seconds',type=int,default=7200);a=p.parse_args()
+    a.out.mkdir(parents=True,exist_ok=False);(a.out/'cache').mkdir();(a.out/'raw').mkdir();started=time.monotonic()
+    protocol={'scope':'all 23 adjacent layer transitions; source attention head contribution to next effective Q/K/V','samples':'4 previously authored zh/en texts, first32 tokens, descriptive','primary_queries':list(range(7,32)), 'influence':'L2 change / baseline token recipient-head L2; QKV score sqrt(mean of three squared relative changes))','linear_QK':'after causal convolution/SiLU and delta-rule L2 normalization; Q additionally divided by sqrt(key dim)','full_QK':'QK norm and partial RoPE applied; K/V repeated to query heads for indexing (not independent KV observations)','single_ablation':'remove complete source head projected output at all tokens; recompute source postattention normalization, MLP, residual and next input normalization','joint_ablation':'weak/strong/random half and all attention heads; one coherent token-varying removal mask for each recipient query head; ranking uses QKV single effects; no fitting','continuous':'effective heads=1/sum(p^2); fraction=effective/H; concentration=(sum(p^2)-1/H)/(1-1/H)','zero_total':'undefined concentration (NaN arrays), count explicitly','cost_budget_seconds':a.seconds,'no_training':True,'keep_all_weights_and_caches':True,'limitations':['not final LM quality or pruning proof','individual effects not additive; normalized scores not true contribution probabilities','token-varying masks are input-specific diagnostic and not a deployable fixed head subset','linear attention does not have an ordinary softmax probability matrix','Q/K/V scales handled separately; combined QKV is a declared balanced relative-effect score']}
+    dump(a.out/'protocol.json',protocol);status={'state':'running','completed_links':[]};dump(a.out/'status.json',status)
+    try:
+        lm=InfluenceLM(a.source/'weights',json.loads((a.source/'model_config.json').read_text()),seconds=a.seconds)
+        samples=json.loads(Path('crossqkv/fresh_samples.json').read_text())['samples'];tok=Tokenizer.from_file(str(a.source/'tokenizer.json'));controls=[]
+        for si,s in enumerate(samples):
+            lm.guard();s['token_ids']=tok.encode(s['text']).ids[:32];ids=np.array([s['token_ids']],dtype=np.int64);cache=a.out/'cache'/s['id'];cache.mkdir()
+            final=lm.baseline(ids,cache);np.save(cache/'final.npy',final)
+            for x in lm.head_sum_errors:controls.append({'sample':s['id'],**x})
+            if si==0:
+                reference,_=StreamingLM.forward(lm,ids);normalized=lm.normalization(final,'norm');err=float(np.linalg.norm(normalized-reference)/max(np.linalg.norm(reference),1e-30));assert err<2e-5;status['canonical_forward_relative_error']=err
+            dump(a.out/'head-sum-controls.json',controls);dump(a.out/'samples.json',samples)
+            for layer in range(23):
+                target=a.out/'raw'/f'{s["id"]}-link-{layer:02d}.npz'
+                with np.load(cache/f'layer-{layer}.npz') as data:loaded={k:data[k] for k in data.files}
+                info=link(lm,loaded,layer,target,31000+si*100+layer)
+                del loaded
+                status['completed_links'].append({'sample':s['id'],**info});status['elapsed_seconds']=time.monotonic()-started;dump(a.out/'status.json',status)
+                print('completed',s['id'],layer,'seconds',round(info['elapsed_seconds'],2),flush=True)
+        status.update(state='completed',elapsed_seconds=time.monotonic()-started,peak_rss_kib=resource.getrusage(resource.RUSAGE_SELF).ru_maxrss);dump(a.out/'status.json',status)
+    except BaseException as e:
+        status.update(state='failed',error=repr(e),elapsed_seconds=time.monotonic()-started);dump(a.out/'status.json',status);raise
+if __name__=='__main__':main()

finetune_compare/PROTOCOL.md

@@ -0,0 +1,42 @@
+# Paired task-finetuning comparison (registered before reading finetuned effects)
+
+Primary checkpoint: Avifenesh/gdn-beta-gate-qwen3.5-2b-grpo,
+HF revision038d8be9bd0edd3883fa019651b02bdf65609468, pure lora/seed0,
+step150 after shared40-step warmup. GSM8K GRPO, not SFT. Selected by smallest
+seed index before observing concentration, not by benchmark score or our metric.
+No gate01/gate02 arm or architecture changes. Exact base revision matches retained
+Qwen/Qwen3.5-2B15852e8c16360a2fea060d615a32b45270f8a8fc.
+
+Download existing18.9MB F32 LoRA tensors, verify official file SHA. All144 tensors
+must form72 A/B pairs on the4 declared GDN projections of18 linear-attention
+layers, rank16 alpha32. Apply FP32 effective W +2BA in streamed row chunks, also
+when decomposing source output heads. Do not quantize merged weights, mutate or
+save over base weights, execute foreign .pt files, train or use GPU. Validate
+against separate base-output +2B(Ax) forward and check head sums/null masks.
+
+Primary comparison exactly matches the reported8.931822/16 statistic: all six
+ordinary attention receivers3/7/11/15/19/23, previous-layer16 donor output heads,
+all32 tokens computed, query7..31, original4 plain-text inputs and same token IDs.
+Reuse corresponding immutable base single-ablation arrays, recompute FT baselines
+and all16 single donor removals, source MLP/residual/recipient normalization exact.
+Use the same sqrt(JS) distance influence, normalized shares and N_eff=1/sum(p²).
+Preserve JS divergence as a sensitivity row; no change of ranking or threshold.
+All4800 per-token/query-head observations are paired. No new joint-mask search:
+the requested statistic depends on single removals, not the previous joint tests.
+
+Secondary task-conditioned comparison: first4 original GSM8K test questions with
+at least32 base-tokenizer tokens (length selection only, before model effects),
+question-only plain text, first32 tokens. Freeze original author repository commit,
+JSONL SHA, item indices, question hashes and exact token IDs. No answers, generation,
+chat-template change or accuracy claim. Compute both base and FT on these same
+questions; never compare old general-text base to new GSM8K FT as if paired.
+
+Report overall median/P10/P90, paired differences, per receiver layer and per text,
+strong/weak-quartile shares, and dominant donor identity agreement. No iid-token
+p-values, no claim over training seeds (one fixed trained seed), no proof of
+GSM8K accuracy improvement or long-context behavior. Positive, negative and near
+zero changes all count as results. Source cohort remains4+4 short texts.
+
+All model work/tests/plots on authorized CPU server8.211.154.134, bounded7200s.
+Preserve all base/FT weights, adapters and caches; no automatic cleanup. Save raw
+arrays, source provenance, SHA manifests, fixed chart gallery and reviewable PR.

finetune_compare/README.md

@@ -0,0 +1,61 @@
+# Existing GSM8K fine-tune: paired head-concentration comparison
+
+First restore the original charts through [the fixed figure gallery](../docs/FIGURE_GALLERY.md).
+The original 8.931822/16, P10=5.784081 and P90=11.672658 refer to sqrt(JS)
+influences of all16 prior-layer heads on the six full-attention receivers.
+
+Selected release: [Avifenesh/gdn-beta-gate-qwen3.5-2b-grpo](https://huggingface.co/Avifenesh/gdn-beta-gate-qwen3.5-2b-grpo),
+pinned038d8be9bd0edd3883fa019651b02bdf65609468, pure lora/seed0. The publisher
+records GSM8K GRPO from a shared warmup, rank16 alpha32, exact same original
+Qwen3.5-2B revision. This is extra RL fine-tuning of the post-trained release,
+not pretraining-to-SFT, and not a beta-gate architecture modification. Our seed
+choice is fixed before seeing effects; no new training is performed.
+
+`lora.py` validates all144 tensors/72 pairs, maps only declared GDN projections,
+and provides read-only FP32 effective row chunks W+2BA. Source head decomposition
+uses the same effective weights. `ExplicitLoRA` adds2B(Ax) independently to the
+base projection as a numerical reference. No BF16 re-rounding is introduced:
+the author's training code also warns that rounding merged increments can erase
+small LoRA changes. Author configuration commit/SHA and public HF metadata are
+saved alongside the protocol; foreign training code and pickle checkpoints are
+not executed.
+
+`run.py` reuses the exact original single-ablation arrays for the original cohort,
+then computes matched finetuned states; it computes both models on4 question-only
+GSM8K test prefixes as a separate cohort. Every input has32 tokens. All16 donor
+heads, six receiver layers andquery7..31 remain identical to the original metric.
+The task corpus is the canonical author's test JSONL at a fixed Git commit, and
+selection uses only the first4 items with at least32 tokenizer tokens. All item
+indices, question hashes and token IDs are recorded; answers are not model inputs.
+
+`report.py` generates paired CDFs, per-layer medians/quantiles, paired change
+histograms, raw observations and tables. `audit.py` independently reconstructs
+effective-head counts via(sum d)^2/sum(d^2), checks pairing, exact old baselines,
+head sums, null masks, adapter/dataset/base SHA and retained external caches.
+`tests/test_finetune_compare.py` checks streamed row arithmetic, an independent
+low-rank forward and head decomposition, plus exact zero-scale restoration.
+
+No iid-token p-values, training-seed generalization, GSM8K accuracy result,
+long-context claim, pruning recommendation or functional-duplicate claim.
+The paired delta median is distinguished from the difference of pooled medians.
+All weights, adapters and caches remain on the authorized CPU server until user
+explicit deletion. Git stores all raw influence arrays, not the model/adapter.
+
+Remote workflow on8.211.154.134:
+```
+OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 venv/bin/python -B -m unittest tests.test_finetune_compare -v
+OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 venv/bin/python -B -m finetune_compare.run --source runs/cpu-qwen35-2b --parent runs/head-dominance --assets runs/finetune-head-comparison --out runs/finetune-head-comparison/NEW_EXPERIMENT
+venv/bin/python -B -m finetune_compare.report runs/finetune-head-comparison/NEW_EXPERIMENT
+venv/bin/python -B -m finetune_compare.audit runs/finetune-head-comparison/NEW_EXPERIMENT runs/head-dominance runs/finetune-head-comparison runs/cpu-qwen35-2b
+```
+
+The supplemental QKV field is measured only on the same six full-attention
+receivers, so its baseline must not be confused with the older76.0% pooled
+QKV effective-head fraction across all23 adjacent transitions. The gallery
+preserves that original table separately; the requested8.931822 metric uses
+exactly the matching six receivers in both studies.
+
+The audit also records all72 low-rank update Frobenius norms (via small Gram
+matrices) and final pre-normalization residual changes per text. These separate
+a stable concentration statistic from an accidentally inactive adapter. Residual
+changes are not logit distributions, accuracy differences or capability scores.

finetune_compare/audit.py

@@ -0,0 +1,74 @@
+"""Independent paired effective-head, source, preservation and delivery checks."""
+import json,hashlib,sys
+from pathlib import Path
+import numpy as np
+
+def sha(path):
+    with Path(path).open('rb') as f:return hashlib.file_digest(f,'sha256').hexdigest()
+def main(root,parent,assets,source):
+    root,parent,assets,source=map(Path,[root,parent,assets,source]);status=json.loads((root/'status.json').read_text());assert status['state']=='completed' and len(status['links'])==96
+    samples=json.loads((root/'samples.json').read_text());assert set(samples)=={'legacy','gsm8k'} and all(len(v)==4 for v in samples.values());assert all(len(s['token_ids'])==32 for ss in samples.values() for s in ss)
+    assert status['explicit_lora_forward_relative_error']<1e-4;rawstats={};expected=set();obs=dict(np.load(root/'observations.npz'));summary=json.loads((root/'summary.json').read_text())
+    def check(x,record):
+        x=np.asarray(x,dtype=np.float64);x=x[np.isfinite(x)];assert record['count']==len(x)
+        for name,value in zip(('p10','median','p90','mean'),[*np.quantile(x,[.1,.5,.9]),x.mean()]):np.testing.assert_allclose(record[name],value,atol=1e-8,rtol=1e-6)
+    for cohort,ss in samples.items():
+        for role in ('base','finetuned'):
+            for sample in ss:
+                for layer in (2,6,10,14,18,22):
+                    name=f'{cohort}-{role}-{sample["id"]}-{layer:02d}.npz';expected.add(name);d=dict(np.load(root/'raw'/name))
+                    for key in ('Q','K','V','QKV','JS'):
+                        assert d['single_'+key].shape==(16,32,8) and np.isfinite(d['single_'+key]).all() and np.min(d['single_'+key])>=0
+                        assert np.max(d['null_'+key])<2e-5
+                    if cohort=='legacy' and role=='base':
+                        with np.load(parent/'raw'/f'{sample["id"]}-link-{layer:02d}.npz') as original:
+                            for key in d:np.testing.assert_array_equal(d[key],original[key])
+                    for metric,impacts in [('distance',np.sqrt(d['single_JS'])),('divergence',d['single_JS']),('QKV',d['single_QKV'])]:
+                        x=impacts[:,7:,:].transpose(1,2,0).astype(np.float64);total=x.sum(-1);den=np.sum(x*x,-1);assert np.all(den>0)
+                        neff=total*total/den
+                        rawstats.setdefault((cohort,role,metric),[]).append(neff.ravel())
+    assert expected=={f.name for f in (root/'raw').glob('*.npz')}
+    for cohort in samples:
+        for metric in ('distance','divergence','QKV'):
+            for role in ('base','finetuned'):
+                x=np.concatenate(rawstats[(cohort,role,metric)]);assert len(x)==4800
+                np.testing.assert_allclose(obs['__'.join((cohort,role,metric,'effective_heads'))],x,rtol=1e-8,atol=1e-8)
+                check(x,summary[cohort][metric][role]['effective_heads'])
+            before=np.concatenate(rawstats[(cohort,'base',metric)]);after=np.concatenate(rawstats[(cohort,'finetuned',metric)]);check(after-before,summary[cohort][metric]['paired']['effective_heads_delta'])
+    base=summary['legacy']['distance']['base']['effective_heads'];np.testing.assert_allclose([base[x] for x in ('median','p10','p90')],[8.93182211792415,5.7840811612328125,11.67265834734814],rtol=1e-10)
+    controls=json.loads((root/'head-sum-controls.json').read_text());assert len(controls)==288 and all(c['relative_l2']<2e-5 for c in controls)
+    refs=json.loads((root/'baseline-references.json').read_text());assert len(refs)==24 and all(sha(v['source'])==v['sha256'] for v in refs)
+    assert sha(assets/'adapter.safetensors')=='93a3f43a4356f84da0d13ecc622fee13e41ba15d237cdaad6094f243899c62d6'
+    dataset=json.loads((root/'gsm8k-source.json').read_text());assert sha(assets/'gsm8k-test.jsonl')==dataset['sha256']
+    oldweights=list((source/'weights').glob('*.safetensors'));assert len(oldweights)==1
+    base_sha=sha(oldweights[0]);assert base_sha=='aa33250c4fc64891ddfaba3a314fd9542ea371843c387178b425fbcc5ed680b1'
+    from pilot.tensors import load_tensors
+    import re
+    tensors=load_tensors(assets);pairs={}
+    for name,t in tensors.items():
+        stem,which=name.split('.lora_');pairs.setdefault(stem,{})[which[0]]=t.read().reshape(t.shape).copy();t.evict()
+    updates=[]
+    for name,pair in pairs.items():
+        a=pair['A'].astype(np.float64);b=pair['B'].astype(np.float64)
+        norm2=4*float(np.sum((b.T@b)*(a@a.T).T))
+        updates.append({'module':name,'delta_frobenius':float(np.sqrt(max(norm2,0))),'A_nonzero':int(np.count_nonzero(a)),'B_nonzero':int(np.count_nonzero(b))})
+    assert len(updates)==72 and any(x['delta_frobenius']>0 for x in updates)
+    (root/'weight-update-summary.json').write_text(json.dumps({'scale':2,'modules':updates,'nonzero_update_modules':sum(x['delta_frobenius']>0 for x in updates),'norm_identity':'||2BA||_F^2 = 4 trace((B^T B)(A A^T))'},indent=2)+'\n')
+    changes=[]
+    for cohort,ss in samples.items():
+        for sample in ss:
+            basefile=parent/'cache'/sample['id']/'final.npy' if cohort=='legacy' else root/'cache'/f"{cohort}-base-{sample['id']}"/'final.npy'
+            ftfile=root/'cache'/f"{cohort}-finetuned-{sample['id']}"/'final.npy'
+            before=np.load(basefile).astype(np.float64);after=np.load(ftfile).astype(np.float64)
+            changes.append({'cohort':cohort,'sample':sample['id'],'final_pre_norm_residual_relative_l2':float(np.linalg.norm(after-before)/np.linalg.norm(before))})
+    assert any(x['final_pre_norm_residual_relative_l2']>0 for x in changes)
+    (root/'hidden-state-changes.json').write_text(json.dumps({'definition':'final residual before final RMSNorm, not logits, accuracy or LM quality','changes':changes},indent=2)+'\n')
+    caches=[{'path':str(p.resolve()),'bytes':p.stat().st_size,'sha256':sha(p)} for p in sorted((root/'cache').rglob('*')) if p.is_file()]
+    assert len(caches)==300
+    (root/'external-caches.json').write_text(json.dumps({'host':'8.211.154.134','base_weights':str(oldweights[0].resolve()),'base_sha256':base_sha,'adapter_path':str((assets/'adapter.safetensors').resolve()),'adapter_sha256':sha(assets/'adapter.safetensors'),'caches':caches,'automatic_deletion':False},indent=2)+'\n')
+    files=list(Path('finetune_compare').glob('*.py'))+[Path('finetune_compare/PROTOCOL.md'),Path('tests/test_finetune_compare.py'),Path('dominance/core.py'),Path('dominance/run.py'),Path('functional/model.py')]
+    (root/'source-hashes.json').write_text(json.dumps({str(f):sha(f) for f in files},indent=2)+'\n')
+    proof={'passed':True,'links':96,'paired_observations_per_cohort_metric':4800,'legacy_baseline_exact':True,'effective_head_formula_independently_recomputed':True,'head_sum_checks':288,'explicit_lora_forward_relative_error':status['explicit_lora_forward_relative_error'],'adapter_sha_verified':True,'base_sha_verified':True,'dataset_sha_verified':True,'retained_cache_files':len(caches),'nonzero_update_modules':sum(x['delta_frobenius']>0 for x in updates),'nonzero_hidden_state_changes_verified':True}
+    (root/'audit.json').write_text(json.dumps(proof,indent=2)+'\n');print(json.dumps(proof,indent=2),flush=True)
+    included=[p for p in root.rglob('*') if p.is_file() and 'cache' not in p.relative_to(root).parts and p.name!='checksums.json'];(root/'checksums.json').write_text(json.dumps({str(p.relative_to(root)):sha(p) for p in sorted(included)},indent=2)+'\n')
+if __name__=='__main__':main(*sys.argv[1:])

finetune_compare/author-configuration-provenance.json

@@ -0,0 +1,12 @@
+{
+  "source_repository": "https://github.com/avifenesh/gdn-beta-gate",
+  "source_revision": "f12f61f24ebe5997aac58667f413dcd1dcd96f54",
+  "lora_configuration_path": "src/gdn_gate/broad/arms.py",
+  "lora_configuration_sha256": "fc5b8de096eaa259ab166b8083bb774f9e14859d566ce82ea15986cc8d6eb5b5",
+  "verified_fields": [
+    "LoraConfig r=rank",
+    "lora_alpha=2*rank",
+    "use_rslora not enabled"
+  ],
+  "effective_scale": 2
+}

finetune_compare/candidate-SHA256SUMS

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finetune_compare/candidate-hf-metadata.json

@@ -0,0 +1,379 @@
+{
+  "_id": "6accb5a881b0c228f413b5e5",
+  "id": "Avifenesh/gdn-beta-gate-qwen3.5-2b-grpo",
+  "private": false,
+  "pipeline_tag": "text-generation",
+  "library_name": "pytorch",
+  "tags": [
+    "pytorch",
+    "lora",
+    "gated-deltanet",
+    "qwen3.5",
+    "beta-gate",
+    "grpo",
+    "reasoning",
+    "math",
+    "safetensors",
+    "research",
+    "text-generation",
+    "en",
+    "dataset:openai/gsm8k",
+    "base_model:Qwen/Qwen3.5-2B",
+    "base_model:adapter:Qwen/Qwen3.5-2B",
+    "license:apache-2.0",
+    "region:us"
+  ],
+  "downloads": 0,
+  "likes": 2,
+  "modelId": "Avifenesh/gdn-beta-gate-qwen3.5-2b-grpo",
+  "author": "Avifenesh",
+  "sha": "038d8be9bd0edd3883fa019651b02bdf65609468",
+  "lastModified": "2026-11-09T19:45:55.000Z",
+  "gated": false,
+  "disabled": false,
+  "widgetData": [
+    {
+      "text": "Hi, what can you help me with?"
+    },
+    {
+      "text": "What is 84 * 3 / 2?"
+    },
+    {
+      "text": "Tell me an interesting fact about the universe!"
+    },
+    {
+      "text": "Explain quantum computing in simple terms."
+    }
+  ],
+  "model-index": null,
+  "config": {},
+  "cardData": {
+    "license": "apache-2.0",
+    "base_model": "Qwen/Qwen3.5-2B",
+    "base_model_relation": "adapter",
+    "library_name": "pytorch",
+    "pipeline_tag": "text-generation",
+    "language": [
+      "en"
+    ],
+    "datasets": [
+      "openai/gsm8k"
+    ],
+    "tags": [
+      "lora",
+      "gated-deltanet",
+      "qwen3.5",
+      "beta-gate",
+      "grpo",
+      "reasoning",
+      "math",
+      "safetensors",
+      "research"
+    ]
+  },
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+  "createdAt": "2026-10-12T10:25:44.000Z",
+  "usedStorage": 388053976
+}

finetune_compare/candidate-lora-seed0-adapter_meta.json

@@ -0,0 +1,22 @@
+{
+ "record": "qwen3.5-2b-grpo",
+ "arm": "lora",
+ "receipt_arm": "ctrl",
+ "seed": 0,
+ "step": 150,
+ "base_model": "Qwen/Qwen3.5-2B",
+ "base_revision": "15852e8c16360a2fea060d615a32b45270f8a8fc",
+ "recipe": "GRPO, correctness-gated length penalty alpha 0.2, 150 steps from the shared warm-up (paper Section 4)",
+ "lora_rank": 16,
+ "lora_alpha": 32,
+ "lora_targets": [
+  "in_proj_qkv",
+  "in_proj_a",
+  "in_proj_b",
+  "out_proj"
+ ],
+ "gate": null,
+ "n_tensors": 144,
+ "n_params": 4727808,
+ "source_sha256": "de2794c085f5096fea53374ba1cea31075071bb6de3f227bb3350b3035970447"
+}

finetune_compare/lora.py

@@ -0,0 +1,47 @@
+"""Read-only FP32 effective weights for an existing pure-LoRA checkpoint."""
+import re
+import numpy as np
+from functional.model import PREFIX,StreamingLM
+from pilot.tensors import load_tensors
+BASE_REVISION='15852e8c16360a2fea060d615a32b45270f8a8fc'
+TARGETS={'in_proj_qkv','in_proj_a','in_proj_b','out_proj'}
+
+class EffectiveWeight:
+    def __init__(self,base,a,b,scale):
+        assert a.ndim==b.ndim==2 and (b.shape[0],a.shape[1])==base.shape and a.shape[0]==b.shape[1]
+        self.base,self.a,self.b,self.scale=base,a,b,scale
+        self.shape,self.numel,self.dtype=base.shape,base.numel,'F32'
+    def read(self,selector=slice(None)):
+        if not isinstance(selector,slice):raise ValueError('Only contiguous row chunks supported')
+        start,stop,step=selector.indices(self.numel);dim=self.shape[1]
+        if step!=1 or start%dim or stop%dim:raise ValueError('Row aligned chunks required')
+        w=self.base.read(slice(start,stop)).reshape(-1,dim)
+        return (w+self.scale*(self.b[start//dim:stop//dim]@self.a)).astype(np.float32).reshape(-1)
+    def evict(self):self.base.evict()
+
+
+def install(lm,directory,meta,multiplier=1.):
+    assert meta['base_model']=='Qwen/Qwen3.5-2B' and meta['base_revision']==BASE_REVISION and meta['gate'] is None and meta['arm']=='lora'
+    tensors=load_tensors(directory);pairs={};pattern=re.compile(r'^base_model\.model\.model\.(layers\.(\d+)\.linear_attn\.(in_proj_qkv|in_proj_a|in_proj_b|out_proj))\.lora_([AB])\.default\.weight$')
+    for name,tensor in tensors.items():
+        match=pattern.fullmatch(name)
+        if not match:raise ValueError('Unexpected trainable tensor: '+name)
+        target=PREFIX+match[1]+'.weight';assert lm.config['layer_types'][int(match[2])]=='linear_attention'
+        assert target in lm.tensors
+        value=tensor.read().reshape(tensor.shape).copy();tensor.evict();assert np.isfinite(value).all()
+        pairs.setdefault(target,{})[match[4]]=value
+    expected={PREFIX+f'layers.{layer}.linear_attn.{role}.weight' for layer,kind in enumerate(lm.config['layer_types']) if kind=='linear_attention' for role in TARGETS}
+    assert set(pairs)==expected and len(tensors)==meta['n_tensors']
+    scale=meta['lora_alpha']/meta['lora_rank']*multiplier
+    for target,pair in pairs.items():
+        assert set(pair)=={'A','B'} and pair['A'].shape[0]==meta['lora_rank']
+        lm.tensors[target]=EffectiveWeight(lm.tensors[target],pair['A'],pair['B'],scale)
+    return pairs,scale
+
+
+class ExplicitLoRA(StreamingLM):
+    """Independent reference: base projection + scaled low-rank output, no W merge."""
+    def linear(self,x,name,rows=256):
+        y=super().linear(x,name,rows);pair=self.pairs.get(PREFIX+name+'.weight')
+        if pair is not None:y=y+self.scale*((x@pair['A'].T)@pair['B'].T)
+        return y.astype(np.float32)

finetune_compare/report.py

@@ -0,0 +1,73 @@
+"""Paired concentration changes from saved immutable intervention arrays."""
+import csv,json,sys
+from pathlib import Path
+import numpy as np
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+from dominance.core import concentration
+
+def stats(x):
+    x=np.asarray(x,dtype=np.float64);v=x[np.isfinite(x)];q=np.quantile(v,[.1,.5,.9])
+    return {'count':int(v.size),'p10':float(q[0]),'median':float(q[1]),'p90':float(q[2]),'mean':float(v.mean())}
+def csvwrite(path,rows):
+    with path.open('w') as f:w=csv.DictWriter(f,fieldnames=list(rows[0]));w.writeheader();w.writerows(rows)
+def main(root):
+    root=Path(root);status=json.loads((root/'status.json').read_text());assert status['state']=='completed';samples=json.loads((root/'samples.json').read_text());pool={};observations={};summary={};rows=[];layer_rows=[]
+    for cohort in ('legacy','gsm8k'):
+        for role in ('base','finetuned'):
+            for sample in samples[cohort]:
+                for source in (2,6,10,14,18,22):
+                    data=np.load(root/'raw'/f'{cohort}-{role}-{sample["id"]}-{source:02d}.npz')
+                    for metric,key,transform in [('distance','JS',np.sqrt),('divergence','JS',lambda x:x),('QKV','QKV',lambda x:x)]:
+                        impacts=transform(data['single_'+key])[:,7:,:].transpose(1,2,0);c=concentration(impacts)
+                        record={'cohort':cohort,'role':role,'sample':sample['id'],'recipient':source+1,'metric':metric}
+                        for name,x in c.items():
+                            observations.setdefault((cohort,role,metric,name),[]).append(x.reshape(-1))
+                            if name!='valid':
+                                for stat,value in stats(x).items():record[name+'_'+stat]=value
+                        pool.setdefault((cohort,role,metric,'recipient'),[]).append(np.full(200,source+1))
+                        pool.setdefault((cohort,role,metric,'sample'),[]).append(np.full(200,sample['id']))
+                        observations.setdefault((cohort,role,metric,'winner'),[]).append(np.argmax(impacts,-1).reshape(-1))
+                        rows.append(record)
+    arrays={k:np.concatenate(v) for k,v in observations.items()};np.savez_compressed(root/'observations.npz',**{'__'.join(k):v for k,v in arrays.items()})
+    for cohort in ('legacy','gsm8k'):
+        summary[cohort]={}
+        for metric in ('distance','divergence','QKV'):
+            item={role:{name:stats(arrays[(cohort,role,metric,name)]) for name in ('effective_heads','effective_fraction','concentration','top1','top25','bottom25','heads90','total')} for role in ('base','finetuned')}
+            before=arrays[(cohort,'base',metric,'effective_heads')];after=arrays[(cohort,'finetuned',metric,'effective_heads')];delta=after-before
+            item['paired']={'effective_heads_delta':stats(delta),'increased_fraction':float(np.mean(delta>0)),'decreased_fraction':float(np.mean(delta<0)),'winner_agreement':float(np.mean(arrays[(cohort,'base',metric,'winner')]==arrays[(cohort,'finetuned',metric,'winner')]))}
+            summary[cohort][metric]=item
+            recipients=np.concatenate(pool[(cohort,'base',metric,'recipient')]);names=np.concatenate(pool[(cohort,'base',metric,'sample')])
+            for layer in (3,7,11,15,19,23):
+                which=recipients==layer;a=stats(before[which]);b=stats(after[which]);d=stats(delta[which]);layer_rows.append({'cohort':cohort,'metric':metric,'recipient':layer,'base_median':a['median'],'base_p10':a['p10'],'base_p90':a['p90'],'finetuned_median':b['median'],'finetuned_p10':b['p10'],'finetuned_p90':b['p90'],'paired_delta_median':d['median']})
+            for sample in samples[cohort]:
+                which=names==sample['id'];item.setdefault('per_text',{})[sample['id']]={'base':stats(before[which]),'finetuned':stats(after[which]),'paired_delta':stats(delta[which])}
+    expected=(8.93182211792415,5.7840811612328125,11.67265834734814);b=summary['legacy']['distance']['base']['effective_heads'];np.testing.assert_allclose([b[k] for k in ['median','p10','p90']],expected,rtol=1e-10)
+    (root/'summary.json').write_text(json.dumps(summary,indent=2)+'\n');csvwrite(root/'per-text-layer.csv',rows);csvwrite(root/'per-layer.csv',layer_rows)
+    figs=root/'figures';figs.mkdir(exist_ok=True)
+    def save(fig,name):
+        for ext in ('png','pdf','svg'):fig.savefig(figs/(name+'.'+ext),dpi=180)
+        plt.close(fig)
+    fig,axs=plt.subplots(1,2,figsize=(13,4.8),layout='constrained')
+    for ax,cohort in zip(axs,['legacy','gsm8k']):
+        for role,label,color in [('base','Original release','C0'),('finetuned','GSM8K GRPO LoRA seed0','C1')]:
+            x=np.sort(arrays[(cohort,role,'distance','effective_heads')]);ax.plot(x,np.arange(1,len(x)+1)/len(x),label=label,color=color)
+        ax.set(title='Original four texts' if cohort=='legacy' else 'Four GSM8K held-out question prefixes',xlabel='Effective donor heads: sqrt(JS) influence',ylabel='Empirical CDF',xlim=(1,16),ylim=(0,1));ax.grid(alpha=.2);ax.legend()
+    fig.suptitle('Same inputs before/after existing task fine-tuning; 32 tokens; one training seed; no training here');save(fig,'paired_effective_heads_cdf')
+    fig,axs=plt.subplots(1,2,figsize=(13,4.8),layout='constrained')
+    for ax,cohort in zip(axs,['legacy','gsm8k']):
+        for role,prefix,color in [('base','base','C0'),('finetuned','finetuned','C1')]:
+            rr=[r for r in layer_rows if r['cohort']==cohort and r['metric']=='distance'];x=[r['recipient'] for r in rr];med=[r[prefix+'_median'] for r in rr];lo=[r[prefix+'_p10'] for r in rr];hi=[r[prefix+'_p90'] for r in rr];ax.plot(x,med,'o-',color=color,label=role);ax.fill_between(x,lo,hi,color=color,alpha=.12)
+        ax.set(title=cohort,xlabel='Full-attention recipient layer index',ylabel='Effective donor heads: median / P10–P90',ylim=(1,16));ax.legend();ax.grid(alpha=.2)
+    fig.suptitle('All six full-attention receivers, sixteen donor heads each; descriptive dependent observations');save(fig,'paired_effective_heads_by_layer')
+    fig,axs=plt.subplots(1,2,figsize=(13,4.8),layout='constrained')
+    for ax,cohort in zip(axs,['legacy','gsm8k']):
+        delta=arrays[(cohort,'finetuned','distance','effective_heads')]-arrays[(cohort,'base','distance','effective_heads')];ax.hist(delta,bins=60,density=True,alpha=.7);ax.axvline(0,color='black',linestyle='--');ax.axvline(np.median(delta),color='C3',label='Paired median');ax.set(title=cohort,xlabel='Finetuned minus original effective head count',ylabel='Density');ax.legend()
+    fig.suptitle('Paired changes; positive = more distributed, negative = more concentrated');save(fig,'paired_effective_heads_delta')
+    lines=['# GSM8K任务微调前后的有效头数','', '比较原Qwen3.5-2B发布权重与同一revision上的纯LoRA seed0（额外GSM8K GRPO，非SFT）。没有训练新模型。主指标为√JS距离消融影响的N_eff；正变化表示更分散，负变化表示更集中。','', '| 输入组 | 原权重中位数 [P10,P90] | 微调后中位数 [P10,P90] | 配对变化中位数 | 增加的观测比例 |','|---|---:|---:|---:|---:|']
+    for cohort,label in [('legacy','原四条文本'),('gsm8k','GSM8K测试题前缀')]:
+        item=summary[cohort]['distance'];a=item['base']['effective_heads'];b=item['finetuned']['effective_heads'];d=item['paired']['effective_heads_delta'];lines.append(f"| {label} | {a['median']:.6f} [{a['p10']:.6f},{a['p90']:.6f}] | {b['median']:.6f} [{b['p10']:.6f},{b['p90']:.6f}] | {d['median']:+.6f} | {item['paired']['increased_fraction']:.1%} |")
+    lines+=['', '每组4条输入×6个接收层×25个query位置×8个接收query头=4800个配对观测；它们不是4800个独立文本样本。前后使用同一原模型tokenizer、完全相同token IDs。原四条输入的基线8.931822/16已精确复现。','', 'GSM8K输入取原作者固定版本test split中前4道至少32token的题目，仅用题目前32token，不含答案，不套不同聊天模板。另算了同组GSM8K输入的原权重基线；没有把旧通用文本与新任务文本混作配对。','', '## 方法与限定','', '所有6个普通注意力接收层和16个上游输出头均保留；每次移除一个上游头在全序列的投影输出，重算中间MLP/残差/归一化和检索分布。微调只改变18个GDN层的指定投影，普通注意力权重不变；隐藏状态和上游贡献仍会变化。','', '采用read-only FP32有效权重W+2BA，未把增量舍入为BF16、未修改底座；另用base projection+2B(Ax)独立前向校验。头输出求和、空消融和源数组复用校验均记录。JS散度及QKV复合相对变化也保留，避免只展示有利度量。这里QKV补充指标仅覆盖相同六个普通注意力接收层，其原基线不能与旧研究全部23连接的76.0%混作同一统计量。','', '单个发布者训练seed、4条短文本/组，仅支持这些输入上的描述性变化；不推广为所有微调都会如此，不报告GSM8K准确率，不据此推断删头可用或最终LM能力。配对变化中位数与两个总体中位数之差不是同一个统计量。','', '[公开微调权重与训练说明](https://huggingface.co/Avifenesh/gdn-beta-gate-qwen3.5-2b-grpo)；固定版本、参数SHA、dataset版本和题目索引见protocol.json/gsm8k-source.json/samples.json。所有权重、适配器和缓存继续保留。']
+    (root/'REPORT.md').write_text('\n'.join(lines)+'\n');print((root/'REPORT.md').read_text(),end='')
+if __name__=='__main__':main(sys.argv[1])

finetune_compare/run.py

@@ -0,0 +1,72 @@
+"""Paired CPU-only existing-adapter comparison for the published sqrt(JS) metric."""
+import argparse,json,hashlib,time,resource,gc
+from pathlib import Path
+import numpy as np
+from tokenizers import Tokenizer
+from dominance.core import InfluenceLM,concentration
+from dominance.run import distances
+from .lora import install,ExplicitLoRA,BASE_REVISION
+SOURCES=(2,6,10,14,18,22)
+
+def dump(p,d):p.write_text(json.dumps(d,indent=2,allow_nan=False)+'\n')
+def single(lm,cache,layer,path):
+    base=lm.recipient(cache['next_raw'],layer+1);hs=cache['heads'].shape[2];t=cache['raw'].shape[1];assert hs==16
+    scores={k:[] for k in ('Q','K','V','QKV','JS')}
+    for start in range(0,hs,4):
+        masks=np.zeros((4,t,hs),dtype=np.float32)
+        for n in range(4):masks[n,:,start+n]=1
+        effects=distances(base,lm.intervene(cache,layer,masks))
+        for k,v in effects.items():scores[k].extend(v)
+    null=distances(base,lm.intervene(cache,layer,np.zeros((1,t,hs),dtype=np.float32)))
+    assert max(float(np.max(v)) for v in null.values())<2e-5
+    payload={'single_'+k:np.asarray(v,dtype=np.float32) for k,v in scores.items()};payload.update({'null_'+k:v for k,v in null.items()});np.savez_compressed(path,**payload)
+
+def main():
+    p=argparse.ArgumentParser();p.add_argument('--source',type=Path,required=True);p.add_argument('--parent',type=Path,required=True);p.add_argument('--assets',type=Path,required=True);p.add_argument('--out',type=Path,required=True);a=p.parse_args();a.out.mkdir(parents=True,exist_ok=False);(a.out/'cache').mkdir();(a.out/'raw').mkdir()
+    started=time.monotonic();meta=json.loads((a.assets/'adapter_meta.json').read_text());assert meta['base_revision']==BASE_REVISION
+    adapter_sha=hashlib.sha256((a.assets/'adapter.safetensors').read_bytes()).hexdigest();assert adapter_sha=='93a3f43a4356f84da0d13ecc622fee13e41ba15d237cdaad6094f243899c62d6'
+    tok=Tokenizer.from_file(str(a.source/'tokenizer.json'));legacy=json.loads((a.parent/'samples.json').read_text());gsm=[]
+    for s in legacy:assert s['token_ids']==tok.encode(s['text']).ids[:32]
+    for index,line in enumerate((a.assets/'gsm8k-test.jsonl').read_text().splitlines()):
+        q=json.loads(line)['question'];ids=tok.encode(q).ids
+        if len(ids)<32:continue
+        gsm.append({'id':f'gsm8k-test-{index:04d}','row_index':index,'text':q,'question_sha256':hashlib.sha256(q.encode()).hexdigest(),'token_ids':ids[:32]})
+        if len(gsm)==4:break
+    assert len(gsm)==4
+    cohorts={'legacy':legacy,'gsm8k':gsm};dump(a.out/'samples.json',cohorts);dump(a.out/'adapter_meta.json',meta);dump(a.out/'gsm8k-source.json',json.loads((a.assets/'gsm8k-source.json').read_text()))
+    protocol={'primary':'same six full-attention receivers and original4x32 token cohort as published8.931822/16','adapter_repo':'Avifenesh/gdn-beta-gate-qwen3.5-2b-grpo','adapter_revision':'038d8be9bd0edd3883fa019651b02bdf65609468','adapter_file':'lora/seed0/adapter.safetensors','adapter_sha256':adapter_sha,'base_revision':BASE_REVISION,'scale':2,'rank':16,'arm':'pure LoRA','training':'publisher-reported GSM8K GRPO; we do no training','sample_choice':'four original texts; first4 canonical GSM8K test questions with >=32tokens, length selection only, first32tokens; question only','source_layers':SOURCES,'primary_queries':list(range(7,32)),'main_metric':'concentration(sqrt(single_JS)); same FP32 tensors and sequence-wide donor-head deletion as prior run','cohort_pairing':'each comparison uses same question/tokenIDs before and after; base legacy arrays reused with elementwise checks','runtime':'read-only FP32 effective W+2BA; no BF16 re-rounding or quantization','controls':'head sum, null mask, explicit low-rank versus effective-weight full forward relL2<1e-4','budget_seconds':7200,'keep_all_weights_and_caches':True,'limitations':['one existing training seed','4 texts per cohort, 32 tokens each','dependent query/head observations, not iid textual samples','no generation or accuracy measurement','GDN targets only; ordinary attention weights stay unchanged','no pruning or functional interchangeability claim']}
+    dump(a.out/'protocol.json',protocol);status={'state':'running','links':[]};dump(a.out/'status.json',status);cfg=json.loads((a.source/'model_config.json').read_text());controls=[];references=[]
+    try:
+        for cohort,samples in cohorts.items():
+            for role in ('base','finetuned'):
+                reuse=cohort=='legacy' and role=='base';lm=None
+                if not reuse:
+                    lm=InfluenceLM(a.source/'weights',cfg,seconds=max(1,7200-(time.monotonic()-started)))
+                    if role=='finetuned':pairs,scale=install(lm,a.assets,meta)
+                for si,s in enumerate(samples):
+                    if time.monotonic()-started>7200:raise TimeoutError('Paired CPU budget')
+                    if not reuse:
+                        cache=a.out/'cache'/f'{cohort}-{role}-{s["id"]}';cache.mkdir();ids=np.array([s['token_ids']],dtype=np.int64);final=lm.baseline(ids,cache);np.save(cache/'final.npy',final)
+                        controls.extend({'cohort':cohort,'role':role,'sample':s['id'],**v} for v in lm.head_sum_errors)
+                        if cohort=='legacy' and role=='finetuned' and si==0:
+                            ref=ExplicitLoRA(a.source/'weights',cfg,seconds=max(1,7200-(time.monotonic()-started)));ref.pairs,ref.scale=pairs,scale;expected,_=ref.forward(ids);actual=lm.normalization(final,'norm');error=float(np.linalg.norm(actual.astype(np.float64)-expected)/np.linalg.norm(expected.astype(np.float64)));assert error<1e-4
+                            status['explicit_lora_forward_relative_error']=error;del ref;gc.collect()
+                    for layer in SOURCES:
+                        out=a.out/'raw'/f'{cohort}-{role}-{s["id"]}-{layer:02d}.npz'
+                        if reuse:
+                            old=a.parent/'raw'/f'{s["id"]}-link-{layer:02d}.npz'
+                            with np.load(old) as d:payload={prefix+k:d[prefix+k] for prefix in ('single_','null_') for k in ('Q','K','V','QKV','JS')}
+                            np.savez_compressed(out,**payload)
+                            with np.load(out) as d:
+                                for k,v in payload.items():np.testing.assert_array_equal(d[k],v)
+                            references.append({'source':str(old.resolve()),'sha256':hashlib.sha256(old.read_bytes()).hexdigest(),'derived':out.name})
+                        else:
+                            with np.load(cache/f'layer-{layer}.npz') as d:loaded={k:d[k] for k in d.files}
+                            single(lm,loaded,layer,out);del loaded
+                        status['links'].append({'cohort':cohort,'role':role,'sample':s['id'],'source':layer,'path':str(out.relative_to(a.out))});status['elapsed_seconds']=time.monotonic()-started;dump(a.out/'status.json',status);print('completed',cohort,role,s['id'],layer,flush=True)
+                    dump(a.out/'head-sum-controls.json',controls);dump(a.out/'baseline-references.json',references)
+                if lm is not None:del lm;gc.collect()
+        status.update(state='completed',elapsed_seconds=time.monotonic()-started,peak_rss_kib=resource.getrusage(resource.RUSAGE_SELF).ru_maxrss);dump(a.out/'status.json',status)
+    except BaseException as e:
+        status.update(state='failed',error=repr(e),elapsed_seconds=time.monotonic()-started);dump(a.out/'status.json',status);raise
+if __name__=='__main__':main()

model_shell/PROTOCOL.md

@@ -0,0 +1,83 @@
+# Actual-model parameter clouds: frozen grouping and null protocol
+
+Assets: retained Qwen3.5-2B original15852e8c16360a2fea060d615a32b45270f8a8fc,
+and pure GSM8K GRPO LoRA seed0 at HF038d8be9bd0edd3883fa019651b02bdf65609468.
+Use the established read-only FP32 W+2BA wrapper. No training, GPU, new models,
+weight mutation, inference requirement or deletion. Every retained asset stays.
+
+## What constitutes a distribution
+
+A model has no unique canonical number of parameter distributions. Enumerate a
+fixed family, not "all possible distributions":
+1. Each ndim>=2 tensor flattened as [axis0, product(other axes)]: row vectors
+   and column vectors, when point count>=16 and feature dimension>=32.
+2. Decoder language/MTP full-attention Q and interleaved gate rows per query
+   head; K/V rows per KV head. GDN concatenated Q/K/V rows per corresponding head.
+   No claim that these groups are disjoint or iid; whole-matrix views overlap.
+3. Primary sample-size stratum has >=64 available points;16..63 is small-sample
+   descriptive only. One-dimensional norm/bias vectors have no cloud of vectors;
+   GDN kernel row dimension4 is low dimensional and excluded with explicit reason.
+   Vision parameters are covered by matrix/flattened views, not vision-head splits.
+
+Include all checkpoint components (language, vision and MTP). Per view choose
+up to512 point indices without replacement, split deterministically into calibration
+and held-out halves. Use full vector coordinates up to dimension8192; only larger
+vectors use a fixed random coordinate subset, labelled projected rather than full.
+Use identical indices, coordinates and null random draws for original/FT.
+No overlap-group parameter savings or total distribution counts are additive.
+
+## Centers and radius/scaling
+
+Analyze origin and calibration-mean centers separately. Fit R_max using calibration
+norms only; do not silently clip held-out points outside R_max. Also fit calibration
+RMS radius. Store held-out maximum, outside-radius mass and radial spread.
+
+Null A: uniform d-dimensional ball. Simulate the SAME center-fitting and calibration
+maximum/RMS fitting before held-out evaluation. For origin, also retain exact
+expected max-fitted null CDF a^d*n_cal/(n_cal+1), a<=1; log probabilities remain
+available and zero Monte Carlo hits are not exact zeros.
+Null B: independent coordinate-uniform box bounded by calibration min/max,
+including asymmetric coordinates. Fit its center/radii using its own calibration
+sample by exactly the same procedure. Record energy/range mismatch and held-out
+coordinate violations; this null is conditional on fitted bounds, not a statement
+that all real vectors lie inside that box.
+Null C: independent coordinate-uniform box with calibration mean mu and standard
+deviation sigma, bounds mu +/- sqrt(3)*sigma (sample variance ddof1). This matches
+first and diagonal second moments in its generating law. Use its own calibration
+and held-out center/max/RMS fits too; RMS-normalized comparisons target shape
+rather than a change of global scale. Matching diagonal moments is not covariance
+matching and does not preserve correlations.
+
+Use512 validation null draws (not iid model observations), same calibration count
+as each data cloud. Reuse isotropic-ball nulls only for identical dimension/count
+and center-fitting rules. Monte Carlo resolution/error must be stated. Fit both
+centers from the same raw draws where applicable; paired FT uses common randomness.
+
+## Metrics and interpretation
+
+Retain measured norm arrays, empirical CDF grids, max-normalized interior fractions
+at0.5/0.75/0.9/0.95/0.99 and outside1, RMS-normalized CDF from0.5 to1.5,
+median radial ratios and radial spread. Prefer probability-point differences over
+astronomical ratios against tiny a^d. Inspect positive AND negative CDF gaps:
+more inner tail plus more outer tail is not global inward stochastic dominance.
+
+Record exact zeros, coordinate energy participation (sum squares)^2/sum fourths,
+coordinate-variance participation, empirical covariance/second-moment participation
+via calibration Gram matrices. Label sample rank ceiling n_cal-1 (mean centered)
+or n_cal (origin); compare to equally sampled nulls, do not interpret the raw
+finite-sample participation as true intrinsic dimension.
+
+Report both full-radius and moment-controlled results for each center, whole-view
+and head subgroups, primary versus small sample/projection strata, original and
+changed72-LoRA-target groups. Unchanged parameter views can reuse identical results
+with explicit references rather than re-read/recompute. Results describe parameters,
+not activations, model quality, functional duplicates, or pruning gains.
+
+Execution budget: one CPU, 512 MiB RSS ceiling, 7200 seconds per invocation;
+checkpoint every group, resume without replacing completed groups. Any incomplete
+coverage is reported before interpretation. No numerical analysis runs locally.
+
+Illustrative radial CDFs use these fixed original/origin views, selected by module
+role before the census completes, not by the resulting similarity/interior score:
+language layer0 linear-attention Q head0, layer3 full-attention Q head0, and layer0
+MLP up-projection rows. Their plots are illustrations, not independent replications.

model_shell/README.md

@@ -0,0 +1,42 @@
+# Actual-model shell census
+
+Read [PROTOCOL.md](PROTOCOL.md) before interpreting the results. This analyzes
+retained parameter arrays, not model activations or task accuracy.
+
+Run only on the authorized CPU server from the repository root:
+
+```sh
+OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 venv/bin/python -m unittest model_shell.test_analysis -v
+OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 venv/bin/python -m model_shell.analyze
+OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 venv/bin/python -m model_shell.report
+```
+
+Inputs are the already retained `runs/cpu-qwen35-2b/weights` and
+`runs/finetune-head-comparison/adapter.safetensors` with their existing JSON
+configuration and metadata. The adapter is evaluated as FP32 W+2BA. No weights
+are modified, no training or inference is run, and no files are deleted.
+
+`runs/model-shell/checkpoints` is a resumable group ledger. Re-running skips
+completed groups; do not reuse a ledger after changing its protocol/code/inputs.
+One invocation stops at 7,200 seconds, RSS above 512 MiB or available memory below
+128 MiB. Resume after addressing a resource stop; do not relax guards silently.
+
+`results/model-shell` contains the report, group inventory, per-profile metric
+CSV, compressed complete CDF/control summaries, and measured calibration/held-out
+radii. The NPZ keys are `GROUPINDEX_MODEL_CENTER_cal` / `_eval`, with zero-based
+group indices into `inventory.json`. Unchanged fine-tune profiles explicitly
+reference the original profiles in `profiles.json.gz`; the CSV expands both.
+Raw null draws are reproducible from the recorded deterministic seeds; null CDFs,
+scales and participation summaries are stored. `audit.json` verifies coverage,
+recomputes empirical CDFs from the saved norms, checks finite-sample rank limits,
+verifies the original asset SHA256 values, and hashes the output artifacts.
+`example_radii.npz` also saves calibration/held-out radii for the three fixed role
+examples and their regenerated nulls; their grid CDFs are checked against the
+original ledger before plotting full empirical step curves from radius zero.
+
+The count of views is defined by the protocol and is not a unique number of
+distributions inherent in a neural network. These overlapping views are equally
+weighted for description; they are not independent replicates. The box nulls
+match fitted marginal ranges or moments, not correlations. The maximum fitted
+radius is not the unknown support boundary. Quantities near numerical or Monte
+Carlo resolution cannot support a definitive population statement.

model_shell/analyze.py

@@ -0,0 +1,124 @@
+"""CPU-only, read-only parameter cloud census with paired uniform references."""
+import argparse, hashlib, json, math, os, time
+from pathlib import Path
+from types import SimpleNamespace
+import numpy as np
+from pilot.tensors import load_tensors
+from finetune_compare.lora import install
+from model_shell.groups import enumerate_groups
+
+MAX_GRID=np.array([.5,.75,.9,.95,.99,1.,1.05,1.1,1.25])
+RMS_GRID=np.linspace(.5,1.5,41)
+
+def seed(key):return int.from_bytes(hashlib.sha256(key.encode()).digest()[:8],'little')
+
+def guard(start):
+    mem=dict((s.split(':')[0],int(s.split()[1])) for s in Path('/proc/meminfo').read_text().splitlines() if s.startswith('MemAvailable:'))
+    rss=int(Path('/proc/self/statm').read_text().split()[1])*os.sysconf('SC_PAGE_SIZE')
+    if rss>512*1024**2 or mem['MemAvailable']<128*1024 or time.monotonic()-start>7200:
+        raise RuntimeError(f'Resource guard: RSS={rss}, available_kB={mem}, elapsed={time.monotonic()-start:.0f}')
+
+def sample(t,g):
+    rng=np.random.default_rng(seed(g['id']+'/indices'))
+    n=min(512,g['population']);d=min(8192,g['dimension'])
+    points=np.sort(rng.choice(g['population'],n,replace=False))
+    features=np.sort(rng.choice(g['dimension'],d,replace=False)) if d<g['dimension'] else np.arange(d)
+    width=math.prod(t.shape[1:]);out=np.empty((n,d),np.float32)
+    if g['orientation']=='rows':
+        for j,i in enumerate(points):
+            row=i+g['start'];out[j]=t.read(slice(row*width,(row+1)*width))[features]
+            if j%32==0:t.evict()
+    else:
+        for j,i in enumerate(features):
+            out[:,j]=t.read(slice(i*width,(i+1)*width))[points]
+            if j%32==0:t.evict()
+    t.evict();out=out[rng.permutation(n)]
+    digest=hashlib.sha256(points.tobytes()+features.tobytes()).hexdigest()
+    return out,digest
+
+def participation(y):
+    gram=y@y.T;energy=float(np.trace(gram));den=float(np.square(gram).sum())
+    return energy**2/den if den else 0.
+
+def geometry(x,ncal,center,raw=False):
+    # Float64 arithmetic; bounded sample matrix, no model-wide conversion.
+    z=x.astype(np.float64);mu=z[:ncal].mean(0) if center=='mean' else np.zeros(z.shape[1])
+    y=z-mu;norm=np.linalg.norm(y,axis=1);cal=norm[:ncal];r=norm[ncal:]
+    maximum=float(cal.max());rms=float(np.sqrt(np.mean(cal**2)))
+    valid=maximum>0 and rms>0
+    a=r/maximum if maximum else np.zeros_like(r);b=r/rms if rms else np.zeros_like(r)
+    coordvar=z[:ncal].var(0,ddof=1);cv=float(coordvar.sum()**2/np.square(coordvar).sum()) if coordvar.any() else 0.
+    e=np.square(y[ncal:]);den=np.square(e).sum(1)
+    pe=np.divide(e.sum(1)**2,den,out=np.zeros(len(e)),where=den>0)
+    out=dict(valid=bool(valid),ncal=ncal,neval=len(r),d=z.shape[1],rmax=maximum,rms=rms,
+        median_max=float(np.median(a)),median_rms=float(np.median(b)),
+        p10_rms=float(np.quantile(b,.1)),p90_rms=float(np.quantile(b,.9)),
+        outside=float(np.mean(a>1)),cdf_max=[float(np.mean(a<=v)) for v in MAX_GRID],
+        cdf_rms=[float(np.mean(b<=v)) for v in RMS_GRID],
+        covariance_participation=participation(y[:ncal]),rank_ceiling=ncal-(center=='mean'),
+        coordinate_variance_participation=cv,point_energy_participation_median=float(np.median(pe)),
+        zero_fraction=float(np.mean(z==0)))
+    if raw:out.update(cal_norm=cal.tolist(),eval_norm=r.tolist())
+    return out
+
+BALL_CACHE={}
+def references(x,ncal,key):
+    d=x.shape[1];cachekey=(d,ncal)
+    if cachekey not in BALL_CACHE:
+        rng=np.random.default_rng(seed(f'ball/{d}/{ncal}'))
+        y=rng.standard_normal((ncal+512,d),dtype=np.float32)
+        y*=((rng.random(len(y))**(1/d))/np.linalg.norm(y,axis=1))[:,None]
+        BALL_CACHE[cachekey]={c:geometry(y,ncal,c) for c in ('origin','mean')}
+    cal=x[:ncal].astype(np.float64);lo=cal.min(0);hi=cal.max(0);mu=cal.mean(0);sd=cal.std(0,ddof=1)
+    rng=np.random.default_rng(seed(key+'/uniform'))
+    u=rng.random((ncal+512,d),dtype=np.float32)
+    refs={'ball':BALL_CACHE[cachekey]}
+    for name,left,right in [('range_box',lo,hi),('moment_box',mu-np.sqrt(3)*sd,mu+np.sqrt(3)*sd)]:
+        y=(left+(right-left)*u).astype(np.float32)
+        refs[name]={c:geometry(y,ncal,c) for c in ('origin','mean')}
+    return refs,dict(heldout_outside_coordinate_range=float(np.mean((x[ncal:]<lo)|(x[ncal:]>hi))),
+                     range_box_expected_energy=float(np.sum((lo**2+lo*hi+hi**2)/3)),
+                     observed_cal_energy=float(np.mean(np.sum(cal**2,axis=1))))
+
+def analyze(x,key):
+    ncal=len(x)//2;refs,extra=references(x,ncal,key);out={}
+    for center in ('origin','mean'):
+        p=geometry(x,ncal,center,True);p['references']=refs; p.update(extra)
+        p['comparisons']={}
+        for name,bycenter in refs.items():
+            ref=bycenter[center];diff=np.array(p['cdf_rms'])-ref['cdf_rms']
+            p['comparisons'][name]=dict(interior90_gap=p['cdf_max'][2]-ref['cdf_max'][2],
+                interior95_gap=p['cdf_max'][3]-ref['cdf_max'][3],
+                rms_cdf_positive_gap=float(max(0,diff.max())),rms_cdf_negative_gap=float(max(0,-diff.min())),
+                median_rms_delta=p['median_rms']-ref['median_rms'],
+                participation_ratio=p['covariance_participation']/ref['covariance_participation'] if ref['covariance_participation'] else None)
+        p['ball_origin_log_expected_cdf90']=float(x.shape[1]*np.log(.9)+np.log(ncal/(ncal+1)))
+        out[center]=p
+    return out
+
+def main():
+    ap=argparse.ArgumentParser();ap.add_argument('--root',default='runs');ap.add_argument('--out',default='runs/model-shell');args=ap.parse_args()
+    root=Path(args.root);out=Path(args.out);out.mkdir(exist_ok=True,parents=True);(out/'checkpoints').mkdir(exist_ok=True)
+    cfg=json.loads((root/'cpu-qwen35-2b/model_config.json').read_text())['text_config']
+    base=load_tensors(root/'cpu-qwen35-2b/weights');ft=SimpleNamespace(tensors=dict(base),config=cfg)
+    meta=json.loads((root/'finetune-head-comparison/adapter_meta.json').read_text())
+    pairs,scale=install(ft,root/'finetune-head-comparison',meta)
+    groups,skips=enumerate_groups(base,cfg)
+    manifest=dict(groups=groups,skips=skips,total_tensors=len(base),parameters=sum(t.numel for t in base.values()),changed_tensors=sorted(pairs),adapter_meta=meta,
+                  max_grid=MAX_GRID.tolist(),rms_grid=RMS_GRID.tolist(),null_eval=512,point_cap=512,feature_cap=8192)
+    (out/'inventory.json').write_text(json.dumps(manifest,indent=2));start=time.monotonic()
+    for i,g in enumerate(groups):
+        target=out/'checkpoints'/f'{i:04d}.json'
+        if target.exists():continue
+        guard(start);x,digest=sample(base[g['tensor']],g);p=analyze(x,g['id']);del x
+        changed=g['tensor'] in pairs
+        if changed:
+            guard(start);x,digest2=sample(ft.tensors[g['tensor']],g);assert digest==digest2
+            q=analyze(x,g['id']);del x
+        else:q=None
+        record=dict(group=g,sampling_sha256=digest,changed=changed,original=p,finetuned=q,finetuned_reuses_original=not changed)
+        temp=target.with_suffix('.tmp');temp.write_text(json.dumps(record,allow_nan=False));temp.replace(target)
+        if i%10==0:print(f'{i+1}/{len(groups)} elapsed={time.monotonic()-start:.1f}s',flush=True)
+    print('COMPLETE',len(groups),flush=True)
+
+if __name__=='__main__':main()

model_shell/groups.py

@@ -0,0 +1,29 @@
+"""Deterministic finite family of matrix/vector clouds; no data-dependent choice."""
+import math,re
+
+def enumerate_groups(tensors,cfg):
+    groups=[];skips=[]
+    for name,t in sorted(tensors.items()):
+        if len(t.shape)<2:skips.append({'name':name,'reason':'one vector, no vector cloud','shape':t.shape});continue
+        rows=t.shape[0];cols=math.prod(t.shape[1:]);count=0
+        for axis,n,d in [('rows',rows,cols),('columns',cols,rows)]:
+            if n>=16 and d>=32:
+                groups.append({'id':name+'::'+axis,'tensor':name,'kind':'matrix','orientation':axis,'start':0,'stop':rows,'population':n,'dimension':d,'stratum':'primary' if n>=64 else 'small_sample'});count+=1
+        if not count:skips.append({'name':name,'reason':'insufficient points or dimension <32','shape':t.shape})
+        if not (name.startswith('model.language_model.layers.') or name.startswith('mtp.layers.')):continue
+        segments=[]
+        if name.endswith('.linear_attn.in_proj_qkv.weight'):
+            kh,kd,vh,vd=[cfg[k] for k in ('linear_num_key_heads','linear_key_head_dim','linear_num_value_heads','linear_value_head_dim')]
+            for role,num,dim,offset in [('Q',kh,kd,0),('K',kh,kd,kh*kd),('V',vh,vd,2*kh*kd)]:
+                for h in range(num):segments.append((role,h,offset+h*dim,offset+(h+1)*dim))
+        elif name.endswith('.self_attn.q_proj.weight'):
+            dim=cfg['head_dim'];assert rows==cfg['num_attention_heads']*2*dim
+            for h in range(cfg['num_attention_heads']):
+                for role,offset in [('Q',0),('gate',dim)]:segments.append((role,h,h*2*dim+offset,h*2*dim+offset+dim))
+        elif name.endswith(('.self_attn.k_proj.weight','.self_attn.v_proj.weight')):
+            role='K' if '.k_proj.' in name else 'V';dim=cfg['head_dim'];assert rows==cfg['num_key_value_heads']*dim
+            for h in range(cfg['num_key_value_heads']):segments.append((role,h,h*dim,(h+1)*dim))
+        for role,h,start,stop in segments:
+            assert stop<=rows
+            groups.append({'id':name+f'::{role}_head{h}','tensor':name,'kind':'head_rows','orientation':'rows','start':start,'stop':stop,'population':stop-start,'dimension':cols,'stratum':'primary' if stop-start>=64 else 'small_sample'})
+    return groups,skips

model_shell/report.py

@@ -0,0 +1,176 @@
+"""Validate complete checkpoints; write compact scientific data, figures and report."""
+import argparse,csv,gzip,hashlib,json,platform,sys
+from pathlib import Path
+import numpy as np
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+from model_shell.analyze import MAX_GRID,RMS_GRID,sample,seed
+from pilot.tensors import load_tensors
+
+def sha(p):
+    h=hashlib.sha256()
+    with p.open('rb') as f:
+        for b in iter(lambda:f.read(1024*1024),b''):h.update(b)
+    return h.hexdigest()
+
+def main():
+    ap=argparse.ArgumentParser();ap.add_argument('--input',default='runs/model-shell');ap.add_argument('--out',default='results/model-shell');a=ap.parse_args()
+    source=Path(a.input);out=Path(a.out);out.mkdir(parents=True,exist_ok=True);figdir=out/'figures';figdir.mkdir(exist_ok=True)
+    inventory=json.loads((source/'inventory.json').read_text());groups=inventory['groups'];records=[];rows=[];radii={}
+    for i,g in enumerate(groups):
+        r=json.loads((source/'checkpoints'/f'{i:04d}.json').read_text());assert r['group']==g
+        assert r['changed']==(g['tensor'] in inventory['changed_tensors'])
+        for model in ('original','finetuned'):
+            profiles=r[model] if r[model] is not None else r['original']
+            for center,p in profiles.items():
+                if center=='origin':
+                    p['ball_log_expected_max_cdf']=[float(p['d']*np.log(z)+np.log(p['ncal']/(p['ncal']+1))) for z in MAX_GRID if z<=1]
+                assert p['valid'] and p['d']==min(8192,g['dimension'])
+                assert p['covariance_participation']<=p['rank_ceiling']+1e-6
+                v=np.asarray(p['eval_norm']);cal=np.asarray(p['cal_norm'])
+                assert abs(p['rmax']-cal.max())<1e-12
+                assert abs(p['outside']-np.mean(v>p['rmax']))<1e-12
+                np.testing.assert_allclose(p['cdf_max'],[np.mean(v<=z*p['rmax']) for z in MAX_GRID])
+                np.testing.assert_allclose(p['cdf_rms'],[np.mean(v<=z*p['rms']) for z in RMS_GRID])
+                key=f'{i}_{model}_{center}';radii[key+'_cal']=cal;radii[key+'_eval']=v
+                row=dict(group_index=i,group=g['id'],model=model,center=center,kind=g['kind'],stratum=g['stratum'],changed=r['changed'],projected=g['dimension']>8192,
+                         dimension=g['dimension'],analyzed_dimension=p['d'],population=g['population'],ncal=p['ncal'],neval=p['neval'],sampling_sha256=r['sampling_sha256'])
+                for k in ('median_max','median_rms','p10_rms','p90_rms','outside','rmax','rms','covariance_participation','rank_ceiling','coordinate_variance_participation','point_energy_participation_median','zero_fraction','heldout_outside_coordinate_range'):
+                    row[k]=p[k]
+                for name,c in p['comparisons'].items():
+                    for k,vv in c.items():row[name+'_'+k]=vv
+                rows.append(row)
+        # Compact profiles keep CDF and controls; raw measured radii live in NPZ.
+        for model in ('original','finetuned'):
+            if r[model] is not None:
+                for p in r[model].values():p.pop('cal_norm');p.pop('eval_norm')
+        records.append(r)
+    with gzip.open(out/'profiles.json.gz','wt',encoding='utf-8') as f:json.dump(records,f,allow_nan=False)
+    np.savez_compressed(out/'measured_radii.npz',**radii)
+    with (out/'metrics.csv').open('w',newline='') as f:
+        w=csv.DictWriter(f,fieldnames=list(rows[0]));w.writeheader();w.writerows(rows)
+    (out/'inventory.json').write_text(json.dumps(inventory,indent=2))
+    def subset(model='original',center='origin',kind=None,changed=None):
+        return [r for r in rows if r['model']==model and r['center']==center and r['stratum']=='primary' and not r['projected'] and (kind is None or r['kind']==kind) and (changed is None or r['changed']==changed)]
+    summary={}
+    for center in ('origin','mean'):
+        for kind in ('matrix','head_rows'):
+            rs=subset(center=center,kind=kind);s={'n':len(rs),
+                'median_zero_fraction':float(np.median([r['zero_fraction'] for r in rs])),
+                'median_coordinate_energy_participation_fraction':float(np.median([r['point_energy_participation_median']/r['analyzed_dimension'] for r in rs])),
+                'median_coordinate_variance_participation_fraction':float(np.median([r['coordinate_variance_participation']/r['analyzed_dimension'] for r in rs]))}
+            for ref in ('ball','range_box','moment_box'):
+                gap=np.array([r[ref+'_interior90_gap'] for r in rs]);pos=np.array([r[ref+'_rms_cdf_positive_gap'] for r in rs]);neg=np.array([r[ref+'_rms_cdf_negative_gap'] for r in rs])
+                s[ref]=dict(median_interior90_gap=float(np.median(gap)),fraction_gap_gt10pp=float(np.mean(gap>.1)),median_positive_rms_gap=float(np.median(pos)),median_negative_rms_gap=float(np.median(neg)),
+                            fraction_two_sided_rms_gt10pp=float(np.mean((pos>.1)&(neg>.1))),median_participation_ratio=float(np.median([r[ref+'_participation_ratio'] for r in rs])))
+            summary[center+'/'+kind]=s
+    paired={}
+    for center in ('origin','mean'):
+        b=subset(center=center,changed=True);t=subset(model='finetuned',center=center,changed=True);assert [r['group'] for r in b]==[r['group'] for r in t]
+        paired[center]={'n':len(b)}
+        for field in ('median_max','median_rms','covariance_participation','moment_box_interior90_gap'):
+            delta=np.array([q[field]-p[field] for p,q in zip(b,t)])
+            paired[center][field]=dict(median=float(np.median(delta)),p10=float(np.quantile(delta,.1)),p90=float(np.quantile(delta,.9)),max_abs=float(np.max(abs(delta))))
+    secondary=[]
+    for model in ('original','finetuned'):
+        for center in ('origin','mean'):
+            for label in ('small_sample','projected'):
+                ss=[r for r in rows if r['model']==model and r['center']==center and (r['stratum']=='small_sample' if label=='small_sample' else r['projected'])]
+                secondary.append(dict(model=model,center=center,stratum=label,n=len(ss),median_max=float(np.median([r['median_max'] for r in ss])),moment_interior90_gap=float(np.median([r['moment_box_interior90_gap'] for r in ss]))))
+    (out/'summary.json').write_text(json.dumps(dict(strata=summary,paired_changed=paired,secondary=secondary),indent=2))
+    plt.rcParams.update({'font.size':10,'axes.spines.top':False,'axes.spines.right':False})
+    def save(fig,name):
+        fig.tight_layout()
+        for ext in ('png','pdf','svg'):fig.savefig(figdir/(name+'.'+ext),dpi=180,bbox_inches='tight')
+        plt.close(fig)
+    fig,axs=plt.subplots(2,2,figsize=(11,7),sharex=True,sharey=True)
+    for i,center in enumerate(('origin','mean')):
+        for j,kind in enumerate(('matrix','head_rows')):
+            rs=subset(center=center,kind=kind);ax=axs[i,j]
+            for ref,label in [('ball','Uniform ball'),('range_box','Range-matched box'),('moment_box','Moment-matched box')]:
+                v=np.sort([r[ref+'_interior90_gap'] for r in rs]);ax.plot(v,np.arange(1,len(v)+1)/len(v),label=label)
+            ax.axvline(0,color='gray',lw=.8);ax.set_title(f'{center} center / {kind}, n={len(rs)}');ax.set_xlabel('F(data r/Rcal <= 0.9) - F(null)');ax.set_ylabel('Fraction of sampled groups')
+    axs[0,0].legend(fontsize=8);fig.suptitle('Actual Qwen3.5-2B: interior mass depends on the reference');save(fig,'interior_reference_sensitivity')
+    fig,axs=plt.subplots(1,2,figsize=(11,4.3))
+    for ax,center in zip(axs,('origin','mean')):
+        for kind,marker in [('matrix','o'),('head_rows','.')]:
+            rs=subset(center=center,kind=kind);ax.scatter([r['moment_box_rms_cdf_positive_gap'] for r in rs],[r['moment_box_rms_cdf_negative_gap'] for r in rs],s=10,alpha=.25,label=kind,marker=marker)
+        ax.axvline(.1,color='gray',lw=.7);ax.axhline(.1,color='gray',lw=.7);ax.set(xlabel='Largest positive CDF gap (more inner tail)',ylabel='Largest negative CDF gap (more outer tail)',title=f'{center} / moment-matched box',xlim=(-.02,1),ylim=(-.02,1));ax.legend()
+    fig.suptitle('RMS-normalized radial shape: both tails can grow together');save(fig,'radial_shape_two_tails')
+    fig,axs=plt.subplots(1,2,figsize=(11,4.3))
+    for ax,center in zip(axs,('origin','mean')):
+        b=subset(center=center,changed=True);t=subset(model='finetuned',center=center,changed=True)
+        x=[r['median_max'] for r in b];y=[r['median_max'] for r in t];ax.scatter(x,y,s=10,alpha=.3);low=min(x+y);high=max(x+y);ax.plot([low,high],[low,high],color='gray',lw=1)
+        ax.set(xlabel='Original median r/Rcal',ylabel='GRPO-LoRA median r/Rcal',title=f'{center}, changed primary groups n={len(b)}')
+    fig.suptitle('Paired same-index comparison; unchanged groups excluded');save(fig,'finetune_paired_radii')
+    fig,axs=plt.subplots(1,2,figsize=(11,4.3))
+    for ax,center in zip(axs,('origin','mean')):
+        for kind in ('matrix','head_rows'):
+            rs=subset(center=center,kind=kind);v=np.sort([r['moment_box_participation_ratio'] for r in rs]);ax.plot(v,np.arange(1,len(v)+1)/len(v),label=kind)
+        ax.set(xlabel='Empirical participation / equal-sample moment-box reference',ylabel='Fraction of groups',title=center);ax.axvline(1,color='gray',lw=.8);ax.legend()
+    fig.suptitle('Finite-sample participation is not the true intrinsic dimension');save(fig,'participation_reference')
+    examples=[('model.language_model.layers.0.linear_attn.in_proj_qkv.weight::Q_head0','L0 GDN Q head0'),
+              ('model.language_model.layers.3.self_attn.q_proj.weight::Q_head0','L3 full-attention Q head0'),
+              ('model.language_model.layers.0.mlp.up_proj.weight::rows','L0 MLP up rows')]
+    fig,axs=plt.subplots(2,3,figsize=(13,7),sharey=True)
+    byid={r['group']['id']:r for r in records}
+    indices={g['id']:i for i,g in enumerate(groups)}
+    base=load_tensors(Path('runs/cpu-qwen35-2b/weights'));example_radii={}
+    for j,(gid,label) in enumerate(examples):
+        p=byid[gid]['original']['origin']
+        g=byid[gid]['group'];x,digest=sample(base[g['tensor']],g);assert digest==byid[gid]['sampling_sha256']
+        ncal=len(x)//2;d=x.shape[1];cal=x[:ncal].astype(np.float64)
+        rng=np.random.default_rng(seed(gid+'/uniform'));u=rng.random((ncal+512,d),dtype=np.float32)
+        mu=cal.mean(0);sd=cal.std(0,ddof=1);lo=mu-np.sqrt(3)*sd;hi=mu+np.sqrt(3)*sd
+        moment=(lo+(hi-lo)*u).astype(np.float32)
+        rng=np.random.default_rng(seed(f'ball/{d}/{ncal}'));ball=rng.standard_normal((ncal+512,d),dtype=np.float32)
+        ball*=((rng.random(len(ball))**(1/d))/np.linalg.norm(ball,axis=1))[:,None]
+        arrays={'Actual weights':(radii[f'{indices[gid]}_original_origin_cal'],radii[f'{indices[gid]}_original_origin_eval'])}
+        for name,ref,raw in [('Uniform ball','ball',ball),('Moment box','moment_box',moment)]:
+            nr=np.linalg.norm(raw.astype(np.float64),axis=1);arrays[name]=(nr[:ncal],nr[ncal:])
+            np.testing.assert_allclose([np.mean(nr[ncal:]/nr[:ncal].max()<=z) for z in MAX_GRID],p['references'][ref]['origin']['cdf_max'])
+            np.testing.assert_allclose([np.mean(nr[ncal:]/np.sqrt(np.mean(nr[:ncal]**2))<=z) for z in RMS_GRID],p['references'][ref]['origin']['cdf_rms'])
+        for name,(nc,ne) in arrays.items():example_radii[f'example{j}_{name.replace(" ","_")}_cal']=nc;example_radii[f'example{j}_{name.replace(" ","_")}_eval']=ne
+        for i,(field,grid) in enumerate([('cdf_max',MAX_GRID),('cdf_rms',RMS_GRID)]):
+            ax=axs[i,j]
+            for name,(nc,ne) in arrays.items():
+                scale=nc.max() if i==0 else np.sqrt(np.mean(nc**2));v=np.sort(ne/scale)
+                ax.step(np.r_[0,v, max(1.5,float(v[-1]))],np.r_[0,np.arange(1,len(v)+1)/len(v),1],where='post',label=name,lw=1.8,ls='-' if name=='Actual weights' else '--')
+            ax.set(title=label,xlabel='r / calibration '+('maximum' if i==0 else 'RMS'),ylabel='Held-out CDF',ylim=(-.03,1.03),xlim=(0,1.5));ax.legend(fontsize=8)
+    np.savez_compressed(out/'example_radii.npz',**example_radii)
+    fig.suptitle('Fixed role examples: maximum-radius and RMS normalization');save(fig,'fixed_role_radial_cdfs')
+    lines=['# 两个实际2B检查点：参数云的球壳检验','',f'完整覆盖：{len(groups)}组/模型，{len(rows)}个模型×中心组合。632个参数张量，2,274,069,824个参数（包含语言、视觉和MTP）。',
+           '','## 分布计数与范围','', '固定规则给出554个矩阵行／列视图、1,004个语言/MTP头视图；视图互相重叠，不能相加成参数节省量。1,522组有至少64个可用点，36组只有16个点；1组高维嵌入列视图使用8,192个抽样坐标。主表只汇总样本充足且没有投影的1,521组。319个一维张量和18个低维卷积核不构成此规则下的高维样本云。',
+           '','## 原模型结果（逐组等权，非独立观测）','', '|中心|视图|组数|相对均匀球：90%半径内质量差中位数|相对矩匹配均匀盒：质量差中位数|矩匹配且RMS归一后，内外两尾差均>10个百分点的组占比|','|---|---|---:|---:|---:|---:|']
+    for key,s in summary.items():
+        c,k=key.split('/');lines.append(f'|{c}|{k}|{s["n"]}|{s["ball"]["median_interior90_gap"]:.3f}|{s["moment_box"]["median_interior90_gap"]:.3f}|{s["moment_box"]["fraction_two_sided_rms_gt10pp"]:.1%}|')
+    lines+=['','这些是描述性质量差，不是显著性检验。正差表示所指定半径以内有更多样本；不能将其自动解释为整条径向分布向内平移。若RMS归一后内外尾均变多，更准确的解释是径向分布更宽。',
+            '', '|中心/视图|逐组零值率中位数|点坐标能量参与率÷维度，中位数|坐标方差参与率÷维度，中位数|','|---|---:|---:|---:|']
+    for key,s in summary.items():lines.append(f'|{key}|{s["median_zero_fraction"]:.6g}|{s["median_coordinate_energy_participation_fraction"]:.4f}|{s["median_coordinate_variance_participation_fraction"]:.4f}|')
+    lines+=['','能量参与率衡量坐标能量集中程度，依赖坐标基；有限样本Gram参与率反映样本间几何，另见图。两者均不是精确非零坐标计数，也不是模型功能的有效维度。',
+            '','## 微调前后','', '原模型是官方Qwen3.5-2B；第二个模型是在同一revision上进行GSM8K GRPO的已有纯LoRA检查点，不是另一个完整模型。72个投影矩阵使用只读W+2BA展开，其余参数共享。972个视图受影响；其中936个属于主分析。未改变视图复用完全相同的分析结果。','', '|中心|指标|配对变化中位数|P10|P90|最大绝对变化|','|---|---|---:|---:|---:|---:|']
+    for c,p in paired.items():
+        for k,v in p.items():
+            if k!='n':lines.append(f'|{c}|{k}|{v["median"]:.6g}|{v["p10"]:.6g}|{v["p90"]:.6g}|{v["max_abs"]:.6g}|')
+    lines+=['','## 单独描述的小样本与坐标抽样组','', '|模型|中心|类别|组数|中位r/Rcal的组间中位数|相对矩匹配盒的90%半径质量差中位数|','|---|---|---|---:|---:|---:|']
+    for s in secondary:lines.append(f'|{s["model"]}|{s["center"]}|{s["stratum"]}|{s["n"]}|{s["median_max"]:.4f}|{s["moment_interior90_gap"]:.4f}|')
+    lines+=['','## 如何读图','', '1. `interior_reference_sensitivity`：不同均匀参照下，90%校准半径内质量差的组间CDF。横轴向右表示更多内侧质量。','2. `radial_shape_two_tails`：匹配矩且按各自校准RMS归一后的最大正／负CDF差。右上角同时增加两尾，并非单纯向内。','3. `finetune_paired_radii`：同一视图的微调前后中位半径；对角线表示不变。','4. `participation_reference`：Gram矩阵参与率相对同样本量参照的比值。受样本秩上限影响，不能当作真实内在维度。',
+            '5. `fixed_role_radial_cdfs`：预先按角色固定的GDN Q、全注意力Q和MLP上投影示例，上排按校准最大半径、下排按RMS归一化。不是按结果挑选最明显的组。',
+            '','## 方法与限制','', '每组最多512个不同参数向量，一半校准、一半留出；模型固定，留出指未用于拟合中心/半径的参数向量，不是独立训练模型。对照每组512个验证向量，最坏单项二项标准误约2.21个百分点；模型留出样本的误差还取决于样本数，且参数向量不保证独立。10个百分点仅是描述性筛选尺度，没有多重检验或总体显著性结论。',
+            '','均匀球、坐标范围均匀盒、匹配均值和逐坐标方差的均匀盒都按同一规则拟合自身中心、最大半径及RMS。均匀球原点中心的精确拟合半径CDF为a^d*ncal/(ncal+1)，保留其对数以避免把模拟零命中误报成零概率。盒对照不是协方差匹配。校准最大半径不是已知总体支撑边界；留出越界率已记录。',
+            '','`origin`保留坐标均值贡献；`mean`使用校准向量均值。没有使用所有数据来选边界、训练或推理；没有删除模型。坐标稀疏性、有限样本参与率与径向内侧质量分别测量，不能相互代替。该实验不能证明更好的模型更靠里，也不能证明功能重复、可剪枝或可复用。',
+            '','复现：`OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 venv/bin/python -m model_shell.analyze`，完成后 `... python -m model_shell.report`。只在已授权CPU服务器运行。分组协议见 `model_shell/PROTOCOL.md`；全量指标在metrics.csv，完整CDF/参照在profiles.json.gz，实测范数在measured_radii.npz。']
+    (out/'REPORT.md').write_text('\n'.join(lines)+'\n')
+    sources={'base':Path('runs/cpu-qwen35-2b/weights/model.safetensors-00001-of-00001.safetensors'),'adapter':Path('runs/finetune-head-comparison/adapter.safetensors'),
+             'config':Path('runs/cpu-qwen35-2b/model_config.json'),'adapter_meta':Path('runs/finetune-head-comparison/adapter_meta.json')}
+    sourcehash={k:dict(path=str(p),bytes=p.stat().st_size,sha256=sha(p)) for k,p in sources.items()}
+    (out/'environment.json').write_text(json.dumps(dict(python=sys.version,platform=platform.platform(),numpy=np.__version__,matplotlib=matplotlib.__version__,
+        source_code_sha256={str(p):sha(p) for p in sorted(Path('model_shell').glob('*.py'))},protocol_sha256=sha(Path('model_shell/PROTOCOL.md')),seed='first 8 SHA256 bytes, little endian; group id plus purpose suffix',
+        thread_limit=1,server='8.211.154.134',training=False,inference=False,gpu=False),indent=2))
+    assert sourcehash['base']['sha256']=='aa33250c4fc64891ddfaba3a314fd9542ea371843c387178b425fbcc5ed680b1'
+    assert sourcehash['adapter']['sha256']=='93a3f43a4356f84da0d13ecc622fee13e41ba15d237cdaad6094f243899c62d6'
+    audit=dict(complete_groups=len(groups),profile_rows=len(rows),all_finite=True,cdf_recomputed=True,sample_rank_checked=True,sources=sourcehash,files={str(p.relative_to(out)):sha(p) for p in sorted(out.rglob('*')) if p.is_file() and p.name!='audit.json'})
+    (out/'audit.json').write_text(json.dumps(audit,indent=2));print(json.dumps(dict(summary=summary,paired=paired),indent=2))
+
+if __name__=='__main__':main()

model_shell/test_analysis.py

@@ -0,0 +1,49 @@
+"""Run on the authorized CPU server: python -m unittest model_shell.test_analysis."""
+import unittest
+import numpy as np
+from model_shell.analyze import sample,geometry,analyze,seed
+from finetune_compare.lora import EffectiveWeight
+
+class ArrayTensor:
+    def __init__(self,x):self.x=x;self.shape=x.shape;self.numel=x.size;self.dtype='F32'
+    def read(self,s=slice(None)):return self.x.reshape(-1)[s].copy()
+    def evict(self):pass
+
+class Checks(unittest.TestCase):
+    def test_sampling_and_lora(self):
+        rng=np.random.default_rng(17);w=rng.normal(size=(80,64)).astype('f');a=rng.normal(size=(2,64)).astype('f');b=rng.normal(size=(80,2)).astype('f')
+        effective=EffectiveWeight(ArrayTensor(w),a,b,2);explicit=ArrayTensor(w+2*b@a)
+        for axis,n,d,start,stop in [('rows',80,64,0,80),('columns',64,80,0,80),('rows',32,64,16,48)]:
+            g=dict(id=axis+str(start),orientation=axis,population=n,dimension=d,start=start,stop=stop)
+            x,h=sample(effective,g);y,k=sample(explicit,g);np.testing.assert_allclose(x,y,atol=2e-6,rtol=1e-5);self.assertEqual(h,k)
+            self.assertEqual(x.shape,(n,d))
+            rr=np.random.default_rng(seed(g['id']+'/indices'));indices=np.sort(rr.choice(n,n,replace=False));order=rr.permutation(n)
+            cloud=explicit.x[start:stop] if axis=='rows' else explicit.x.T
+            np.testing.assert_allclose(y,cloud[indices][order],atol=0,rtol=0)
+    def test_center_scale_and_rank(self):
+        rng=np.random.default_rng(5);x=rng.normal(size=(128,96)).astype('f')
+        for center in ('origin','mean'):
+            p=geometry(x,64,center);q=geometry(x*3,64,center)
+            np.testing.assert_allclose(p['cdf_rms'],q['cdf_rms']);self.assertLessEqual(p['covariance_participation'],p['rank_ceiling']+1e-6)
+        p=geometry(x,64,'mean');q=geometry(x+4,64,'mean');np.testing.assert_allclose(p['median_rms'],q['median_rms'],rtol=1e-6)
+        self.assertFalse(geometry(np.zeros((32,64)),16,'mean')['valid'])
+    def test_ball_fit_and_sparse_counterexample(self):
+        rng=np.random.default_rng(33);d=32;ncal=256
+        # Analytic expectation over independent calibration maxima.
+        c=rng.random((20000,ncal))**(1/d);v=rng.random(20000)**(1/d)
+        observed=np.mean(v<=.99*c.max(1));expected=.99**d*ncal/(ncal+1)
+        self.assertLess(abs(observed-expected),.015)
+        radii=rng.random(512)**(1/d);x=np.zeros((512,d),dtype='f');x[:,0]=radii
+        p=geometry(x,256,'origin');self.assertGreater(p['median_max'],.9)
+        # Sparse support alone retains the full-dimensional radial law.
+        self.assertLess(p['cdf_max'][2],.08)
+        x[:,0]=rng.random(512)**.5
+        self.assertGreater(geometry(x,256,'origin')['cdf_max'][2],.65)
+        sphere=rng.normal(size=(512,d));sphere/=np.linalg.norm(sphere,axis=1)[:,None]
+        self.assertEqual(geometry(sphere,256,'origin')['cdf_max'][2],0.)
+    def test_reproducible_controls(self):
+        x=np.random.default_rng(7).normal(size=(32,40)).astype('f');a=analyze(x,'test');b=analyze(x,'test')
+        self.assertEqual(a,b)
+        self.assertTrue(a['mean']['valid'])
+
+if __name__=='__main__':unittest.main()

results/block-activation/REPORT.md

@@ -0,0 +1,25 @@
+# 实测激活率：SiLU 门控模型
+
+覆盖24层、4条中英文文本，每条32 token；所有投影输出及 MLP 门控中间量，按64/128输出通道分块。
+
+| 输出 | 精确零比例均值 | 0.01 RMS以下均值 | 0.1 RMS以下均值 | 32 token内曾活跃通道比例均值 | 90%能量所需通道比例均值 |
+|---|---:|---:|---:|---:|---:|
+| mlp.gate_proj.output | 0.0000% | 0.7811% | 7.8005% | 100.0000% | 63.3063% |
+| mlp.silu_gate | 0.0000% | 0.5031% | 5.1789% | 100.0000% | 70.1228% |
+| mlp.gated_product | 0.0000% | 3.1636% | 21.0460% | 100.0000% | 54.8421% |
+| self_attn.q_proj.output | 0.0000% | 1.2760% | 12.4831% | 100.0000% | 44.0277% |
+| self_attn.k_proj.output | 0.0000% | 1.2245% | 12.0834% | 100.0000% | 53.4587% |
+| self_attn.v_proj.output | 0.0000% | 1.3741% | 13.4814% | 100.0000% | 59.9447% |
+
+128通道块×文本观测共27600条，整个32 token窗口全为精确零的块观测数为0。
+
+表中是整模块逐文本统计再取均值；每个分块的独立统计见 metrics.csv。阈值是各自RMS的比例；阈值下激活率=1−近零比例。能量集中不等于可删除或可替换。
+
+仅测该模型、四条短文本；不推断所有语言/长上下文。SiLU不把负值整体截零，负数不等于静默。未执行删块/跳算的质量验证。权重和既有缓存全部保留。
+
+CPU测量耗时268.6秒，峰值RSS151.6 MiB。
+
+mlp.gated_product: 128通道块在0.1 RMS阈值上的激活率范围 10.47%–89.04%。
+self_attn.q_proj.output: 128通道块在0.1 RMS阈值上的激活率范围 75.49%–100.00%。
+self_attn.k_proj.output: 128通道块在0.1 RMS阈值上的激活率范围 81.20%–93.95%。
+self_attn.v_proj.output: 128通道块在0.1 RMS阈值上的激活率范围 76.66%–91.89%。

results/block-activation/aggregates.json

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+{
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+}

results/block-activation/audit.json

@@ -0,0 +1,7 @@
+{
+  "passed": true,
+  "rows": 83616,
+  "layers": 24,
+  "samples": 4,
+  "all_zero_128_blocks": 0
+}

results/block-activation/checksums.json

@@ -0,0 +1,18 @@
+{
+  "REPORT.md": "f7f05bdbf9c9d096fe685ebb49ee15ff9866a99071302f9d7710b99b1a2e7db4",
+  "aggregates.json": "57dfef79b14e73dafd39840661abacd8b44447884e640bb40b86f90ec441ee4d",
+  "audit.json": "2a67fde20f6765d0dbfa783ff31c548f8135af5d74b6fc0e27a3e71c9cbdffb1",
+  "figures/block_activity.pdf": "354cbc89dfc6da6d6611c6effc9dfefc6762e3da1d52bfba89cc11be8c8a9247",
+  "figures/block_activity.png": "ddcfa1f0de30f4f8bdece16b3451ca1b5bf419fac5a5bb9bb944301983efaeec",
+  "figures/block_activity.svg": "da85920dd0fc32ba439b650c8b695757d42eb72768b4017a78109cb8a2f24216",
+  "figures/mlp_activation.pdf": "6eef102d1ecc7c21206da862f79d4edaed77a695f7c46d53550c3dc6f9087ee5",
+  "figures/mlp_activation.png": "0e1a01ea5b21274eaa757c1c9cd493524003220eadcf7aa378e0eb39a37a7c40",
+  "figures/mlp_activation.svg": "d7b0a67d2d1d501e6fa70aa4a38010bead93a450423e5cd6b3b1284794353c8f",
+  "final-tests.txt": "7b52cfeffca483471297e30aa9658bc2c5c6585b682f5ee5fbbcc2e1b2b8abe4",
+  "first-attempt.txt": "7a1f686db3b7412b681d516bdd83bb8ed7ab2a758a44a2d84c4c7968017d9c91",
+  "full-tests.txt": "55fe658a01d15ed380f2b5cd01058f2374997ffde945025262e92fd966f0dc3b",
+  "metrics.csv": "8f6205e7f446f83567485275f57f63ce64233eee711a4be89c30b37182772e4d",
+  "run.txt": "9fe01020a3ac92db51a17042eef6af73f854dd2e81220d7a04cd5a843c6ff9ba",
+  "source-hashes.json": "34d1c164e787ec6ec5b77d4bc056da2e72365e1cd4b5089741f2b979a3c8beb3",
+  "summary.json": "1e7c3bace11673d10cb927d689348bf00790a9b074ccafed6ce44d898410ef98"
+}

results/block-activation/final-tests.txt

@@ -0,0 +1,7 @@
+test_known_activity (tests.test_activation.ActivationTests.test_known_activity) ... ok
+test_zero_and_scale (tests.test_activation.ActivationTests.test_zero_and_scale) ... ok
+
+----------------------------------------------------------------------
+Ran 2 tests in 0.002s
+
+OK

results/block-activation/first-attempt.txt

@@ -0,0 +1,24 @@
+completed fresh-zh-1 20904
+completed fresh-zh-2 41808
+completed fresh-en-1 62712
+Traceback (most recent call last):
+  File "/opt/exp-model-parameter-similarity/activation_probe.py", line 44, in <module>
+    if __name__=='__main__':main()
+                            ~~~~^^
+  File "/opt/exp-model-parameter-similarity/activation_probe.py", line 39, in main
+    lm.forward(np.array([ids],dtype=np.int64)); print('completed',s['id'],len(lm.rows),flush=True)
+    ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/opt/exp-model-parameter-similarity/functional/model.py", line 220, in forward
+    h = h + self.mlp(n,donor)
+            ~~~~~~~~^^^^^^^^^
+  File "/opt/exp-model-parameter-similarity/activation_probe.py", line 30, in mlp
+    return self.linear(g*u,p+'down_proj')
+           ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
+  File "/opt/exp-model-parameter-similarity/activation_probe.py", line 22, in linear
+    y=super().linear(x,name,rows); self.record(y,name+'.output')
+  File "/opt/exp-model-parameter-similarity/functional/model.py", line 130, in linear
+    self.guard()
+    ~~~~~~~~~~^^
+  File "/opt/exp-model-parameter-similarity/functional/model.py", line 111, in guard
+    raise MemoryError(f'Sustained host swap pressure plus constrained memory: available={available} KiB, process_swap={own_swap_kib} KiB')
+MemoryError: Sustained host swap pressure plus constrained memory: available=989040 KiB, process_swap=132360 KiB

results/block-activation/full-tests.txt

@@ -0,0 +1,33 @@
+test_known_activity (test_activation.ActivationTests.test_known_activity) ... ok
+test_zero_and_scale (test_activation.ActivationTests.test_zero_and_scale) ... ok
+test_assignment_against_exhaustive_and_inverse (test_crossqkv.CrossQKVTests.test_assignment_against_exhaustive_and_inverse) ... ok
+test_cached_mode_rejects_future_state_reuse (test_crossqkv.CrossQKVTests.test_cached_mode_rejects_future_state_reuse) ... ok
+test_controls_cover_multiple_projection_layers (test_crossqkv.CrossQKVTests.test_controls_cover_multiple_projection_layers) ... ok
+test_head_matrix_metrics_and_gram_are_diagnostic (test_crossqkv.CrossQKVTests.test_head_matrix_metrics_and_gram_are_diagnostic) ... ok
+test_probability_identity_and_nontrivial_queries (test_crossqkv.CrossQKVTests.test_probability_identity_and_nontrivial_queries) ... ok
+test_profile_matches_canonical_and_head_sum (test_crossqkv.CrossQKVTests.test_profile_matches_canonical_and_head_sum) ... ok
+test_recipient_value_gate_output_and_gqa_group_stay_fixed (test_crossqkv.CrossQKVTests.test_recipient_value_gate_output_and_gqa_group_stay_fixed) ... ok
+test_topk_tail_rank_can_change_with_tiny_probability_difference (test_crossqkv.CrossQKVTests.test_topk_tail_rank_can_change_with_tiny_probability_difference) ... ok
+test_causal_convolution_and_norm_boundary (test_functional.FunctionalTests.test_causal_convolution_and_norm_boundary) ... ok
+test_causality_and_identical_function_metrics (test_functional.FunctionalTests.test_causality_and_identical_function_metrics) ... ok
+test_neuron_function_metrics_and_targeted_replacement (test_functional.FunctionalTests.test_neuron_function_metrics_and_targeted_replacement) ... ok
+test_nonrotary_gain_identity_and_rotary_negative_control (test_functional.FunctionalTests.test_nonrotary_gain_identity_and_rotary_negative_control) ... ok
+test_streamed_projection_embedding_and_output_head (test_functional.FunctionalTests.test_streamed_projection_embedding_and_output_head) ... ok
+test_uniform_rmsnorm_function_bound_is_tight (test_functional.FunctionalTests.test_uniform_rmsnorm_function_bound_is_tight) ... ok
+test_3d_reader_and_exact_block (test_pilot.PilotTests.test_3d_reader_and_exact_block) ... ok
+test_bfloat16 (test_pilot.PilotTests.test_bfloat16) ... ok
+test_directional_replacement_error_and_scaling (test_pilot.PilotTests.test_directional_replacement_error_and_scaling) ... ok
+test_end_to_end_synthetic_groups_and_savings (test_pilot.PilotTests.test_end_to_end_synthetic_groups_and_savings) ... Inventory: 4 tensors, 33792 bytes
+tensor: sampling 4 objects
+block: sampling 22 objects
+block: verified 100/109
+{"model": "synthetic", "revision": "test", "tensor_count": 4, "parameter_count": 8448, "model_weight_bytes": 33792, "ndim_counts": {"2": 2, "3": 2}, "elapsed_seconds": 0.3087193910032511, "analysis_budget_seconds": 60, "peak_rss_kib": 57700, "python": "3.14.4", "numpy": "2.5.4", "seed": 20261109, "limitations": ["No inference or functional equivalence measured", "No exhaustive pair search", "Block search uses per-role/shape/scale reservoirs of 256", "Different block scales overlap; savings must not be added", "Block savings count only verified disjoint sampled blocks, no extrapolation", "Spectral probes use aligned bounded subvolumes, not whole tensors", "Shared encoding is theoretical, not a runnable compressed checkpoint", "No cross-role grouping, neuron permutation alignment, or training"], "completed": true}
+ok
+test_no_transitive_chaining (test_pilot.PilotTests.test_no_transitive_chaining) ... ok
+test_ranged_download_preserves_prefix (test_pilot.PilotTests.test_ranged_download_preserves_prefix) ... Weight download 36/36 bytes (100.0%)
+ok
+
+----------------------------------------------------------------------
+Ran 22 tests in 33.253s
+
+OK

results/block-activation/run.txt

@@ -0,0 +1,4 @@
+completed fresh-zh-1 20904
+completed fresh-zh-2 20904
+completed fresh-en-1 20904
+completed fresh-en-2 20904

results/block-activation/source-hashes.json

@@ -0,0 +1,8 @@
+{
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+  "activation/audit.py": "7dc8af7c31de379a50bdcbd3c41243eb190e6a50e8ec00a30182e82a34e73627",
+  "activation/run.py": "cba62a2d8b6344625f233309b166b091028154890d93e48278eb338cbcd19b18",
+  "activation/__init__.py": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+  "tests/test_activation.py": "dee1756b63cd03c2051cdc92d678d2e27aff639fbfd3d3cb3169140760da4c68",
+  "functional/model.py": "48a2a97d0445dc6e08ac7f27a935637a7553417d0982eb94122d6cb02bbb499c"
+}

results/block-activation/summary.json

@@ -0,0 +1,180 @@
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+  "training": false,
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+  ]
+}

results/finetune-comparison/REPORT.md

@@ -0,0 +1,22 @@
+# GSM8K任务微调前后的有效头数
+
+比较原Qwen3.5-2B发布权重与同一revision上的纯LoRA seed0（额外GSM8K GRPO，非SFT）。没有训练新模型。主指标为√JS距离消融影响的N_eff；正变化表示更分散，负变化表示更集中。
+
+| 输入组 | 原权重中位数 [P10,P90] | 微调后中位数 [P10,P90] | 配对变化中位数 | 增加的观测比例 |
+|---|---:|---:|---:|---:|
+| 原四条文本 | 8.931822 [5.784081,11.672658] | 8.918301 [5.776576,11.683940] | -0.007827 | 45.6% |
+| GSM8K测试题前缀 | 8.906046 [6.013504,11.801882] | 8.904117 [5.982593,11.818115] | -0.006747 | 47.2% |
+
+每组4条输入×6个接收层×25个query位置×8个接收query头=4800个配对观测；它们不是4800个独立文本样本。前后使用同一原模型tokenizer、完全相同token IDs。原四条输入的基线8.931822/16已精确复现。
+
+GSM8K输入取原作者固定版本test split中前4道至少32token的题目，仅用题目前32token，不含答案，不套不同聊天模板。另算了同组GSM8K输入的原权重基线；没有把旧通用文本与新任务文本混作配对。
+
+## 方法与限定
+
+所有6个普通注意力接收层和16个上游输出头均保留；每次移除一个上游头在全序列的投影输出，重算中间MLP/残差/归一化和检索分布。微调只改变18个GDN层的指定投影，普通注意力权重不变；隐藏状态和上游贡献仍会变化。
+
+采用read-only FP32有效权重W+2BA，未把增量舍入为BF16、未修改底座；另用base projection+2B(Ax)独立前向校验。头输出求和、空消融和源数组复用校验均记录。JS散度及QKV复合相对变化也保留，避免只展示有利度量。这里QKV补充指标仅覆盖相同六个普通注意力接收层，其原基线不能与旧研究全部23连接的76.0%混作同一统计量。
+
+单个发布者训练seed、4条短文本/组，仅支持这些输入上的描述性变化；不推广为所有微调都会如此，不报告GSM8K准确率，不据此推断删头可用或最终LM能力。配对变化中位数与两个总体中位数之差不是同一个统计量。
+
+[公开微调权重与训练说明](https://huggingface.co/Avifenesh/gdn-beta-gate-qwen3.5-2b-grpo)；固定版本、参数SHA、dataset版本和题目索引见protocol.json/gsm8k-source.json/samples.json。所有权重、适配器和缓存继续保留。

results/finetune-comparison/adapter_meta.json

@@ -0,0 +1,22 @@
+{
+  "record": "qwen3.5-2b-grpo",
+  "arm": "lora",
+  "receipt_arm": "ctrl",
+  "seed": 0,
+  "step": 150,
+  "base_model": "Qwen/Qwen3.5-2B",
+  "base_revision": "15852e8c16360a2fea060d615a32b45270f8a8fc",
+  "recipe": "GRPO, correctness-gated length penalty alpha 0.2, 150 steps from the shared warm-up (paper Section 4)",
+  "lora_rank": 16,
+  "lora_alpha": 32,
+  "lora_targets": [
+    "in_proj_qkv",
+    "in_proj_a",
+    "in_proj_b",
+    "out_proj"
+  ],
+  "gate": null,
+  "n_tensors": 144,
+  "n_params": 4727808,
+  "source_sha256": "de2794c085f5096fea53374ba1cea31075071bb6de3f227bb3350b3035970447"
+}

results/finetune-comparison/audit-output.txt

@@ -0,0 +1,15 @@
+{
+  "passed": true,
+  "links": 96,
+  "paired_observations_per_cohort_metric": 4800,
+  "legacy_baseline_exact": true,
+  "effective_head_formula_independently_recomputed": true,
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+  "explicit_lora_forward_relative_error": 8.33777860933606e-07,
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+  "base_sha_verified": true,
+  "dataset_sha_verified": true,
+  "retained_cache_files": 300,
+  "nonzero_update_modules": 72,
+  "nonzero_hidden_state_changes_verified": true
+}

results/finetune-comparison/audit.json

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+  "explicit_lora_forward_relative_error": 8.33777860933606e-07,
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+  "base_sha_verified": true,
+  "dataset_sha_verified": true,
+  "retained_cache_files": 300,
+  "nonzero_update_modules": 72,
+  "nonzero_hidden_state_changes_verified": true
+}

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+}

results/finetune-comparison/final-tests.txt

@@ -0,0 +1,49 @@
+test_3d_reader_and_exact_block (tests.test_pilot.PilotTests.test_3d_reader_and_exact_block) ... ok
+test_bfloat16 (tests.test_pilot.PilotTests.test_bfloat16) ... ok
+test_directional_replacement_error_and_scaling (tests.test_pilot.PilotTests.test_directional_replacement_error_and_scaling) ... ok
+test_end_to_end_synthetic_groups_and_savings (tests.test_pilot.PilotTests.test_end_to_end_synthetic_groups_and_savings) ... Inventory: 4 tensors, 33792 bytes
+tensor: sampling 4 objects
+block: sampling 22 objects
+block: verified 100/109
+{"model": "synthetic", "revision": "test", "tensor_count": 4, "parameter_count": 8448, "model_weight_bytes": 33792, "ndim_counts": {"2": 2, "3": 2}, "elapsed_seconds": 0.09112083166837692, "analysis_budget_seconds": 60, "peak_rss_kib": 55772, "python": "3.14.4", "numpy": "2.5.4", "seed": 20261109, "limitations": ["No inference or functional equivalence measured", "No exhaustive pair search", "Block search uses per-role/shape/scale reservoirs of 256", "Different block scales overlap; savings must not be added", "Block savings count only verified disjoint sampled blocks, no extrapolation", "Spectral probes use aligned bounded subvolumes, not whole tensors", "Shared encoding is theoretical, not a runnable compressed checkpoint", "No cross-role grouping, neuron permutation alignment, or training"], "completed": true}
+ok
+test_no_transitive_chaining (tests.test_pilot.PilotTests.test_no_transitive_chaining) ... ok
+test_ranged_download_preserves_prefix (tests.test_pilot.PilotTests.test_ranged_download_preserves_prefix) ... Weight download 36/36 bytes (100.0%)
+ok
+test_causal_convolution_and_norm_boundary (tests.test_functional.FunctionalTests.test_causal_convolution_and_norm_boundary) ... ok
+test_causality_and_identical_function_metrics (tests.test_functional.FunctionalTests.test_causality_and_identical_function_metrics) ... ok
+test_neuron_function_metrics_and_targeted_replacement (tests.test_functional.FunctionalTests.test_neuron_function_metrics_and_targeted_replacement) ... ok
+test_nonrotary_gain_identity_and_rotary_negative_control (tests.test_functional.FunctionalTests.test_nonrotary_gain_identity_and_rotary_negative_control) ... ok
+test_streamed_projection_embedding_and_output_head (tests.test_functional.FunctionalTests.test_streamed_projection_embedding_and_output_head) ... ok
+test_uniform_rmsnorm_function_bound_is_tight (tests.test_functional.FunctionalTests.test_uniform_rmsnorm_function_bound_is_tight) ... ok
+test_assignment_against_exhaustive_and_inverse (tests.test_crossqkv.CrossQKVTests.test_assignment_against_exhaustive_and_inverse) ... ok
+test_cached_mode_rejects_future_state_reuse (tests.test_crossqkv.CrossQKVTests.test_cached_mode_rejects_future_state_reuse) ... ok
+test_controls_cover_multiple_projection_layers (tests.test_crossqkv.CrossQKVTests.test_controls_cover_multiple_projection_layers) ... ok
+test_head_matrix_metrics_and_gram_are_diagnostic (tests.test_crossqkv.CrossQKVTests.test_head_matrix_metrics_and_gram_are_diagnostic) ... ok
+test_probability_identity_and_nontrivial_queries (tests.test_crossqkv.CrossQKVTests.test_probability_identity_and_nontrivial_queries) ... ok
+test_profile_matches_canonical_and_head_sum (tests.test_crossqkv.CrossQKVTests.test_profile_matches_canonical_and_head_sum) ... ok
+test_recipient_value_gate_output_and_gqa_group_stay_fixed (tests.test_crossqkv.CrossQKVTests.test_recipient_value_gate_output_and_gqa_group_stay_fixed) ... ok
+test_topk_tail_rank_can_change_with_tiny_probability_difference (tests.test_crossqkv.CrossQKVTests.test_topk_tail_rank_can_change_with_tiny_probability_difference) ... ok
+test_known_activity (tests.test_activation.ActivationTests.test_known_activity) ... ok
+test_zero_and_scale (tests.test_activation.ActivationTests.test_zero_and_scale) ... ok
+test_continuous_limits_and_undefined_zero (tests.test_dominance.DominanceTests.test_continuous_limits_and_undefined_zero) ... ok
+test_joint_recomputation_null_and_saved_raw (tests.test_dominance.DominanceTests.test_joint_recomputation_null_and_saved_raw) ... baseline layer 0
+baseline layer 1
+ok
+test_js_identity_and_disjoint (tests.test_dominance.DominanceTests.test_js_identity_and_disjoint) ... ok
+test_mask_ranks_vary_by_token_and_recipient (tests.test_dominance.DominanceTests.test_mask_ranks_vary_by_token_and_recipient) ... ok
+test_mixed_layer_forward_and_effective_qkv (tests.test_dominance.DominanceTests.test_mixed_layer_forward_and_effective_qkv) ... baseline layer 0
+baseline layer 1
+baseline layer 0
+baseline layer 1
+ok
+test_effective_rows_equal_explicit_low_rank_output (tests.test_finetune_compare.FineTuneTests.test_effective_rows_equal_explicit_low_rank_output) ... ok
+test_forward_and_head_decomposition_match_independent_overlay (tests.test_finetune_compare.FineTuneTests.test_forward_and_head_decomposition_match_independent_overlay) ... baseline layer 0
+baseline layer 1
+ok
+test_zero_scale_preserves_original_and_bad_metadata_rejected (tests.test_finetune_compare.FineTuneTests.test_zero_scale_preserves_original_and_bad_metadata_rejected) ... ok
+
+----------------------------------------------------------------------
+Ran 30 tests in 23.124s
+
+OK

results/finetune-comparison/gsm8k-source.json

@@ -0,0 +1,9 @@
+{
+  "repository": "https://github.com/openai/grade-school-math",
+  "revision": "3101c7d5072418e28b9008a6636bde82a006892c",
+  "url": "https://raw.githubusercontent.com/openai/grade-school-math/3101c7d5072418e28b9008a6636bde82a006892c/grade_school_math/data/test.jsonl",
+  "sha256": "3730d312f6e3440559ace48831e51066acaca737f6eabec99bccb9e4b3c39d14",
+  "split": "test",
+  "format": "original author JSONL",
+  "rows": 1319
+}

results/finetune-comparison/hidden-state-changes.json

@@ -0,0 +1,45 @@
+{
+  "definition": "final residual before final RMSNorm, not logits, accuracy or LM quality",
+  "changes": [
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+      "sample": "fresh-zh-1",
+      "final_pre_norm_residual_relative_l2": 0.021898728857078685
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+      "sample": "fresh-zh-2",
+      "final_pre_norm_residual_relative_l2": 0.03829561458389978
+    },
+    {
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+      "sample": "fresh-en-1",
+      "final_pre_norm_residual_relative_l2": 0.02432907925877068
+    },
+    {
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+      "sample": "fresh-en-2",
+      "final_pre_norm_residual_relative_l2": 0.034934933219615986
+    },
+    {
+      "cohort": "gsm8k",
+      "sample": "gsm8k-test-0000",
+      "final_pre_norm_residual_relative_l2": 0.03901556767460501
+    },
+    {
+      "cohort": "gsm8k",
+      "sample": "gsm8k-test-0002",
+      "final_pre_norm_residual_relative_l2": 0.04043223109300957
+    },
+    {
+      "cohort": "gsm8k",
+      "sample": "gsm8k-test-0003",
+      "final_pre_norm_residual_relative_l2": 0.045740534396548564
+    },
+    {
+      "cohort": "gsm8k",
+      "sample": "gsm8k-test-0004",
+      "final_pre_norm_residual_relative_l2": 0.04190792993325029
+    }
+  ]
+}

results/finetune-comparison/per-layer.csv

@@ -0,0 +1,37 @@
+cohort,metric,recipient,base_median,base_p10,base_p90,finetuned_median,finetuned_p10,finetuned_p90,paired_delta_median
+legacy,distance,3,8.739316033368453,4.950963035160957,10.863499972573624,8.731708727318438,4.9232497064004646,10.84212771798773,-0.02037733856762003
+legacy,distance,7,9.4363090529414,6.25085771836616,11.556565692274562,9.423484801453295,6.21315757199869,11.527370171007142,-0.019801758117782065
+legacy,distance,11,11.22490926849138,7.737020182887724,12.908045076028829,11.250679957928547,7.73525088572658,12.928017935515697,0.0038455741574283664
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results/finetune-comparison/protocol.json

@@ -0,0 +1,62 @@
+{
+  "primary": "same six full-attention receivers and original4x32 token cohort as published8.931822/16",
+  "adapter_repo": "Avifenesh/gdn-beta-gate-qwen3.5-2b-grpo",
+  "adapter_revision": "038d8be9bd0edd3883fa019651b02bdf65609468",
+  "adapter_file": "lora/seed0/adapter.safetensors",
+  "adapter_sha256": "93a3f43a4356f84da0d13ecc622fee13e41ba15d237cdaad6094f243899c62d6",
+  "base_revision": "15852e8c16360a2fea060d615a32b45270f8a8fc",
+  "scale": 2,
+  "rank": 16,
+  "arm": "pure LoRA",
+  "training": "publisher-reported GSM8K GRPO; we do no training",
+  "sample_choice": "four original texts; first4 canonical GSM8K test questions with >=32tokens, length selection only, first32tokens; question only",
+  "source_layers": [
+    2,
+    6,
+    10,
+    14,
+    18,
+    22
+  ],
+  "primary_queries": [
+    7,
+    8,
+    9,
+    10,
+    11,
+    12,
+    13,
+    14,
+    15,
+    16,
+    17,
+    18,
+    19,
+    20,
+    21,
+    22,
+    23,
+    24,
+    25,
+    26,
+    27,
+    28,
+    29,
+    30,
+    31
+  ],
+  "main_metric": "concentration(sqrt(single_JS)); same FP32 tensors and sequence-wide donor-head deletion as prior run",
+  "cohort_pairing": "each comparison uses same question/tokenIDs before and after; base legacy arrays reused with elementwise checks",
+  "runtime": "read-only FP32 effective W+2BA; no BF16 re-rounding or quantization",
+  "controls": "head sum, null mask, explicit low-rank versus effective-weight full forward relL2<1e-4",
+  "budget_seconds": 7200,
+  "keep_all_weights_and_caches": true,
+  "limitations": [
+    "one existing training seed",
+    "4 texts per cohort, 32 tokens each",
+    "dependent query/head observations, not iid textual samples",
+    "no generation or accuracy measurement",
+    "GDN targets only; ordinary attention weights stay unchanged",
+    "no pruning or functional interchangeability claim"
+  ]
+}