diff --git a/README.md b/README.md
index ba88e06..00d8049 100644
--- a/README.md
+++ b/README.md
@@ -38,3 +38,7 @@ 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.
diff --git a/activation/README.md b/activation/README.md
new file mode 100644
index 0000000..1711bc9
--- /dev/null
+++ b/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.
diff --git a/activation/__init__.py b/activation/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/activation/audit.py b/activation/audit.py
new file mode 100644
index 0000000..f8a2e68
--- /dev/null
+++ b/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())
diff --git a/activation/report.py b/activation/report.py
new file mode 100644
index 0000000..f671941
--- /dev/null
+++ b/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))
diff --git a/activation/run.py b/activation/run.py
new file mode 100644
index 0000000..13b1421
--- /dev/null
+++ b/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()
diff --git a/results/block-activation/REPORT.md b/results/block-activation/REPORT.md
new file mode 100644
index 0000000..dab15e1
--- /dev/null
+++ b/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%。
diff --git a/results/block-activation/aggregates.json b/results/block-activation/aggregates.json
new file mode 100644
index 0000000..e65c6cb
--- /dev/null
+++ b/results/block-activation/aggregates.json
@@ -0,0 +1,200 @@
+{
+ "mlp.gate_proj.output": {
+ "observations": 96,
+ "zero_rate": {
+ "min": 0.0,
+ "mean": 0.0,
+ "max": 0.0
+ },
+ "negative_rate": {
+ "min": 0.6825663248697916,
+ "mean": 0.8063652250501846,
+ "max": 0.9117279052734375
+ },
+ "near_001_rms": {
+ "min": 0.005289713541666667,
+ "mean": 0.007811493343777125,
+ "max": 0.010442097981770834
+ },
+ "near_01_rms": {
+ "min": 0.053070068359375,
+ "mean": 0.07800536685519749,
+ "max": 0.10291544596354167
+ },
+ "ever_active_001": {
+ "min": 1.0,
+ "mean": 1.0,
+ "max": 1.0
+ },
+ "energy90_channel_fraction": {
+ "min": 0.5314127604166666,
+ "mean": 0.6330634223090278,
+ "max": 0.78955078125
+ }
+ },
+ "mlp.silu_gate": {
+ "observations": 96,
+ "zero_rate": {
+ "min": 0.0,
+ "mean": 0.0,
+ "max": 0.0
+ },
+ "negative_rate": {
+ "min": 0.6825663248697916,
+ "mean": 0.8063652250501846,
+ "max": 0.9117279052734375
+ },
+ "near_001_rms": {
+ "min": 0.0018768310546875,
+ "mean": 0.0050310028923882385,
+ "max": 0.0082244873046875
+ },
+ "near_01_rms": {
+ "min": 0.020375569661458332,
+ "mean": 0.05178917778862848,
+ "max": 0.09897359212239583
+ },
+ "ever_active_001": {
+ "min": 1.0,
+ "mean": 1.0,
+ "max": 1.0
+ },
+ "energy90_channel_fraction": {
+ "min": 0.04736328125,
+ "mean": 0.7012278238932291,
+ "max": 0.8186848958333334
+ }
+ },
+ "mlp.gated_product": {
+ "observations": 96,
+ "zero_rate": {
+ "min": 0.0,
+ "mean": 0.0,
+ "max": 0.0
+ },
+ "negative_rate": {
+ "min": 0.4942118326822917,
+ "mean": 0.5002292527092828,
+ "max": 0.5078125
+ },
+ "near_001_rms": {
+ "min": 0.020960489908854168,
+ "mean": 0.03163560231526693,
+ "max": 0.07338460286458333
+ },
+ "near_01_rms": {
+ "min": 0.16080220540364584,
+ "mean": 0.21046002705891928,
+ "max": 0.4075215657552083
+ },
+ "ever_active_001": {
+ "min": 1.0,
+ "mean": 1.0,
+ "max": 1.0
+ },
+ "energy90_channel_fraction": {
+ "min": 0.06380208333333333,
+ "mean": 0.5484212239583334,
+ "max": 0.7263997395833334
+ }
+ },
+ "self_attn.q_proj.output": {
+ "observations": 24,
+ "zero_rate": {
+ "min": 0.0,
+ "mean": 0.0,
+ "max": 0.0
+ },
+ "negative_rate": {
+ "min": 0.6834945678710938,
+ "mean": 0.7360480626424154,
+ "max": 0.7523574829101562
+ },
+ "near_001_rms": {
+ "min": 0.01090240478515625,
+ "mean": 0.012759526570638021,
+ "max": 0.01556396484375
+ },
+ "near_01_rms": {
+ "min": 0.106048583984375,
+ "mean": 0.1248311996459961,
+ "max": 0.148590087890625
+ },
+ "ever_active_001": {
+ "min": 1.0,
+ "mean": 1.0,
+ "max": 1.0
+ },
+ "energy90_channel_fraction": {
+ "min": 0.413330078125,
+ "mean": 0.440277099609375,
+ "max": 0.4931640625
+ }
+ },
+ "self_attn.k_proj.output": {
+ "observations": 24,
+ "zero_rate": {
+ "min": 0.0,
+ "mean": 0.0,
+ "max": 0.0
+ },
+ "negative_rate": {
+ "min": 0.47601318359375,
+ "mean": 0.49783579508463544,
+ "max": 0.52325439453125
+ },
+ "near_001_rms": {
+ "min": 0.00909423828125,
+ "mean": 0.01224517822265625,
+ "max": 0.01629638671875
+ },
+ "near_01_rms": {
+ "min": 0.095458984375,
+ "mean": 0.1208343505859375,
+ "max": 0.15106201171875
+ },
+ "ever_active_001": {
+ "min": 1.0,
+ "mean": 1.0,
+ "max": 1.0
+ },
+ "energy90_channel_fraction": {
+ "min": 0.41015625,
+ "mean": 0.5345865885416666,
+ "max": 0.65234375
+ }
+ },
+ "self_attn.v_proj.output": {
+ "observations": 24,
+ "zero_rate": {
+ "min": 0.0,
+ "mean": 0.0,
+ "max": 0.0
+ },
+ "negative_rate": {
+ "min": 0.49267578125,
+ "mean": 0.5059967041015625,
+ "max": 0.51666259765625
+ },
+ "near_001_rms": {
+ "min": 0.0084228515625,
+ "mean": 0.01374053955078125,
+ "max": 0.02447509765625
+ },
+ "near_01_rms": {
+ "min": 0.087890625,
+ "mean": 0.1348139444986979,
+ "max": 0.22296142578125
+ },
+ "ever_active_001": {
+ "min": 1.0,
+ "mean": 1.0,
+ "max": 1.0
+ },
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diff --git a/results/block-activation/audit.json b/results/block-activation/audit.json
new file mode 100644
index 0000000..bd78c1a
--- /dev/null
+++ b/results/block-activation/audit.json
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+{
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+ "rows": 83616,
+ "layers": 24,
+ "samples": 4,
+ "all_zero_128_blocks": 0
+}
\ No newline at end of file
diff --git a/results/block-activation/checksums.json b/results/block-activation/checksums.json
new file mode 100644
index 0000000..f294197
--- /dev/null
+++ b/results/block-activation/checksums.json
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diff --git a/results/block-activation/figures/block_activity.pdf b/results/block-activation/figures/block_activity.pdf
new file mode 100644
index 0000000..426b743
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diff --git a/results/block-activation/figures/block_activity.png b/results/block-activation/figures/block_activity.png
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diff --git a/results/block-activation/figures/block_activity.svg b/results/block-activation/figures/block_activity.svg
new file mode 100644
index 0000000..b851400
--- /dev/null
+++ b/results/block-activation/figures/block_activity.svg
@@ -0,0 +1,1821 @@
+
+
+
diff --git a/results/block-activation/figures/mlp_activation.pdf b/results/block-activation/figures/mlp_activation.pdf
new file mode 100644
index 0000000..77f766c
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diff --git a/results/block-activation/figures/mlp_activation.png b/results/block-activation/figures/mlp_activation.png
new file mode 100644
index 0000000..a0fdef9
Binary files /dev/null and b/results/block-activation/figures/mlp_activation.png differ
diff --git a/results/block-activation/figures/mlp_activation.svg b/results/block-activation/figures/mlp_activation.svg
new file mode 100644
index 0000000..9cd3745
--- /dev/null
+++ b/results/block-activation/figures/mlp_activation.svg
@@ -0,0 +1,2410 @@
+
+
+
diff --git a/results/block-activation/final-tests.txt b/results/block-activation/final-tests.txt
new file mode 100644
index 0000000..6476276
--- /dev/null
+++ b/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
diff --git a/results/block-activation/first-attempt.txt b/results/block-activation/first-attempt.txt
new file mode 100644
index 0000000..c5eeb7a
--- /dev/null
+++ b/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