diff --git a/.gitattributes b/.gitattributes
index 7cfdd5b..04aa34c 100644
--- a/.gitattributes
+++ b/.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
diff --git a/README.md b/README.md
index 00d8049..346748b 100644
--- a/README.md
+++ b/README.md
@@ -42,3 +42,12 @@ Full resolved versions and run provenance accompany each result. See the SOP for
 ## 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.
diff --git a/dominance/PROTOCOL.md b/dominance/PROTOCOL.md
new file mode 100644
index 0000000..76c1cdd
--- /dev/null
+++ b/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.
diff --git a/dominance/README.md b/dominance/README.md
new file mode 100644
index 0000000..0feda3f
--- /dev/null
+++ b/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.
diff --git a/dominance/__init__.py b/dominance/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/dominance/audit.py b/dominance/audit.py
new file mode 100644
index 0000000..269a19a
--- /dev/null
+++ b/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])
diff --git a/dominance/core.py b/dominance/core.py
new file mode 100644
index 0000000..46fdea8
--- /dev/null
+++ b/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)
diff --git a/dominance/report.py b/dominance/report.py
new file mode 100644
index 0000000..5de7f28
--- /dev/null
+++ b/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])
diff --git a/dominance/run.py b/dominance/run.py
new file mode 100644
index 0000000..2d7bb15
--- /dev/null
+++ b/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()
diff --git a/results/head-dominance/REPORT.md b/results/head-dominance/REPORT.md
new file mode 100644
index 0000000..063af5a
--- /dev/null
+++ b/results/head-dominance/REPORT.md
@@ -0,0 +1,48 @@
+# 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 |
+|---|---:|---:|---:|---:|---:|
+| Q | 0.736 [0.506,0.861] | 18.6% | 46.0% | 10.0% | 12.0 |
+| K | 0.749 [0.517,0.879] | 18.4% | 45.2% | 10.3% | 12.0 |
+| V | 0.723 [0.479,0.862] | 19.0% | 46.6% | 9.5% | 12.0 |
+| QKV | 0.760 [0.531,0.878] | 18.1% | 44.6% | 10.6% | 12.0 |
+| JS | 0.244 [0.117,0.434] | 42.3% | 82.3% | 1.1% | 6.0 |
+| sqrtJS | 0.558 [0.362,0.730] | 21.7% | 56.5% | 6.8% | 11.0 |
+
+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 |
+|---|---:|---:|---:|---:|
+| linear_attention | 0.747 | 0.773 | 0.736 | 0.778 |
+| full_attention | 0.677 | 0.562 | 0.629 | 0.645 |
+
+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] |
+|---|---:|---:|
+| weak_half | 0.09035 [0.03148,0.23354] | 0.001413 [0.000220,0.006160] |
+| strong_half | 0.20918 [0.09105,0.48031] | 0.025745 [0.004460,0.088616] |
+| random_half | 0.16420 [0.06186,0.39711] | 0.013815 [0.002647,0.055241] |
+| all_heads | 0.22908 [0.09930,0.51739] | 0.028837 [0.004936,0.100283] |
+
+配对观测：QKV弱半数的实际联合影响小于强半数的比例为95.6%；强/弱影响比中位数为2.358。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。
+
+CPU测量耗时2636.4秒，峰值RSS155.9 MiB；无训练、GPU或清理。
diff --git a/results/head-dominance/analysis-summary.json b/results/head-dominance/analysis-summary.json
new file mode 100644
index 0000000..d724d24
--- /dev/null
+++ b/results/head-dominance/analysis-summary.json
@@ -0,0 +1,1490 @@
+{
+  "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": {
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diff --git a/results/head-dominance/concentration-by-link.csv b/results/head-dominance/concentration-by-link.csv
new file mode 100644
index 0000000..038d056
--- /dev/null
+++ b/results/head-dominance/concentration-by-link.csv
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diff --git a/results/head-dominance/concentration-curve.csv b/results/head-dominance/concentration-curve.csv
new file mode 100644
index 0000000..c276091
--- /dev/null
+++ b/results/head-dominance/concentration-curve.csv
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diff --git a/results/head-dominance/final-tests.txt b/results/head-dominance/final-tests.txt
new file mode 100644
index 0000000..202f31e
--- /dev/null
+++ b/results/head-dominance/final-tests.txt
@@ -0,0 +1,42 @@
+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": 0.09181240666657686, "analysis_budget_seconds": 60, "peak_rss_kib": 55188, "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_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
+
+----------------------------------------------------------------------
+Ran 25 tests in 22.226s
+
+OK
diff --git a/results/head-dominance/head-sum-controls.json b/results/head-dominance/head-sum-controls.json
new file mode 100644
index 0000000..2809e5e
--- /dev/null
+++ b/results/head-dominance/head-sum-controls.json
@@ -0,0 +1,482 @@
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diff --git a/results/head-dominance/joint-by-link.csv b/results/head-dominance/joint-by-link.csv
new file mode 100644
index 0000000..89484e4
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diff --git a/results/head-dominance/mainline-activation-tests.txt b/results/head-dominance/mainline-activation-tests.txt
new file mode 100644
index 0000000..1c3e190
--- /dev/null
+++ b/results/head-dominance/mainline-activation-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 0.038s
+
+OK
diff --git a/results/head-dominance/observations.npz b/results/head-dominance/observations.npz
new file mode 100644
index 0000000..0fbb74d
Binary files /dev/null and b/results/head-dominance/observations.npz differ
diff --git a/results/head-dominance/primary-analysis-summary.json b/results/head-dominance/primary-analysis-summary.json
new file mode 100644
index 0000000..a37aaf3
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+    "fresh-zh-1",
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+  ],
+  "winner_identity_file": "winner-identities.csv"
+}
diff --git a/results/head-dominance/prior-official-reference-check.json b/results/head-dominance/prior-official-reference-check.json
new file mode 100644
index 0000000..29ed680
--- /dev/null
+++ b/results/head-dominance/prior-official-reference-check.json
@@ -0,0 +1,288 @@
+{
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+    },
+    {
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+      ],
+      "max_abs_error": 3.5762786865234375e-07,
+      "atol": 2e-05,
+      "rtol": 2e-05,
+      "passed": true
+    },
+    {
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+      "max_abs_error": 3.5762786865234375e-07,
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+  ],
+  "limitations": "FP32 CPU semantics on tiny synthetic configurations; not full-checkpoint BF16 kernel equivalence"
+}
\ No newline at end of file
diff --git a/results/head-dominance/protocol.json b/results/head-dominance/protocol.json
new file mode 100644
index 0000000..c580248
--- /dev/null
+++ b/results/head-dominance/protocol.json
@@ -0,0 +1,48 @@
+{
+  "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": [
+    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
+  ],
+  "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": 7200,
+  "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"
+  ]
+}
diff --git a/results/head-dominance/raw/fresh-en-1-link-00.npz b/results/head-dominance/raw/fresh-en-1-link-00.npz
new file mode 100644
index 0000000..f2c7cea
Binary files /dev/null and b/results/head-dominance/raw/fresh-en-1-link-00.npz differ
diff --git a/results/head-dominance/raw/fresh-en-1-link-01.npz b/results/head-dominance/raw/fresh-en-1-link-01.npz
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diff --git a/results/head-dominance/report-output.txt b/results/head-dominance/report-output.txt
new file mode 100644
index 0000000..c4ccab3
--- /dev/null
+++ b/results/head-dominance/report-output.txt
@@ -0,0 +1,49 @@
+# 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 |
+|---|---:|---:|---:|---:|---:|
+| Q | 0.736 [0.506,0.861] | 18.6% | 46.0% | 10.0% | 12.0 |
+| K | 0.749 [0.517,0.879] | 18.4% | 45.2% | 10.3% | 12.0 |
+| V | 0.723 [0.479,0.862] | 19.0% | 46.6% | 9.5% | 12.0 |
+| QKV | 0.760 [0.531,0.878] | 18.1% | 44.6% | 10.6% | 12.0 |
+| JS | 0.244 [0.117,0.434] | 42.3% | 82.3% | 1.1% | 6.0 |
+| sqrtJS | 0.558 [0.362,0.730] | 21.7% | 56.5% | 6.8% | 11.0 |
+
+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 |
+|---|---:|---:|---:|---:|
+| linear_attention | 0.747 | 0.773 | 0.736 | 0.778 |
+| full_attention | 0.677 | 0.562 | 0.629 | 0.645 |
+
+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] |
+|---|---:|---:|
+| weak_half | 0.09035 [0.03148,0.23354] | 0.001413 [0.000220,0.006160] |
+| strong_half | 0.20918 [0.09105,0.48031] | 0.025745 [0.004460,0.088616] |
+| random_half | 0.16420 [0.06186,0.39711] | 0.013815 [0.002647,0.055241] |
+| all_heads | 0.22908 [0.09930,0.51739] | 0.028837 [0.004936,0.100283] |
+
+配对观测：QKV弱半数的实际联合影响小于强半数的比例为95.6%；强/弱影响比中位数为2.358。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。
+
+CPU测量耗时2636.4秒，峰值RSS155.9 MiB；无训练、GPU或清理。
+
diff --git a/results/head-dominance/run-log.txt b/results/head-dominance/run-log.txt
new file mode 100644
index 0000000..a06f983
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diff --git a/results/head-dominance/samples.json b/results/head-dominance/samples.json
new file mode 100644
index 0000000..fc4a3ad
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+[
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diff --git a/results/head-dominance/source-hashes.json b/results/head-dominance/source-hashes.json
new file mode 100644
index 0000000..bdd991a
--- /dev/null
+++ b/results/head-dominance/source-hashes.json
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+{
+  "dominance/report.py": "d765b4bb7542d8858bf7ac12e08c0187d36bc0c17eadf47e38778a30f5ed92ca",
+  "dominance/core.py": "547ccd60b357dfe4ffcfb2fa5e6ddfb7cdae29e9325e6e10e6fb99dc1542158e",
+  "dominance/audit.py": "d87db9483f73a8082c5f242c94f5f2b79cee690f96dc502b1e15d6a7cc4cf8a0",
+  "dominance/run.py": "34b6067480d95522646da5918f2b2ae4d06a608f179fa76e4732319b872707a6",
+  "dominance/__init__.py": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+  "dominance/PROTOCOL.md": "6ecac0c86e611e6324aa946320967f460aef762e691d8941527e81a5209e3d6b",
+  "dominance/README.md": "1888986693b8292f29c15cafef766dd32fde6bda188a3f404287df62f20068cb",
+  "tests/test_dominance.py": "bbb0f84ba8b536ab5db9a8568d9ecbf26a6a463ae135efab52bb9e4a20501b9e",
+  "functional/model.py": "48a2a97d0445dc6e08ac7f27a935637a7553417d0982eb94122d6cb02bbb499c",
+  "crossqkv/fresh_samples.json": "d3f53fc98fa9d061a3df42f24fe307b693b41a9ddf74c8377dd52096663c40a9"
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diff --git a/results/head-dominance/status.json b/results/head-dominance/status.json
new file mode 100644
index 0000000..60d6afb
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+}
diff --git a/results/head-dominance/winner-identities.csv b/results/head-dominance/winner-identities.csv
new file mode 100644
index 0000000..41cf989
--- /dev/null
+++ b/results/head-dominance/winner-identities.csv
@@ -0,0 +1,417 @@
+sample,source,metric,most_frequent_winner_fraction,distinct_winning_heads,head_counts
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+fresh-zh-1,1,Q,0.2925,12,"[73, 0, 5, 32, 6, 31, 0, 0, 117, 6, 32, 6, 13, 0, 67, 12]"
+fresh-zh-1,1,K,0.4175,9,"[61, 0, 0, 36, 3, 21, 0, 0, 167, 0, 22, 3, 0, 0, 79, 8]"
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diff --git a/tests/test_dominance.py b/tests/test_dominance.py
new file mode 100644
index 0000000..ebc9ab6
--- /dev/null
+++ b/tests/test_dominance.py
@@ -0,0 +1,78 @@
+"""Independent algebra, mixed-layer canonical, and joint-mask intervention checks."""
+import json,struct,tempfile,unittest
+from pathlib import Path
+from unittest.mock import patch
+import numpy as np
+from dominance.core import InfluenceLM,concentration,masks_from_influences,js_rows
+from dominance.run import distances,link
+from functional.model import StreamingLM,delta_rule,attention
+from crossqkv.core import QKVModel
+
+
+def fixture(path,kinds):
+    rng=np.random.default_rng(922);h=16;nh=4;dim=4
+    c={'rms_norm_eps':1e-6,'hidden_size':h,'head_dim':dim,'num_attention_heads':nh,'num_key_value_heads':2,'rope_parameters':{'rope_theta':10000000.,'partial_rotary_factor':.5},'linear_num_key_heads':2,'linear_num_value_heads':4,'linear_key_head_dim':4,'linear_value_head_dim':4,'layer_types':kinds}
+    a={}
+    def add(name,shape):a['model.language_model.'+name]=(rng.standard_normal(shape)*.15).astype(np.float32)
+    add('embed_tokens.weight',(19,h));add('norm.weight',(h,))
+    for l,kind in enumerate(kinds):
+        p=f'layers.{l}.'
+        for k in ('input_layernorm','post_attention_layernorm'):add(p+k+'.weight',(h,))
+        for k,shape in [('gate_proj',(24,h)),('up_proj',(24,h)),('down_proj',(h,24))]:add(p+'mlp.'+k+'.weight',shape)
+        if kind=='full_attention':
+            for k,shape in [('q_proj',(32,h)),('k_proj',(8,h)),('v_proj',(8,h)),('o_proj',(h,16)),('q_norm',(4,)),('k_norm',(4,))]:add(p+'self_attn.'+k+'.weight',shape)
+        else:
+            for k,shape in [('in_proj_qkv',(32,h)),('in_proj_z',(16,h)),('in_proj_a',(4,h)),('in_proj_b',(4,h)),('out_proj',(h,16)),('conv1d',(32,1,4)),('norm',(4,))]:add(p+'linear_attn.'+k+'.weight',shape)
+            add(p+'linear_attn.A_log',(4,));add(p+'linear_attn.dt_bias',(4,))
+    header={};body=b''
+    for k,v in a.items():
+        raw=v.tobytes();header[k]={'shape':list(v.shape),'dtype':'F32','data_offsets':[len(body),len(body)+len(raw)]};body+=raw
+    j=json.dumps(header).encode();Path(path,'model.safetensors').write_bytes(struct.pack('<Q',len(j))+j+body)
+    return c
+
+
+class DominanceTests(unittest.TestCase):
+    def test_continuous_limits_and_undefined_zero(self):
+        x=concentration([[1,1,1,1],[4,0,0,0],[0,0,0,0]])
+        np.testing.assert_allclose(x['effective_heads'][:2],[4,1]);np.testing.assert_allclose(x['concentration'][:2],[0,1]);self.assertFalse(x['valid'][2]);self.assertTrue(np.isnan(x['effective_heads'][2]))
+        self.assertAlmostEqual(float(concentration([3.,1.])['effective_heads']),1.6)
+        self.assertAlmostEqual(float(concentration([30.,10.])['effective_heads']),1.6)
+        with self.assertRaises(ValueError):concentration([-1,2])
+    def test_mask_ranks_vary_by_token_and_recipient(self):
+        d=np.arange(4*3*2).reshape(4,3,2).astype(float);d[:,1]=d[::-1,1]
+        w=masks_from_influences(d);s=masks_from_influences(d,False)
+        np.testing.assert_array_equal(w.sum(-1),2);np.testing.assert_array_equal(w+s,1)
+        self.assertFalse(np.array_equal(w[:,0],w[:,1]))
+    def test_js_identity_and_disjoint(self):
+        np.testing.assert_allclose(js_rows([[1,0]],[[1,0]]),0)
+        np.testing.assert_allclose(js_rows([[1,0]],[[0,1]]),np.log(2))
+    def test_mixed_layer_forward_and_effective_qkv(self):
+        for kinds in [('linear_attention','full_attention'),('full_attention','linear_attention')]:
+            with tempfile.TemporaryDirectory() as d:
+                cfg=fixture(d,list(kinds));lm=InfluenceLM(d,cfg,enforce_memory=False);ids=np.array([[1,2,3,4,5,6,7,8,9]])
+                cache=Path(d)/'cache';cache.mkdir();final=lm.baseline(ids,cache);ref,_=StreamingLM.forward(lm,ids)
+                # baseline returns pre-final-norm residual state, canonical returns normalized state.
+                np.testing.assert_allclose(lm.normalization(final,'norm'),ref,atol=2e-6,rtol=2e-6)
+                for layer in (0,1):
+                    raw=np.load(cache/f'layer-{layer}.npz')['raw'];obs=lm.recipient(raw,layer);h=lm.normalization(raw,f'layers.{layer}.input_layernorm')
+                    if kinds[layer]=='full_attention':
+                        expected=QKVModel(d,cfg,enforce_memory=False).parts(h,layer,heads=False)
+                        np.testing.assert_allclose(obs['P'],expected['p'],atol=2e-6)
+                        np.testing.assert_allclose(obs['Q'],expected['q'],atol=2e-6)
+                    else:
+                        with patch('functional.model.delta_rule',wraps=delta_rule) as fn:lm.linear_attention(h,layer)
+                        q,k,v=fn.call_args.args[:3]
+                        q=q/np.sqrt(np.sum(q*q,-1,keepdims=True)+1e-6)/np.sqrt(cfg['linear_key_head_dim']);k=k/np.sqrt(np.sum(k*k,-1,keepdims=True)+1e-6)
+                        for key,val in [('Q',q),('K',k),('V',v)]:np.testing.assert_allclose(obs[key],val,atol=2e-6)
+    def test_joint_recomputation_null_and_saved_raw(self):
+        with tempfile.TemporaryDirectory() as d:
+            cfg=fixture(d,['full_attention','linear_attention']);lm=InfluenceLM(d,cfg,enforce_memory=False);ids=np.array([[1,2,3,4,5,6,7,8,9]])
+            cache=Path(d)/'cache';cache.mkdir();lm.baseline(ids,cache)
+            data=dict(np.load(cache/'layer-0.npz'));masks=np.ones((1,9,4),dtype=np.float32)
+            actual=lm.intervene(data,0,masks);mid=data['raw']+data['out']-data['heads'].sum(2)
+            next_raw=mid+lm.mlp(lm.normalization(mid,'layers.0.post_attention_layernorm'),0)
+            expected=lm.recipient(next_raw,1)
+            for k in expected:np.testing.assert_allclose(actual[k],expected[k],atol=2e-6)
+            info=link(lm,data,0,Path(d)/'effects.npz',99);self.assertEqual(info['source_heads'],4);self.assertLess(info['null_max'],1e-6)
+            raw=dict(np.load(Path(d)/'effects.npz'));self.assertEqual(raw['single_Q'].shape,(4,9,4));self.assertEqual(raw['joint_weak_half_Q'].shape,(9,4))
+            np.testing.assert_array_equal(raw['mask_weak_half']+raw['mask_strong_half'],1)