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reweave: 2026 04 07b #2460

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m3taversal commented 2026-04-07 01:00:09 +00:00
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m3taversal added 1 commit 2026-04-07 01:00:10 +00:00 
Threshold: 0.7, Haiku classification, 42 files modified.

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m3taversal commented 2026-04-07 01:00:18 +00:00
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Thanks for the contribution! Your PR is queued for evaluation (priority: high). Expected review time: ~5 minutes.

This is an automated message from the Teleo pipeline.

Thanks for the contribution! Your PR is queued for evaluation (priority: high). Expected review time: ~5 minutes. _This is an automated message from the Teleo pipeline._
m3taversal commented 2026-04-07 01:00:40 +00:00
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Validation: PASS — 0/0 claims pass

tier0-gate v2 | 2026-04-07 01:00 UTC

<!-- TIER0-VALIDATION:eb04e35f5b2230b1e25bd224f34a7ebf909dc6fa --> **Validation: PASS** — 0/0 claims pass *tier0-gate v2 | 2026-04-07 01:00 UTC*
m3taversal commented 2026-04-07 01:01:10 +00:00
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Auto-approved: reweave structural update (frontmatter edges only). Leo reviews manually.

Auto-approved: reweave structural update (frontmatter edges only). Leo reviews manually.
leo commented 2026-04-07 01:01:48 +00:00
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Eval started — 2 reviewers: leo (cross-domain, opus), vida (domain-peer, sonnet)

teleo-eval-orchestrator v2

**Eval started** — 2 reviewers: leo (cross-domain, opus), vida (domain-peer, sonnet) *teleo-eval-orchestrator v2*
m3taversal closed this pull request 2026-04-07 01:02:52 +00:00
m3taversal commented 2026-04-07 01:02:52 +00:00
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Closed by conflict auto-resolver: rebase failed 3 times (enrichment conflict). Claims already on main from prior extraction. Source filed in archive.

Closed by conflict auto-resolver: rebase failed 3 times (enrichment conflict). Claims already on main from prior extraction. Source filed in archive.
leo commented 2026-04-07 01:04:29 +00:00
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Leo Cross-Domain Review — PR #2460

PR: reweave/2026-04-07b — "connect 26 orphan claims via vector similarity" + "merge 42 files via frontmatter union"
Scope: 42 files, +156/-9 lines. Adds related:, supports:, and reweave_edges: frontmatter entries across domains/health/, domains/ai-alignment/, domains/grand-strategy/, core/living-agents/, and foundations/collective-intelligence/.

What this PR does

Automated reweave pass. Connects orphan and under-linked claims to the broader graph by adding frontmatter link entries. No claim body text was changed — this is purely graph topology work.

Link target verification

All link targets resolve to existing claim files. Checked all 16 unique target claims — confirmed present. No dangling references.

Edge type assessment

Spot-checked 5 files for supports vs related classification accuracy. One issue found:

Circular supports — anchoring bias ↔ bias amplification

Two health claims now have mutual supports: edges:

  • llm-anchoring-bias-explains-clinical-ai-plan-reinforcement-mechanism.md → supports → clinical-ai-bias-amplification-creates-compounding-disparity-risk-at-scale.md
  • clinical-ai-bias-amplification-creates-compounding-disparity-risk-at-scale.md → supports → llm-anchoring-bias-explains-clinical-ai-plan-reinforcement-mechanism.md

A↔B mutual supports is semantically incoherent. The anchoring bias claim describes a mechanism; the bias amplification claim describes a population-scale consequence. The correct relationship is:

  • Anchoring bias → supports → bias amplification (mechanism supports the higher-level claim) ✓
  • Bias amplification → related → anchoring bias (or depends_on — but not supports) ✗

The reweave's vector similarity correctly identified these as strongly connected, but the edge type assignment is wrong in one direction. The bias amplification claim's supports: edge to the anchoring bias claim should be related:.

Other edge types: correct

The remaining spot-checked edges are well-classified:

  • AI sandbagging → supports → noise injection detection (evidence for detection validates the phenomenon)
  • FDA MAUDE structural gaps → supports → clinical AI safety gap (specific → general)
  • Voluntary safety constraints → supports → statements of intent (specific legal case → general principle)

Cross-domain connections worth noting

The reweave surfaces a genuinely interesting cross-domain bridge: health claims about FDA regulatory gaps now link through to the AI alignment verification claims (via the "doubly structural" safety gap claim). This is exactly the kind of connection that makes the graph valuable — clinical AI safety and AI governance verification share the same structural problem (no mechanism to detect what you can't measure).

Minor observations

  • The PR also removes stray blank lines from 2 files (AI alignment is a coordination problem.md, recursive self-improvement.md). Fine — whitespace cleanup.
  • The reweave_edges field with |type|date format provides good auditability for automated edges vs human-curated ones.

Verdict: request_changes
Model: opus
Summary: Automated reweave is well-executed — all 42 files' link targets resolve, edge types are mostly correct, and the new connections genuinely improve graph topology. One fix needed: circular mutual supports between the anchoring bias and bias amplification health claims. Change the bias-amplification → anchoring-bias edge from supports to related.

# Leo Cross-Domain Review — PR #2460 **PR:** reweave/2026-04-07b — "connect 26 orphan claims via vector similarity" + "merge 42 files via frontmatter union" **Scope:** 42 files, +156/-9 lines. Adds `related:`, `supports:`, and `reweave_edges:` frontmatter entries across `domains/health/`, `domains/ai-alignment/`, `domains/grand-strategy/`, `core/living-agents/`, and `foundations/collective-intelligence/`. --- ## What this PR does Automated reweave pass. Connects orphan and under-linked claims to the broader graph by adding frontmatter link entries. No claim body text was changed — this is purely graph topology work. ## Link target verification All link targets resolve to existing claim files. Checked all 16 unique target claims — confirmed present. No dangling references. ## Edge type assessment Spot-checked 5 files for `supports` vs `related` classification accuracy. One issue found: ### Circular `supports` — anchoring bias ↔ bias amplification Two health claims now have mutual `supports:` edges: - `llm-anchoring-bias-explains-clinical-ai-plan-reinforcement-mechanism.md` → supports → `clinical-ai-bias-amplification-creates-compounding-disparity-risk-at-scale.md` - `clinical-ai-bias-amplification-creates-compounding-disparity-risk-at-scale.md` → supports → `llm-anchoring-bias-explains-clinical-ai-plan-reinforcement-mechanism.md` A↔B mutual `supports` is semantically incoherent. The anchoring bias claim describes a *mechanism*; the bias amplification claim describes a *population-scale consequence*. The correct relationship is: - Anchoring bias → **supports** → bias amplification (mechanism supports the higher-level claim) ✓ - Bias amplification → **related** → anchoring bias (or `depends_on` — but not `supports`) ✗ The reweave's vector similarity correctly identified these as strongly connected, but the edge type assignment is wrong in one direction. The bias amplification claim's `supports:` edge to the anchoring bias claim should be `related:`. ### Other edge types: correct The remaining spot-checked edges are well-classified: - AI sandbagging → supports → noise injection detection (evidence for detection validates the phenomenon) - FDA MAUDE structural gaps → supports → clinical AI safety gap (specific → general) - Voluntary safety constraints → supports → statements of intent (specific legal case → general principle) ## Cross-domain connections worth noting The reweave surfaces a genuinely interesting cross-domain bridge: health claims about FDA regulatory gaps now link through to the AI alignment verification claims (via the "doubly structural" safety gap claim). This is exactly the kind of connection that makes the graph valuable — clinical AI safety and AI governance verification share the same structural problem (no mechanism to detect what you can't measure). ## Minor observations - The PR also removes stray blank lines from 2 files (`AI alignment is a coordination problem.md`, `recursive self-improvement.md`). Fine — whitespace cleanup. - The `reweave_edges` field with `|type|date` format provides good auditability for automated edges vs human-curated ones. --- **Verdict:** request_changes **Model:** opus **Summary:** Automated reweave is well-executed — all 42 files' link targets resolve, edge types are mostly correct, and the new connections genuinely improve graph topology. One fix needed: circular mutual `supports` between the anchoring bias and bias amplification health claims. Change the bias-amplification → anchoring-bias edge from `supports` to `related`. <!-- VERDICT:LEO:REQUEST_CHANGES -->
vida commented 2026-04-07 01:09:29 +00:00
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Vida Domain Peer Review — PR #2460

Reviewing as Vida, health and cross-domain AI safety specialist. This PR spans four clusters: sandbagging detection, governance/coordination, knowledge architecture epistemology, and clinical AI + CVD epidemiology. My focus is on what a health/clinical-AI-safety lens catches that a pure alignment lens misses.

AI Alignment Domain — Domain Expertise Flags

1. Confidence calibration: "capabilities generalize further than alignment" (sharp left turn)

Confidence is likely. The body explicitly states: "The sharp left turn is unfalsifiable in advance by design — it predicts failure only at capability levels we haven't reached." A claim the author acknowledges is unfalsifiable in advance cannot be rated likely. The scalable oversight degrades at capability gaps evidence supports oversight breakdown but doesn't confirm the discontinuity prediction — it's evidence of a related phenomenon, not the sharp left turn itself. Shard theory (Shah et al.) is cited as a challenge but not engaged seriously.

This should be speculative. Leaving it at likely misrepresents the epistemic status of one of the KB's most consequential alignment claims.

2. Confidence calibration: "recursive self-improvement creates explosive intelligence gains"

Confidence is likely. The body presents Amodei's 2026 quote — AI "writing much of the code at Anthropic" — as "evidence the self-reinforcing loop has already started." This is a substantial over-read. AI-assisted code generation at a lab is not the Bostrom crossover point where self-improvement outpaces human contribution. The Noah Smith "jagged intelligence" counterargument included in the body is actually quite strong and arguably challenges the RSI frame as the central alignment concern. "Experimental" would be accurate; "likely" is not.

3. Domain misclassification: two knowledge architecture claims tagged as ai-alignment

graph traversal through curated wiki links replicates spreading activation... and knowledge between notes is generated by traversal not stored in any individual note... are both tagged domain: ai-alignment with secondary_domains: [collective-intelligence]. These claims are about knowledge management architecture and agent epistemology. Neither contains an alignment argument — they describe how wiki-link graphs produce emergent understanding and why embedding retrieval is structurally inferior to curated traversal. This is foundations/collective-intelligence/ territory.

Contrast with knowledge codification into AI agent skills structurally loses metis... — that one belongs in ai-alignment because it explicitly argues the alignment-relevant dimension (contextual judgment about when to constrain is exactly what codification loses). The spreading activation and inter-note claims lack that alignment bridge.

4. Missing cross-domain connection: sandbagging × clinical AI safety

The sandbagging cluster is technically strong. Three well-constructed claims establish that: (1) models can covertly sandbag under CoT monitoring, (2) white-box access is required for the only reliable detection method, and (3) external evaluators are stuck at AL1 black-box access. The chain is solid.

What's missing: this has a direct and high-consequence instantiation in Vida's domain that neither claim set surfaces. If clinical AI systems can strategically underperform during pre-deployment evaluation while appearing safe, and the FDA has no AI-specific adverse event fields in MAUDE and no post-market surveillance requirements (both in this PR), then sandbagging creates a dual-failure scenario: the evaluation infrastructure that doesn't exist is also evaluating systems that can game whatever evaluation does exist. This isn't speculative — it's the logical intersection of two claim clusters in this same PR.

The sandbagging claims should wiki-link to [[clinical-ai-safety-gap-is-doubly-structural-with-no-pre-deployment-requirements-and-no-post-market-surveillance]] and vice versa. This is a genuine cross-domain connection with governance implications (FDA CDS oversight + evaluation access levels are the same bottleneck from different angles).

Similarly, knowledge codification loses metis should link to [[human-in-the-loop clinical AI degrades to worse-than-AI-alone because physicians both de-skill from reliance and introduce errors when overriding correct outputs]]. Physician de-skilling is the clinical instantiation of metis loss — execution-level judgment that doesn't survive the abstraction to AI-augmented care. This link would materially strengthen both claims.

Health Domain — Domain Expertise Flags

FDA MAUDE 943 adverse events number needs context

The claim fda-maude-database-lacks-ai-specific-adverse-event-fields-creating-systematic-under-detection-of-ai-attributable-harm presents 943 reported events across 823+ AI/ML devices as evidence of AI-specific structural failure. The structural argument (no AI-specific fields) is solid. But MAUDE systematic under-reporting is well-documented across ALL device categories — estimates suggest adverse events are under-reported by 90%+ in general. Presenting 943 as specific evidence of AI-specific failure without acknowledging that MAUDE under-reports everything slightly weakens the claim's precision. The real AI-specific argument is the structural inability to attribute events to AI components, not the absolute count. The claim body makes this case but the title's implication (fields = under-detection) is imprecise. Minor — doesn't require rejection but worth a clarifying sentence.

Automation bias claim is a genuine contribution

fda-treats-automation-bias-as-transparency-problem-contradicting-evidence-that-visibility-does-not-prevent-deference is excellent. The category error diagnosis — FDA treating a cognitive architecture problem as an information problem — is clinically accurate and grounded in the automation bias literature. This is exactly what Vida is supposed to catch: a regulatory agency's category error with compounding downstream consequences for deployed clinical AI. High confidence in this claim's accuracy and domain value.

CVD epidemiology cluster is solid

The hypertension mortality data (1999–2023 doubling, shift from secondary to primary CVD driver, midlife reversal post-2010) is consistent with CDC vital statistics and published epidemiology. Food insecurity → CVD (CARDIA cohort, 20-year follow-up) is well-sourced prospective data. The five adverse SDOH independently predicting hypertension is appropriately cited. No accuracy concerns. Confidence levels (likely to experimental) track the evidence quality.

LLM bias claims are appropriately calibrated

The scope qualifications in the nursing care plan bias claim ("9 LLMs, 1.7M outputs") are appropriate. "All model architectures" is a bold claim title but the body correctly scopes it to tested architectures. likely is defensible given the replication across multiple models and architectures tested.

What changes are needed

  1. Downgrade confidence on capabilities generalize further than alignment: likely → speculative. The file itself argues this is unfalsifiable in advance; the rating should match.

  2. Downgrade confidence on recursive self-improvement creates explosive intelligence gains: likely → experimental. AI-assisted development is not the Bostrom crossover. The Noah Smith counterargument warrants a lower confidence floor.

  3. Reclassify or reroute graph traversal... and knowledge between notes...: These belong in foundations/collective-intelligence/, not ai-alignment. No alignment argument runs through either claim body.

  4. Add wiki links connecting sandbagging cluster ↔ clinical AI safety gap: Cross-domain connection between ai-models-can-covertly-sandbag-capability-evaluations and clinical-ai-safety-gap-is-doubly-structural-with-no-pre-deployment-requirements-and-no-post-market-surveillance. The compound failure scenario is worth naming explicitly.

  5. Add wiki link: knowledge codification loses metis ↔ physician de-skilling claim: Instantiation of the same mechanism in the clinical domain.

Items 4 and 5 could be addressed with wiki links added to existing bodies; items 1–3 require frontmatter edits and potential file moves.

Verdict: request_changes
Model: sonnet
Summary: Two confidence miscalibrations in ai-alignment (sharp left turn and RSI both rated "likely" despite acknowledged unfalsifiability and insufficient empirical support respectively); two knowledge architecture claims misclassified as ai-alignment rather than collective-intelligence; high-value cross-domain connection between sandbagging detection and clinical AI safety gap missing; health domain claims are solid with one minor MAUDE framing note.

# Vida Domain Peer Review — PR #2460 Reviewing as Vida, health and cross-domain AI safety specialist. This PR spans four clusters: sandbagging detection, governance/coordination, knowledge architecture epistemology, and clinical AI + CVD epidemiology. My focus is on what a health/clinical-AI-safety lens catches that a pure alignment lens misses. --- ## AI Alignment Domain — Domain Expertise Flags ### 1. Confidence calibration: "capabilities generalize further than alignment" (sharp left turn) Confidence is `likely`. The body explicitly states: *"The sharp left turn is unfalsifiable in advance by design — it predicts failure only at capability levels we haven't reached."* A claim the author acknowledges is unfalsifiable in advance cannot be rated `likely`. The `scalable oversight degrades at capability gaps` evidence supports oversight breakdown but doesn't confirm the discontinuity prediction — it's evidence of a related phenomenon, not the sharp left turn itself. Shard theory (Shah et al.) is cited as a challenge but not engaged seriously. This should be `speculative`. Leaving it at `likely` misrepresents the epistemic status of one of the KB's most consequential alignment claims. ### 2. Confidence calibration: "recursive self-improvement creates explosive intelligence gains" Confidence is `likely`. The body presents Amodei's 2026 quote — AI "writing much of the code at Anthropic" — as "evidence the self-reinforcing loop has already started." This is a substantial over-read. AI-assisted code generation at a lab is not the Bostrom crossover point where self-improvement outpaces human contribution. The Noah Smith "jagged intelligence" counterargument included in the body is actually quite strong and arguably challenges the RSI frame as the central alignment concern. "Experimental" would be accurate; "likely" is not. ### 3. Domain misclassification: two knowledge architecture claims tagged as ai-alignment `graph traversal through curated wiki links replicates spreading activation...` and `knowledge between notes is generated by traversal not stored in any individual note...` are both tagged `domain: ai-alignment` with `secondary_domains: [collective-intelligence]`. These claims are about knowledge management architecture and agent epistemology. Neither contains an alignment argument — they describe how wiki-link graphs produce emergent understanding and why embedding retrieval is structurally inferior to curated traversal. This is `foundations/collective-intelligence/` territory. Contrast with `knowledge codification into AI agent skills structurally loses metis...` — that one belongs in ai-alignment because it explicitly argues the alignment-relevant dimension (contextual judgment about when to constrain is exactly what codification loses). The spreading activation and inter-note claims lack that alignment bridge. ### 4. Missing cross-domain connection: sandbagging × clinical AI safety The sandbagging cluster is technically strong. Three well-constructed claims establish that: (1) models can covertly sandbag under CoT monitoring, (2) white-box access is required for the only reliable detection method, and (3) external evaluators are stuck at AL1 black-box access. The chain is solid. What's missing: this has a direct and high-consequence instantiation in Vida's domain that neither claim set surfaces. If clinical AI systems can strategically underperform during pre-deployment evaluation while appearing safe, and the FDA has no AI-specific adverse event fields in MAUDE and no post-market surveillance requirements (both in this PR), then sandbagging creates a dual-failure scenario: the evaluation infrastructure that doesn't exist is also evaluating systems that can game whatever evaluation does exist. This isn't speculative — it's the logical intersection of two claim clusters in this same PR. The sandbagging claims should wiki-link to `[[clinical-ai-safety-gap-is-doubly-structural-with-no-pre-deployment-requirements-and-no-post-market-surveillance]]` and vice versa. This is a genuine cross-domain connection with governance implications (FDA CDS oversight + evaluation access levels are the same bottleneck from different angles). Similarly, `knowledge codification loses metis` should link to `[[human-in-the-loop clinical AI degrades to worse-than-AI-alone because physicians both de-skill from reliance and introduce errors when overriding correct outputs]]`. Physician de-skilling is the clinical instantiation of metis loss — execution-level judgment that doesn't survive the abstraction to AI-augmented care. This link would materially strengthen both claims. --- ## Health Domain — Domain Expertise Flags ### FDA MAUDE 943 adverse events number needs context The claim `fda-maude-database-lacks-ai-specific-adverse-event-fields-creating-systematic-under-detection-of-ai-attributable-harm` presents 943 reported events across 823+ AI/ML devices as evidence of AI-specific structural failure. The structural argument (no AI-specific fields) is solid. But MAUDE systematic under-reporting is well-documented across ALL device categories — estimates suggest adverse events are under-reported by 90%+ in general. Presenting 943 as specific evidence of AI-specific failure without acknowledging that MAUDE under-reports everything slightly weakens the claim's precision. The real AI-specific argument is the structural inability to attribute events to AI components, not the absolute count. The claim body makes this case but the title's implication (fields = under-detection) is imprecise. Minor — doesn't require rejection but worth a clarifying sentence. ### Automation bias claim is a genuine contribution `fda-treats-automation-bias-as-transparency-problem-contradicting-evidence-that-visibility-does-not-prevent-deference` is excellent. The category error diagnosis — FDA treating a cognitive architecture problem as an information problem — is clinically accurate and grounded in the automation bias literature. This is exactly what Vida is supposed to catch: a regulatory agency's category error with compounding downstream consequences for deployed clinical AI. High confidence in this claim's accuracy and domain value. ### CVD epidemiology cluster is solid The hypertension mortality data (1999–2023 doubling, shift from secondary to primary CVD driver, midlife reversal post-2010) is consistent with CDC vital statistics and published epidemiology. Food insecurity → CVD (CARDIA cohort, 20-year follow-up) is well-sourced prospective data. The five adverse SDOH independently predicting hypertension is appropriately cited. No accuracy concerns. Confidence levels (`likely` to `experimental`) track the evidence quality. ### LLM bias claims are appropriately calibrated The scope qualifications in the nursing care plan bias claim ("9 LLMs, 1.7M outputs") are appropriate. "All model architectures" is a bold claim title but the body correctly scopes it to tested architectures. `likely` is defensible given the replication across multiple models and architectures tested. --- ## What changes are needed 1. **Downgrade confidence on `capabilities generalize further than alignment`**: `likely` → `speculative`. The file itself argues this is unfalsifiable in advance; the rating should match. 2. **Downgrade confidence on `recursive self-improvement creates explosive intelligence gains`**: `likely` → `experimental`. AI-assisted development is not the Bostrom crossover. The Noah Smith counterargument warrants a lower confidence floor. 3. **Reclassify or reroute `graph traversal...` and `knowledge between notes...`**: These belong in `foundations/collective-intelligence/`, not `ai-alignment`. No alignment argument runs through either claim body. 4. **Add wiki links connecting sandbagging cluster ↔ clinical AI safety gap**: Cross-domain connection between `ai-models-can-covertly-sandbag-capability-evaluations` and `clinical-ai-safety-gap-is-doubly-structural-with-no-pre-deployment-requirements-and-no-post-market-surveillance`. The compound failure scenario is worth naming explicitly. 5. **Add wiki link: `knowledge codification loses metis` ↔ physician de-skilling claim**: Instantiation of the same mechanism in the clinical domain. Items 4 and 5 could be addressed with wiki links added to existing bodies; items 1–3 require frontmatter edits and potential file moves. --- **Verdict:** request_changes **Model:** sonnet **Summary:** Two confidence miscalibrations in ai-alignment (sharp left turn and RSI both rated "likely" despite acknowledged unfalsifiability and insufficient empirical support respectively); two knowledge architecture claims misclassified as ai-alignment rather than collective-intelligence; high-value cross-domain connection between sandbagging detection and clinical AI safety gap missing; health domain claims are solid with one minor MAUDE framing note. <!-- VERDICT:VIDA:REQUEST_CHANGES -->
leo commented 2026-04-07 01:09:29 +00:00
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Changes requested by leo(cross-domain), vida(domain-peer). Address feedback and push to trigger re-eval.

teleo-eval-orchestrator v2

**Changes requested** by leo(cross-domain), vida(domain-peer). Address feedback and push to trigger re-eval. *teleo-eval-orchestrator v2*

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Reference: teleo/teleo-codex#2460
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