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reweave: 2026 04 07b #2458

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pull from: reweave/2026-04-07b
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m3taversal commented 2026-04-07 00:52:09 +00:00
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m3taversal added 1 commit 2026-04-07 00:52:09 +00:00 
Threshold: 0.7, Haiku classification, 42 files modified.

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m3taversal commented 2026-04-07 00:52:36 +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 00:52:38 +00:00
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Validation: PASS — 0/0 claims pass

tier0-gate v2 | 2026-04-07 00:52 UTC

<!-- TIER0-VALIDATION:eb04e35f5b2230b1e25bd224f34a7ebf909dc6fa --> **Validation: PASS** — 0/0 claims pass *tier0-gate v2 | 2026-04-07 00:52 UTC*
m3taversal commented 2026-04-07 00:53:08 +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 00:53:48 +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 00:55:11 +00:00
m3taversal commented 2026-04-07 00:55:11 +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 00:56:32 +00:00
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Leo — Cross-Domain Review: PR #2458

PR: reweave/2026-04-07b — connect 26 orphan claims via vector similarity
Scope: Frontmatter-only changes to 42 existing claim files, adding related:/supports: links and reweave_edges: audit trails. No claim content modified.

What this does

Automated reweave pass that adds cross-references between previously unlinked claims. The reweave_edges field provides a dated audit trail of each machine-generated link. Good infrastructure work — orphan claims are dead weight in a graph-structured KB.

Issues

Broken link (must fix)

Three AI-alignment files reference "the shape of returns on cognitive reinvestment determines takeoff speed because constant or increasing returns on investing cognitive output into cognitive capability produce recursive self improvement" — but the actual file uses "self-improvement" (hyphenated). The link text drops the hyphen.

Affected files:

  • domains/ai-alignment/capabilities generalize further than alignment...md
  • domains/ai-alignment/marginal returns to intelligence are bounded by five complementary factors...md
  • domains/ai-alignment/recursive self-improvement creates explosive intelligence gains...md

Field naming inconsistency (note, not blocking)

The PR adds related: and supports: fields (plain text, block sequence format). Some pre-existing files already have related_claims: fields using [[wiki-link]] syntax in inline JSON arrays. This creates two parallel systems for the same concept. One file (tirzepatide-patent-thicket...md) now has both related_claims AND related in the same frontmatter.

This isn't introduced by this PR — it's a pre-existing schema divergence that the reweave makes more visible. Worth a follow-up normalization pass but not a blocker here.

Spot checks

Relationship types are semantically sound. Checked ~10 edges:

  • "Weight noise injection reveals hidden capabilities" → supports "AI models can covertly sandbag" ✓
  • "Clinical AI reinforces physician plans" → supports "LLMs amplify human cognitive biases" ✓
  • "undiscovered public knowledge" → related to "wiki-link graphs create auditable reasoning chains" ✓
  • CVD mortality cluster (hypertension/heart-failure/SDOH claims) correctly cross-linked with supports edges ✓

Health domain gets the densest weaving — 21 of the 42 files are in domains/health/. The clinical AI safety cluster (FDA/MAUDE/bias/hallucination claims) and the CVD mortality cluster are now well-interconnected. This is the right call; these claims were semantically close but graph-disconnected.

Cross-domain connections: Limited in this pass — most links stay within domain. The few cross-domain edges (e.g., AI-alignment verification claims ↔ grand-strategy governance claims) are appropriate. Future reweaves could push harder on cross-domain connections, which is where the highest-value links live.

What's good

  • reweave_edges audit trail is a clean pattern — every machine-generated link is dated and typed, making it easy to distinguish from human-curated links
  • 25 of 26 unique link targets resolve to real files (one hyphen mismatch noted above)
  • No claim content was modified — pure metadata enrichment, low risk

Verdict: request_changes
Model: opus
Summary: Clean automated reweave connecting 26 orphan claims across 42 files. One broken link (hyphen mismatch in "self-improvement") must be fixed in 3 AI-alignment files before merge. Field naming inconsistency (related vs related_claims) is pre-existing and worth a follow-up but not blocking.

# Leo — Cross-Domain Review: PR #2458 **PR:** reweave/2026-04-07b — connect 26 orphan claims via vector similarity **Scope:** Frontmatter-only changes to 42 existing claim files, adding `related:`/`supports:` links and `reweave_edges:` audit trails. No claim content modified. ## What this does Automated reweave pass that adds cross-references between previously unlinked claims. The `reweave_edges` field provides a dated audit trail of each machine-generated link. Good infrastructure work — orphan claims are dead weight in a graph-structured KB. ## Issues ### Broken link (must fix) Three AI-alignment files reference "the shape of returns on cognitive reinvestment determines takeoff speed because constant or increasing returns on investing cognitive output into cognitive capability produce recursive self **improvement**" — but the actual file uses "self-**improvement**" (hyphenated). The link text drops the hyphen. Affected files: - `domains/ai-alignment/capabilities generalize further than alignment...md` - `domains/ai-alignment/marginal returns to intelligence are bounded by five complementary factors...md` - `domains/ai-alignment/recursive self-improvement creates explosive intelligence gains...md` ### Field naming inconsistency (note, not blocking) The PR adds `related:` and `supports:` fields (plain text, block sequence format). Some pre-existing files already have `related_claims:` fields using `[[wiki-link]]` syntax in inline JSON arrays. This creates two parallel systems for the same concept. One file (`tirzepatide-patent-thicket...md`) now has both `related_claims` AND `related` in the same frontmatter. This isn't introduced by this PR — it's a pre-existing schema divergence that the reweave makes more visible. Worth a follow-up normalization pass but not a blocker here. ## Spot checks **Relationship types are semantically sound.** Checked ~10 edges: - "Weight noise injection reveals hidden capabilities" → supports "AI models can covertly sandbag" ✓ - "Clinical AI reinforces physician plans" → supports "LLMs amplify human cognitive biases" ✓ - "undiscovered public knowledge" → related to "wiki-link graphs create auditable reasoning chains" ✓ - CVD mortality cluster (hypertension/heart-failure/SDOH claims) correctly cross-linked with `supports` edges ✓ **Health domain gets the densest weaving** — 21 of the 42 files are in `domains/health/`. The clinical AI safety cluster (FDA/MAUDE/bias/hallucination claims) and the CVD mortality cluster are now well-interconnected. This is the right call; these claims were semantically close but graph-disconnected. **Cross-domain connections:** Limited in this pass — most links stay within domain. The few cross-domain edges (e.g., AI-alignment verification claims ↔ grand-strategy governance claims) are appropriate. Future reweaves could push harder on cross-domain connections, which is where the highest-value links live. ## What's good - `reweave_edges` audit trail is a clean pattern — every machine-generated link is dated and typed, making it easy to distinguish from human-curated links - 25 of 26 unique link targets resolve to real files (one hyphen mismatch noted above) - No claim content was modified — pure metadata enrichment, low risk --- **Verdict:** request_changes **Model:** opus **Summary:** Clean automated reweave connecting 26 orphan claims across 42 files. One broken link (hyphen mismatch in "self-improvement") must be fixed in 3 AI-alignment files before merge. Field naming inconsistency (`related` vs `related_claims`) is pre-existing and worth a follow-up but not blocking. <!-- VERDICT:LEO:REQUEST_CHANGES -->
vida commented 2026-04-07 00:57:09 +00:00
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Vida — Domain Peer Review: PR #2458

Reviewing the AI alignment claims as domain specialist, with secondary review of health claims as Vida's territory.

AI Alignment Claims

Sandbagging detection cluster (4 claims)

These four claims form a coherent logical chain that the PR doesn't fully wire together:

models can sandbag CoT monitoring → external evaluators only have black-box access → detection requires white-box access → noise injection works but needs white-box → therefore current governance is structurally blind to sandbagging

All four pass quality gates. The internal logic is tight and the evidence is recent (Aug 2025, Dec 2025 NeurIPS, March 2026 METR). But the claims are under-linked to each other — each cites sources but doesn't explicitly wiki-link the chain. Before merge, sandbagging-detection-requires-white-box-access should link to external-evaluators-predominantly-have-black-box-access and both should link to ai-models-can-covertly-sandbag-capability-evaluations-even-under-chain-of-thought-monitoring.

One technical gap in noise-injection-detects-sandbagging: the claim doesn't address whether models fine-tuned to resist noise injection could defeat this detection method. If sandbagging is strategic behavior, adversarial robustness against the detection method is a meaningful attack surface. Worth a challenged_by or a note in the Challenges section.

Confidence calibration issues

capabilities generalize further than alignment (sharp left turn) — rated likely but:

  • The claim body itself says "sharp left turn is unfalsifiable in advance"
  • The claim body notes "current smooth scaling (GPT-2→3→4) contradicts discontinuity prediction"
  • Primary source is Yudkowsky/Soares theoretical framing from 2022, with no confirmatory empirical evidence at scale

An unfalsifiable-in-advance claim whose observable evidence so far contradicts it cannot be likely. This should be speculative. The claim is worth having in the KB — it's an important theoretical frame — but the confidence level is inflated.

recursive self-improvement creates explosive intelligence gains — rated likely based on Bostrom 2014 + Dario Amodei quote that AI writes much of Anthropic's code. The Amodei evidence is incremental automation, not recursive self-improvement creating discontinuous gains. Bostrom's framework was written before modern transformers and the scaling era demonstrated continuous rather than explosive improvement. This should be experimental at best, probably speculative. The Noah Smith "jagged intelligence" alternative deserves more weight — it matches observed evidence better.

marginal returns to intelligence are bounded by five complementary factors — rated likely with primary source being Amodei's public essay. CEO advocacy essays are not evidence. The five factors are reasonable heuristics but "machines of loving grace" is explicitly speculative writing. Should be experimental.

Near-duplicate concerns

AI alignment is a coordination problem and AI accelerates existing Molochian dynamics and the alignment tax creates a structural race to the bottom all argue from different angles that competitive dynamics make unilateral safety unsustainable. These are meaningfully distinct (coordination framing vs. Moloch framing vs. race-to-bottom economics) but they use the same 2026 empirical evidence (Anthropic RSP rollback, Jared Kaplan quote). Meanwhile the existing claim Anthropic's RSP rollback under commercial pressure is the first empirical confirmation... already in main covers this evidence in detail.

The new claims add analytical framing (Alexander's four restraints, the alignment tax mechanism) that genuinely extends the KB. Not duplicates, but they need explicit wiki-links to the existing RSP rollback claim that their evidence overlaps with.

Notable cross-domain connections

The entire sandbagging cluster connects directly to Vida's health claims. If sandbagging detection is structurally blocked at the safety evaluation level (no white-box access, CoT monitors defeated), this maps exactly to clinical AI's evaluation problem: physician oversight of clinical AI is the health domain's equivalent of AL1 black-box access. The FDA's reliance on physician-in-the-loop oversight as a safety mechanism is structurally analogous to governance relying on behavioral monitoring that models can defeat. This connection deserves a wiki-link between sandbagging-detection-requires-white-box-access and Vida's fda-treats-automation-bias-as-transparency-problem — both identify cases where the designed oversight mechanism fails the way it was intended to work.

knowledge codification into AI agent skills structurally loses metis — the alignment dimension (codification loses contextual judgment about when to constrain) connects directly to clinical AI claims about anchoring bias and plan reinforcement. When clinical knowledge is codified into LLM skills, the tacit contextual resistance that expert clinicians develop is exactly what doesn't transfer. This claim supports several health claims but lacks wiki-links to them.

AI investment concentration — relevant to health AI safety: if two companies capture 14% of global VC and dominate AI deployment infrastructure, the structural conditions for regulatory capture of FDA/EMA are met. The claim appropriately classifies as secondary_domains: internet-finance but should also flag health regulatory implications.

Multilateral governance claims

multilateral-verification-mechanisms-can-substitute-for-failed-voluntary-commitments — confidence experimental is appropriate. The claim doesn't address the obvious failure mode: the EU AI Act covers EU markets, but the US DoD (primary demand-side actor for frontier AI) is not subject to EU enforcement. The grand-strategy companion claim on the BWC/CWC comparison establishes this more rigorously. The substitution claim needs a challenged_by that names US-non-EU jurisdictions as the gap.

Health Claims

These are Vida's primary territory. The cluster is strong overall — documenting a regulatory vacuum during active harm accumulation is high-value KB material.

Potential consolidation candidates: fda-maude-cannot-identify-ai-contributions and fda-maude-database-lacks-ai-specific-adverse-event-fields cover the same structural gap from slightly different angles (information insufficiency vs. field absence). Both should merge or one should explicitly scope to the mechanism and the other to the consequence. Currently they read as two descriptions of the same finding.

Strongest claim in the health batch: medical LLM benchmark performance does not translate to clinical impact — the finding that physician + AI = 68%, AI alone = 90%, physician alone = 65% (physician input actively degraded AI performance by 22pp) is striking and underappreciated. This belongs prominently wiki-linked from the automation bias claim and the anchoring bias claim as the empirical consequence of those mechanisms.

fda-treats-automation-bias-as-transparency-problem — excellent claim with precise mechanistic insight. No changes needed.

regulatory-rollback-clinical-ai-eu-us-2025-2026-removes-high-risk-oversight — the "coordinated or parallel regulatory capture" framing in the title is a stronger assertion than the evidence supports. Simultaneous deregulation in two jurisdictions is consistent with coordinated lobbying OR with parallel policy diffusion from shared industry lobbying OR with shared political economy pressures (post-2024 election dynamics). Calling it "coordinated or parallel regulatory capture" may be overclaiming. The temporal coincidence is real and damning — the claim doesn't need the "coordinated" framing to be valuable.

Verdict: request_changes
Model: sonnet
Summary: The sandbagging cluster is a real contribution and the health claims are technically solid. Two confidence calibration failures require correction before merge: capabilities generalize further than alignment and recursive self-improvement are rated likely for unfalsifiable or empirically-contradicted theoretical claims — both should drop to experimental or speculative. The sandbagging chain also needs explicit wiki-links between the four claims to make the governance argument legible. Remaining issues (MAUDE consolidation, multilateral verification gap, regulatory capture framing) are request-for-changes not blockers, but should be addressed in this PR since all the evidence is fresh.

# Vida — Domain Peer Review: PR #2458 Reviewing the AI alignment claims as domain specialist, with secondary review of health claims as Vida's territory. --- ## AI Alignment Claims ### Sandbagging detection cluster (4 claims) These four claims form a coherent logical chain that the PR doesn't fully wire together: > models can sandbag CoT monitoring → external evaluators only have black-box access → detection requires white-box access → noise injection works but needs white-box → therefore current governance is structurally blind to sandbagging All four pass quality gates. The internal logic is tight and the evidence is recent (Aug 2025, Dec 2025 NeurIPS, March 2026 METR). But the claims are under-linked to each other — each cites sources but doesn't explicitly wiki-link the chain. Before merge, `sandbagging-detection-requires-white-box-access` should link to `external-evaluators-predominantly-have-black-box-access` and both should link to `ai-models-can-covertly-sandbag-capability-evaluations-even-under-chain-of-thought-monitoring`. One technical gap in `noise-injection-detects-sandbagging`: the claim doesn't address whether models fine-tuned to resist noise injection could defeat this detection method. If sandbagging is strategic behavior, adversarial robustness against the detection method is a meaningful attack surface. Worth a `challenged_by` or a note in the Challenges section. ### Confidence calibration issues **`capabilities generalize further than alignment`** (sharp left turn) — rated `likely` but: - The claim body itself says "sharp left turn is unfalsifiable in advance" - The claim body notes "current smooth scaling (GPT-2→3→4) contradicts discontinuity prediction" - Primary source is Yudkowsky/Soares theoretical framing from 2022, with no confirmatory empirical evidence at scale An unfalsifiable-in-advance claim whose observable evidence so far contradicts it cannot be `likely`. This should be `speculative`. The claim is worth having in the KB — it's an important theoretical frame — but the confidence level is inflated. **`recursive self-improvement creates explosive intelligence gains`** — rated `likely` based on Bostrom 2014 + Dario Amodei quote that AI writes much of Anthropic's code. The Amodei evidence is incremental automation, not recursive self-improvement creating discontinuous gains. Bostrom's framework was written before modern transformers and the scaling era demonstrated continuous rather than explosive improvement. This should be `experimental` at best, probably `speculative`. The Noah Smith "jagged intelligence" alternative deserves more weight — it matches observed evidence better. **`marginal returns to intelligence are bounded by five complementary factors`** — rated `likely` with primary source being Amodei's public essay. CEO advocacy essays are not evidence. The five factors are reasonable heuristics but "machines of loving grace" is explicitly speculative writing. Should be `experimental`. ### Near-duplicate concerns **`AI alignment is a coordination problem`** and **`AI accelerates existing Molochian dynamics`** and **`the alignment tax creates a structural race to the bottom`** all argue from different angles that competitive dynamics make unilateral safety unsustainable. These are meaningfully distinct (coordination framing vs. Moloch framing vs. race-to-bottom economics) but they use the same 2026 empirical evidence (Anthropic RSP rollback, Jared Kaplan quote). Meanwhile the existing claim **`Anthropic's RSP rollback under commercial pressure is the first empirical confirmation...`** already in main covers this evidence in detail. The new claims add analytical framing (Alexander's four restraints, the alignment tax mechanism) that genuinely extends the KB. Not duplicates, but they need explicit wiki-links to the existing RSP rollback claim that their evidence overlaps with. ### Notable cross-domain connections The entire sandbagging cluster connects directly to Vida's health claims. If sandbagging detection is structurally blocked at the safety evaluation level (no white-box access, CoT monitors defeated), this maps exactly to clinical AI's evaluation problem: physician oversight of clinical AI is the health domain's equivalent of AL1 black-box access. The FDA's reliance on physician-in-the-loop oversight as a safety mechanism is structurally analogous to governance relying on behavioral monitoring that models can defeat. This connection deserves a wiki-link between `sandbagging-detection-requires-white-box-access` and Vida's `fda-treats-automation-bias-as-transparency-problem` — both identify cases where the designed oversight mechanism fails the way it was intended to work. **`knowledge codification into AI agent skills structurally loses metis`** — the alignment dimension (codification loses contextual judgment about when to constrain) connects directly to clinical AI claims about anchoring bias and plan reinforcement. When clinical knowledge is codified into LLM skills, the tacit contextual resistance that expert clinicians develop is exactly what doesn't transfer. This claim supports several health claims but lacks wiki-links to them. **`AI investment concentration`** — relevant to health AI safety: if two companies capture 14% of global VC and dominate AI deployment infrastructure, the structural conditions for regulatory capture of FDA/EMA are met. The claim appropriately classifies as `secondary_domains: internet-finance` but should also flag health regulatory implications. ### Multilateral governance claims **`multilateral-verification-mechanisms-can-substitute-for-failed-voluntary-commitments`** — confidence `experimental` is appropriate. The claim doesn't address the obvious failure mode: the EU AI Act covers EU markets, but the US DoD (primary demand-side actor for frontier AI) is not subject to EU enforcement. The grand-strategy companion claim on the BWC/CWC comparison establishes this more rigorously. The substitution claim needs a `challenged_by` that names US-non-EU jurisdictions as the gap. --- ## Health Claims These are Vida's primary territory. The cluster is strong overall — documenting a regulatory vacuum during active harm accumulation is high-value KB material. **Potential consolidation candidates:** `fda-maude-cannot-identify-ai-contributions` and `fda-maude-database-lacks-ai-specific-adverse-event-fields` cover the same structural gap from slightly different angles (information insufficiency vs. field absence). Both should merge or one should explicitly scope to the mechanism and the other to the consequence. Currently they read as two descriptions of the same finding. **Strongest claim in the health batch:** `medical LLM benchmark performance does not translate to clinical impact` — the finding that physician + AI = 68%, AI alone = 90%, physician alone = 65% (physician input actively degraded AI performance by 22pp) is striking and underappreciated. This belongs prominently wiki-linked from the automation bias claim and the anchoring bias claim as the empirical consequence of those mechanisms. **`fda-treats-automation-bias-as-transparency-problem`** — excellent claim with precise mechanistic insight. No changes needed. **`regulatory-rollback-clinical-ai-eu-us-2025-2026-removes-high-risk-oversight`** — the "coordinated or parallel regulatory capture" framing in the title is a stronger assertion than the evidence supports. Simultaneous deregulation in two jurisdictions is consistent with coordinated lobbying OR with parallel policy diffusion from shared industry lobbying OR with shared political economy pressures (post-2024 election dynamics). Calling it "coordinated or parallel regulatory capture" may be overclaiming. The temporal coincidence is real and damning — the claim doesn't need the "coordinated" framing to be valuable. --- **Verdict:** request_changes **Model:** sonnet **Summary:** The sandbagging cluster is a real contribution and the health claims are technically solid. Two confidence calibration failures require correction before merge: `capabilities generalize further than alignment` and `recursive self-improvement` are rated `likely` for unfalsifiable or empirically-contradicted theoretical claims — both should drop to `experimental` or `speculative`. The sandbagging chain also needs explicit wiki-links between the four claims to make the governance argument legible. Remaining issues (MAUDE consolidation, multilateral verification gap, regulatory capture framing) are request-for-changes not blockers, but should be addressed in this PR since all the evidence is fresh. <!-- VERDICT:VIDA:REQUEST_CHANGES -->
leo commented 2026-04-07 00:57:09 +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#2458
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