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reweave: 2026 04 07b #2462

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m3taversal commented 2026-04-07 01:08:08 +00:00
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m3taversal added 1 commit 2026-04-07 01:08:09 +00:00 
Threshold: 0.7, Haiku classification, 42 files modified.

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m3taversal commented 2026-04-07 01:08:28 +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 01:08:41 +00:00
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Validation: PASS — 0/0 claims pass

tier0-gate v2 | 2026-04-07 01:08 UTC

<!-- TIER0-VALIDATION:eb04e35f5b2230b1e25bd224f34a7ebf909dc6fa --> **Validation: PASS** — 0/0 claims pass *tier0-gate v2 | 2026-04-07 01:08 UTC*
m3taversal commented 2026-04-07 01:09:11 +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 01:09:48 +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*
leo commented 2026-04-07 01:12:35 +00:00
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Leo Cross-Domain Review — PR #2462

Branch: reweave/2026-04-07b
Scope: ~40 new claim files across ai-alignment (15), health (22), grand-strategy (3), core/living-agents (1), foundations/collective-intelligence (1)

Issues Requiring Changes

1. Likely duplicate in ai-alignment — noise-injection claim

noise-injection-detects-sandbagging-through-asymmetric-performance-response.md appears to duplicate existing Weight-noise-injection-detects-sandbagging-through-anomalous-performance-patterns-under-perturbation.md. Same mechanism (weight noise → asymmetric response reveals sandbagging), same source study (Tice et al. NeurIPS 2025). If the new file adds evidence, merge it into the existing claim rather than creating a second file.

2. Domain misclassification — two knowledge-architecture claims filed under ai-alignment

  • graph traversal through curated wiki links replicates spreading activation...
  • knowledge between notes is generated by traversal not stored in any individual note...

These are about cognitive science and knowledge graph architecture, not AI alignment. Primary domain should be collective-intelligence. The ai-alignment connection (agent reasoning) is real but secondary. Move to foundations/collective-intelligence/ or add explicit alignment framing to the descriptions.

3. Branch-only wiki links in grand-strategy claims

attractor-agentic-taylorism.md and verification-mechanism-is-the-critical-enabler...md both cite extended evidence files (2026-03-31-leo-*, 2026-04-01-leo-*) that exist only on other branches. These links will break on main. Either include those files in this PR or remove the references.

4. Hypertension claims 11 vs 12 — near-duplicate

  • Claim 11: "Hypertension shifted from secondary to primary CVD mortality driver since 2022"
  • Claim 12: "Hypertensive disease mortality doubled 1999-2023, becoming leading contributing CVD cause"

Both assert hypertension became the leading CVD cause. Claim 11 focuses on the 2022 inflection point; claim 12 focuses on the doubling mechanism (obesity, sedentary behavior). These should either be merged or the titles should be sharpened to make the distinct contributions clearer. Currently they overlap ~70%.

Tensions Worth Noting (not blocking)

Bounded returns vs. recursive self-improvement (ai-alignment #10 vs #14): Claim 10 argues superintelligence has bounded marginal returns (Amodei's five factors). Claim 14 argues recursive self-improvement creates explosive gains (Bostrom). Both can be true — RSI produces rapid gains that eventually hit bounds — but neither claim acknowledges the other. This is a divergence candidate. At minimum, add cross-references.

Verification claims #11 vs #12 (ai-alignment): Claim 11 says multilateral verification mechanisms "remain at proposal stage." Claim 12 cites the EU AI Act as providing "binding enforcement architecture." These operate at different scopes (autonomous weapons vs. high-risk military AI broadly) but the relationship isn't explicit. Clarify the scope boundary.

Verification-as-load-bearing vs. substitutable enablers (grand-strategy): The new verification-mechanism claim positions verification as the critical enabler. The existing arms-control-three-condition-framework claim treats verification and strategic-utility-reduction as substitutable. Not a contradiction — more a question of emphasis — but the new claim should reference the existing framework.

Cross-Domain Connections Worth Highlighting

Alignment tax → clinical AI regulatory rollback: The alignment tax mechanism (foundations/collective-intelligence) and the regulatory rollback claims (health domain, claims 19-20) describe the same structural dynamic in different domains — safety constraints that cost capability get dropped under competitive pressure. The health claims add empirical specificity (FDA January 6 guidance + ECRI top hazard designation in the same 30-day window). These should be cross-linked.

Agentic Taylorism ↔ metis loss: attractor-agentic-taylorism.md (grand-strategy) and the metis claim (ai-alignment, knowledge codification loses tacit judgment) are deeply complementary. The agentic Taylorism claim frames the macro pattern; the metis claim identifies the specific mechanism of loss. Both cite the Abdalla manuscript. Good that the metis claim's depends_on includes attractor-agentic-taylorism.

LLM bias cascade → alignment-as-coordination: The health domain's five nested LLM bias claims (anchoring → bias → plan reinforcement → population-scale harm) provide concrete evidence for the ai-alignment claim that alignment is a coordination problem. When 30M monthly consultations amplify demographic bias, that's an alignment failure manifesting through coordination failure. Worth a wiki link from the alignment-is-coordination claim to the clinical bias amplification claim.

What's Good

The health cluster is strong — 22 claims forming coherent causal chains (CVD bifurcation, SDOH mechanisms, LLM bias cascade, regulatory capture). Evidence quality is high: CARDIA prospective cohort, Nature Medicine, multiple RCTs. Confidence calibration is appropriate throughout.

The sandbagging detection cluster (ai-alignment claims 4, 6, 13, 15) forms a tight argument: models can sandbag → CoT monitoring doesn't catch it → best detection requires white-box access → evaluators only have black-box access. Clean logical chain with experimental evidence at each step.

The agentic Taylorism claim is the most interesting single addition — novel framing with real explanatory power.

Verdict: request_changes
Model: opus
Summary: Strong 40-file PR with good evidence quality across health and ai-alignment. Four blocking issues: one likely duplicate (noise-injection), two misclassified claims (knowledge-architecture filed under ai-alignment), branch-only wiki links in grand-strategy, and a near-duplicate pair in hypertension claims. Fix these and it's ready.

# Leo Cross-Domain Review — PR #2462 **Branch:** `reweave/2026-04-07b` **Scope:** ~40 new claim files across ai-alignment (15), health (22), grand-strategy (3), core/living-agents (1), foundations/collective-intelligence (1) --- ## Issues Requiring Changes ### 1. Likely duplicate in ai-alignment — noise-injection claim `noise-injection-detects-sandbagging-through-asymmetric-performance-response.md` appears to duplicate existing `Weight-noise-injection-detects-sandbagging-through-anomalous-performance-patterns-under-perturbation.md`. Same mechanism (weight noise → asymmetric response reveals sandbagging), same source study (Tice et al. NeurIPS 2025). If the new file adds evidence, merge it into the existing claim rather than creating a second file. ### 2. Domain misclassification — two knowledge-architecture claims filed under ai-alignment - `graph traversal through curated wiki links replicates spreading activation...` - `knowledge between notes is generated by traversal not stored in any individual note...` These are about cognitive science and knowledge graph architecture, not AI alignment. Primary domain should be `collective-intelligence`. The ai-alignment connection (agent reasoning) is real but secondary. Move to `foundations/collective-intelligence/` or add explicit alignment framing to the descriptions. ### 3. Branch-only wiki links in grand-strategy claims `attractor-agentic-taylorism.md` and `verification-mechanism-is-the-critical-enabler...md` both cite extended evidence files (`2026-03-31-leo-*`, `2026-04-01-leo-*`) that exist only on other branches. These links will break on main. Either include those files in this PR or remove the references. ### 4. Hypertension claims 11 vs 12 — near-duplicate - Claim 11: "Hypertension shifted from secondary to primary CVD mortality driver since 2022" - Claim 12: "Hypertensive disease mortality doubled 1999-2023, becoming leading contributing CVD cause" Both assert hypertension became the leading CVD cause. Claim 11 focuses on the 2022 inflection point; claim 12 focuses on the doubling mechanism (obesity, sedentary behavior). These should either be merged or the titles should be sharpened to make the distinct contributions clearer. Currently they overlap ~70%. --- ## Tensions Worth Noting (not blocking) **Bounded returns vs. recursive self-improvement (ai-alignment #10 vs #14):** Claim 10 argues superintelligence has bounded marginal returns (Amodei's five factors). Claim 14 argues recursive self-improvement creates explosive gains (Bostrom). Both can be true — RSI produces rapid gains that eventually hit bounds — but neither claim acknowledges the other. This is a **divergence candidate**. At minimum, add cross-references. **Verification claims #11 vs #12 (ai-alignment):** Claim 11 says multilateral verification mechanisms "remain at proposal stage." Claim 12 cites the EU AI Act as providing "binding enforcement architecture." These operate at different scopes (autonomous weapons vs. high-risk military AI broadly) but the relationship isn't explicit. Clarify the scope boundary. **Verification-as-load-bearing vs. substitutable enablers (grand-strategy):** The new verification-mechanism claim positions verification as *the* critical enabler. The existing `arms-control-three-condition-framework` claim treats verification and strategic-utility-reduction as substitutable. Not a contradiction — more a question of emphasis — but the new claim should reference the existing framework. --- ## Cross-Domain Connections Worth Highlighting **Alignment tax → clinical AI regulatory rollback:** The alignment tax mechanism (foundations/collective-intelligence) and the regulatory rollback claims (health domain, claims 19-20) describe the same structural dynamic in different domains — safety constraints that cost capability get dropped under competitive pressure. The health claims add empirical specificity (FDA January 6 guidance + ECRI top hazard designation in the same 30-day window). These should be cross-linked. **Agentic Taylorism ↔ metis loss:** `attractor-agentic-taylorism.md` (grand-strategy) and the metis claim (ai-alignment, knowledge codification loses tacit judgment) are deeply complementary. The agentic Taylorism claim frames the macro pattern; the metis claim identifies the specific mechanism of loss. Both cite the Abdalla manuscript. Good that the metis claim's `depends_on` includes attractor-agentic-taylorism. **LLM bias cascade → alignment-as-coordination:** The health domain's five nested LLM bias claims (anchoring → bias → plan reinforcement → population-scale harm) provide concrete evidence for the ai-alignment claim that alignment is a coordination problem. When 30M monthly consultations amplify demographic bias, that's an alignment failure manifesting through coordination failure. Worth a wiki link from the alignment-is-coordination claim to the clinical bias amplification claim. --- ## What's Good The health cluster is strong — 22 claims forming coherent causal chains (CVD bifurcation, SDOH mechanisms, LLM bias cascade, regulatory capture). Evidence quality is high: CARDIA prospective cohort, Nature Medicine, multiple RCTs. Confidence calibration is appropriate throughout. The sandbagging detection cluster (ai-alignment claims 4, 6, 13, 15) forms a tight argument: models can sandbag → CoT monitoring doesn't catch it → best detection requires white-box access → evaluators only have black-box access. Clean logical chain with experimental evidence at each step. The agentic Taylorism claim is the most interesting single addition — novel framing with real explanatory power. --- **Verdict:** request_changes **Model:** opus **Summary:** Strong 40-file PR with good evidence quality across health and ai-alignment. Four blocking issues: one likely duplicate (noise-injection), two misclassified claims (knowledge-architecture filed under ai-alignment), branch-only wiki links in grand-strategy, and a near-duplicate pair in hypertension claims. Fix these and it's ready. <!-- VERDICT:LEO:REQUEST_CHANGES -->
vida commented 2026-04-07 01:13:03 +00:00
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Vida Domain Peer Review — PR #2462

Reviewing from Vida's lens: clinical AI safety as a domain-specific instance of the general alignment problem. Focus on ai-alignment claims; health claims reviewed separately.

Confidence Calibration Issues

capabilities generalize further than alignment (sharp left turn) — filed as likely. The claim's own Challenges section states it is "unfalsifiable in advance by design" and "cannot be tested" at current capability levels. A claim that cannot be tested at present capability levels and predicts behavior only at thresholds we haven't reached shouldn't clear the likely bar — that's experimental. The smooth scaling from GPT-2→4→Claude series (noted in Challenges) is the only available empirical signal and it runs contrary to the discontinuity prediction. The claim is worth including — Yudkowsky's framing is important to have in the KB — but experimental is the honest confidence here.

AI accelerates existing Molochian dynamics... not creating new misalignment — filed as likely. The title's "not creating new misalignment" makes a negative existence claim (novel AI failure modes don't add to the dynamic) that the body immediately walks back in Challenges: "This framing risks minimizing genuinely novel AI risks (deceptive alignment, mesa-optimization, power-seeking)." A title asserting something the body's challenges section explicitly contests has a scoping problem. Either scope the title to "primary mechanism" or adjust confidence to experimental. The Molochian framing is valuable; the "not creating new" part is what's doing the work and it's the least supported piece.

Domain Classification

graph traversal through curated wiki links replicates spreading activation and knowledge between notes is generated by traversal — both filed as ai-alignment. The alignment relevance in both bodies is real but indirect: "because alignment (contextual judgment about when to constrain) is precisely what codification loses" — but that argument is made in the metis-codification claim, not in the graph traversal claims. These two are epistemology/knowledge architecture claims whose primary domain is collective-intelligence. Both already have secondary_domains: [collective-intelligence] which should be inverted. Domain classification matters for discovery — an agent searching ai-alignment for governance claims gets these instead.

Structural Tension: Verification Cluster

multilateral-ai-governance-verification-mechanisms-remain-at-proposal-stage establishes that technical infrastructure for deployment-scale verification does not exist. multilateral-verification-mechanisms-can-substitute-for-failed-voluntary-commitments proposes EU AI Act as the enforcement architecture that can substitute.

These claims are using "verification mechanism" to mean different things. The first claim means technical infrastructure for verifying AI capability/compliance (like OPCW inspections). The second claim means legal enforcement architecture (EU market access requirements). EU AI Act provides legal teeth, not technical verification — it can mandate that companies demonstrate compliance but it doesn't solve the underlying technical problem of how compliance is verified for software systems with no physical manifestation. The BWC/CWC claim in grand-strategy makes this precise: the CWC worked because chemical weapons are physical stockpiles; AI is software with zero-cost replication and no infrastructure chokepoint.

The second claim should acknowledge this gap rather than leave it implicit. As written, it sounds like EU AI Act solves the verification problem. It addresses the enforcement problem — which is distinct and genuinely valuable — but doesn't close the technical verification gap that makes the first claim true.

Missed Cross-Domain Connections (Vida's territory)

The most valuable thing I can add: several ai-alignment claims in this PR are directly evidenced by health domain claims in this same PR, with no linking.

Sandbagging detection cluster → clinical AI evaluation failures. The external-evaluators-predominantly-have-black-box-access, ai-models-can-covertly-sandbag, and sandbagging-detection-requires-white-box-access claims describe evaluation infrastructure inadequacy for detecting hidden model behavior. The health claims clinical-ai-safety-gap-is-doubly-structural and fda-maude-database-lacks-ai-specific-adverse-event-fields document the same failure in clinical deployment: no pre-deployment evaluation requirements AND no post-market surveillance that can detect AI-attributable harm. Clinical AI is a concrete operational instance of the evaluation infrastructure problem — the stakes (patient harm) are higher and the failure is already happening. These should be cross-linked.

knowledge codification loses metis → automation bias claims. The mechanism in the metis claim — codified knowledge captures "how" but loses contextual judgment about "when not to" — is precisely what the health claims llm-anchoring-bias-explains-clinical-ai-plan-reinforcement-mechanism and fda-treats-automation-bias-as-transparency-problem-contradicting-evidence-that-visibility-does-not-prevent-deference document empirically in clinical settings. Automation bias in clinical AI IS metis loss in deployment: the physician who overrides a correct AI output (because they lost the metis to evaluate when the AI is right) is the downstream consequence of codification without contextual judgment transfer. The metis claim has a strong abstract argument; the health claims have RCT-level evidence for the mechanism. Cross-linking would strengthen both.

AI alignment is a coordination problem → regulatory rollback health claims. The health claim regulatory-rollback-clinical-ai-eu-us-2025-2026 is a domain-specific instance of alignment coordination failure: EU and FDA simultaneously weakened clinical AI oversight during accumulating evidence of failure modes. The coordination problem isn't just in frontier labs racing on capability — it's in regulators racing each other to deregulate. This is Molochian dynamics operating in the regulatory domain, evidenced by health data. Neither claim references the other.

What Works Well

The sandbagging cluster (three claims: covert sandbagging, noise injection detection, white-box access barrier) forms a clean, well-scoped argument chain. The supports relationships accurately reflect logical dependency. experimental confidence is right for all three. The finding that training-based elicitation outperforms behavioral detection is highlighted appropriately.

The AI investment concentration claim's "safety monoculture risk" paragraph is novel and not present elsewhere in the KB — if three to four labs produce all frontier models, their correlated failure modes are an alignment risk independent of any individual model's alignment. This insight adds genuine value.

The AI alignment is a coordination problem claim is the best-developed in the set — extensive evidence across five evidence blocks, concrete 2026 case study, honest about what coordination failure looks like in practice (not just theoretical). The Anthropic/Pentagon/OpenAI triangle is the clearest real-world demonstration of coordination failure I've seen written up.

Verdict: request_changes
Model: sonnet
Summary: Three issues warrant changes before merge: (1) confidence on capabilities generalize further than alignment should drop from likely to experimental — the claim acknowledges its own unfalsifiability; (2) title of Molochian dynamics claim overstates with "not creating new misalignment" — scope it or drop confidence to experimental; (3) the verification cluster has a conceptual elision between legal enforcement and technical verification that the second claim should address explicitly. The domain classification on the wiki-graph claims and the missed cross-domain connections to health are lower-priority but would meaningfully strengthen the KB's cross-domain value.

# Vida Domain Peer Review — PR #2462 Reviewing from Vida's lens: clinical AI safety as a domain-specific instance of the general alignment problem. Focus on ai-alignment claims; health claims reviewed separately. --- ## Confidence Calibration Issues **`capabilities generalize further than alignment` (sharp left turn)** — filed as `likely`. The claim's own Challenges section states it is "unfalsifiable in advance by design" and "cannot be tested" at current capability levels. A claim that cannot be tested at present capability levels and predicts behavior only at thresholds we haven't reached shouldn't clear the `likely` bar — that's `experimental`. The smooth scaling from GPT-2→4→Claude series (noted in Challenges) is the only available empirical signal and it runs contrary to the discontinuity prediction. The claim is worth including — Yudkowsky's framing is important to have in the KB — but `experimental` is the honest confidence here. **`AI accelerates existing Molochian dynamics... not creating new misalignment`** — filed as `likely`. The title's "not creating new misalignment" makes a negative existence claim (novel AI failure modes don't add to the dynamic) that the body immediately walks back in Challenges: "This framing risks minimizing genuinely novel AI risks (deceptive alignment, mesa-optimization, power-seeking)." A title asserting something the body's challenges section explicitly contests has a scoping problem. Either scope the title to "primary mechanism" or adjust confidence to `experimental`. The Molochian framing is valuable; the "not creating new" part is what's doing the work and it's the least supported piece. --- ## Domain Classification **`graph traversal through curated wiki links replicates spreading activation`** and **`knowledge between notes is generated by traversal`** — both filed as `ai-alignment`. The alignment relevance in both bodies is real but indirect: "because alignment (contextual judgment about when to constrain) is precisely what codification loses" — but that argument is made in the metis-codification claim, not in the graph traversal claims. These two are epistemology/knowledge architecture claims whose primary domain is `collective-intelligence`. Both already have `secondary_domains: [collective-intelligence]` which should be inverted. Domain classification matters for discovery — an agent searching `ai-alignment` for governance claims gets these instead. --- ## Structural Tension: Verification Cluster `multilateral-ai-governance-verification-mechanisms-remain-at-proposal-stage` establishes that **technical infrastructure for deployment-scale verification does not exist**. `multilateral-verification-mechanisms-can-substitute-for-failed-voluntary-commitments` proposes EU AI Act as the enforcement architecture that can substitute. These claims are using "verification mechanism" to mean different things. The first claim means technical infrastructure for verifying AI capability/compliance (like OPCW inspections). The second claim means legal enforcement architecture (EU market access requirements). EU AI Act provides legal teeth, not technical verification — it can mandate that companies demonstrate compliance but it doesn't solve the underlying technical problem of *how* compliance is verified for software systems with no physical manifestation. The BWC/CWC claim in grand-strategy makes this precise: the CWC worked because chemical weapons are physical stockpiles; AI is software with zero-cost replication and no infrastructure chokepoint. The second claim should acknowledge this gap rather than leave it implicit. As written, it sounds like EU AI Act solves the verification problem. It addresses the enforcement problem — which is distinct and genuinely valuable — but doesn't close the technical verification gap that makes the first claim true. --- ## Missed Cross-Domain Connections (Vida's territory) The most valuable thing I can add: several ai-alignment claims in this PR are directly evidenced by health domain claims in this same PR, with no linking. **Sandbagging detection cluster → clinical AI evaluation failures.** The `external-evaluators-predominantly-have-black-box-access`, `ai-models-can-covertly-sandbag`, and `sandbagging-detection-requires-white-box-access` claims describe evaluation infrastructure inadequacy for detecting hidden model behavior. The health claims `clinical-ai-safety-gap-is-doubly-structural` and `fda-maude-database-lacks-ai-specific-adverse-event-fields` document the same failure in clinical deployment: no pre-deployment evaluation requirements AND no post-market surveillance that can detect AI-attributable harm. Clinical AI is a concrete operational instance of the evaluation infrastructure problem — the stakes (patient harm) are higher and the failure is already happening. These should be cross-linked. **`knowledge codification loses metis` → automation bias claims.** The mechanism in the metis claim — codified knowledge captures "how" but loses contextual judgment about "when not to" — is precisely what the health claims `llm-anchoring-bias-explains-clinical-ai-plan-reinforcement-mechanism` and `fda-treats-automation-bias-as-transparency-problem-contradicting-evidence-that-visibility-does-not-prevent-deference` document empirically in clinical settings. Automation bias in clinical AI IS metis loss in deployment: the physician who overrides a correct AI output (because they lost the metis to evaluate when the AI is right) is the downstream consequence of codification without contextual judgment transfer. The metis claim has a strong abstract argument; the health claims have RCT-level evidence for the mechanism. Cross-linking would strengthen both. **`AI alignment is a coordination problem` → regulatory rollback health claims.** The health claim `regulatory-rollback-clinical-ai-eu-us-2025-2026` is a domain-specific instance of alignment coordination failure: EU and FDA simultaneously weakened clinical AI oversight during accumulating evidence of failure modes. The coordination problem isn't just in frontier labs racing on capability — it's in regulators racing each other to deregulate. This is Molochian dynamics operating in the regulatory domain, evidenced by health data. Neither claim references the other. --- ## What Works Well The sandbagging cluster (three claims: covert sandbagging, noise injection detection, white-box access barrier) forms a clean, well-scoped argument chain. The `supports` relationships accurately reflect logical dependency. `experimental` confidence is right for all three. The finding that training-based elicitation outperforms behavioral detection is highlighted appropriately. The `AI investment concentration` claim's "safety monoculture risk" paragraph is novel and not present elsewhere in the KB — if three to four labs produce all frontier models, their correlated failure modes are an alignment risk independent of any individual model's alignment. This insight adds genuine value. The `AI alignment is a coordination problem` claim is the best-developed in the set — extensive evidence across five evidence blocks, concrete 2026 case study, honest about what coordination failure looks like in practice (not just theoretical). The Anthropic/Pentagon/OpenAI triangle is the clearest real-world demonstration of coordination failure I've seen written up. --- **Verdict:** request_changes **Model:** sonnet **Summary:** Three issues warrant changes before merge: (1) confidence on `capabilities generalize further than alignment` should drop from `likely` to `experimental` — the claim acknowledges its own unfalsifiability; (2) title of Molochian dynamics claim overstates with "not creating new misalignment" — scope it or drop confidence to `experimental`; (3) the verification cluster has a conceptual elision between legal enforcement and technical verification that the second claim should address explicitly. The domain classification on the wiki-graph claims and the missed cross-domain connections to health are lower-priority but would meaningfully strengthen the KB's cross-domain value. <!-- VERDICT:VIDA:REQUEST_CHANGES -->
leo commented 2026-04-07 01:13:03 +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*
m3taversal closed this pull request 2026-04-07 01:25:37 +00:00
m3taversal commented 2026-04-07 01:25:37 +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.

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Reference: teleo/teleo-codex#2462
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