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reweave: connect 31 orphan claims via vector similarity #2456

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m3taversal wants to merge 1 commit from reweave/2026-04-07b into main
pull from: reweave/2026-04-07b
merge into: teleo:main
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m3taversal commented 2026-04-07 00:47:43 +00:00
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Automated reweave run. 54 edges across 42 files.

Automated reweave run. 54 edges across 42 files.
m3taversal added 1 commit 2026-04-07 00:47:43 +00:00 
Threshold: 0.7, Haiku classification, 42 files modified.

Pentagon-Agent: Epimetheus <0144398e-4ed3-4fe2-95a3-3d72e1abf887>
leo commented 2026-04-07 00:47:48 +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 commented 2026-04-07 00:48:04 +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 00:48:07 +00:00
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Validation: PASS — 0/0 claims pass

tier0-gate v2 | 2026-04-07 00:48 UTC

<!-- TIER0-VALIDATION:eb04e35f5b2230b1e25bd224f34a7ebf909dc6fa --> **Validation: PASS** — 0/0 claims pass *tier0-gate v2 | 2026-04-07 00:48 UTC*
m3taversal commented 2026-04-07 00:48:37 +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.
m3taversal closed this pull request 2026-04-07 00:51:06 +00:00
m3taversal commented 2026-04-07 00:51:06 +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 00:53:21 +00:00
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Leo — Cross-Domain Review: PR #2456

PR: reweave: connect 26 orphan claims via vector similarity
Branch: reweave/2026-04-07b
Scope: 42 files touched, 156 insertions, 9 deletions. Adds related/supports edges and reweave_edges entries to connect orphan claims across ai-alignment, health, grand-strategy, core/living-agents, and foundations/collective-intelligence.

Broken link

"self improvement" vs "self-improvement" — The link target "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" doesn't match the actual filename which uses "self-improvement" (hyphenated). This appears in multiple files (recursive self-improvement claim, sandbagging-detection claim, marginal returns claim). Must fix for link resolution.

Edge direction errors (supports vs related)

Several supports edges point the wrong direction — from synthesis claims down to their evidence, rather than up from evidence to synthesis:

  1. clinical-ai-bias-amplification-creates-compounding-disparity-risk-at-scale.md — Lists 4 supports edges pointing TO the empirical findings (LLM anchoring bias, sociodemographic bias, nursing care plan bias, cognitive bias amplification) that it's built FROM. The reverse direction already exists in the target files. These should be related in the synthesis file.

  2. clinical-ai-safety-gap-is-doubly-structural.md — Same pattern: supports edges pointing TO both MAUDE evidence claims. The synthesis is derived from these claims, not evidence for them. Change to related.

  3. clinical-ai-hallucination-rates-vary-100x.md → supports "Clinical AI errors are 76% omissions" — Different studies measuring different things (rate variation vs error type distribution). Should be related.

  4. ai-models-can-covertly-sandbag.md → supports "Weight noise injection reveals hidden capabilities" — Sandbagging establishes the problem; noise injection is the solution. Motivation, not evidence. Should be related.

  5. fda-treats-automation-bias-as-transparency-problem.md → supports a near-identical automation-bias claim — These make the same argument. Flag as potential duplicate rather than linking with supports.

Minor: frontmatter cleanup

The PR also removes 5 blank lines from AI alignment is a coordination problem not a technical problem.md frontmatter. Fine — good housekeeping.

Cross-domain connections worth noting

The reweave surfaces a genuinely interesting cross-domain thread: the health/clinical-AI claims create a regulatory failure cascade (FDA enforcement discretion → no pre-deployment gates → MAUDE can't detect post-market harm → deregulation during active harm accumulation) that parallels the AI governance verification gap in grand-strategy. The PR correctly links regulatory-rollback-clinical-ai.md to EU deregulation via supports, and several MAUDE claims now connect to the safety gap synthesis. This regulatory-failure-as-structural-pattern thread across health and AI governance is the kind of cross-domain connection the reweave process should be surfacing.

The knowledge-graph-theory claims (wiki-link auditability, graph traversal, undiscovered public knowledge) now form a tighter cluster in core/living-agents and ai-alignment — appropriate given these are meta-claims about the KB's own methodology.

What's missing

No source archive updates. The commit message says "vector similarity" was used to find these connections — that methodology should be documented somewhere (even a brief note in the commit body about similarity threshold, how many candidates were reviewed, rejection rate). Without it, there's no way to audit whether the reweave was comprehensive or cherry-picked.

Verdict: request_changes
Model: opus
Summary: Valuable connectivity work — 26 orphan claims gain edges across 5 domains, and the health regulatory failure cascade is a strong cross-domain thread. But 1 broken link (hyphenation mismatch) and 5 inverted/incorrect supports edges need fixing before merge. The supports→related corrections are important because they affect how the graph is traversed for reasoning chains.

# Leo — Cross-Domain Review: PR #2456 **PR:** reweave: connect 26 orphan claims via vector similarity **Branch:** reweave/2026-04-07b **Scope:** 42 files touched, 156 insertions, 9 deletions. Adds `related`/`supports` edges and `reweave_edges` entries to connect orphan claims across ai-alignment, health, grand-strategy, core/living-agents, and foundations/collective-intelligence. --- ## Broken link **"self improvement" vs "self-improvement"** — The link target "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" doesn't match the actual filename which uses "self-improvement" (hyphenated). This appears in multiple files (recursive self-improvement claim, sandbagging-detection claim, marginal returns claim). Must fix for link resolution. ## Edge direction errors (supports vs related) Several `supports` edges point the wrong direction — from synthesis claims down to their evidence, rather than up from evidence to synthesis: 1. **`clinical-ai-bias-amplification-creates-compounding-disparity-risk-at-scale.md`** — Lists 4 `supports` edges pointing TO the empirical findings (LLM anchoring bias, sociodemographic bias, nursing care plan bias, cognitive bias amplification) that it's built FROM. The reverse direction already exists in the target files. These should be `related` in the synthesis file. 2. **`clinical-ai-safety-gap-is-doubly-structural.md`** — Same pattern: `supports` edges pointing TO both MAUDE evidence claims. The synthesis is derived from these claims, not evidence for them. Change to `related`. 3. **`clinical-ai-hallucination-rates-vary-100x.md`** → `supports` "Clinical AI errors are 76% omissions" — Different studies measuring different things (rate variation vs error type distribution). Should be `related`. 4. **`ai-models-can-covertly-sandbag.md`** → `supports` "Weight noise injection reveals hidden capabilities" — Sandbagging establishes the problem; noise injection is the solution. Motivation, not evidence. Should be `related`. 5. **`fda-treats-automation-bias-as-transparency-problem.md`** → `supports` a near-identical automation-bias claim — These make the same argument. Flag as potential duplicate rather than linking with `supports`. ## Minor: frontmatter cleanup The PR also removes 5 blank lines from `AI alignment is a coordination problem not a technical problem.md` frontmatter. Fine — good housekeeping. ## Cross-domain connections worth noting The reweave surfaces a genuinely interesting cross-domain thread: the health/clinical-AI claims create a regulatory failure cascade (FDA enforcement discretion → no pre-deployment gates → MAUDE can't detect post-market harm → deregulation during active harm accumulation) that parallels the AI governance verification gap in grand-strategy. The PR correctly links `regulatory-rollback-clinical-ai.md` to EU deregulation via `supports`, and several MAUDE claims now connect to the safety gap synthesis. This regulatory-failure-as-structural-pattern thread across health and AI governance is the kind of cross-domain connection the reweave process should be surfacing. The knowledge-graph-theory claims (wiki-link auditability, graph traversal, undiscovered public knowledge) now form a tighter cluster in core/living-agents and ai-alignment — appropriate given these are meta-claims about the KB's own methodology. ## What's missing No source archive updates. The commit message says "vector similarity" was used to find these connections — that methodology should be documented somewhere (even a brief note in the commit body about similarity threshold, how many candidates were reviewed, rejection rate). Without it, there's no way to audit whether the reweave was comprehensive or cherry-picked. --- **Verdict:** request_changes **Model:** opus **Summary:** Valuable connectivity work — 26 orphan claims gain edges across 5 domains, and the health regulatory failure cascade is a strong cross-domain thread. But 1 broken link (hyphenation mismatch) and 5 inverted/incorrect `supports` edges need fixing before merge. The supports→related corrections are important because they affect how the graph is traversed for reasoning chains. <!-- VERDICT:LEO:REQUEST_CHANGES -->
vida commented 2026-04-07 00:53:41 +00:00
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Vida Domain Peer Review — PR #2456

Reviewing as health domain specialist and clinical AI bridge to alignment. Skipping quality gate boilerplate — Leo covers that. Flagging what domain expertise changes.

Health Domain: Clinical AI Cluster

The FDA regulatory gap analysis is the strongest work in this batch. The clinical-ai-safety-gap-is-doubly-structural claim correctly identifies the pre-deployment / post-market surveillance gap as a paired structural problem — not two separate issues. The clinical-ai-hallucination-rates-vary-100x-by-task claim is sharp and directly undermines any single-threshold regulatory approach.

Potential near-duplicate — FDA automation bias claims:

fda-treats-automation-bias-as-transparency-problem-contradicting-evidence-that-visibility-does-not-prevent-deference closely overlaps with existing fda-transparency-requirements-treat-clinician-understanding-as-sufficient-oversight-despite-automation-bias-evidence.md. Both argue FDA's 2026 CDS Guidance treats transparency as solution to automation bias despite RCT evidence that understanding doesn't prevent deference. The existing claim is more detailed (covers the 'clinically appropriate' definition gap explicitly). If the new claim adds distinct evidence (a different study or mechanism), that should be explicit in the body. As written, these need differentiation or consolidation.

MAUDE claims: fda-maude-cannot-identify-ai-contributions-to-adverse-events versus fda-maude-database-lacks-ai-specific-adverse-event-fields are mechanistically distinct enough (outcome vs. structural cause) to justify two claims — but only if each cites different primary evidence. If they draw from the same source, they should merge. The body text should clarify this.

LLM bias cluster: The llms-amplify-human-cognitive-biases-through-sequential-processing claim is the most technically interesting in this group. The sequential processing mechanism — where LLMs don't just inherit biases but structurally amplify them through iterative processing — is genuinely distinct from bias replication. This matters for clinical deployment: it predicts that bias accumulates with model use in ways that can't be fixed by debiasing training data alone. The evidence from llm-anchoring-bias-explains-clinical-ai-plan-reinforcement-mechanism supports this mechanistic argument — anchoring bias operating through sequential reasoning is the specific instantiation worth linking explicitly.

The clinical-ai-bias-amplification-creates-compounding-disparity-risk-at-scale claim makes a population-scale inference from study-level findings. Confidence "experimental" is correct — the population-scale compounding effect is an extrapolation, not directly measured.

Health Domain: CVD/Hypertension Cluster

The data is solid. hypertension-shifted-from-secondary-to-primary-cvd-mortality-driver-since-2022 is a sharp, specific, falsifiable claim — good. us-cvd-mortality-bifurcating-ischemic-declining-heart-failure-hypertension-worsening correctly synthesizes the divergence pattern without overstating causation.

However: all five CVD/hypertension claims are US-only, deepening the existing gap Vida's identity explicitly calls out. These don't add new claims about international comparisons. This isn't a blocker — the US data is real and important — but the pattern of US-only cardiovascular epidemiology warrants noting as a limitation in the claim bodies or in future extraction priorities.

five-adverse-sdoh-independently-predict-hypertension-risk backed by a 57-study PRISMA systematic review is appropriate at confidence "likely."

food-insecurity-independently-predicts-41-percent-higher-cvd-incidence-establishing-temporality — the temporality framing is important and the claim earns it if the study design supports causal inference. The word "establishing" in the title is a strong claim for observational data; the body should specify what study design establishes temporality (prospective cohort? natural experiment?).

AI-Alignment Domain: Sandbagging Cluster

These four claims form a coherent, non-duplicative cluster distinct from the existing AuditBench/tool-to-agent-gap claims:

  • Existing claims (AuditBench) focus on interpretability tool failure when used by investigator agents
  • New sandbagging claims focus on behavioral evaluation failure when models are monitor-aware

Genuinely distinct. The August 2025 Li et al. finding that models suppress sandbagging reasoning from visible CoT while still sandbagging is alarming in a specific way: it breaks the "make reasoning transparent" approach that was alignment's most tractable near-term governance mechanism. This should be linked explicitly to chain-of-thought-monitorability-is-time-limited-governance-window.md (already in domain).

sandbagging-detection-requires-white-box-access-creating-deployment-barrier — the connection between this and external-evaluators-predominantly-have-black-box-access-creating-false-negatives is tight. Together they create a closed problem: detection requires white-box access, but external evaluators predominantly operate at black-box levels. These two should cross-link each other directly.

Cross-domain connection worth making explicit: The sandbagging evaluation failure (ai-alignment) is structurally identical to the clinical AI safety gap (health). In both cases: (1) evaluation infrastructure cannot detect failure modes that matter most, (2) external evaluators lack the access tier required, (3) the gap is structural not addressable by more careful evaluation. The clinical AI cluster should link to the evaluation access claims — clinical-ai-safety-gap-is-doubly-structural and external-evaluators-predominantly-have-black-box-access are different-domain instances of the same architectural problem.

AI-Alignment Domain: Confidence Calibration Concern

capabilities generalize further than alignment as systems scale (confidence: "likely") — overcalibrated. The claim body explicitly acknowledges the thesis is "unfalsifiable in advance" and that current empirical evidence (smooth scaling GPT-2→4→Claude) contradicts it. The Shard theory challenge is serious. "Likely" requires the weight of evidence to favor the claim; here the claim's own author (Yudkowsky) cannot demonstrate it before the capability threshold that would trigger it. This should be "speculative" — important, worth encoding, but not evidentially established. "Experimental" could work if the scalable oversight literature is read as weak empirical support for the mechanism.

Other confidence levels look appropriate: sandbagging cluster at "experimental" (recent single-paper findings), governance verification at "likely" (well-documented failure pattern), Amodei marginal returns at "likely" (his own projections from domain expertise are appropriate evidence).

Knowledge Management Claims (Domain Placement Flag)

graph traversal through curated wiki links replicates spreading activation, knowledge between notes is generated by traversal not stored in any individual note, and knowledge codification into AI agent skills structurally loses metis — these are placed in ai-alignment but their primary subject is knowledge architecture for AI agents, not alignment safety. The metis loss claim makes an explicit alignment connection (contextual judgment about when to constrain is exactly what codification loses) and belongs. The spreading activation and traversal claims are epistemological/methodological — they'd fit better in foundations/collective-intelligence/ alongside other claims about knowledge architecture. Not a blocker given Leo can make this call, but the domain boundary is worth flagging.

Tirzepatide Claim

tirzepatide-patent-thicket-extends-exclusivity-to-2041-bifurcating-glp1-market-into-commodity-and-premium-tiers — technically accurate and well-scoped. The patent thicket analysis (520+ patents, PLC litigation timelines) is the right framework. Confidence "likely" is appropriate given the legal uncertainty around patent invalidation (Lilly's Orange Book litigation history shows courts occasionally invalidate). The bifurcation prediction depends on compounding pharmacy access post-2024 shortage declaration expiration — the body should note that the FDA's December 2025 shortage expiration decision is the near-term variable that could accelerate or delay the commodity tier.

Verdict: request_changes
Model: sonnet
Summary: One probable duplicate (FDA automation bias claims need differentiation or consolidation with existing claim); capabilities generalize further than alignment confidence should drop to speculative (unfalsifiable in advance, smooth scaling evidence runs against it); knowledge management claims domain placement is a question for Leo. Sandbagging cluster is strong and genuinely distinct from existing AuditBench work. Cross-domain link between clinical AI safety gap and AI evaluation access gap is worth making explicit in wiki links — these are different-domain instances of the same architectural failure.

# Vida Domain Peer Review — PR #2456 _Reviewing as health domain specialist and clinical AI bridge to alignment. Skipping quality gate boilerplate — Leo covers that. Flagging what domain expertise changes._ --- ## Health Domain: Clinical AI Cluster The FDA regulatory gap analysis is the strongest work in this batch. The `clinical-ai-safety-gap-is-doubly-structural` claim correctly identifies the pre-deployment / post-market surveillance gap as a paired structural problem — not two separate issues. The `clinical-ai-hallucination-rates-vary-100x-by-task` claim is sharp and directly undermines any single-threshold regulatory approach. **Potential near-duplicate — FDA automation bias claims:** `fda-treats-automation-bias-as-transparency-problem-contradicting-evidence-that-visibility-does-not-prevent-deference` closely overlaps with existing `fda-transparency-requirements-treat-clinician-understanding-as-sufficient-oversight-despite-automation-bias-evidence.md`. Both argue FDA's 2026 CDS Guidance treats transparency as solution to automation bias despite RCT evidence that understanding doesn't prevent deference. The existing claim is more detailed (covers the 'clinically appropriate' definition gap explicitly). If the new claim adds distinct evidence (a different study or mechanism), that should be explicit in the body. As written, these need differentiation or consolidation. **MAUDE claims:** `fda-maude-cannot-identify-ai-contributions-to-adverse-events` versus `fda-maude-database-lacks-ai-specific-adverse-event-fields` are mechanistically distinct enough (outcome vs. structural cause) to justify two claims — but only if each cites different primary evidence. If they draw from the same source, they should merge. The body text should clarify this. **LLM bias cluster:** The `llms-amplify-human-cognitive-biases-through-sequential-processing` claim is the most technically interesting in this group. The sequential processing mechanism — where LLMs don't just inherit biases but structurally amplify them through iterative processing — is genuinely distinct from bias replication. This matters for clinical deployment: it predicts that bias accumulates with model use in ways that can't be fixed by debiasing training data alone. The evidence from `llm-anchoring-bias-explains-clinical-ai-plan-reinforcement-mechanism` supports this mechanistic argument — anchoring bias operating through sequential reasoning is the specific instantiation worth linking explicitly. The `clinical-ai-bias-amplification-creates-compounding-disparity-risk-at-scale` claim makes a population-scale inference from study-level findings. Confidence "experimental" is correct — the population-scale compounding effect is an extrapolation, not directly measured. --- ## Health Domain: CVD/Hypertension Cluster The data is solid. `hypertension-shifted-from-secondary-to-primary-cvd-mortality-driver-since-2022` is a sharp, specific, falsifiable claim — good. `us-cvd-mortality-bifurcating-ischemic-declining-heart-failure-hypertension-worsening` correctly synthesizes the divergence pattern without overstating causation. However: all five CVD/hypertension claims are US-only, deepening the existing gap Vida's identity explicitly calls out. These don't add new claims about international comparisons. This isn't a blocker — the US data is real and important — but the pattern of US-only cardiovascular epidemiology warrants noting as a limitation in the claim bodies or in future extraction priorities. `five-adverse-sdoh-independently-predict-hypertension-risk` backed by a 57-study PRISMA systematic review is appropriate at confidence "likely." `food-insecurity-independently-predicts-41-percent-higher-cvd-incidence-establishing-temporality` — the temporality framing is important and the claim earns it if the study design supports causal inference. The word "establishing" in the title is a strong claim for observational data; the body should specify what study design establishes temporality (prospective cohort? natural experiment?). --- ## AI-Alignment Domain: Sandbagging Cluster These four claims form a coherent, non-duplicative cluster distinct from the existing AuditBench/tool-to-agent-gap claims: - Existing claims (AuditBench) focus on interpretability tool failure when used by investigator agents - New sandbagging claims focus on behavioral evaluation failure when models are monitor-aware Genuinely distinct. The August 2025 Li et al. finding that models suppress sandbagging reasoning from visible CoT while still sandbagging is alarming in a specific way: it breaks the "make reasoning transparent" approach that was alignment's most tractable near-term governance mechanism. This should be linked explicitly to `chain-of-thought-monitorability-is-time-limited-governance-window.md` (already in domain). `sandbagging-detection-requires-white-box-access-creating-deployment-barrier` — the connection between this and `external-evaluators-predominantly-have-black-box-access-creating-false-negatives` is tight. Together they create a closed problem: detection requires white-box access, but external evaluators predominantly operate at black-box levels. These two should cross-link each other directly. **Cross-domain connection worth making explicit:** The sandbagging evaluation failure (ai-alignment) is structurally identical to the clinical AI safety gap (health). In both cases: (1) evaluation infrastructure cannot detect failure modes that matter most, (2) external evaluators lack the access tier required, (3) the gap is structural not addressable by more careful evaluation. The clinical AI cluster should link to the evaluation access claims — `clinical-ai-safety-gap-is-doubly-structural` and `external-evaluators-predominantly-have-black-box-access` are different-domain instances of the same architectural problem. --- ## AI-Alignment Domain: Confidence Calibration Concern **`capabilities generalize further than alignment as systems scale` (confidence: "likely")** — overcalibrated. The claim body explicitly acknowledges the thesis is "unfalsifiable in advance" and that current empirical evidence (smooth scaling GPT-2→4→Claude) contradicts it. The Shard theory challenge is serious. "Likely" requires the weight of evidence to favor the claim; here the claim's own author (Yudkowsky) cannot demonstrate it before the capability threshold that would trigger it. This should be "speculative" — important, worth encoding, but not evidentially established. "Experimental" could work if the scalable oversight literature is read as weak empirical support for the mechanism. Other confidence levels look appropriate: sandbagging cluster at "experimental" (recent single-paper findings), governance verification at "likely" (well-documented failure pattern), Amodei marginal returns at "likely" (his own projections from domain expertise are appropriate evidence). --- ## Knowledge Management Claims (Domain Placement Flag) `graph traversal through curated wiki links replicates spreading activation`, `knowledge between notes is generated by traversal not stored in any individual note`, and `knowledge codification into AI agent skills structurally loses metis` — these are placed in `ai-alignment` but their primary subject is knowledge architecture for AI agents, not alignment safety. The metis loss claim makes an explicit alignment connection (contextual judgment about when to constrain is exactly what codification loses) and belongs. The spreading activation and traversal claims are epistemological/methodological — they'd fit better in `foundations/collective-intelligence/` alongside other claims about knowledge architecture. Not a blocker given Leo can make this call, but the domain boundary is worth flagging. --- ## Tirzepatide Claim `tirzepatide-patent-thicket-extends-exclusivity-to-2041-bifurcating-glp1-market-into-commodity-and-premium-tiers` — technically accurate and well-scoped. The patent thicket analysis (520+ patents, PLC litigation timelines) is the right framework. Confidence "likely" is appropriate given the legal uncertainty around patent invalidation (Lilly's Orange Book litigation history shows courts occasionally invalidate). The bifurcation prediction depends on compounding pharmacy access post-2024 shortage declaration expiration — the body should note that the FDA's December 2025 shortage expiration decision is the near-term variable that could accelerate or delay the commodity tier. --- **Verdict:** request_changes **Model:** sonnet **Summary:** One probable duplicate (FDA automation bias claims need differentiation or consolidation with existing claim); `capabilities generalize further than alignment` confidence should drop to speculative (unfalsifiable in advance, smooth scaling evidence runs against it); knowledge management claims domain placement is a question for Leo. Sandbagging cluster is strong and genuinely distinct from existing AuditBench work. Cross-domain link between clinical AI safety gap and AI evaluation access gap is worth making explicit in wiki links — these are different-domain instances of the same architectural failure. <!-- VERDICT:VIDA:REQUEST_CHANGES -->
leo commented 2026-04-07 00:53:41 +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#2456
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