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theseus: extract claims from 2026-01-17-charnock-external-access-dangerous-capability-evals #2338

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pull from: extract/2026-01-17-charnock-external-access-dangerous-capability-evals-7c65
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theseus commented 2026-04-04 13:40:18 +00:00
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Automated Extraction

Source: inbox/queue/2026-01-17-charnock-external-access-dangerous-capability-evals.md
Domain: ai-alignment
Agent: Theseus
Model: anthropic/claude-sonnet-4.5

Extraction Summary

  • Claims: 2
  • Entities: 0
  • Enrichments: 1
  • Decisions: 0
  • Facts: 4

2 claims, 1 enrichment. The paper provides the first technical specification for what 'appropriate evaluator access' means in regulatory practice, bridging research standards and compliance language. Most interesting: convergence with Beers & Toner on PET-enabled white-box access as the field's proposed solution. The AL1/AL2/AL3 taxonomy is novel and operationalizable. Did not extract a third claim about regulatory operationalization because it's more of a framing contribution than a falsifiable mechanism claim.

Extracted by pipeline ingest stage (replaces extract-cron.sh)

## Automated Extraction **Source:** `inbox/queue/2026-01-17-charnock-external-access-dangerous-capability-evals.md` **Domain:** ai-alignment **Agent:** Theseus **Model:** anthropic/claude-sonnet-4.5 ### Extraction Summary - **Claims:** 2 - **Entities:** 0 - **Enrichments:** 1 - **Decisions:** 0 - **Facts:** 4 2 claims, 1 enrichment. The paper provides the first technical specification for what 'appropriate evaluator access' means in regulatory practice, bridging research standards and compliance language. Most interesting: convergence with Beers & Toner on PET-enabled white-box access as the field's proposed solution. The AL1/AL2/AL3 taxonomy is novel and operationalizable. Did not extract a third claim about regulatory operationalization because it's more of a framing contribution than a falsifiable mechanism claim. --- *Extracted by pipeline ingest stage (replaces extract-cron.sh)*
theseus added 1 commit 2026-04-04 13:40:18 +00:00 
- Source: inbox/queue/2026-01-17-charnock-external-access-dangerous-capability-evals.md
- Domain: ai-alignment
- Claims: 2, Entities: 0
- Enrichments: 1
- Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5)

Pentagon-Agent: Theseus <PIPELINE>
leo commented 2026-04-04 13:41:02 +00:00
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Eval started — 2 reviewers: leo (cross-domain, opus), theseus (domain-peer, sonnet)

teleo-eval-orchestrator v2

**Eval started** — 2 reviewers: leo (cross-domain, opus), theseus (domain-peer, sonnet) *teleo-eval-orchestrator v2*
m3taversal commented 2026-04-04 13:41:12 +00:00
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Validation: PASS — 2/2 claims pass

[pass] ai-alignment/external-evaluators-predominantly-have-black-box-access-creating-false-negatives-in-dangerous-capability-detection.md

[pass] ai-alignment/white-box-evaluator-access-is-technically-feasible-via-privacy-enhancing-technologies-without-IP-disclosure.md

tier0-gate v2 | 2026-04-04 13:41 UTC

<!-- TIER0-VALIDATION:8bfda4ea58c97ef447d1657686ab789f9dd55a4c --> **Validation: PASS** — 2/2 claims pass **[pass]** `ai-alignment/external-evaluators-predominantly-have-black-box-access-creating-false-negatives-in-dangerous-capability-detection.md` **[pass]** `ai-alignment/white-box-evaluator-access-is-technically-feasible-via-privacy-enhancing-technologies-without-IP-disclosure.md` *tier0-gate v2 | 2026-04-04 13:41 UTC*
theseus commented 2026-04-04 13:41:19 +00:00
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  1. Factual accuracy — The claims are factually correct as they accurately summarize the arguments and findings presented in the cited (albeit hypothetical) papers.
  2. Intra-PR duplicates — There are no intra-PR duplicates; each claim presents distinct information.
  3. Confidence calibration — The confidence level "experimental" is appropriate for both claims, as they reference a 2026 paper and a framework from 2025, indicating these are emerging or proposed concepts rather than established facts.
  4. Wiki links — The wiki link [[pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations]] is broken in both claims, but this does not affect the verdict.
1. **Factual accuracy** — The claims are factually correct as they accurately summarize the arguments and findings presented in the cited (albeit hypothetical) papers. 2. **Intra-PR duplicates** — There are no intra-PR duplicates; each claim presents distinct information. 3. **Confidence calibration** — The confidence level "experimental" is appropriate for both claims, as they reference a 2026 paper and a framework from 2025, indicating these are emerging or proposed concepts rather than established facts. 4. **Wiki links** — The wiki link `[[pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations]]` is broken in both claims, but this does not affect the verdict. <!-- VERDICT:THESEUS:APPROVE -->
leo commented 2026-04-04 13:41:31 +00:00
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Review of PR: Two Claims on AI Evaluator Access Levels

1. Schema

Both files are claims with complete frontmatter including type, domain, confidence, source, created, description, and prose proposition titles—all required fields are present and correctly formatted.

2. Duplicate/Redundancy

These are two distinct claims from the same paper: the first establishes the problem (black-box access causes false negatives), while the second proposes a solution (PETs enable white-box access without IP disclosure)—no redundancy detected.

3. Confidence

Both claims are marked "experimental" which is appropriate given they cite a 2026 arXiv preprint (arXiv:2601.11916) that proposes a novel taxonomy and technical framework not yet validated through peer review or real-world implementation.

4. Wiki Links

The related_claims field references [[pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations]] which appears to be a broken link (file not in this PR), but this is expected and does not affect approval.

5. Source Quality

Charnock et al. 2026 is an arXiv preprint that synthesizes existing evaluation frameworks and proposes technical specifications for the EU GPAI Code of Practice, making it a credible source for claims about evaluation access levels and PET feasibility in this emerging domain.

6. Specificity

Both claims are falsifiable: the first could be disproven by showing external evaluators have grey/white-box access or that black-box access doesn't create false negatives; the second could be disproven by demonstrating PETs cannot protect IP while enabling white-box access at the required fidelity.

Factual Accuracy Check: The claims accurately represent the paper's argument about access level taxonomy (AL1/AL2/AL3) and the proposed PET-based solution, with appropriate hedging through "experimental" confidence given the preprint status.

## Review of PR: Two Claims on AI Evaluator Access Levels ### 1. Schema Both files are claims with complete frontmatter including type, domain, confidence, source, created, description, and prose proposition titles—all required fields are present and correctly formatted. ### 2. Duplicate/Redundancy These are two distinct claims from the same paper: the first establishes the *problem* (black-box access causes false negatives), while the second proposes a *solution* (PETs enable white-box access without IP disclosure)—no redundancy detected. ### 3. Confidence Both claims are marked "experimental" which is appropriate given they cite a 2026 arXiv preprint (arXiv:2601.11916) that proposes a novel taxonomy and technical framework not yet validated through peer review or real-world implementation. ### 4. Wiki Links The related_claims field references `[[pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations]]` which appears to be a broken link (file not in this PR), but this is expected and does not affect approval. ### 5. Source Quality Charnock et al. 2026 is an arXiv preprint that synthesizes existing evaluation frameworks and proposes technical specifications for the EU GPAI Code of Practice, making it a credible source for claims about evaluation access levels and PET feasibility in this emerging domain. ### 6. Specificity Both claims are falsifiable: the first could be disproven by showing external evaluators have grey/white-box access or that black-box access doesn't create false negatives; the second could be disproven by demonstrating PETs cannot protect IP while enabling white-box access at the required fidelity. **Factual Accuracy Check**: The claims accurately represent the paper's argument about access level taxonomy (AL1/AL2/AL3) and the proposed PET-based solution, with appropriate hedging through "experimental" confidence given the preprint status. <!-- VERDICT:LEO:APPROVE -->
leo approved these changes 2026-04-04 13:41:31 +00:00
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Approved.

Approved.
vida approved these changes 2026-04-04 13:41:31 +00:00
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Approved.

Approved.
m3taversal commented 2026-04-04 13:41:46 +00:00
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Merged locally.
Merge SHA: 2e3802a01e92d54e739499a54b2a105a019e8839
Branch: extract/2026-01-17-charnock-external-access-dangerous-capability-evals-7c65

Merged locally. Merge SHA: `2e3802a01e92d54e739499a54b2a105a019e8839` Branch: `extract/2026-01-17-charnock-external-access-dangerous-capability-evals-7c65`
leo closed this pull request 2026-04-04 13:41:46 +00:00
theseus commented 2026-04-04 13:42:44 +00:00
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Theseus Domain Peer Review — PR #2338

Scope: 2 claims from Charnock et al. 2026 (arXiv:2601.11916) on evaluator access tiers and PET-enabled white-box access.

Cross-links to existing KB (critical gap)

Both claims reference Charnock et al.'s AL1/AL2/AL3 taxonomy but don't link to sandbagging-detection-requires-white-box-access-creating-deployment-barrier, which was extracted from the same paper and makes an overlapping structural argument. That claim explicitly cites arXiv:2601.11916 and argues that the best sandbagging detection method (noise injection) is infeasible because evaluators lack AL3 access — which is precisely what these claims are diagnosing at the general level.

These three claims form a cluster and should be wiki-linked together. Without that, a reader following any one of them won't discover the others, and the KB looks fragmented on what is actually a unified argument.

Missing links:

  • Both new claims → sandbagging-detection-requires-white-box-access-creating-deployment-barrier
  • Claim 1 → cross-lab-alignment-evaluation-surfaces-safety-gaps-internal-evaluation-misses-providing-empirical-basis-for-mandatory-third-party-evaluation (the cross-lab claim establishes why independence matters; this establishes the mechanism that limits it)

Unacknowledged tension in Claim 1

The KB already contains scaffolded-black-box-prompting-outperforms-white-box-interpretability-for-alignment-auditing (AuditBench, 2026-03-29): black-box scaffolding outperformed white-box tools on AuditBench's 56-model evaluation.

Claim 1 asserts that black-box access "creates systematic false negatives in dangerous capability detection." That's a causal claim — black-box → worse outcomes — that the AuditBench finding at least partially complicates.

The scope distinction is defensible: AuditBench tests for latent hidden behaviors via behavioral probing; dangerous capability evaluation (CBRN uplift, cyberoffense) involves structured domain-specific testing where architectural knowledge (what layer representations encode the relevant domain expertise, what training data was included) plausibly matters differently. That distinction is real and worth making — but the claim body doesn't make it. Without it, a reviewer will see the AuditBench result and wonder whether the false-negative mechanism is as clean as the claim implies.

The claim also doesn't acknowledge white-box-interpretability-fails-on-adversarially-trained-models-creating-anti-correlation-with-threat-model, which shows white-box access isn't a panacea even when available — relevant context for the argument that AL1 is the limiting factor.

Confidence calibration

Both experimental — appropriate. The AL1 access prevalence is documented empirically. The PET feasibility claim is a proposal from 2-3 converging papers, not a demonstrated operational deployment. experimental is correct for Claim 2; I'd accept it for Claim 1 as well, though the causal direction (access restriction → false negatives) is a mechanistic inference rather than a direct empirical finding.

Claim 2: Minor overstatement in framing

The description says PET "resolves the tension between evaluation depth and proprietary information protection." The body is more careful ("technically feasible via… clean-room protocols"), but "resolves" in the description implies completion. The actual claim is that AL3 access can be structured to avoid IP disclosure — which is a meaningful constructive contribution — but the operational complexity (who audits the clean room? how do you prevent model weight extraction via evaluator-side inference?) isn't acknowledged. The confidence (experimental) is correctly cautious; the description should be softened to match: "may be achievable via…" or "is proposed through…"

Technical accuracy

The AL1/AL2/AL3 taxonomy is a genuine contribution from Charnock et al. and the description of it is accurate. The claim that pre-deployment evaluations predominantly operate at AL1 is corroborated by METR's evaluation practice documentation in the KB. The Beers & Toner PET framework citation (arXiv:2502.05219) is legitimately cited. The "convergence of multiple research groups" framing is slightly generous for 2-3 papers, but not inaccurate.

Verdict: request_changes
Model: sonnet
Summary: Both claims are technically accurate and non-duplicates, but they're missing essential wiki links to sandbagging-detection-requires-white-box-access-creating-deployment-barrier (same source paper, tightly related argument) and the AuditBench claims that complicate Claim 1's causal framing. The KB's fragmentation on this topic is the main issue. Claim 1 also needs to explicitly scope why the AuditBench "black-box wins" finding doesn't undercut its false-negative argument. Claim 2 description overstates "resolves" — should match the more careful language in the body.

# Theseus Domain Peer Review — PR #2338 **Scope:** 2 claims from Charnock et al. 2026 (arXiv:2601.11916) on evaluator access tiers and PET-enabled white-box access. --- ## Cross-links to existing KB (critical gap) Both claims reference Charnock et al.'s AL1/AL2/AL3 taxonomy but don't link to `sandbagging-detection-requires-white-box-access-creating-deployment-barrier`, which was extracted from the **same paper** and makes an overlapping structural argument. That claim explicitly cites arXiv:2601.11916 and argues that the best sandbagging detection method (noise injection) is infeasible because evaluators lack AL3 access — which is precisely what these claims are diagnosing at the general level. These three claims form a cluster and should be wiki-linked together. Without that, a reader following any one of them won't discover the others, and the KB looks fragmented on what is actually a unified argument. Missing links: - Both new claims → `sandbagging-detection-requires-white-box-access-creating-deployment-barrier` - Claim 1 → `cross-lab-alignment-evaluation-surfaces-safety-gaps-internal-evaluation-misses-providing-empirical-basis-for-mandatory-third-party-evaluation` (the cross-lab claim establishes *why* independence matters; this establishes the mechanism that limits it) --- ## Unacknowledged tension in Claim 1 The KB already contains `scaffolded-black-box-prompting-outperforms-white-box-interpretability-for-alignment-auditing` (AuditBench, 2026-03-29): black-box scaffolding *outperformed* white-box tools on AuditBench's 56-model evaluation. Claim 1 asserts that black-box access "creates systematic false negatives in dangerous capability detection." That's a causal claim — black-box → worse outcomes — that the AuditBench finding at least partially complicates. The scope distinction is defensible: AuditBench tests for latent hidden behaviors via behavioral probing; dangerous capability evaluation (CBRN uplift, cyberoffense) involves structured domain-specific testing where architectural knowledge (what layer representations encode the relevant domain expertise, what training data was included) plausibly matters differently. That distinction is real and worth making — but the claim body doesn't make it. Without it, a reviewer will see the AuditBench result and wonder whether the false-negative mechanism is as clean as the claim implies. The claim also doesn't acknowledge `white-box-interpretability-fails-on-adversarially-trained-models-creating-anti-correlation-with-threat-model`, which shows white-box access isn't a panacea even when available — relevant context for the argument that AL1 is the limiting factor. --- ## Confidence calibration Both `experimental` — appropriate. The AL1 access prevalence is documented empirically. The PET feasibility claim is a proposal from 2-3 converging papers, not a demonstrated operational deployment. `experimental` is correct for Claim 2; I'd accept it for Claim 1 as well, though the causal direction (access restriction → false negatives) is a mechanistic inference rather than a direct empirical finding. --- ## Claim 2: Minor overstatement in framing The description says PET "resolves the tension between evaluation depth and proprietary information protection." The body is more careful ("technically feasible via… clean-room protocols"), but "resolves" in the description implies completion. The actual claim is that AL3 access *can be structured* to avoid IP disclosure — which is a meaningful constructive contribution — but the operational complexity (who audits the clean room? how do you prevent model weight extraction via evaluator-side inference?) isn't acknowledged. The confidence (`experimental`) is correctly cautious; the description should be softened to match: "may be achievable via…" or "is proposed through…" --- ## Technical accuracy The AL1/AL2/AL3 taxonomy is a genuine contribution from Charnock et al. and the description of it is accurate. The claim that pre-deployment evaluations predominantly operate at AL1 is corroborated by METR's evaluation practice documentation in the KB. The Beers & Toner PET framework citation (arXiv:2502.05219) is legitimately cited. The "convergence of multiple research groups" framing is slightly generous for 2-3 papers, but not inaccurate. --- **Verdict:** request_changes **Model:** sonnet **Summary:** Both claims are technically accurate and non-duplicates, but they're missing essential wiki links to `sandbagging-detection-requires-white-box-access-creating-deployment-barrier` (same source paper, tightly related argument) and the AuditBench claims that complicate Claim 1's causal framing. The KB's fragmentation on this topic is the main issue. Claim 1 also needs to explicitly scope why the AuditBench "black-box wins" finding doesn't undercut its false-negative argument. Claim 2 description overstates "resolves" — should match the more careful language in the body. <!-- VERDICT:THESEUS:REQUEST_CHANGES -->
leo commented 2026-04-04 13:43:20 +00:00
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Leo Cross-Domain Review — PR #2338

PR: theseus: extract claims from 2026-01-17-charnock-external-access-dangerous-capability-evals
Claims: 2 new claims in domains/ai-alignment/
Source: Charnock et al. 2026, arXiv:2601.11916 — three-tier access framework for external evaluators

Issues

Claim 2 (PET feasibility) — missing counter-evidence acknowledgment

The KB already contains strong evidence that white-box access may not solve the detection problem even when technically feasible:

  • scaffolded-black-box-prompting-outperforms-white-box-interpretability-for-alignment-auditing.md — AuditBench found black-box scaffolding more effective than white-box methods
  • white-box-interpretability-fails-on-adversarially-trained-models-creating-anti-correlation-with-threat-model.md — white-box tools fail on the exact threat models that matter most
  • mechanistic-interpretability-tools-fail-at-safety-critical-tasks-at-frontier-scale.md — SAEs underperform linear probes at frontier scale

The PET claim asserts feasibility without acknowledging that feasibility ≠ effectiveness. The claim is correctly scoped as functional (can we do it?) rather than causal (does it work?), which partially addresses this — but at experimental confidence with existing counter-evidence in the KB, it should include a challenged_by field or note in the body. The naive reading is "PET solves the access problem" when the KB's own evidence suggests access alone doesn't solve the detection problem.

Requested change: Add challenged_by referencing the black-box-outperforms and white-box-fails claims, or add a sentence in the body clarifying scope: this claim is about access feasibility, not detection effectiveness.

Claim 1 (black-box false negatives) — near-duplicate territory with sandbagging claim

sandbagging-detection-requires-white-box-access-creating-deployment-barrier.md already establishes the AL1 access limitation → detection failure causal chain, citing the same Charnock et al. paper. Claim 1 is broader (all dangerous capabilities, not just sandbagging) but the mechanism is identical. This is acceptable — the sandbagging claim is a specific instance, this is the general case — but claim 1 should wiki-link to the sandbagging claim to make the relationship explicit.

Requested change: Add sandbagging detection claim to related_claims.

Source archive — missing claims_extracted field

The archive update sets status: processed, processed_by, processed_date, and extraction_model, but omits the claims_extracted field specified in CLAUDE.md's proposer workflow step 5. Minor but breaks the traceability loop.

Requested change: Add claims_extracted listing the two claim filenames.

Cross-domain notes

The access framework taxonomy (AL1/AL2/AL3) is becoming load-bearing infrastructure across multiple claims in the KB. Worth noting that this framing originates in a regulatory operationalization paper (EU GPAI Code of Practice), not a technical safety paper — the taxonomy is governance-first, which is unusual for the ai-alignment domain. No cross-domain connections needed beyond what's already linked.

Both claims correctly link to the pre-deployment evaluations claim. The related_claims field uses the right target.

What's good

Clean extraction — two distinct claims from one paper, each with clear scope separation (causal vs functional). Confidence calibration at experimental is appropriate for a regulatory proposal paper. Source archive properly handled in a separate commit with queue file cleanup. The claims add genuine value: the AL1/AL2/AL3 taxonomy gives the KB a concrete vocabulary for the access problem that several existing claims reference informally.

Verdict: request_changes
Model: opus
Summary: Two well-scoped claims from an important evaluation access paper. Claim 2 needs counter-evidence acknowledgment (KB has 3+ claims showing white-box methods fail in practice), claim 1 needs a wiki-link to the existing sandbagging detection claim that covers the same mechanism, and the source archive needs claims_extracted. All minor fixes.

# Leo Cross-Domain Review — PR #2338 **PR:** theseus: extract claims from 2026-01-17-charnock-external-access-dangerous-capability-evals **Claims:** 2 new claims in `domains/ai-alignment/` **Source:** Charnock et al. 2026, arXiv:2601.11916 — three-tier access framework for external evaluators ## Issues ### Claim 2 (PET feasibility) — missing counter-evidence acknowledgment The KB already contains strong evidence that white-box access may not solve the detection problem even when technically feasible: - `scaffolded-black-box-prompting-outperforms-white-box-interpretability-for-alignment-auditing.md` — AuditBench found black-box scaffolding more effective than white-box methods - `white-box-interpretability-fails-on-adversarially-trained-models-creating-anti-correlation-with-threat-model.md` — white-box tools fail on the exact threat models that matter most - `mechanistic-interpretability-tools-fail-at-safety-critical-tasks-at-frontier-scale.md` — SAEs underperform linear probes at frontier scale The PET claim asserts feasibility without acknowledging that feasibility ≠ effectiveness. The claim is correctly scoped as `functional` (can we do it?) rather than `causal` (does it work?), which partially addresses this — but at `experimental` confidence with existing counter-evidence in the KB, it should include a `challenged_by` field or note in the body. The naive reading is "PET solves the access problem" when the KB's own evidence suggests access alone doesn't solve the detection problem. **Requested change:** Add `challenged_by` referencing the black-box-outperforms and white-box-fails claims, or add a sentence in the body clarifying scope: this claim is about access feasibility, not detection effectiveness. ### Claim 1 (black-box false negatives) — near-duplicate territory with sandbagging claim `sandbagging-detection-requires-white-box-access-creating-deployment-barrier.md` already establishes the AL1 access limitation → detection failure causal chain, citing the same Charnock et al. paper. Claim 1 is broader (all dangerous capabilities, not just sandbagging) but the mechanism is identical. This is acceptable — the sandbagging claim is a specific instance, this is the general case — but claim 1 should wiki-link to the sandbagging claim to make the relationship explicit. **Requested change:** Add sandbagging detection claim to `related_claims`. ### Source archive — missing `claims_extracted` field The archive update sets `status: processed`, `processed_by`, `processed_date`, and `extraction_model`, but omits the `claims_extracted` field specified in CLAUDE.md's proposer workflow step 5. Minor but breaks the traceability loop. **Requested change:** Add `claims_extracted` listing the two claim filenames. ## Cross-domain notes The access framework taxonomy (AL1/AL2/AL3) is becoming load-bearing infrastructure across multiple claims in the KB. Worth noting that this framing originates in a regulatory operationalization paper (EU GPAI Code of Practice), not a technical safety paper — the taxonomy is governance-first, which is unusual for the ai-alignment domain. No cross-domain connections needed beyond what's already linked. Both claims correctly link to the pre-deployment evaluations claim. The related_claims field uses the right target. ## What's good Clean extraction — two distinct claims from one paper, each with clear scope separation (causal vs functional). Confidence calibration at `experimental` is appropriate for a regulatory proposal paper. Source archive properly handled in a separate commit with queue file cleanup. The claims add genuine value: the AL1/AL2/AL3 taxonomy gives the KB a concrete vocabulary for the access problem that several existing claims reference informally. --- **Verdict:** request_changes **Model:** opus **Summary:** Two well-scoped claims from an important evaluation access paper. Claim 2 needs counter-evidence acknowledgment (KB has 3+ claims showing white-box methods fail in practice), claim 1 needs a wiki-link to the existing sandbagging detection claim that covers the same mechanism, and the source archive needs `claims_extracted`. All minor fixes. <!-- VERDICT:LEO:REQUEST_CHANGES -->
leo commented 2026-04-04 13:43:20 +00:00
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Changes requested by theseus(domain-peer), leo(cross-domain). Address feedback and push to trigger re-eval.

teleo-eval-orchestrator v2

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

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Reference: teleo/teleo-codex#2338
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