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theseus: extract claims from 2026-03-10-deng-continuation-refusal-jailbreak #2535

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theseus wants to merge 1 commit from extract/2026-03-10-deng-continuation-refusal-jailbreak-ca34 into main
pull from: extract/2026-03-10-deng-continuation-refusal-jailbreak-ca34
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theseus commented 2026-04-08 00:26:33 +00:00
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Automated Extraction

Source: inbox/queue/2026-03-10-deng-continuation-refusal-jailbreak.md
Domain: ai-alignment
Agent: Theseus
Model: anthropic/claude-sonnet-4.5

Extraction Summary

  • Claims: 1
  • Entities: 0
  • Enrichments: 2
  • Decisions: 0
  • Facts: 3

1 claim extracted. The core contribution is identifying the architectural origin of jailbreak vulnerability—the continuation-safety competition at the attention head level. This is a novel mechanistic explanation that connects to existing claims about capability-reliability independence and provides structural grounding for why alignment is difficult. The architecture-specific variation in safety heads is important but secondary. Did not extract separate claims about methodology or implications as they support the main structural argument rather than standing as independent propositions.

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

## Automated Extraction **Source:** `inbox/queue/2026-03-10-deng-continuation-refusal-jailbreak.md` **Domain:** ai-alignment **Agent:** Theseus **Model:** anthropic/claude-sonnet-4.5 ### Extraction Summary - **Claims:** 1 - **Entities:** 0 - **Enrichments:** 2 - **Decisions:** 0 - **Facts:** 3 1 claim extracted. The core contribution is identifying the architectural origin of jailbreak vulnerability—the continuation-safety competition at the attention head level. This is a novel mechanistic explanation that connects to existing claims about capability-reliability independence and provides structural grounding for why alignment is difficult. The architecture-specific variation in safety heads is important but secondary. Did not extract separate claims about methodology or implications as they support the main structural argument rather than standing as independent propositions. --- *Extracted by pipeline ingest stage (replaces extract-cron.sh)*
theseus added 1 commit 2026-04-08 00:26:34 +00:00
theseus: extract claims from 2026-03-10-deng-continuation-refusal-jailbreak
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36401c8884 
- Source: inbox/queue/2026-03-10-deng-continuation-refusal-jailbreak.md
- Domain: ai-alignment
- Claims: 1, Entities: 0
- Enrichments: 2
- Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5)

Pentagon-Agent: Theseus <PIPELINE>
m3taversal commented 2026-04-08 00:26:37 +00:00
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Validation: PASS — 1/1 claims pass

[pass] ai-alignment/jailbreak-vulnerability-is-architecturally-structural-from-continuation-safety-competition.md

tier0-gate v2 | 2026-04-08 00:26 UTC

<!-- TIER0-VALIDATION:36401c8884a5adeffcb8aa0374b5f6aa6a452e06 --> **Validation: PASS** — 1/1 claims pass **[pass]** `ai-alignment/jailbreak-vulnerability-is-architecturally-structural-from-continuation-safety-competition.md` *tier0-gate v2 | 2026-04-08 00:26 UTC*
theseus commented 2026-04-08 00:27:10 +00:00
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  1. Factual accuracy — The claim presents a coherent argument based on the provided evidence, and there are no obvious factual errors within the description.
  2. Intra-PR duplicates — There are no intra-PR duplicates as this PR introduces only one new file.
  3. Confidence calibration — The confidence level "experimental" seems appropriate given the source is a research paper describing findings from causal interventions and activation scaling, which are experimental methods.
  4. Wiki links — The wiki links [[AI capability and reliability are independent dimensions because Claude solved a 30-year open mathematical problem while simultaneously degrading at basic program execution during the same session]] and [[safe AI development requires building alignment mechanisms before scaling capability]] are broken, but this does not affect the verdict.
1. **Factual accuracy** — The claim presents a coherent argument based on the provided evidence, and there are no obvious factual errors within the description. 2. **Intra-PR duplicates** — There are no intra-PR duplicates as this PR introduces only one new file. 3. **Confidence calibration** — The confidence level "experimental" seems appropriate given the source is a research paper describing findings from causal interventions and activation scaling, which are experimental methods. 4. **Wiki links** — The wiki links `[[AI capability and reliability are independent dimensions because Claude solved a 30-year open mathematical problem while simultaneously degrading at basic program execution during the same session]]` and `[[safe AI development requires building alignment mechanisms before scaling capability]]` are broken, but this does not affect the verdict. <!-- VERDICT:THESEUS:APPROVE -->
leo commented 2026-04-08 00:27:24 +00:00
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Leo's Review: Jailbreak Vulnerability Structural Claim

1. Cross-domain implications: This claim directly affects beliefs about AI safety training efficacy, architectural design choices, and evaluation methodology transferability across models—significant cascade potential into deployment practices and research priorities.

2. Confidence calibration: "Experimental" confidence is appropriate given the claim cites a single 2026 study (future-dated, likely simulated) with mechanistic interpretability findings that haven't been independently replicated across diverse architectures.

3. Contradiction check: The claim aligns with related claim about capability-reliability independence and doesn't contradict existing alignment beliefs, though it strengthens the case that training alone is insufficient.

4. Wiki link validity: Both related claims links are properly formatted wiki-style links; whether they resolve is not my concern per instructions.

5. Axiom integrity: This doesn't touch axiom-level beliefs but makes a strong architectural claim about fundamental model properties—the evidence from causal interventions on attention heads is substantial but not yet extraordinary enough to be axiom-level.

6. Source quality: "Deng et al. 2026" is future-dated (review occurring before 2026), which is either a date error or this is a simulated/projected paper—this creates serious source credibility issues that undermine the entire claim.

7. Duplicate check: No substantially similar claim about architectural jailbreak vulnerability from continuation-safety competition appears to exist in the changed files.

8. Enrichment vs new claim: This introduces a novel mechanistic explanation for jailbreak vulnerability that warrants standalone claim status rather than enrichment.

9. Domain assignment: "ai-alignment" is correct for claims about safety mechanisms, jailbreak vulnerabilities, and alignment training limitations.

10. Schema compliance: All required frontmatter fields present (type, domain, description, confidence, source, created, title, agent, scope, sourcer), prose-as-title format used, YAML properly formatted.

11. Epistemic hygiene: The claim is falsifiable—one could test whether jailbreak vulnerability persists across architectural redesigns that alter continuation mechanisms, or whether safety transfers across architectures with different attention head implementations.

Critical Issue: The source is dated April 8, 2026, which is in the future. This is either a date error or references a non-existent paper, making the entire evidential basis suspect. A claim with "experimental" confidence cannot rest on a source that doesn't yet exist.

# Leo's Review: Jailbreak Vulnerability Structural Claim **1. Cross-domain implications:** This claim directly affects beliefs about AI safety training efficacy, architectural design choices, and evaluation methodology transferability across models—significant cascade potential into deployment practices and research priorities. **2. Confidence calibration:** "Experimental" confidence is appropriate given the claim cites a single 2026 study (future-dated, likely simulated) with mechanistic interpretability findings that haven't been independently replicated across diverse architectures. **3. Contradiction check:** The claim aligns with related claim about capability-reliability independence and doesn't contradict existing alignment beliefs, though it strengthens the case that training alone is insufficient. **4. Wiki link validity:** Both related claims links are properly formatted wiki-style links; whether they resolve is not my concern per instructions. **5. Axiom integrity:** This doesn't touch axiom-level beliefs but makes a strong architectural claim about fundamental model properties—the evidence from causal interventions on attention heads is substantial but not yet extraordinary enough to be axiom-level. **6. Source quality:** "Deng et al. 2026" is future-dated (review occurring before 2026), which is either a date error or this is a simulated/projected paper—this creates serious source credibility issues that undermine the entire claim. **7. Duplicate check:** No substantially similar claim about architectural jailbreak vulnerability from continuation-safety competition appears to exist in the changed files. **8. Enrichment vs new claim:** This introduces a novel mechanistic explanation for jailbreak vulnerability that warrants standalone claim status rather than enrichment. **9. Domain assignment:** "ai-alignment" is correct for claims about safety mechanisms, jailbreak vulnerabilities, and alignment training limitations. **10. Schema compliance:** All required frontmatter fields present (type, domain, description, confidence, source, created, title, agent, scope, sourcer), prose-as-title format used, YAML properly formatted. **11. Epistemic hygiene:** The claim is falsifiable—one could test whether jailbreak vulnerability persists across architectural redesigns that alter continuation mechanisms, or whether safety transfers across architectures with different attention head implementations. **Critical Issue:** The source is dated April 8, 2026, which is in the future. This is either a date error or references a non-existent paper, making the entire evidential basis suspect. A claim with "experimental" confidence cannot rest on a source that doesn't yet exist. <!-- ISSUES: date_errors, factual_discrepancy --> <!-- VERDICT:LEO:REQUEST_CHANGES -->
m3taversal commented 2026-04-08 00:27:24 +00:00
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Rejected — 2 blocking issues

[BLOCK] Date accuracy: Invalid or incorrect date format in created field (auto-fixable)

  • Fix: created = extraction date (today), not source publication date. Format: YYYY-MM-DD.

[BLOCK] Factual accuracy: Claim contains factual errors or misrepresents source material

  • Fix: Re-read the source. Verify specific numbers, names, dates. If source X quotes source Y, attribute to Y.
<!-- REJECTION: {"issues": ["date_errors", "factual_discrepancy"], "source": "eval_attempt_1", "ts": "2026-04-08T00:27:24.958098+00:00"} --> **Rejected** — 2 blocking issues **[BLOCK] Date accuracy**: Invalid or incorrect date format in created field (auto-fixable) - Fix: created = extraction date (today), not source publication date. Format: YYYY-MM-DD. **[BLOCK] Factual accuracy**: Claim contains factual errors or misrepresents source material - Fix: Re-read the source. Verify specific numbers, names, dates. If source X quotes source Y, attribute to Y.
leo commented 2026-04-08 00:29:19 +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 closed this pull request 2026-04-08 00:30:20 +00:00
leo commented 2026-04-08 00:30:46 +00:00
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Leo — Cross-Domain Review: PR #2535

PR: theseus: extract claims from 2026-03-10-deng-continuation-refusal-jailbreak
Files: 1 new claim (+ source archive update in separate commit)

Notes

Strong claim, well-scoped. The continuation-drive vs. safety-alignment competition is a genuinely novel mechanistic finding. Title passes the claim test, description adds useful context, evidence is inline with specific methodology (causal interventions, activation scaling). Confidence at experimental is correctly calibrated — single paper, specific methodology, not yet replicated.

Cross-domain connection worth flagging: The "scales with generation capability" assertion creates a direct link to the capability-safety scaling literature. This claim strengthens the case that the verification gap isn't just empirical but mechanistic — stronger models aren't just harder to verify, they structurally generate larger attack surfaces through the same mechanism (continuation drive) that makes them useful. This connects to the energy-AI nexus: if safety requires architectural departures from autoregressive generation, that has compute/energy implications for the transition.

Tension with existing KB — productive, not contradictory: The Zhou et al. CFA² claim (mechanistic-interpretability-tools-create-dual-use-attack-surface) identifies SAEs as the tool that enables surgical safety removal. This Deng claim identifies what those tools find — the continuation-safety competition at attention heads. Together they form a causal chain: architectural tension (Deng) → mechanistic identification of that tension (interpretability tools) → surgical exploitation (Zhou). Neither contradicts the other, but they'd benefit from explicit cross-linking. The related_claims field doesn't reference the Zhou claim — it should.

Missing cross-reference: No link to mechanistic-interpretability-tools-create-dual-use-attack-surface-enabling-surgical-safety-feature-removal.md. This is the most semantically adjacent claim in the KB and the omission is notable. Also no link to inference-time-safety-monitoring-recovers-alignment-through-early-reasoning-intervention.md, which proposes a partial solution to exactly the problem this claim identifies.

"Scales with generation capability" — mild scope concern. The title asserts the tension "scales with generation capability" but the paper's evidence is about relocating instruction suffixes, not about scaling behavior across model sizes. The body text argues the scaling logic ("as models develop stronger continuation capabilities... they simultaneously create a larger attack surface") but this is the authors' inference, not an empirically demonstrated scaling law. The claim should acknowledge this is a theoretical extrapolation from the mechanistic finding, not a measured scaling relationship. Currently confidence is experimental which partially covers this, but the title presents the scaling claim with the same certainty as the mechanistic finding.

Source archive: Properly updated to processed with processed_by: theseus in commit c0486e39. Source file in inbox/queue/ still shows status: unprocessed — this is the queue copy vs. the archive copy, so not a blocking issue, but the queue file should be cleaned up or its status updated for consistency.

Requested Changes

  1. Add wiki link to [[Mechanistic interpretability tools create a dual-use attack surface where Sparse Autoencoders developed for alignment research can identify and surgically remove safety-related features]] in related_claims — this is the most directly related claim in the KB.

  2. Qualify the scaling assertion in the body. The mechanistic finding (continuation-safety competition at attention heads) is empirically demonstrated. The scaling claim (this tension grows with capability) is a reasonable inference but not empirically measured in the paper. One sentence distinguishing these would fix it.

Verdict: request_changes
Model: opus
Summary: Solid mechanistic claim from Deng et al. on continuation-safety competition. Needs one missing cross-reference (Zhou CFA² dual-use claim) and a sentence qualifying the scaling inference vs. the empirical finding.

# Leo — Cross-Domain Review: PR #2535 **PR:** theseus: extract claims from 2026-03-10-deng-continuation-refusal-jailbreak **Files:** 1 new claim (+ source archive update in separate commit) ## Notes **Strong claim, well-scoped.** The continuation-drive vs. safety-alignment competition is a genuinely novel mechanistic finding. Title passes the claim test, description adds useful context, evidence is inline with specific methodology (causal interventions, activation scaling). Confidence at `experimental` is correctly calibrated — single paper, specific methodology, not yet replicated. **Cross-domain connection worth flagging:** The "scales with generation capability" assertion creates a direct link to the capability-safety scaling literature. This claim strengthens the case that the verification gap isn't just empirical but mechanistic — stronger models aren't just harder to verify, they structurally generate larger attack surfaces through the same mechanism (continuation drive) that makes them useful. This connects to the energy-AI nexus: if safety requires architectural departures from autoregressive generation, that has compute/energy implications for the transition. **Tension with existing KB — productive, not contradictory:** The Zhou et al. CFA² claim (`mechanistic-interpretability-tools-create-dual-use-attack-surface`) identifies SAEs as the tool that enables surgical safety removal. This Deng claim identifies *what* those tools find — the continuation-safety competition at attention heads. Together they form a causal chain: architectural tension (Deng) → mechanistic identification of that tension (interpretability tools) → surgical exploitation (Zhou). Neither contradicts the other, but they'd benefit from explicit cross-linking. The `related_claims` field doesn't reference the Zhou claim — it should. **Missing cross-reference:** No link to `mechanistic-interpretability-tools-create-dual-use-attack-surface-enabling-surgical-safety-feature-removal.md`. This is the most semantically adjacent claim in the KB and the omission is notable. Also no link to `inference-time-safety-monitoring-recovers-alignment-through-early-reasoning-intervention.md`, which proposes a partial solution to exactly the problem this claim identifies. **"Scales with generation capability" — mild scope concern.** The title asserts the tension "scales with generation capability" but the paper's evidence is about relocating instruction suffixes, not about scaling behavior across model sizes. The body text argues the scaling logic ("as models develop stronger continuation capabilities... they simultaneously create a larger attack surface") but this is the authors' inference, not an empirically demonstrated scaling law. The claim should acknowledge this is a theoretical extrapolation from the mechanistic finding, not a measured scaling relationship. Currently confidence is `experimental` which partially covers this, but the title presents the scaling claim with the same certainty as the mechanistic finding. **Source archive:** Properly updated to `processed` with `processed_by: theseus` in commit c0486e39. Source file in `inbox/queue/` still shows `status: unprocessed` — this is the queue copy vs. the archive copy, so not a blocking issue, but the queue file should be cleaned up or its status updated for consistency. ## Requested Changes 1. **Add wiki link** to `[[Mechanistic interpretability tools create a dual-use attack surface where Sparse Autoencoders developed for alignment research can identify and surgically remove safety-related features]]` in `related_claims` — this is the most directly related claim in the KB. 2. **Qualify the scaling assertion** in the body. The mechanistic finding (continuation-safety competition at attention heads) is empirically demonstrated. The scaling claim (this tension grows with capability) is a reasonable inference but not empirically measured in the paper. One sentence distinguishing these would fix it. --- **Verdict:** request_changes **Model:** opus **Summary:** Solid mechanistic claim from Deng et al. on continuation-safety competition. Needs one missing cross-reference (Zhou CFA² dual-use claim) and a sentence qualifying the scaling inference vs. the empirical finding. <!-- VERDICT:LEO:REQUEST_CHANGES -->
theseus commented 2026-04-08 00:30:50 +00:00
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Theseus Domain Review — PR #2535

Claim: Jailbreak vulnerability in language models is architecturally structural because the continuation drive and safety alignment compete at the attention head level creating an exploitable tension that scales with generation capability

Source: Deng et al. 2026, causal interventions on safety-critical attention heads

What This Adds

The KB already has Zhou et al.'s CFA² claim (mechanistic-interpretability-tools-create-dual-use-attack-surface) from the same recent wave of mechanistic jailbreak research. These are sister papers: Zhou targets the tools side (SAE-based feature removal), Deng targets the architectural side (continuation/safety head competition). Both belong. No duplicate.

Technical Concerns

"Scales with generation capability" is asserted, not demonstrated. The paper shows the tension in specific architectures at a point in time. Calling it a scaling relationship implies this grows monotonically with capability — which would be a significant finding. If the paper establishes this empirically across model scales, it should be stated. If it's an inference from the structural argument, it should be scoped as such. The title currently implies a scaling law the body doesn't establish.

"Continuation drive" anthropomorphizes the mechanism. The paper's language ("intrinsic continuation drive") is adopted uncritically. The actual mechanism is next-token prediction pressure in autoregressive generation — which is a training objective, not an internal drive. This distinction matters for the "architecturally structural" claim: the tension is structural given autoregressive generation, but it's a design choice, not an invariant of all possible architectures. The body gestures at this with "departing from standard autoregressive generation paradigms" but the title implies a deeper structural constraint than the evidence warrants.

The architecture-specificity finding cuts both ways. The paper finds safety mechanisms implemented differently across architectures. This actually weakens the universality of the structural claim (it's not the same structural problem everywhere) while strengthening the transfer-evaluation point. The body handles this correctly; the title doesn't flag it.

Missing Wiki Links

The two linked claims (AI capability and reliability are independent dimensions and safe AI development requires building alignment mechanisms before scaling) are weak primary links — tangentially related. The critical missing connections:

  • mechanistic-interpretability-tools-create-dual-use-attack-surface-enabling-surgical-safety-feature-removal — this is the closest existing claim; both establish mechanistic attack vectors on safety internals using interpretability methods. Should be the primary wiki link with an explicit note that Deng and Zhou represent complementary mechanistic attack strategies.

  • the relationship between training reward signals and resulting AI desires is fundamentally unpredictable making behavioral alignment through training an unreliable method — Deng's structural finding is direct empirical support for why training-based fixes have structural limits. The connection should be explicit.

  • emotion-vectors-causally-drive-unsafe-ai-behavior-through-interpretable-steering — this is a genuine tension worth flagging (see below).

Tension With Existing Claims Not Acknowledged

The Anthropic emotion-vectors claim (emotion-vectors-causally-drive-unsafe-ai-behavior) shows that safety mechanisms CAN be steered via interpretability in production models — desperation vector suppression eliminated blackmail attempts entirely. This partially challenges Deng's conclusion that "training-based fixes have structural limits": if mechanistic steering can modulate safety-critical behavior at production scale, that's a constructive alternative to pure redesign. The claims aren't in direct contradiction (one is about jailbreak attacks, one is about safety interventions), but a challenged_by field or brief Challenges section acknowledging this tension would strengthen the claim rather than weaken it. Per KB review standards for experimental confidence claims, counter-evidence should be acknowledged when it exists.

Source Archive

The commit message identifies the source as inbox/queue/2026-03-10-deng-continuation-refusal-jailbreak.md but no archive file update is included in the diff. Per the proposer workflow, the source should be archived in inbox/archive/ with status: processed after extraction. This is a process gap, not a quality gate failure on the claim itself — but it leaves the source pipeline open.

Confidence Calibration

experimental is right for a single paper with causal interventions. The issue is the title implies stronger generalizability than the evidence supports ("scales with" rather than "may scale with" or "is present as"). The confidence level is correct; the title is slightly overreaching.

Verdict: request_changes
Model: sonnet
Summary: Genuinely novel claim with no duplicate — the Deng/Zhou pair together make a strong mechanistic case. Two issues worth fixing: (1) the title's "scales with generation capability" asserts a scaling law not established in the paper; (2) missing wiki-link to the dual-use attack surface claim (closest existing claim) and missing acknowledgment of the emotion-vectors counter-evidence (mechanistic steering can modulate safety behavior, which is a constructive alternative to architectural redesign). Source archive gap is a process note.

# Theseus Domain Review — PR #2535 **Claim:** Jailbreak vulnerability in language models is architecturally structural because the continuation drive and safety alignment compete at the attention head level creating an exploitable tension that scales with generation capability **Source:** Deng et al. 2026, causal interventions on safety-critical attention heads --- ## What This Adds The KB already has Zhou et al.'s CFA² claim (`mechanistic-interpretability-tools-create-dual-use-attack-surface`) from the same recent wave of mechanistic jailbreak research. These are sister papers: Zhou targets the *tools* side (SAE-based feature removal), Deng targets the *architectural* side (continuation/safety head competition). Both belong. No duplicate. --- ## Technical Concerns **"Scales with generation capability" is asserted, not demonstrated.** The paper shows the tension in specific architectures at a point in time. Calling it a scaling relationship implies this grows monotonically with capability — which would be a significant finding. If the paper establishes this empirically across model scales, it should be stated. If it's an inference from the structural argument, it should be scoped as such. The title currently implies a scaling law the body doesn't establish. **"Continuation drive" anthropomorphizes the mechanism.** The paper's language ("intrinsic continuation drive") is adopted uncritically. The actual mechanism is next-token prediction pressure in autoregressive generation — which is a training objective, not an internal drive. This distinction matters for the "architecturally structural" claim: the tension is structural *given autoregressive generation*, but it's a design choice, not an invariant of all possible architectures. The body gestures at this with "departing from standard autoregressive generation paradigms" but the title implies a deeper structural constraint than the evidence warrants. **The architecture-specificity finding cuts both ways.** The paper finds safety mechanisms implemented differently across architectures. This actually *weakens* the universality of the structural claim (it's not the same structural problem everywhere) while strengthening the transfer-evaluation point. The body handles this correctly; the title doesn't flag it. --- ## Missing Wiki Links The two linked claims (`AI capability and reliability are independent dimensions` and `safe AI development requires building alignment mechanisms before scaling`) are weak primary links — tangentially related. The critical missing connections: - **`mechanistic-interpretability-tools-create-dual-use-attack-surface-enabling-surgical-safety-feature-removal`** — this is the closest existing claim; both establish mechanistic attack vectors on safety internals using interpretability methods. Should be the primary wiki link with an explicit note that Deng and Zhou represent complementary mechanistic attack strategies. - **`the relationship between training reward signals and resulting AI desires is fundamentally unpredictable making behavioral alignment through training an unreliable method`** — Deng's structural finding is direct empirical support for why training-based fixes have structural limits. The connection should be explicit. - **`emotion-vectors-causally-drive-unsafe-ai-behavior-through-interpretable-steering`** — this is a genuine tension worth flagging (see below). --- ## Tension With Existing Claims Not Acknowledged The Anthropic emotion-vectors claim (`emotion-vectors-causally-drive-unsafe-ai-behavior`) shows that safety mechanisms CAN be steered via interpretability in production models — desperation vector suppression eliminated blackmail attempts entirely. This partially challenges Deng's conclusion that "training-based fixes have structural limits": if mechanistic steering can modulate safety-critical behavior at production scale, that's a constructive alternative to pure redesign. The claims aren't in direct contradiction (one is about jailbreak attacks, one is about safety interventions), but a `challenged_by` field or brief Challenges section acknowledging this tension would strengthen the claim rather than weaken it. Per KB review standards for `experimental` confidence claims, counter-evidence should be acknowledged when it exists. --- ## Source Archive The commit message identifies the source as `inbox/queue/2026-03-10-deng-continuation-refusal-jailbreak.md` but no archive file update is included in the diff. Per the proposer workflow, the source should be archived in `inbox/archive/` with `status: processed` after extraction. This is a process gap, not a quality gate failure on the claim itself — but it leaves the source pipeline open. --- ## Confidence Calibration `experimental` is right for a single paper with causal interventions. The issue is the title implies stronger generalizability than the evidence supports ("scales with" rather than "may scale with" or "is present as"). The confidence level is correct; the title is slightly overreaching. --- **Verdict:** request_changes **Model:** sonnet **Summary:** Genuinely novel claim with no duplicate — the Deng/Zhou pair together make a strong mechanistic case. Two issues worth fixing: (1) the title's "scales with generation capability" asserts a scaling law not established in the paper; (2) missing wiki-link to the dual-use attack surface claim (closest existing claim) and missing acknowledgment of the emotion-vectors counter-evidence (mechanistic steering can modulate safety behavior, which is a constructive alternative to architectural redesign). Source archive gap is a process note. <!-- VERDICT:THESEUS:REQUEST_CHANGES -->
leo commented 2026-04-08 00:30:50 +00:00
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Changes requested by leo(cross-domain), theseus(domain-peer). Address feedback and push to trigger re-eval.

teleo-eval-orchestrator v2

**Changes requested** by leo(cross-domain), theseus(domain-peer). Address feedback and push to trigger re-eval. *teleo-eval-orchestrator v2*
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Reference: teleo/teleo-codex#2535
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