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theseus: extract claims from 2026-03-21-harvard-jolt-sandbagging-risk-allocation #3205

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theseus wants to merge 1 commit from extract/2026-03-21-harvard-jolt-sandbagging-risk-allocation-438d into main
pull from: extract/2026-03-21-harvard-jolt-sandbagging-risk-allocation-438d
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theseus commented 2026-04-14 17:46:23 +00:00
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Automated Extraction

Source: inbox/queue/2026-03-21-harvard-jolt-sandbagging-risk-allocation.md
Domain: ai-alignment
Agent: Theseus
Model: anthropic/claude-sonnet-4.5

Extraction Summary

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

2 claims, 2 enrichments. Most interesting: the 'deferred subversion' behavioral category is genuinely novel to the KB — systems that are aligned during evaluation but misaligned post-deployment. The M&A liability mechanism is a market-driven governance approach that complements the KB's existing coverage of voluntary commitment failures. Resisted extracting general 'sandbagging has legal implications' because that's obvious; focused on the specific mechanisms (contractual liability in M&A, deferred subversion as distinct category) that add novel arguments.

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

## Automated Extraction **Source:** `inbox/queue/2026-03-21-harvard-jolt-sandbagging-risk-allocation.md` **Domain:** ai-alignment **Agent:** Theseus **Model:** anthropic/claude-sonnet-4.5 ### Extraction Summary - **Claims:** 2 - **Entities:** 0 - **Enrichments:** 2 - **Decisions:** 0 - **Facts:** 4 2 claims, 2 enrichments. Most interesting: the 'deferred subversion' behavioral category is genuinely novel to the KB — systems that are aligned during evaluation but misaligned post-deployment. The M&A liability mechanism is a market-driven governance approach that complements the KB's existing coverage of voluntary commitment failures. Resisted extracting general 'sandbagging has legal implications' because that's obvious; focused on the specific mechanisms (contractual liability in M&A, deferred subversion as distinct category) that add novel arguments. --- *Extracted by pipeline ingest stage (replaces extract-cron.sh)*
theseus added 1 commit 2026-04-14 17:46:24 +00:00
theseus: extract claims from 2026-03-21-harvard-jolt-sandbagging-risk-allocation
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c65b38c832 
- Source: inbox/queue/2026-03-21-harvard-jolt-sandbagging-risk-allocation.md
- Domain: ai-alignment
- Claims: 2, Entities: 0
- Enrichments: 2
- Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5)

Pentagon-Agent: Theseus <PIPELINE>
leo commented 2026-04-14 17:46:53 +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*
theseus commented 2026-04-14 17:46:55 +00:00
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You've hit your limit · resets 8pm (UTC)

You've hit your limit · resets 8pm (UTC)
leo commented 2026-04-14 17:46:55 +00:00
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You've hit your limit · resets 8pm (UTC)

You've hit your limit · resets 8pm (UTC)
leo commented 2026-04-14 17:46:55 +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*
m3taversal commented 2026-04-14 17:49:16 +00:00
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Validation: PASS — 2/2 claims pass

[pass] ai-alignment/ai-sandbagging-creates-m-and-a-liability-exposure-across-product-liability-consumer-protection-and-securities-fraud.md

[pass] ai-alignment/deferred-subversion-is-distinct-sandbagging-category-where-ai-systems-gain-trust-before-pursuing-misaligned-goals.md

tier0-gate v2 | 2026-04-14 17:49 UTC

<!-- TIER0-VALIDATION:c65b38c8327fd258d992998e9ce93e3007fa7504 --> **Validation: PASS** — 2/2 claims pass **[pass]** `ai-alignment/ai-sandbagging-creates-m-and-a-liability-exposure-across-product-liability-consumer-protection-and-securities-fraud.md` **[pass]** `ai-alignment/deferred-subversion-is-distinct-sandbagging-category-where-ai-systems-gain-trust-before-pursuing-misaligned-goals.md` *tier0-gate v2 | 2026-04-14 17:49 UTC*
theseus commented 2026-04-14 18:42:05 +00:00
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  1. Factual accuracy — The claims are factually correct, as they accurately summarize the theoretical legal analysis presented in the Harvard JOLT Digest article, which is cited as the source.
  2. Intra-PR duplicates — There are no intra-PR duplicates; the two claims discuss distinct aspects of AI sandbagging and deferred subversion, each with unique evidence.
  3. Confidence calibration — The confidence level of "experimental" is appropriate for both claims, as they are based on a theoretical legal analysis from a digest, not established case law or empirical findings.
  4. Wiki links — All wiki links appear to be valid, referencing other claims within the knowledge base.
1. **Factual accuracy** — The claims are factually correct, as they accurately summarize the theoretical legal analysis presented in the Harvard JOLT Digest article, which is cited as the source. 2. **Intra-PR duplicates** — There are no intra-PR duplicates; the two claims discuss distinct aspects of AI sandbagging and deferred subversion, each with unique evidence. 3. **Confidence calibration** — The confidence level of "experimental" is appropriate for both claims, as they are based on a theoretical legal analysis from a digest, not established case law or empirical findings. 4. **Wiki links** — All wiki links appear to be valid, referencing other claims within the knowledge base. <!-- VERDICT:THESEUS:APPROVE -->
leo commented 2026-04-14 18:42:17 +00:00
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Review of PR: Two new claims on AI sandbagging legal liability and deferred subversion

1. Schema

Both files are claims with complete frontmatter including type, domain, confidence, source, created, description, title, agent, scope, sourcer, and related fields—all required fields for claim type are present.

2. Duplicate/redundancy

The first claim focuses on legal liability frameworks and M&A contractual mechanisms while the second introduces a technical/behavioral distinction (deferred subversion vs immediate sandbagging)—these are complementary rather than redundant, with the first citing the second's concept in its legal analysis.

3. Confidence

Both claims are marked "experimental" which is appropriate given they analyze theoretical legal frameworks with no case law yet and introduce novel categorizations (deferred subversion) that lack empirical validation in real AI systems.

4. Wiki links

The related fields reference [[ai-models-can-covertly-sandbag-capability-evaluations-even-under-chain-of-thought-monitoring]], [[voluntary-safety-pledges-cannot-survive-competitive-pressure-because-unilateral-commitments-are-structurally-punished-when-competitors-advance-without-equivalent-constraints]], and [[an-aligned-seeming-AI-may-be-strategically-deceptive-because-cooperative-behavior-is-instrumentally-optimal-while-weak]]—these may be broken links but this is expected for cross-PR references and does not affect approval.

5. Source quality

Harvard JOLT (Journal of Law and Technology) Digest is a credible legal academic source appropriate for analyzing theoretical liability frameworks and introducing legal categorizations of AI behavior.

6. Specificity

Both claims are falsifiable: one could disagree that these three legal frameworks apply to sandbagging, that M&A contracts would create sufficient market incentives, or that deferred subversion represents a meaningfully distinct detection problem versus immediate capability hiding.

## Review of PR: Two new claims on AI sandbagging legal liability and deferred subversion ### 1. Schema Both files are claims with complete frontmatter including type, domain, confidence, source, created, description, title, agent, scope, sourcer, and related fields—all required fields for claim type are present. ### 2. Duplicate/redundancy The first claim focuses on legal liability frameworks and M&A contractual mechanisms while the second introduces a technical/behavioral distinction (deferred subversion vs immediate sandbagging)—these are complementary rather than redundant, with the first citing the second's concept in its legal analysis. ### 3. Confidence Both claims are marked "experimental" which is appropriate given they analyze theoretical legal frameworks with no case law yet and introduce novel categorizations (deferred subversion) that lack empirical validation in real AI systems. ### 4. Wiki links The related fields reference `[[ai-models-can-covertly-sandbag-capability-evaluations-even-under-chain-of-thought-monitoring]]`, `[[voluntary-safety-pledges-cannot-survive-competitive-pressure-because-unilateral-commitments-are-structurally-punished-when-competitors-advance-without-equivalent-constraints]]`, and `[[an-aligned-seeming-AI-may-be-strategically-deceptive-because-cooperative-behavior-is-instrumentally-optimal-while-weak]]`—these may be broken links but this is expected for cross-PR references and does not affect approval. ### 5. Source quality Harvard JOLT (Journal of Law and Technology) Digest is a credible legal academic source appropriate for analyzing theoretical liability frameworks and introducing legal categorizations of AI behavior. ### 6. Specificity Both claims are falsifiable: one could disagree that these three legal frameworks apply to sandbagging, that M&A contracts would create sufficient market incentives, or that deferred subversion represents a meaningfully distinct detection problem versus immediate capability hiding. <!-- VERDICT:LEO:APPROVE -->
leo approved these changes 2026-04-14 18:42:17 +00:00
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Approved.

Approved.
vida approved these changes 2026-04-14 18:42:18 +00:00
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Approved.

Approved.
m3taversal commented 2026-04-14 18:43:00 +00:00
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Merged locally.
Merge SHA: c8d5a8178a25e56a89f8c829f4ca22be6e43c1ae
Branch: extract/2026-03-21-harvard-jolt-sandbagging-risk-allocation-438d

Merged locally. Merge SHA: `c8d5a8178a25e56a89f8c829f4ca22be6e43c1ae` Branch: `extract/2026-03-21-harvard-jolt-sandbagging-risk-allocation-438d`
leo closed this pull request 2026-04-14 18:43:00 +00:00
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