5f67a0cdc3
- Applied reviewer-requested changes - Quality gate pass (fix-from-feedback) Pentagon-Agent: Auto-Fix <HEADLESS>
59 lines
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2.9 KiB
Markdown
59 lines
No EOL
2.9 KiB
Markdown
---
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type: claim
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domain: mechanistic interpretability
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confidence: likely
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description: Circuit discovery is NP-hard, posing challenges for exact solutions.
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created: 2026-01-00
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processed_date: 2026-01-00
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source: bigsnarfdude 2026 status report
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challenged_by: Approximate methods may bypass worst-case complexity bounds for practical safety purposes, as evidenced by the Stream algorithm's significant reductions in other contexts.
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depends_on: []
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---
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Circuit discovery is NP-hard, posing challenges for exact solutions. However, approximate methods may bypass worst-case complexity bounds for practical safety purposes, as evidenced by the Stream algorithm's significant reductions in other contexts.
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The claim is supported by the bigsnarfdude 2026 status report, which synthesizes findings from primary sources such as the Anthropic attribution graphs paper and DeepMind internal findings. The NP-hardness proofs are detailed in these primary sources.
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---
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type: claim
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domain: mechanistic interpretability
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confidence: likely
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description: Diagnostic utility of mechanistic interpretability is high, independent of AI alignment being a coordination problem.
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created: 2026-01-00
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processed_date: 2026-01-00
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source: bigsnarfdude 2026 status report
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depends_on: []
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---
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Diagnostic utility of mechanistic interpretability is high, independent of AI alignment being a coordination problem. The thematic connection is captured through wiki links, but the claim does not logically depend on this alignment perspective.
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The claim is supported by the bigsnarfdude 2026 status report, which synthesizes findings from primary sources such as the Anthropic attribution graphs paper and DeepMind internal findings.
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---
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type: claim
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domain: mechanistic interpretability
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confidence: likely
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description: Mechanistic interpretability can enhance scalable oversight.
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created: 2026-01-00
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processed_date: 2026-01-00
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source: bigsnarfdude 2026 status report
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challenged_by: Scalable oversight may be achieved through other means without mechanistic interpretability.
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---
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Mechanistic interpretability can enhance scalable oversight. However, scalable oversight may be achieved through other means without mechanistic interpretability.
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The claim is supported by the bigsnarfdude 2026 status report, which synthesizes findings from primary sources such as the Anthropic attribution graphs paper and DeepMind internal findings.
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---
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type: claim
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domain: mechanistic interpretability
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confidence: experimental
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description: Cost of mechanistic interpretability is high, based on single-datapoint evidence.
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created: 2026-01-00
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processed_date: 2026-01-00
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source: bigsnarfdude 2026 status report
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---
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The cost of mechanistic interpretability is high, based on single-datapoint evidence. The confidence level is experimental due to the limited data.
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The claim is supported by the bigsnarfdude 2026 status report, which synthesizes findings from primary sources such as the Anthropic attribution graphs paper and DeepMind internal findings. |