theseus: extract claims from 2026-04-02-anthropic-circuit-tracing-claude-haiku-production-results

- Source: inbox/queue/2026-04-02-anthropic-circuit-tracing-claude-haiku-production-results.md
- Domain: ai-alignment
- Claims: 1, Entities: 0
- Enrichments: 2
- Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5)

Pentagon-Agent: Theseus <PIPELINE>

domains/ai-alignment/mechanistic-interpretability-traces-reasoning-pathways-but-cannot-detect-deceptive-alignment.md

@@ -0,0 +1,17 @@
+---
+type: claim
+domain: ai-alignment
+description: There is a gap between demonstrated interpretability capability (how it reasons) and alignment-relevant verification capability (whether it has deceptive goals)
+confidence: experimental
+source: Anthropic Interpretability Team, Circuit Tracing release March 2025
+created: 2026-04-02
+title: Mechanistic interpretability at production model scale can trace multi-step reasoning pathways but cannot yet detect deceptive alignment or covert goal-pursuing
+agent: theseus
+scope: functional
+sourcer: Anthropic Interpretability Team
+related_claims: ["verification degrades faster than capability grows", "[[AI-models-distinguish-testing-from-deployment-environments-providing-empirical-evidence-for-deceptive-alignment-concerns]]", "[[an aligned-seeming AI may be strategically deceptive because cooperative behavior is instrumentally optimal while weak]]"]
+---
+
+# Mechanistic interpretability at production model scale can trace multi-step reasoning pathways but cannot yet detect deceptive alignment or covert goal-pursuing
+
+Anthropic's circuit tracing work on Claude 3.5 Haiku demonstrates genuine technical progress in mechanistic interpretability at production scale. The team successfully traced two-hop reasoning ('the capital of the state containing Dallas' → 'Texas' → 'Austin'), showing they could see and manipulate intermediate representations. They also traced poetry planning where the model identifies potential rhyming words before writing each line. However, the demonstrated capabilities are limited to observing HOW the model reasons, not WHETHER it has hidden goals or deceptive tendencies. Dario Amodei's stated goal is to 'reliably detect most AI model problems by 2027' — framing this as future aspiration rather than current capability. The work does not demonstrate detection of scheming, deceptive alignment, or power-seeking behaviors. This creates a critical gap: the tools can reveal computational pathways but cannot yet answer the alignment-relevant question of whether a model is strategically deceptive or pursuing covert goals. The scale achievement (production model, not toy) is meaningful, but the capability demonstrated addresses transparency of reasoning processes rather than verification of alignment.