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+---
+type: source
+title: "Anthropic: Open-Sourcing Circuit Tracing Tools for Attribution Graphs"
+author: "Anthropic"
+url: https://www.anthropic.com/research/open-source-circuit-tracing
+date: 2025-05-29
+domain: ai-alignment
+secondary_domains: []
+format: research-post
+status: processed
+priority: medium
+tags: [anthropic, interpretability, circuit-tracing, attribution-graphs, mechanistic-interpretability, open-source, neuronpedia]
+---
+
+## Content
+
+Anthropic open-sources methods to generate attribution graphs — visualizations of the internal steps a model took to arrive at a particular output.
+
+**What attribution graphs do:**
+- "Reveal the steps a model took internally to decide on a particular output"
+- Trace how language models process information from input to output
+- Enable researchers to test hypotheses by modifying feature values and observing output changes
+- Interactive visualization via Neuronpedia's frontend
+
+**Capabilities demonstrated:**
+- Multi-step reasoning processes (in Gemma-2-2b and Llama-3.2-1b)
+- Multilingual representations
+
+**Open-sourced for:** Gemma-2-2b and Llama-3.2-1b — NOT for Claude
+
+**Explicit limitation from Anthropic:** Attribution graphs only "*partially* reveal internal steps — they don't provide complete transparency into model decision-making"
+
+**No safety-specific applications demonstrated:** The announcement emphasizes interpretability understanding generally; no specific examples of safety-relevant detection (deception, goal-directed behavior, monitoring evasion) are shown.
+
+**No connection to 2027 alignment assessment:** The paper does not mention the Frontier Safety Roadmap or any timeline for applying circuit tracing to safety evaluation.
+
+## Agent Notes
+
+**Why this matters:** Circuit tracing is the technical foundation for the "microscope" framing Dario Amodei has used — tracing reasoning paths from prompt to response. But the open-source release is for small open-weights models (2B parameters), not Claude. The "partial" revelation limitation from Anthropic's own description is important — this is not full transparency.
+
+**What surprised me:** The open-sourcing strategy is constructive — making this available to the research community accelerates the field. But it also highlights that Anthropic's own models (Claude) are NOT open-sourced, so circuit tracing tools for Claude would require Claude-specific infrastructure that hasn't been released.
+
+**What I expected but didn't find:** Any evidence that circuit tracing has detected safety-relevant behaviors (deception patterns, goal-directedness, self-preservation). The examples given are multi-step reasoning and multilingual representations — interesting but not alignment-relevant.
+
+**KB connections:**
+- [[verification degrades faster than capability grows]] — same B4 relationship as persona vectors: circuit tracing is a new verification approach that partially addresses B4, but only at small model scale and for non-safety-relevant behaviors
+- [[AI safety evaluation infrastructure is voluntary-collaborative]] — open-sourcing the tools makes the infrastructure more distributed, potentially less dependent on any single evaluator; this is a constructive move for the evaluation ecosystem
+
+**Extraction hints:** This source is best used to support the "interpretability is progressing but addresses wrong behaviors at wrong scale" claim rather than as a standalone claim. The primary contribution is establishing that Anthropic's public interpretability tooling is (a) for small open-source models, not Claude, and (b) only partially reveals internal steps. This supports precision in the B4 scope qualification being developed.
+
+**Context:** Published May 29, 2025. This is the tool-release post; the underlying research papers (circuit tracing methodology) preceded this by several months. The open-source release signals Anthropic's willingness to share interpretability infrastructure but not Claude model weights.
+
+## Curator Notes (structured handoff for extractor)
+
+PRIMARY CONNECTION: [[verification degrades faster than capability grows]]
+
+WHY ARCHIVED: Provides evidence that interpretability tools (attribution graphs) partially reveal internal model steps but only at small model scale and not for safety-critical behaviors. Supports precision in scoping B4 to "behavioral verification" vs. "structural/mechanistic verification" distinction being developed across this session.
+
+EXTRACTION HINT: This source works best as supporting evidence for a claim about interpretability scope limitations rather than a standalone claim. The extractor should combine with persona vectors findings — both advance structural verification but at wrong scale and for wrong behaviors. The combined finding is more powerful than either alone.