#activation-patching News & Analysis

4 articles tagged with #activation-patching. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AINeutralarXiv – CS AI · Apr 207/10

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Hallucination as Trajectory Commitment: Causal Evidence for Asymmetric Attractor Dynamics in Transformer Generation

Researchers demonstrate through causal experiments that hallucinations in language models arise from early trajectory commitments governed by asymmetric attractor dynamics. Using controlled prompt bifurcation on Qwen2.5-1.5B, they show that 44% of test prompts diverge into factual or hallucinated outputs at the first token, with activation patterns revealing that corrupting correct trajectories is far easier than recovering hallucinated ones—suggesting hallucination represents a stable but difficult-to-escape attractor state.

AINeutralarXiv – CS AI · May 126/10

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Belief or Circuitry? Causal Evidence for In-Context Graph Learning

Researchers present causal evidence that large language models learn in-context through dual mechanisms combining genuine structure inference with local pattern-matching, rather than relying on either approach alone. Using graph random-walk tasks and activation patching techniques, they demonstrate that LLMs simultaneously encode multiple competing graph topologies in orthogonal representational subspaces and show that late-layer circuits causally drive graph-preference predictions.

AINeutralarXiv – CS AI · May 116/10

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Where's the Plan? Locating Latent Planning in Language Models with Lightweight Mechanistic Interventions

Researchers investigated how language models develop internal representations of future constraints during text generation using rhyming-couplet completion as a test case. Across three major model families (Qwen, Gemma, Llama), only Gemma-3-27B demonstrated causal reliance on future-planning representations, with a critical handoff point at layer 30 localized to five attention heads.

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AINeutralarXiv – CS AI · May 96/10

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Patch-Effect Graph Kernels for LLM Interpretability

Researchers propose a novel framework for understanding transformer neural networks by converting activation patching data into graph structures analyzable through machine learning techniques. The approach demonstrates that localized graph features can effectively preserve and classify circuit-level computational patterns in language models like GPT-2, providing a systematic method for mechanistic interpretability research.