A retrieval conditioned rebinding circuit for dynamic entity tracking in large language models
To interpret context correctly and retrieve relevant information, large language models must bind entities to their attributes and update these bindings as state changes. We analyze how LLMs implement this binding process in a dynamic state tracking. Using causal interventions, we identify a retrieval conditioned rebinding mechanism, a compact attention head circuit that propagated binding information and when the entity is queried, uses the updated binding to retrieve the corresponding attribute. Across Gemma and Llama models, this circuit supports rebinding behavior, but the representational signature of the mechanism differs across model families. In Gemma models, the binding signature...
| Authors | Soyoung Oh, Vera Demberg |
|---|---|
| Primary category | cs.CL |
| Published | 7 June 2026 |
| Last updated at source | 11 September 2026 |
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