aiqbench analysis
This research identifies a specific attention head circuit, termed a "retrieval conditioned rebinding mechanism," that enables large language models to dynamically track entity attributes as context evolves. By applying causal interventions across Gemma and Llama architectures, the study reveals distinct representational signatures for this state-tracking process depending on the model family. The findings provide critical interpretability insights into how modern LLMs maintain coherent internal states without relying on external memory modules.
A retrieval conditioned rebinding circuit for dynamic entity tracking in large language models
arXiv:2606.08644v2 Announce Type: replace Abstract: 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 is clearly expressed in the query/key subspaces of the relevant attention heads, whereas in Llama models, the binding information is carried primarily in key vectors. Overall, our results reveal an interpretable mechanism for context dependent state tracking in LLMs.
Abstract: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 is clearly expressed in the query/key subspaces of the relevant attention heads, whereas in Llama models, the binding information is carried primarily in key vectors. Overall, our results reveal an interpretable mechanism for context dependent state tracking in LLMs.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2606.08644 [cs.CL]
(or arXiv:2606.08644v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.08644 Focus to learn more arXiv-issued DOI via DataCite
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