arXiv:2606.08644v1 Announce Type: cross
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 encodes swap relevant binding information and reinstates it at readout. 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.
ChatGPT in healthcare: perceptions, ethical considerations, and practice implications among healthcare professionals in Ecuador and other countries in the Americas: a cross-sectional survey study
BackgroundGenerative artificial intelligence tools, such as ChatGPT, are increasingly discussed in healthcare; however, evidence from Latin American professional settings is limited and must be interpreted