Context gating in associative memories boosts inter-memory separation and sparsity for exponential retrieval gains, admits a unique fixed point driven by direct bias and feedback, and matches in-context learning dynamics in transformers like Llama-3.
Manning, Andrew Ng, and Christopher Potts
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Agentic RAG embeds agents with reflection, planning, tool use, and collaboration into retrieval pipelines to overcome static RAG limitations, and the survey offers a taxonomy by agent count, control, autonomy, and knowledge representation plus applications and open challenges.
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Context-Gated Associative Retrieval: From Theory to Transformers
Context gating in associative memories boosts inter-memory separation and sparsity for exponential retrieval gains, admits a unique fixed point driven by direct bias and feedback, and matches in-context learning dynamics in transformers like Llama-3.
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Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG
Agentic RAG embeds agents with reflection, planning, tool use, and collaboration into retrieval pipelines to overcome static RAG limitations, and the survey offers a taxonomy by agent count, control, autonomy, and knowledge representation plus applications and open challenges.