LLM Agents as Nonlinear RNNs with Exposed Hidden States
akbirthko · x · 2026-08-14
The author proposes a novel perspective: viewing LLM agents as nonlinear RNNs.
Unlike traditional RNNs, the "hidden state" of an agent is externalized into the context window, making it interpretable and monitorable. The author references Friedrich Hayek to support this concept and notes in a quoted discussion that by using reinforcement learning (RL) combined with a large scratchpad, models may not need to store all information directly within their weights.
Related event: New Perspective Views LLM Agents as Non-linear RNNs(2 posts)→
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