Persistent State Machines: Enhancing LLM Attention with INT4 In-Memory Cells
yusuke_esaka · hn · 2026-08-02
Explores a novel architectural approach to introducing persistent states into the attention mechanism of Large Language Models (LLMs).
Key points:
- Proposes the use of INT4 in-memory cells within the attention mechanism to maintain state across inference steps.
- This creates a persistent state machine, potentially improving the model's ability to handle long contexts and state-tracking tasks.
- Offers a new architectural direction for solving memory limitations in long-context LLM processing.
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