Bridging Inference-Time Scaling and Episodic Memory with Action-Centric Graphs
Recent advancements in inference-time scaling have significantly unlocked the complex reasoning capabilities of Large Language Models~(LLMs). However, for agents, these approaches suffer from a critical inefficiency, operating in a stateless manner and engaging in redundant search processes. Existing memory mechanisms largely rely on the reasoning capabilities of LLMs, leading to prohibitive computational costs. In this paper, we propose a novel framework, \textit{GAMER}~(Graph-based Action-centric Memory with Episodic Reasoning), that bridges the gap between inference scaling and episodic memory. Our approach models historical reasoning as a dynamic \textit{Action-Centric Graph}. By deco...
| Authors | Xu Zheng, Chaohao Lin, Zhuomin Chen et al. |
|---|---|
| Primary category | cs.AI |
| Published | 29 July 2026 |
| Last updated at source | 29 July 2026 |
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