Work

SAMem: State-Aware Memory as a Fine-Grained Memory for LLM Agents in Decision-Making

Year: 2026

Type: conference-paper

Abstract: Existing LLM-based agents primarily utilize coarse-grained experiential memory, where experiences are retrieved based on global task or scene context.While effective in simple settings, such coarse-grained memory lacks the situational alignment required for complex multi-step decision-making.As a result, recalled experiences often fail to match the agent's current state, blurring reasoning focus and leading to inaccurate decisions at critical steps.To this end, we propose State-Aware memory(SAMem), a new fine-grained memory paradigm for LLM agents that explicitly aligns memory retrieval with the current state.Instead of storing and reusing globally shared experiences, SAMem organizes memory at the level of state-specific reasoning thoughts, enabling the agent to retrieve only the most relevant experience for the current decision context.This state-conditioned memory allows the agent to focus on the most informative reasoning cues at each step, rather than being distracted by task-level but state-misaligned guidance.Extensive experiments on complex decision-making benchmarks demonstrate that SAMem outperforms existing experiential memory approaches, achieving superior performance and substantially improved tasksolving efficiency.These results indicate that state-aware, fine-grained memory enhances the decision-making capabilities of LLM agents.

Source: Findings of the Association for Computational Linguistics: ACL (Findings of ACL)

DOI: 10.18653/v1/2026.findings-acl.722