Papers by Shiyue Cao
SAMem: State-Aware Memory as a Fine-Grained Memory for LLM Agents in Decision-Making (2026.findings-acl)
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| Challenge: | Existing experiential memory approaches rely on task-level memory, but this lacks the situational alignment required for complex multi-step decision-making. |
| Approach: | They propose a new fine-grained memory paradigm that aligns memory retrieval with the current state instead of storing and reusing globally shared experiences. |
| Outcome: | Experiments on complex decision-making benchmarks show that the proposed state-aware memory outperforms existing experiential memory approaches and significantly improves task-solving efficiency. |