Papers with CAME-Bench
Grounding Agent Memory in Contextual Intent (2026.findings-acl)
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| Challenge: | Large language models are deployed in long-horizon tasks that require agents to track interleaved goals, resolve references to prior information, and coordinate actions over extended trajectories. |
| Approach: | They propose an agentic memory system that indexes each trajectory step with a structured retrieval cue, contextual intent, and retrieves history by matching the current step’s intent. |
| Outcome: | The proposed system outperforms the strongest benchmark by 35.6%, with the largest gains as trajectory length increases. |