When Stored Evidence Stops Being Usable: Scale-Conditioned Evaluation of Agent Memory
📰 ArXiv cs.AI
Learn to evaluate agent memory with a scale-conditioned protocol to assess evidence usability as irrelevant sessions accumulate
Action Steps
- Implement a scale-conditioned evaluation protocol for agent memory
- Hold task evidence fixed for each query while adding irrelevant sessions
- Log agent performance and evidence usability over time
- Analyze the impact of irrelevant sessions on evidence usability
- Apply the protocol to various agent memory models to compare their performance
Who Needs to Know This
AI researchers and engineers working on agent memory and evidence preservation can benefit from this protocol to improve their models' performance and robustness
Key Insight
💡 Evidence usability degrades as irrelevant sessions accumulate, highlighting the need for robust agent memory models
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🤖 Evaluate agent memory with a scale-conditioned protocol to assess evidence usability #AI #AgentMemory
Key Takeaways
Learn to evaluate agent memory with a scale-conditioned protocol to assess evidence usability as irrelevant sessions accumulate
Full Article
Title: When Stored Evidence Stops Being Usable: Scale-Conditioned Evaluation of Agent Memory
Abstract:
arXiv:2605.07313v1 Announce Type: new Abstract: Memory-agent evaluations report fixed-snapshot accuracy or retrieval quality, but these scores do not show whether evidence remains usable as irrelevant sessions (sessions not annotated as task-relevant evidence for the query) accumulate. We present a scale-conditioned evaluation protocol for agent memory under evidence-preserving growth: for each query, task evidence is held fixed while irrelevant sessions are added. The protocol logs agent--memor
Abstract:
arXiv:2605.07313v1 Announce Type: new Abstract: Memory-agent evaluations report fixed-snapshot accuracy or retrieval quality, but these scores do not show whether evidence remains usable as irrelevant sessions (sessions not annotated as task-relevant evidence for the query) accumulate. We present a scale-conditioned evaluation protocol for agent memory under evidence-preserving growth: for each query, task evidence is held fixed while irrelevant sessions are added. The protocol logs agent--memor
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