AI

Capability-Driven Self-Evolution of Agent Memory

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 07/10/2026, 01:18:25 UTC.

Briefings

This article presents a new method for improving agent memory by using capability-driven evolution instead of holistic performance-based approaches. The proposed PrisMem system uses dependency-aware capability selection and trace-guided integration to enhance memory programs. Experiments show PrisMem outperforms baselines by 10.54 percentage points on BEAM-1M and 7.83 percentage points on LongMemEval-M, demonstrating effectiveness on million-token histories.

Key points

• The system PrisMem uses dependency-aware capability selection to prioritize memory targets with potential cross-capability benefits.
• Trace-guided integration compares evaluated programs on paired differential cases to consolidate complementary gains into a unified memory program.

Context

Existing approaches typically adopt holistic evolution, deriving revision directions from mixed feedback and judging progress by overall performance. This can obscure optimization directions and hide capability-specific gains offset by regressions elsewhere. The new capability-driven evolution method addresses these limitations by focusing on individual capability dimensions.

Limitations

The evidence is limited to experimental results on BEAM-1M and LongMemEval-M benchmarks. The study does not provide details on the specific agents used, the exact training data distribution, or the long-term stability of the evolved memory programs beyond the reported performance metrics.

Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 24/10/2026, 18:23:20 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.

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