Agents
RefCon: Iterative Refinement and Contrastive Memory Extraction for Context-Evolving Agent
arXiv:2609.39143v1 Announce Type: new Abstract: Long-horizon agent interactions generate useful but noisy experience, and retraining models to absorb it is expensive. Context-evolving agents therefore
arXiv:2609.39143v1 Announce Type: new Abstract: Long-horizon agent interactions generate useful but noisy experience, and retraining models to absorb it is expensive. Context-evolving agents therefore need memory extraction methods that improve with more test-time compute without relying on gold labels. We propose RefCon, which combines sequential self-refinement with parallel self-contrast to extract higher-quality memories without gold labels. Evaluated on AppWorld and BFCL-V3 across multiple context-evolving agent frameworks, RefCon delivers strong and consistent gains, including relative improvements of 21.6% on ACE and 16.6% on ReMe over no-scaling baselines, while a diversity-focused variant (DivCon) achieves a 35.5% gain on ReasoningBank. RefCon consistently outperforms existing baselines without ground-truth labels, and generalizes across model scales and to software engineering tasks, where it surpasses even ground-truth baselines. We further analyze the accuracy-token trade-off and scaling behavior, showing RefCon maintains favorable efficiency and continues to improve as more trajectories are used, unlike diversity-only scaling which saturates earlier.
Related
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- Remember the Decision, Not the Description: A Rate-Distortion Framework for Agent Memory
- Experience Compression Spectrum: Unifying Memory, Skills, and Rules in LLM Agents
- The Retriever Should Remember: Experience-Amortized Reranking for Long-Term Agent Memory
- MindMemOS: A Portable and Self-Evolving Memory Operating Layer for AI Agents
Source: arXiv cs.AI | 2026-10-01