The paper introduces Online Meta-Control of Experience (OMCE), a framework that updates an agent’s experience memory and learns a meta-control policy during test-time adaptation. OMCE uses a lightweight contextual-bandit meta-controller to choose among four cognitive actions—DIRECT, EPISODE, GUIDE, and DISTILL—based on compact retrieval signals, thereby deciding when to invoke experience, which memory format to use, and when to consolidate new guidance. Experiments on four online adaptation benchmarks across four backbones show that OMCE can improve first-attempt success by up to 20.5 percentage points over the strongest baseline while reducing per-task token use by a factor of 5.7 to 62.5 relative to ACE with GPT‑5.2, and controlled ablations confirm the benefits of learned routing and ongoing policy adaptation.
By Zeyuan Zhao, Yuanyuan Shi, Peng Yu, Lingyue Fu, Shao Zhang, Ying Wen
arXiv:2607. 13591v1 Announce Type: cross Abstract: Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks.
By Eric Hanchen Jiang, Zhi Zhang, Yuchen Wu, Levina Li, Dong Liu, Xiao Liang, Rui Sun, Yubei Li, Edward Sun, Haozheng Luo, Zhaolu Kang, Aylin Caliskan, Kai-Wei Chang, Ying Nian Wu
arXiv:2607. 29241v1 Announce Type: cross Abstract: Optimizing modern recommender models still depends heavily on engineers manually iterating over architectural, objective, and training-strategy changes.
By Haoran Ling, Yuecheng Li, Zeyu Song, Jing Yao, Shuwen Kang, Chi Lu, Wenjin Wu, Peng Jiang
Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks. Yet nearly all existing approaches, from graph-structured memories to reflective insight stores, access memory through fixed, hand-designed heuristics.
Adaptive Memory Index Evolution for Agents (AMIE) is a lightweight plug‑in layer that evolves per‑memory access state from deployment retrievals without altering stored content or retraining the host memory system. It distills a semantic teacher’s keep/suppress judgments into a retrieval‑conditioned policy and accumulates selective actions into bounded state that reweights subsequent host retrievals. Across five retrieval‑based hosts and three benchmarks, trained AMIE improves downstream F1 or accuracy in all 15 comparisons (0.70–3.37 percentage points) and improves retrieval in all 10 comparisons, demonstrating that persistent memory access can be adapted independently of memory content.
By Yuntong Hu, Tongli Su, Yuanfu Sun, Shuo Ma, Qiaoyu Tan, Liang Zhao
arXiv:2608.21946v1 Announce Type: cross
Abstract: Reinforcement learning with outcome-based objectives such as GRPO enables LLM-based agents to solve complex, long-horizon tasks, yet the reusable exp...
By Can Xie, Yuyi Zhou, Wen Yang, Ziyi zhang, Siyao Song, Yingzhuo Deng, Shuo Ren, Jiajun Zhang