arXiv AI June 10, 2026 By Qingcan Kang, Liu Mingyang, Shixiong Kai, Kaichao Liang, Tao Zhong, Mingxuan Yuan

Learning What to Remember: Observability-Safe Memory Retention via Constrained Optimization for Long-Horizon Language Agents

arXiv:2606. 10616v1 Announce Type: new Abstract: Long-horizon language agents accumulate observations, reasoning traces, and retrieved facts that exceed their finite context windows, making memory retention a fundamental resource-allocation problem.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

agents

arXiv AI

Jul 28

MemChain: Learning Interpretable Memory Traces for Memory-Augmented LLM Agents

arXiv:2607. 24097v1 Announce Type: new Abstract: Memory-augmented LLM agents typically answer queries by retrieving relevant memories and feeding them directly to an answer model.

By Yiwen Ma, Songjun Tu, Qichao Zhang, Dong Li, Linjing Li, Dongbin Zhao

llms agents reinforcement-learning benchmarks

arXiv AI

Jul 21

Retain or Consolidate? Budget-Dependent Operator Selection for Language Agent Memory

arXiv:2607. 17545v1 Announce Type: new Abstract: Language agents depend on memory across interactions.

By Qingcan Kang, Mingyang Liu, Shixiong Kai, Kaichao Liang, Zhentao Tang, Yuqi Cui, Tao Zhong, Mingxuan Yuan

llms agents benchmarks safety

arXiv Machine Learning

Aug 4

Stop When Memory Suffices: Evidence-Conditioned Progressive Execution for LLM Agents

arXiv:2608. 01285v1 Announce Type: new Abstract: The continued development of LLMs toward persistent and adaptive intelligence increasingly requires long-term memory mechanisms that preserve and reuse information across interactions.

By Yidan Lin, Kaixiang Wang, Jiong Lou, Jie Li

llms agents efficiency

arXiv AI

Aug 5

Verifiable Memory: Learning Unified Memory Management with Local and Global Verifiers for Large Language Model Agents

arXiv:2608. 03137v1 Announce Type: new Abstract: Large language model (LLM) agents must retain reusable information, control a bounded active context, and recover earlier evidence during long-horizon interaction.

By Xiaolong Sun, Qichao Wang, Hangyu Li, Liang Chen

llms agents fine-tuning benchmarks

arXiv AI

Jul 16

Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents

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

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Jul 9

UP: Unbounded Positive Asymmetric Optimization for Breaking the Exploration-Stability Dilemma

arXiv:2607. 06987v1 Announce Type: new Abstract: Reinforcement learning (RL) has become the standard paradigm for enhancing the complex reasoning capabilities of large language models (LLMs).

By Chongyu Fan, Pengfei Liu, Jingjia Huang, Sijia Liu, Yi Lin

llms reinforcement-learning multimodal

Related stories

MemChain: Learning Interpretable Memory Traces for Memory-Augmented LLM Agents

Retain or Consolidate? Budget-Dependent Operator Selection for Language Agent Memory

Stop When Memory Suffices: Evidence-Conditioned Progressive Execution for LLM Agents

Verifiable Memory: Learning Unified Memory Management with Local and Global Verifiers for Large Language Model Agents

Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents

UP: Unbounded Positive Asymmetric Optimization for Breaking the Exploration-Stability Dilemma