Hugging Face Trending Papers July 9, 2026

Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents

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In long-horizon tasks, decision-relevant state is often scattered across an expanding trajectory, while the action agent must surface it and act. As trajectories grow, task requirements, environment facts, prior attempts, diagnoses, and open subgoals can be buried in the context window or pushed beyond it, failing to influence decisions when needed.

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arXiv AI

Jul 10

Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents

arXiv:2607. 08716v1 Announce Type: new Abstract: In long-horizon tasks, decision-relevant state is often scattered across an expanding trajectory, while the action agent must surface it and act.

By Yifan Wu, Lizhu Zhang, Yuhang Zhou, Mingyi Wang, Bo Peng, Serena Li, Xiangjun Fan, Zhuokai Zhao

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arXiv AI

Jul 14

The Compliance Trap: Diagnosing How AI Agents Consume Conflicting Memory

arXiv:2607. 10608v1 Announce Type: new Abstract: Memory is becoming a core component of long-horizon AI agents, allowing agents to reuse past experience when operating web browsers, software tools, and other interactive environments.

By Yixiong Chen, Xinyi Bai, Alan Yuille

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arXiv AI

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Selective Memory Retention for Long-Horizon LLM Agents

arXiv:2606. 29178v1 Announce Type: new Abstract: When does retention matter for memory-augmented LLM agents?

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arXiv Machine Learning

Jun 16

HiMPO: Hindsight-Informed Memory Policy Optimization for Less-Entangled Credit in Long-Horizon Agents

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arXiv AI

Jul 15

PM-Bench: Evaluating Prospective Memory in LLM Agents

arXiv:2607. 12385v1 Announce Type: new Abstract: A significant challenge in agentic AI is prospective memory: the ability to execute an intention at a specific future cue or state while other activities are ongoing.

By Genglin Liu, Saadia Gabriel

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arXiv Machine Learning

Aug 4

RoMeRL: Balancing Feedback Coverage and the Memory-Reward Trap in Self-Evolving Agent Memory via Reduced-Order Utility States

arXiv:2608. 02508v1 Announce Type: new Abstract: Learning-based memory systems for self-evolving LLM agents face two tightly coupled challenges.

By Yi Yang, Zhennan Chen, Yihong Zhuang, Tiehan Fan, Yinan Chen, Jian Li, Jian Yang, Ying Tai

llms agents reinforcement-learning

Related stories

Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents

The Compliance Trap: Diagnosing How AI Agents Consume Conflicting Memory

Selective Memory Retention for Long-Horizon LLM Agents

HiMPO: Hindsight-Informed Memory Policy Optimization for Less-Entangled Credit in Long-Horizon Agents

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RoMeRL: Balancing Feedback Coverage and the Memory-Reward Trap in Self-Evolving Agent Memory via Reduced-Order Utility States