arXiv AI
Jun 6
Memory is Reconstructed, Not Retrieved: Graph Memory for LLM Agents
arXiv:2606. 06036v1 Announce Type: new Abstract: Despite recent progress, LLM agents still struggle with reasoning over long interaction histories.
E-mem: Multi-agent based Episodic Context Reconstruction for LLM Agent Memory
arXiv:2601. 21714v5 Announce Type: replace Abstract: The evolution of Large Language Model (LLM) agents towards System~2 reasoning, characterized by deliberative, high-precision problem-solving, requires maintaining rigorous logical integrity over extended horizons.
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arXiv AI
Jun 11
Organize then Retrieve: Hierarchical Memory Navigation for Efficient Agents
arXiv:2606. 11680v1 Announce Type: new Abstract: Large language model (LLM) agents struggle with long-horizon tasks due to their inherent statelessness, requiring all task-relevant information to be encoded in growing input contexts.
arXiv AI
1d ago
HyMem: Hierarchical Context Management for Long-Horizon Agents via Information Isolation
arXiv:2608. 15703v1 Announce Type: new Abstract: Large language model (LLM) agents often perform poorly on complex, long-horizon tasks because their context becomes increasingly cluttered over time.
arXiv AI
Jun 2
MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation
arXiv:2606. 00610v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has become an essential method for mitigating hallucinations in Large Language Models (LLMs) by leveraging external knowledge.
arXiv AI
Aug 6
Hierarchical Graph Memory for LLM Agents with Path-level Localization and Rewrite
arXiv:2608. 05095v1 Announce Type: new Abstract: Agents for long term reasoning require a memory that can be efficiently and effectively updated over time, as new facts and external feedback continue to arrive.
arXiv AI
Jun 2
ACON: Optimizing Context Compression for Long-horizon LLM Agents
arXiv:2510. 00615v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as agents in dynamic real-world environments, where success depends on maintaining precise records of actions and observations.
arXiv AI
Jun 9
Goal-Oriented Reasoning for RAG-based Memory in Conversational Agentic LLM Systems
arXiv:2605. 12213v2 Announce Type: replace Abstract: LLM-based conversational AI agents struggle to maintain coherent behavior over long horizons due to limited context.
arXiv AI
Jun 18
ActMem: Bridging the Gap Between Memory Retrieval and Reasoning in LLM Agents
arXiv:2603. 00026v2 Announce Type: replace-cross Abstract: Memory management is essential for LLM agents in long-term interactions.
arXiv AI
Jun 29
Agentic Episodic Control
arXiv:2506. 01442v2 Announce Type: replace Abstract: Reinforcement learning (RL) remains fundamentally limited by poor data efficiency and weak generalization.
Hugging Face Trending Papers
Aug 5
Hierarchical Graph Memory for LLM Agents with Path-level Localization and Rewrite
Agents for long term reasoning require a memory that can be efficiently and effectively updated over time, as new facts and external feedback continue to arrive. Recently, graph memory has been adopted to offer structural organization for multi-hop retrieval and reasoning.
arXiv AI
Jun 30
HMARS: A Hierarchical Multi-Agent Memory System for Long-Context Reasoning
arXiv:2606. 28349v1 Announce Type: cross Abstract: Long-context reasoning requires models to access, retrieve, and integrate evidence scattered across documents, dialogues, and accumulated interaction histories.
arXiv AI
Jul 21
RECON: Benchmarking Agent Memory for Compositional Reasoning over Long Contexts
arXiv:2607. 16716v1 Announce Type: new Abstract: Large language models and LLM-based agents are widely used as personal chat assistants, enterprise copilots, and autonomous workflow agents.