Hugging Face Trending Papers
All-in-One Memory: Process-Rewarded Reinforcement Learning for Memory Agents
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arXiv AI
6d ago
All-in-One Memory: Process-Rewarded Reinforcement Learning for Memory Agents
All-in-One Memory (AiO-Mem) is a reinforcement learning framework that unifies memory retrieval, writing, and stopping decisions into a single reward space. It compiles diverse histories into traceable multi‑view events, uses a unified policy actor to generate interaction trajectories, and employs a trajectory‑conditioned process reward model trained with supervised fine‑tuning and DPO. Experiments on long‑context memory benchmarks show AiO‑Mem consistently improves answer accuracy compared to static memory systems and other RL‑trained agents.
arXiv AI
Sep 2
Learning to Remember: End-to-End Training of Memory Agents for Long-Context Reasoning
The paper introduces the Unified Memory Agent (UMA), a system that builds a query‑agnostic external memory from a data stream and reuses it across multiple question‑answering sessions. UMA employs a single policy to manage a structured Memory Bank via CRUD operations and uses Task‑Stratified GRPO to supervise memory maintenance based on QA trajectory rewards. The authors also present Ledger‑QA, a benchmark for long‑horizon state tracking, and demonstrate that UMA outperforms other methods on test‑time learning and accurate‑retrieval tasks, with UMA‑Specialist further improving performance after task adaptation.
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.
arXiv AI
Aug 12
MESA:Task-Adaptive Multi-Structure Evidence Selection for Long-Horizon Agent Memory
arXiv:2608. 10108v1 Announce Type: new Abstract: Long-horizon agents accumulate trajectories spanning hundreds of interleaved reasoning, action, and observation steps, where answering a query may depend on evidence buried far back in the history.
arXiv AI
Jun 8
AdMem: Advanced Memory for Task-solving Agents
arXiv:2606. 06787v1 Announce Type: new Abstract: Large Language Models (LLMs) show promise as tool-using agents but remain limited in long-horizon tasks that require remembering, organizing, and reusing knowledge.
arXiv AI
Sep 30
Learning to Retrieve Missing Evidence for Long-Term Memory QA
arXiv:2609.37443v1 Announce Type: cross Abstract: Long-term memory enables language models to use past interactions in future conversations. However, evidence needed to answer a question may be scatt...