Persistent Memory for Language Models

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48 papers in the last four weeks, up 200% on the four weeks before. 0.3% of all new papers.

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MemBuilder: Reinforcing LLMs for Long-Term Memory Construction via Attributed Dense Rewards
Jan 9, 2026Zhiyu Shen, Ziming Wu, Fuming Lai +2Reinforcement Learning Memory-Augmented Language Models
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China · Tencent Inc.
Beyond Static Summarization: Proactive Memory Extraction for LLM Agents
Jan 8, 2026Chengyuan Yang, Zequn Sun, Wei Wei +1LLM Agent Memory Persistent Memory for Language Models
State Key Laboratory for Novel Software Technology, Nanjing University, China · National Institute of Healthcare Data Science, Nanjing University, China
Continual Memorization of Factoids in Language Models
Nov 11, 2024Howard Chen, Jiayi Geng, Adithya Bhaskar +2Continual Learning for LLMs Memorization in Language Models
Princeton Language and Intelligence (PLI), Princeton University

Jan 9, 2026, cs.CLJ/K move · Enter open · S save

MemBuilder: Reinforcing LLMs for Long-Term Memory Construction via Attributed Dense Rewards

Zhiyu Shen, Ziming Wu, Fuming Lai, Shaobing Lian+1

School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China · Tencent Inc.

Reinforcement Learning Memory-Augmented Language Models Memorization in Language Models Persistent Memory for Language Models LLM Memory Long-Term Conversational Memory+1

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Maintaining consistency in long-term dialogues remains a fundamental challenge for LLMs, as standard retrieval mechanisms often fail to capture the temporal evolution of historical states. While memory-augmented frameworks offer a structured alternative, current systems rely on static prompting of closed-source models or suffer from ineffective training paradigms with sparse rewards. We introduce MemBuilder, a reinforcement learning framework that trains models to orchestrate multi-dimensional memory construction with attributed dense rewards. MemBuilder addresses two key challenges: (1) Sparse Trajectory-Level Rewards: we employ synthetic session-level question generation to provide dense intermediate rewards across extended trajectories; and (2) Multi-Dimensional Memory Attribution: we introduce contribution-aware gradient weighting that scales policy updates based on each component's downstream impact. Experimental results show that MemBuilder enables a 4B-parameter model to outperform state-of-the-art closed-source baselines, exhibiting strong generalization across long-term dialogue benchmarks.

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Latest papers 213

MemBuilder: Reinforcing LLMs for Long-Term Memory Construction via Attributed Dense Rewards

Beyond Static Summarization: Proactive Memory Extraction for LLM Agents

Continual Memorization of Factoids in Language Models