Memory-T1: Reinforcement Learning for Temporal Reasoning in Multi-session Agents
Temporal reasoning over long, multi-session dialogues is a critical capability for conversational agents. However, existing works and our pilot study have shown that as dialogue histories grow in length and accumulate noise, current long-context models struggle to accurately identify temporally pertinent information, significantly impairing reasoning performance. To address this, we introduce Memory-T1, a framework that learns a time-aware memory selection policy using reinforcement learning (RL). It employs a coarse-to-fine strategy, first pruning the dialogue history into a candidate set using temporal and relevance filters, followed by an RL agent that selects the precise evidence sessions. The RL training is guided by a multi-level reward function optimizing (i) answer accuracy, (ii) evidence grounding, and (iii) temporal consistency. In particular, the temporal consistency reward provides a dense signal by evaluating alignment with the query time scope at both the session-level (chronological proximity) and the utterance-level (chronological fidelity), enabling the agent to resolve subtle chronological ambiguities. On the Time-Dialog benchmark, Memory-T1 boosts a 7B model to an overall score of 67.0\%, establishing a new state-of-the-art performance for open-source models and outperforming a 14B baseline by 10.2\%. Ablation studies show temporal consistency and evidence grounding rewards jointly contribute to a 15.0\% performance gain. Moreover, Memory-T1 maintains robustness up to 128k tokens, where baseline models collapse, proving effectiveness against noise in extensive dialogue histories. The code and datasets are publicly available at https://github.com/Elvin-Yiming-Du/Memory-T1/
694b8232746a34b55dd53e36
Yiming Du
claimed_verified
2025-12-25T20:45:12.306Z
68954ff742413a5846d9d80a
/avatars/1751b607a0a65ba63400d34487f3b75c.svg
ElvinDu
ElvinDu518
user
ElvinDu518
694b8232746a34b55dd53e37
Baojun Wang
694b8232746a34b55dd53e38
Yifan Xiang
694b8232746a34b55dd53e39
Zhaowei Wang
claimed_verified
2025-12-25T20:45:14.880Z
62281c11236b7b2eefa7f198
https://cdn-avatars.huggingface.co/v1/production/uploads/62281c11236b7b2eefa7f198/O-LoLaDkIoWcP19mzkNgS.jpeg
Zhaowei Wang
ZhaoweiWang
user
ZhaoweiWang
694b8232746a34b55dd53e3a
Wenyu Huang
694b8232746a34b55dd53e3b
Boyang Xue
694b8232746a34b55dd53e3c
Bin Liang
694b8232746a34b55dd53e3d
Xingshan Zeng
694b8232746a34b55dd53e3e
Fei Mi
694b8232746a34b55dd53e3f
Haoli Bai
694b8232746a34b55dd53e40
Lifeng Shang
694b8232746a34b55dd53e41
Jeff Z. Pan
694b8232746a34b55dd53e42
Yuxin Jiang
claimed_verified
2025-12-29T14:24:46.391Z
63c20105726f62e411fbe882
https://cdn-avatars.huggingface.co/v1/production/uploads/63c20105726f62e411fbe882/2UsU9O2psbDjJzz-sAmGH.jpeg
Yuxin Jiang
YuxinJiang
user
YuxinJiang
694b8232746a34b55dd53e43
Kam-Fai Wong
Memory-T1: Reinforcement Learning for Temporal Reasoning in Multi-session Agents
Temporal reasoning over long, multi-session dialogues is a critical capability for conversational agents. However, existing works and our pilot study have shown that as dialogue histories grow in length and accumulate noise, current long-context models struggle to accurately identify temporally pertinent information, significantly impairing reasoning performance. To address this, we introduce Memory-T1, a framework that learns a time-aware memory selection policy using reinforcement learning (RL). It employs a coarse-to-fine strategy, first pruning the dialogue history into a candidate set using temporal and relevance filters, followed by an RL agent that selects the precise evidence sessions. The RL training is guided by a multi-level reward function optimizing (i) answer accuracy, (ii) evidence grounding, and (iii) temporal consistency. In particular, the temporal consistency reward provides a dense signal by evaluating alignment with the query time scope at both the session-level (chronological proximity) and the utterance-level (chronological fidelity), enabling the agent to resolve subtle chronological ambiguities. On the Time-Dialog benchmark, Memory-T1 boosts a 7B model to an overall score of 67.0\%, establishing a new state-of-the-art performance for open-source models and outperforming a 14B baseline by 10.2\%. Ablation studies show temporal consistency and evidence grounding rewards jointly contribute to a 15.0\% performance gain. Moreover, Memory-T1 maintains robustness up to 128k tokens, where baseline models collapse, proving effectiveness against noise in extensive dialogue histories. The code and datasets are publicly available at https://github.com/Elvin-Yiming-Du/Memory-T1/
Memory-T1, a framework using reinforcement learning, enhances temporal reasoning in long dialogues by selecting relevant sessions and ensuring temporal consistency, achieving state-of-the-art performance on the Time-Dialog benchmark.
reinforcement learning RL temporal reasoning dialogue history time-aware memory coarse-to-fine strategy temporal filters evidence grounding temporal consistency reward function session-level utterance-level chronological proximity chronological fidelity Time-Dialog benchmark performance gain robustness