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| Autori principali: | , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2512.20092 |
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| _version_ | 1866908730273038336 |
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| author | Du, Yiming Wang, Baojun Xiang, Yifan Wang, Zhaowei Huang, Wenyu Xue, Boyang Liang, Bin Zeng, Xingshan Mi, Fei Bai, Haoli Shang, Lifeng Pan, Jeff Z. Jiang, Yuxin Wong, Kam-Fai |
| author_facet | Du, Yiming Wang, Baojun Xiang, Yifan Wang, Zhaowei Huang, Wenyu Xue, Boyang Liang, Bin Zeng, Xingshan Mi, Fei Bai, Haoli Shang, Lifeng Pan, Jeff Z. Jiang, Yuxin Wong, Kam-Fai |
| contents | 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/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_20092 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Memory-T1: Reinforcement Learning for Temporal Reasoning in Multi-session Agents Du, Yiming Wang, Baojun Xiang, Yifan Wang, Zhaowei Huang, Wenyu Xue, Boyang Liang, Bin Zeng, Xingshan Mi, Fei Bai, Haoli Shang, Lifeng Pan, Jeff Z. Jiang, Yuxin Wong, Kam-Fai Computation and Language 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/ |
| title | Memory-T1: Reinforcement Learning for Temporal Reasoning in Multi-session Agents |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2512.20092 |