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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.01620 |
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| _version_ | 1866911190260645888 |
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| author | Liu, Peidong Lin, Junjiang Wang, Shaowen Xu, Yao Li, Haiqing Xie, Xuhao Wu, Siyi Li, Hao |
| author_facet | Liu, Peidong Lin, Junjiang Wang, Shaowen Xu, Yao Li, Haiqing Xie, Xuhao Wu, Siyi Li, Hao |
| contents | Contextual Markov Decision Processes (CMDPs) offer a framework for sequential decision-making under external signals, but existing methods often fail to generalize in high-dimensional or unstructured contexts, resulting in excessive computation and unstable performance. We propose an information-theoretic summarization approach that uses large language models (LLMs) to compress contextual inputs into low-dimensional, semantically rich summaries. These summaries augment states by preserving decision-critical cues while reducing redundancy. Building on the notion of approximate context sufficiency, we provide, to our knowledge, the first regret bounds and a latency-entropy trade-off characterization for CMDPs. Our analysis clarifies how informativeness impacts computational cost. Experiments across discrete, continuous, visual, and recommendation benchmarks show that our method outperforms raw-context and non-context baselines, improving reward, success rate, and sample efficiency, while reducing latency and memory usage. These findings demonstrate that LLM-based summarization offers a scalable and interpretable solution for efficient decision-making in context-rich, resource-constrained environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_01620 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning to Decide with Just Enough: Information-Theoretic Context Summarization for CMDPs Liu, Peidong Lin, Junjiang Wang, Shaowen Xu, Yao Li, Haiqing Xie, Xuhao Wu, Siyi Li, Hao Artificial Intelligence Contextual Markov Decision Processes (CMDPs) offer a framework for sequential decision-making under external signals, but existing methods often fail to generalize in high-dimensional or unstructured contexts, resulting in excessive computation and unstable performance. We propose an information-theoretic summarization approach that uses large language models (LLMs) to compress contextual inputs into low-dimensional, semantically rich summaries. These summaries augment states by preserving decision-critical cues while reducing redundancy. Building on the notion of approximate context sufficiency, we provide, to our knowledge, the first regret bounds and a latency-entropy trade-off characterization for CMDPs. Our analysis clarifies how informativeness impacts computational cost. Experiments across discrete, continuous, visual, and recommendation benchmarks show that our method outperforms raw-context and non-context baselines, improving reward, success rate, and sample efficiency, while reducing latency and memory usage. These findings demonstrate that LLM-based summarization offers a scalable and interpretable solution for efficient decision-making in context-rich, resource-constrained environments. |
| title | Learning to Decide with Just Enough: Information-Theoretic Context Summarization for CMDPs |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2510.01620 |