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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/2601.03261 |
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| _version_ | 1866912805849923584 |
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| author | Lu, Shuo Xu, Yinuo Cheng, Jianjie He, Lingxiao Wang, Meng Liang, Jian |
| author_facet | Lu, Shuo Xu, Yinuo Cheng, Jianjie He, Lingxiao Wang, Meng Liang, Jian |
| contents | Deep Research agents predominantly optimize search policies to maximize retrieval probability. However, we identify a critical bottleneck: the retrieval-utilization gap, where models fail to use gold evidence even after it is retrieved, due to context blindness in noisy environments. To bridge this gap, we propose DeepResearch-Slice, a simple yet effective neuro-symbolic framework. Unlike implicit attention, our approach predicts precise span indices to perform a deterministic hard filter before reasoning. Extensive evaluations across six benchmarks show substantial robustness gains. Applying our method to frozen backbones yields a 73 percent relative improvement, from 19.1 percent to 33.0 percent, effectively mitigating noise without requiring parameter updates to the reasoning model. These results highlight the need for explicit grounding mechanisms in open-ended research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_03261 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DeepResearch-Slice: Bridging the Retrieval-Utilization Gap via Explicit Text Slicing Lu, Shuo Xu, Yinuo Cheng, Jianjie He, Lingxiao Wang, Meng Liang, Jian Computation and Language Artificial Intelligence Deep Research agents predominantly optimize search policies to maximize retrieval probability. However, we identify a critical bottleneck: the retrieval-utilization gap, where models fail to use gold evidence even after it is retrieved, due to context blindness in noisy environments. To bridge this gap, we propose DeepResearch-Slice, a simple yet effective neuro-symbolic framework. Unlike implicit attention, our approach predicts precise span indices to perform a deterministic hard filter before reasoning. Extensive evaluations across six benchmarks show substantial robustness gains. Applying our method to frozen backbones yields a 73 percent relative improvement, from 19.1 percent to 33.0 percent, effectively mitigating noise without requiring parameter updates to the reasoning model. These results highlight the need for explicit grounding mechanisms in open-ended research. |
| title | DeepResearch-Slice: Bridging the Retrieval-Utilization Gap via Explicit Text Slicing |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2601.03261 |