BEAR: Budgeted Evidence Allocation for Multi-Document Reasoning
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| Format: | Preprint |
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2026
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| _version_ | 1866917537139130368 |
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| author | Sun, Lin Zhang, Linglin Huang, Jingang Jia, Change Cheng, Zhengwei Zhang, Xiangzheng |
| author_facet | Sun, Lin Zhang, Linglin Huang, Jingang Jia, Change Cheng, Zhengwei Zhang, Xiangzheng |
| contents | We argue that multi-document reasoning is constrained not only by how much text a model can read, but also by how limited query-time evidence budget is allocated across documents and semantic granularities. Full-context inference exposes the model to broad evidence non-selectively and at high per-query cost, while flat chunk retrieval often returns locally relevant passages that are weakly organized for cross-document synthesis. We present \textbf{BEAR}, a framework for structured evidence allocation that builds hierarchical semantic indices offline and performs coarse-to-fine evidence access at query time through complementary \emph{exploration} and \emph{recovery} paths. This coarse-to-fine design can be viewed as structured evidence allocation under a fixed evidence-context budget. Across synthetic and real-world benchmarks, BEAR performs particularly strongly on DragonBall, remains competitive with strong retrieval-based baselines on HotpotQA, and yields the best retrieval-based result on 2Wiki under our evaluated protocol, while operating under substantially smaller \emph{query-time evidence budgets} than the reported long-context references. Additional analyses suggest that the gains are associated with hierarchy as an allocation substrate together with complementary exploration and recovery, rather than semantic chunking alone. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_18116 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | BEAR: Budgeted Evidence Allocation for Multi-Document Reasoning Sun, Lin Zhang, Linglin Huang, Jingang Jia, Change Cheng, Zhengwei Zhang, Xiangzheng Computation and Language We argue that multi-document reasoning is constrained not only by how much text a model can read, but also by how limited query-time evidence budget is allocated across documents and semantic granularities. Full-context inference exposes the model to broad evidence non-selectively and at high per-query cost, while flat chunk retrieval often returns locally relevant passages that are weakly organized for cross-document synthesis. We present \textbf{BEAR}, a framework for structured evidence allocation that builds hierarchical semantic indices offline and performs coarse-to-fine evidence access at query time through complementary \emph{exploration} and \emph{recovery} paths. This coarse-to-fine design can be viewed as structured evidence allocation under a fixed evidence-context budget. Across synthetic and real-world benchmarks, BEAR performs particularly strongly on DragonBall, remains competitive with strong retrieval-based baselines on HotpotQA, and yields the best retrieval-based result on 2Wiki under our evaluated protocol, while operating under substantially smaller \emph{query-time evidence budgets} than the reported long-context references. Additional analyses suggest that the gains are associated with hierarchy as an allocation substrate together with complementary exploration and recovery, rather than semantic chunking alone. |
| title | BEAR: Budgeted Evidence Allocation for Multi-Document Reasoning |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.18116 |