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arXiv Computation and Language By Michael Theologitis, Dean Light, Shuyue Stella Li, Benjamin Newman, Yulia Tsvetkov, Dan Suciu

Realize What Matters: Principled Context Representation for Large-Scale Reasoning

Read the original on arXiv Computation and Language →

The paper introduces R3Con, a framework that applies principled design rules—derived from cognitive relevance theory—to construct effective context representations for large-scale reasoning tasks. By systematically organizing vast, heterogeneous information into manageable structures, R3Con outperforms nine state‑of‑the‑art baselines on two large‑document reasoning benchmarks, achieving up to 20 percentage points higher accuracy. Notably, R3Con enables smaller models (4B–9B) to surpass larger 35B models and even Claude Code, demonstrating that principled context representation can reduce dependence on model scale.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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