SOURCE-LINKED INTELLIGENCE

EVAR: Evidence-Validated Hypothesis Admission for Budget-Aware Narrative Reasoning

arXiv · AI, language, vision and robotics · article · Aug 30, 2026 · UTC

Large language models (LLMs) often produce fluent but weakly grounded conclusions when reasoning over non-interactive, long-form narratives. A central failure mode is that unsupported intermediate hypotheses can enter the reasoning trajectory and contaminate subsequent inference, especially when evidence is scattered across distant parts of the story. To address this problem, we propose EVAR, an evidence-validated hypothesis admission framework for budget-aware narrative reasoning. EVAR first compiles the narrative into an immutable evidence store of source-linked atomic claims and assigns an

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