Neuro-Symbolic Reasoning Framework for Long-Document Understanding

Multimodal long-document understanding remains challenging because relevant evidence is sparsely distributed across lengthy and redundant documents. Existing retrieval-based methods optimize page-level relevance only, yet individually relevant pages may still fail to collectively provide all the evidence required by a question. Moreover, cross-page reasoning is often generated without explicit verification, allowing unsupported steps and logical gaps to remain hidden under incomplete evidence. We therefore formulate long-document understanding as a neuro-symbolic reasoning process that couples neural evidence discovery with symbolic verification. Neural models derive question-specific evidence requirements, localize complementary evidence across pages, and construct a structured reasoning hypothesis. Symbolic constraints evaluate evidence coverage, validate intermediate reasoning, and enforce answer conditions. Failed checks trigger a bounded repair loop to retrieve missing evidence and revise the reasoning hypothesis, while unresolved cases lead to abstention. Experiments on three multimodal long-document benchmarks demonstrate state-of-the-art performance, improved evidence localization and token efficiency, and interpretable evidence navigation with auditable verification traces.