OracleResolve: Hypothesis-Guided Evidence Reasoning for Oracle Bone Script Decipherment

Oracle bone script (OBS) decipherment requires reasoning across heterogeneous evidence, including glyph morphology, inscriptional context, diachronic character forms, and philological scholarship. Existing computational approaches increasingly incorporate such evidence, yet fixed evidence pipelines and generic tool-routing agents do not explicitly couple competing interpretations with the selection of subsequent evidence. This limitation is particularly important in OBS decipherment, where multiple morphologically plausible readings may require different contextual, historical, or philological evidence to distinguish, and where the correct interpretation may be absent from the initial candidate set. We introduce OracleResolve, a hypothesis-guided research agent that maintains candidate interpretations as persistent hypotheses together with supporting evidence, contradicting evidence, scores, and unresolved questions. Morphology-based Glyph and Component Experts first construct the initial hypothesis set from the target glyph. During subsequent reasoning, OracleResolve identifies unresolved disagreements among the current hypotheses and selectively queries contextual, diachronic, or philological evidence. Retrieved evidence is explicitly evaluated to revise the hypothesis state and subsequent investigation; when the accumulated evidence cannot adequately support the current candidate set, an open-candidate retrieval module expands the hypothesis space with new interpretations. We evaluate OracleResolve on a class-ID-disjoint OBS decipherment setting with an additional stricter reading-unseen subset, while preventing evaluation source groups from entering the contextual retrieval corpus. Controlled comparisons against direct multimodal reasoning, a fixed evidence pipeline, and a generic tool-routing agent show that OracleResolve improves decipherment reliability, better recovers from incorrect initial preferences and incomplete hypothesis spaces, without increasing average evidence-tool usage. These results support hypothesis-guided evidence reasoning as a promising framework for computational ancient-script decipherment.