Task-Specific Multimodal Question Answering Agents via Confidence Calibration and Incremental Reasoning for QANTA 2026

Quizbowl-style question answering, where answers must be given as clues are incrementally revealed, tests both confidence calibration and reasoning under uncertainty. This paper describes a submission using two specialized agents: one deciding when to answer tossup questions using confidence calibration and numeric reasoning safeguards, and another handling bonus questions with structured, multimodal reasoning. Achieving the top leaderboard score without retrieval pipelines or ensembles, the system shows lightweight task-specific strategies can be highly effective. Applications include efficient, resource-constrained multimodal QA systems for trivia, education, and other domains requiring calibrated confidence under partial information. Authors: Nirjhar Das, Md. Al-Mamun Provath Paper: https://arxiv.org/abs/2607.09623v1