他们用GPT-4.1-mini和GPT-4.1做两个不同任务，靠置信度校准和数值推理拿了QANTA 2026最高分0.402，不是靠堆模型而是靠策略。
本文提出参加QANTA 2026挑战（ICML 2026 EMM-QA研讨会）的双智能体多模态问答系统。Tossup任务使用GPT-4.1-mini模型，通过置信度校准和领域特定数值推理策略减少过度自信。Bonus任务使用GPT-4.1模型，结合导引感知推理和结构化关系推理。系统在整体排行榜上取得最高分0.402，其中Tossup得分0.238，Bonus效果得分0.164。结果表明轻量级任务特定推理策略在资源受限的多模态问答基准上表现强劲。
Task-Specific Multimodal Question Answering Agents via Confidence Calibration and Incremental Reasoning for QANTA 2026
We present our submission to the QANTA 2026 shared challenge at the ICML 2026 Workshop on Efficient Multimodal Question Answering (EMM-QA). Quanta evaluates multimodal quizbowl systems that answer pyramid-style questions from incrementally revealed text and accompanying images while operating under realistic efficiency constraints. The challenge consists of two distinct tasks: Tossup questions, which require deciding when to answer under uncertainty, and Bonus questions, which emphasize accurate answer selection and human adoption. To address these differing objectives, we develop a task-specific two-agent architecture. Our Tossup agent utilizes a GPT-4o-mini-class model (referred to as GPT-4.1-mini in the competition logs) with confidence-calibrated answering and a domain-specific numeric reasoning policy that reduces overconfident predictions from isolated quantitative clues. Our Bonus agent uses GPT-4o-class model (referred to as GPT-4.1) with leadin-aware reasoning, structured relational reasoning, and multimodal evidence integration to improve exact answer selection. Rather than relying on a retrieval pipeline or model ensembles, our approach emphasizes efficient reasoning policies and confidence calibration within a hosted-only environment. Our system achieved the highest overall leaderboard score of 0.402, including a Tossup score of 0.238 and a Bonus Effect score of 0.164. The results demonstrate that lightweight, task-specific reasoning strategies can provide strong performance on resource-constrained multimodal question answering benchmarks.