Task-Specific Multimodal Question Answering Agents for QANTA 2026 via Confidence Calibration and Incremental Reasoning
Score
77论文提供了任务特定双代理架构在竞赛场景下的完整设计经验，方法细节丰富，对类似多模态问答问题有较高信息增量。
论文结构清晰，方法描述具体，实验结果量化充分，并公开了排行榜最高分，可信度强。
提出的轻量级推理策略可在资源受限环境中直接应用，对工业界部署高效多模态系统有明确参考价值。
论文提交于2026年内容，涉及最新多模态问答挑战，研究内容紧跟前沿。
来源为arXiv预印本，作者为竞赛参赛者，实验结果可验证，信源可信度较高。
Base 85 × T2 Source weight ×0.9 = 77
Curation line 70Selected
Why selected
The paper achieved the highest score in the QANTA 2026 challenge, with a clean and effective method supported by solid experimental evidence.
AI commentary
「Although this paper focuses on a single competition, its two-agent architecture is clever: Tossup and Bonus tackle different optimization objectives, and it achieves first place without retrieval or ensembles, relying solely on reasoning strategies and confidence calibration. It offers valuable insights for those working on multimodal QA, agents, or resource-constrained AI systems. I particularly appreciate the dedicated numeric reasoning design to prevent overconfidence, a detail often overlooked in real products.」
Quick overview
This paper presents a submission to the QANTA 2026 shared challenge at the ICML 2026 Workshop on Efficient Multimodal Question Answering. Quanta evaluates multimodal quizbowl systems that answer pyramid-style questions from incrementally revealed text and images under realistic efficiency constraints. The challenge involves two tasks: Tossup (deciding when to answer under uncertainty) and Bonus (accurate answer selection and human adoption). The authors develop a task-specific two-agent architecture: the Tossup agent uses a GPT-4o-mini-class model with confidence-calibrated answering and domain-specific numeric reasoning to reduce overconfident predictions; the Bonus agent uses a GPT-4o-class model with leadin-aware reasoning, structured relational reasoning, and multimodal evidence integration for precise answer selection. Without retrieval pipelines or model ensembles, the system achieves the highest overall leaderboard score of 0.402 (Tossup 0.238, Bonus Effect 0.164), demonstrating that lightweight, task-specific reasoning strategies can deliver strong performance on resource-constrained multimodal QA benchmarks.
- Introduces a task-specific two-agent architecture for Tossup (deciding when to answer under uncertainty) and Bonus (accurate answer selection).
- Tossup agent uses confidence calibration and domain-specific numeric reasoning to reduce overconfident predictions from isolated numeric clues.
- Bonus agent integrates leadin-aware reasoning, structured relational reasoning, and multimodal evidence integration.
- Without retrieval or model ensembles, the system achieves the highest leaderboard score of 0.402 in a hosted-only environment.
Full text
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