# Reflective Dialogue between Teacher and Solver Agents for Video Question Answering Source: [Reflective Dialogue between Teacher and Solver Agents for Video Question Answering]() Collection index: [llms.txt]() Data updated: 2026-05-29T05:00:05.519Z Authors: Takuya Murakawa, Toru Tamaki Published: 2026-05-27T03:08:12.000Z ## Abstract Various approaches have been proposed to adapt Vision-Language Models (VLMs) to specialized domains for Video Question Answering, including fine-tuning and in-context learning. However, acquiring task-specific knowledge at the inference phase from only a small labeled support set without fine-tuning remains a challenge. In this paper, we propose a method that achieves adaptation solely through inference-time context injection. Our method first constructs a Reflective Dialogue (RD) -- a multi-turn conversation between two agents, in which Teacher poses each support question and delivers correctness feedback, and Solver answers and provides visual grounding explanations (or reflections) for both correct and incorrect answers. This dialogue history is then used as context at the inference phase. Experiments on the EgoCross benchmark demonstrate that our method outperforms both a baseline zero-shot setting and a standard in-context learning approach that passes support set examples directly, achieving 3rd place in the Open-source Track of the 1st Cross-Domain EgoCross Challenge at the CVPR 2026 EgoVis Workshop, for which this paper also serves as a technical report. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Video Question Answering on EgoCross (test)](): Video Question Answering; EgoCross (test); Animal Accuracy: 79.2. ## Original sources and implementation [Original paper]() [PDF]()