Disagreement-Based Cross-Model Routing for Implicit Video Question Answering
- model Claude Opus 4.8
- model Gemini 3.1 Pro Preview
- person Durga Sandeep Saluru
Researchers have introduced a new method, disagreement-based cross-model routing, that improves accuracy in implicit video question answering by routing difficult questions to a second model, achieving a +1.43 average accuracy gain[1].
The method, which requires no labels and no training, is particularly effective in categories dependent on cross-shot reference resolution, such as Motion & Trajectory (+5.49), Inferred Counting (+3.45), and Vertical Spatial Reasoning (+1.82)[1]. Meanwhile, a new benchmark, TopBench, has been proposed to assess Large Language Models (LLMs) in tabular question answering with implicit prediction tasks. TopBench consists of 779 samples across four sub-tasks and highlights the struggle of current models with intent recognition, often defaulting to simple lookups[2]. Accurate intent disambiguation is a prerequisite for leading predictive behaviors, according to the researchers behind TopBench[2]. The disagreement-based cross-model routing method was submitted on 31 May 2026, while TopBench was first submitted on 30 Apr 2026 and updated on 17 Jun 2026[1][2].
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Background sources we checked (7)
- arxiv.org ↗ We study multiple-choice video question answering on the ImplicitQA benchmark, where the correct answer is never explicitly shown but must be inferred from off-screen events, line-of-sight cues, causal structure, and cross-shot spatial layout. On this benchmark a single frontier …
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