# Evidence-Backed Video Question Answering

Source: [arXiv](https://arxiv.org/abs/2607.11862v1)  
Feed7 permalink: https://feed7.dev/p/2607-11862v1-18as4nc  
Published: Unknown  
Trust: Needs Review (needs_review)

## Why Included

E-VQA requires video answers to include tracked pixel-level evidence, revealing when good QA scores hide weak perception and supplying grounded training data.

## Source Summary

**E-VQA** requires a semantic answer plus temporal segments and dense tracked segmentation masks. Its human-verified ST-Evidence benchmark finds that QA accuracy can diverge from visual grounding, a gap the authors say scaling alone does not close.

## Practical Implication

For video agents, evaluate whether the cited object persists through motion, occlusion, and deformation rather than scoring text alone. The accompanying **160k-scale ST-Evidence-Instruct** dataset provides training examples that tie reasoning to visible evidence.

## Agent-Ready Context

**E-VQA** requires a semantic answer plus temporal segments and dense tracked segmentation masks. Its human-verified ST-Evidence benchmark finds that QA accuracy can diverge from visual grounding, a gap the authors say scaling alone does not close.

For video agents, evaluate whether the cited object persists through motion, occlusion, and deformation rather than scoring text alone. The accompanying **160k-scale ST-Evidence-Instruct** dataset provides training examples that tie reasoning to visible evidence.

On a **7B model**, fine-tuning beats a size-matched UniPixel baseline by **+27.2 t-mean and +13.8 J&F**. Those gains are specific to the reported setup and grounding metrics; the material does not establish equivalent gains for downstream video-agent tasks.

## Context Map

- Layer: benchmark
- Domains: video
- Topics: generative-media, agent-evals, benchmark-integrity

## Uncertainty

- On a **7B model**, fine-tuning beats a size-matched UniPixel baseline by **+27.2 t-mean and +13.8 J&F**. Those gains are specific to the reported setup and grounding metrics; the material does not establish equivalent gains for downstream video-agent tasks.

## Agent Instruction

Use this item as source-backed context. Do not invent claims beyond the linked source. If this item conflicts with another source, call out the conflict.