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Ego2Web: A Web Agent Benchmark Grounded in Egocentric Videos

CVPR 2026 · Multi-agent systems and games › LLM agents · approach: GUI agent memory and trajectories · 0 citations (OpenAlex)

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Introduces Ego2Web, a 500-task benchmark pairing egocentric videos with live web tasks, plus Ego2WebJudge, an MLLM-as-judge that grounds web-agent success in video evidence.

Problem

Existing web-agent benchmarks ignore the user's physical surroundings, so they cannot evaluate agents that must perceive egocentric video and then act on the web.

Approach

Synthesize video-grounded web tasks from Qwen3-VL captions via an LLM planner, with human verification, and evaluate agents on live websites with an LLM judge that additionally uses annotated egocentric keyframes/clips and keypoints (extending WebJudge).

Key results

Surprising findings

Limitations

Experimental setup

Datasets: Ego4D EgoSchema clips

Benchmarks: Ego2Web

Metrics: success rate agreement rate with human evaluation

Baselines: SeeAct Browser Use with GPT-4.1 Browser Use with Gemini-3-Flash Claude Sonnet 3.7 Computer Use Claude Sonnet 4.5 Computer Use GPT-5.4 WebVoyager WebJudge

Builds on: WebJudge WebVoyager Ego4D EgoSchema

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