# Long-Video Question Answering

The work introduces PlaylistEval, an agentic framework that builds video-language judge benchmarks without human annotation by automatically generating questions whose evidence is scattered across two distant segments of ~100-hour playlists and whose wrong answers differ only visually, yielding PlaylistBench with 630 pairs across seven domains; on a stratified subset of 152 pairs it agrees with human judgments 93.0% of the time (IAA 0.781), while evaluating 17 omnimodal and multimodal models from eight families shows frontier judges reach only 75.4% pairwise accuracy, open-source judge models perform far behind, retrieval improves accuracy by up to 10.5 points yet the best retriever finds both relevant segments in the top 10 only 37.

The work introduces PlaylistEval, an agentic framework that builds video-language judge benchmarks without human annotation by automatically generating questions whose evidence is scattered across two distant segments of ~100-hour playlists and whose wrong answers differ only visually, yielding PlaylistBench with 630 pairs across seven domains; on a stratified subset of 152 pairs it agrees with human judgments 93.0% of the time (IAA 0.781), while evaluating 17 omnimodal and multimodal models from eight families shows frontier judges reach only 75.4% pairwise accuracy, open-source judge models perform far behind, retrieval improves accuracy by up to 10.5 points yet the best retriever finds both relevant segments in the top 10 only 37.

The work introduces Grounded Entity Biographies (GEB), a long-video memory framework that groups visually grounded observations of the same physical instance across clips into retrievable biographies and retrieves a biography alongside episodic evidence at question time; across four benchmarks including day-long and week-long recordings it improves both multiple-choice and open-ended question answering over prior memory frameworks, reaching 72.0% accuracy on EgoLifeQA, 4.4 percentage points above the best published result, with ablations showing that grounded identity association and biography reading each contribute and that additional descriptions alone do not fully recover the gains.

The work introduces Grounded Entity Biographies (GEB), a long-video memory framework that groups visually grounded observations of the same physical instance across clips into retrievable biographies and retrieves a biography alongside episodic evidence at question time; across four benchmarks including day-long and week-long recordings it improves both multiple-choice and open-ended question answering over prior memory frameworks, reaching 72.0% accuracy on EgoLifeQA, 4.4 percentage points above the best published result, with ablations showing that grounded identity association and biography reading each contribute and that additional descriptions alone do not fully recover the gains.