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arXiv · 2606.03301

SagaQA: A Multi-hop Reasoning Benchmark for Long-form Narrative Understanding in TV Series

Galann Pennec·Zhengyuan Liu·Nicholas Asher·Philippe Muller·Nancy F. Chen
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Abstract

We introduce SagaQA, a long-form video benchmark for multi-hop reasoning over full-length TV series. Existing video reasoning benchmarks often emphasize local understanding of adjacent frames or clips. SagaQA addresses this gap by requiring high-level comprehension of extended multimodal narratives in entire TV shows. A distinguishing feature of SagaQA is the granularity of its reasoning steps. Our dataset necessitates long-range reasoning hops to connect information across completely different episodes. This requires models to reason over entire events and actions, demanding a deep understanding of the show's narration and progression at a multimodal level. Motivated by recent progress in agentic methods, we further study how different planning strategies handle such complex reasoning. We categorize these approaches into three classes-Parallel, Sequential, and Hybrid planners-and evaluate their ability to generate coherent and complete reasoning plans. Our results on SagaQA suggest that hybrid planners consistently produce higher-quality plans and exhibit stronger capabilities for complex, high-level narrative understanding in TV shows.

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Galann Pennec, Zhengyuan Liu, Nicholas Asher, Philippe Muller, Nancy F. Chen. 2026-06-02. SagaQA: A Multi-hop Reasoning Benchmark for Long-form Narrative Understanding in TV Series. https://arxiv.org/abs/2606.03301

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