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arxiv: 2606.03301 · v1 · pith:AFEAQXBCnew · submitted 2026-06-02 · 💻 cs.CL · cs.CV

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 This is my paper

Pith reviewed 2026-06-28 10:13 UTC · model grok-4.3

classification 💻 cs.CL cs.CV
keywords SagaQAmulti-hop reasoninglong-form videoTV seriesnarrative understandingplanning strategieshybrid planners
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The pith

Hybrid planners generate higher-quality reasoning plans than parallel or sequential ones for multi-hop questions spanning full TV series.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

SagaQA introduces a benchmark that tests models on multi-hop reasoning requiring connections across completely different episodes in TV shows rather than adjacent clips. The work evaluates three classes of planning strategies for producing coherent reasoning plans on these long-range narrative tasks. Results show hybrid planners consistently yield better plans and stronger high-level narrative understanding. This setup fills a gap left by benchmarks limited to short video segments. A sympathetic reader would care because current video models often fail when forced to track plot threads over many episodes.

Core claim

SagaQA is a long-form video benchmark for multi-hop reasoning over full-length TV series that necessitates long-range reasoning hops to connect information across completely different episodes. When evaluating planning strategies categorized into Parallel, Sequential, and Hybrid planners, hybrid planners consistently produce higher-quality plans and exhibit stronger capabilities for complex, high-level narrative understanding in TV shows.

What carries the argument

The SagaQA dataset, which demands long-range hops across episodes, together with the three planner classes (Parallel, Sequential, Hybrid) that generate reasoning plans for the questions.

If this is right

  • Hybrid planners produce higher-quality plans than parallel or sequential planners on SagaQA tasks.
  • Hybrid planners exhibit stronger capabilities for complex high-level narrative understanding in TV shows.
  • Agentic methods using hybrid planning improve handling of extended multimodal narratives.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Hybrid planning may also help on other long-context tasks such as book or podcast reasoning where information is spread over distant sections.
  • Benchmark designers could add controls that explicitly block single-episode shortcuts to strengthen claims about required hop distance.
  • Models trained with hybrid planners might generalize better to full-season plot tracking than those using only sequential or parallel strategies.

Load-bearing premise

The questions in SagaQA genuinely require long-range multi-hop reasoning across different episodes and cannot be solved with local information from single episodes or dataset shortcuts.

What would settle it

Showing that a large fraction of SagaQA questions can be answered correctly using only information contained in one episode or a short contiguous segment.

Figures

Figures reproduced from arXiv: 2606.03301 by Galann Pennec, Nancy F. Chen, Nicholas Asher, Philippe Muller, Zhengyuan Liu.

Figure 1
Figure 1. Figure 1: Our data generation pipeline for SAGAQA. 1) Given a list of 20 consecutive episodes E20 and their corresponding annotated summaries from SummScreen3D, we prompt an LLM to generate synthetic QA pairs that satisfy three key criteria: multi-episode, multi-hop and multi-modal. In the example shown, the generated question requires four reasoning hops, each being logically connected to the previous one in the re… view at source ↗
Figure 2
Figure 2. Figure 2: Statistics of SAGAQA. We provide the distri￾bution of the maximum distance between two episodes involved in the multi-hop reasoning sequence as well as the distribution of the number of episodes involved in answering per question for all the questions in our dataset. 3.2 Dataset Statistics On average, each question involves approxi￾mately 4.2 episodes and rarely exceeds 7 episodes, reflecting the high numb… view at source ↗
Figure 3
Figure 3. Figure 3: Existing strategies for video planning. From left to right: 1) the Parallel Planner splits a composite question into a set of independent subquestions; 2) the Sequential Planner generates subquestions one at a time, progressively working toward an answer; 3) the Hybrid Planner combines the two previous strategies by iteratively producing independent requests to the tools while eventually constructing an an… view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces SagaQA, a benchmark dataset for multi-hop reasoning over full-length TV series that requires connecting information across different episodes. It categorizes planning approaches for agentic reasoning into Parallel, Sequential, and Hybrid planners, evaluates their performance on SagaQA, and concludes that hybrid planners generate higher-quality plans and demonstrate stronger capabilities for complex narrative understanding.

Significance. If the benchmark questions are shown to require genuine long-range cross-episode reasoning without shortcuts, SagaQA could fill an important gap in existing video reasoning benchmarks that focus on local clips. The empirical comparison of planner types could provide useful guidance for designing agents for long-form multimodal narratives, though the current lack of validation details limits immediate impact.

major comments (2)
  1. [Abstract] Abstract: The central claim that SagaQA 'necessitates long-range reasoning hops to connect information across completely different episodes' and requires 'deep understanding of the show's narration' is presented without any described validation (e.g., human performance with single-episode context, adversarial filtering, or ablation on episode span). This assumption is load-bearing for interpreting the hybrid-planner superiority result.
  2. [Abstract / Results] The manuscript provides no quantitative results, model implementation details, or dataset statistics in the abstract, and the full text does not appear to include human baselines or artifact checks that would confirm the questions cannot be solved locally. This undermines the ability to assess whether performance differences reflect reasoning depth.
minor comments (1)
  1. The categorization of planners into three classes is introduced without clear definitions or pseudocode for each strategy, making it difficult to reproduce the experimental setup.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on SagaQA. The comments correctly identify areas where additional validation and presentation details would strengthen the manuscript. We address each major comment below and commit to revisions that directly respond to the concerns raised.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that SagaQA 'necessitates long-range reasoning hops to connect information across completely different episodes' and requires 'deep understanding of the show's narration' is presented without any described validation (e.g., human performance with single-episode context, adversarial filtering, or ablation on episode span). This assumption is load-bearing for interpreting the hybrid-planner superiority result.

    Authors: We agree that the abstract states the long-range requirement without accompanying validation metrics. The full manuscript describes the annotation protocol in which human annotators were explicitly instructed to generate questions whose answers require information spanning multiple distinct episodes, with a verification step to exclude questions solvable from any single episode. However, we did not report quantitative human performance under single-episode context or ablation studies on episode span. We will add a new validation subsection that includes (1) human accuracy when restricted to single-episode context, (2) results after adversarial filtering for local shortcuts, and (3) an ablation varying the number of episodes provided. These additions will directly support the central claim and allow readers to assess the hybrid-planner results in context. revision: yes

  2. Referee: [Abstract / Results] The manuscript provides no quantitative results, model implementation details, or dataset statistics in the abstract, and the full text does not appear to include human baselines or artifact checks that would confirm the questions cannot be solved locally. This undermines the ability to assess whether performance differences reflect reasoning depth.

    Authors: We acknowledge that the abstract currently contains no numbers and that the submitted version lacks explicit human baselines or local-solvability checks. We will revise the abstract to report key dataset statistics (number of questions, average reasoning hops, distribution of episode spans) together with the main performance deltas among planner types. In parallel, the new validation subsection mentioned above will incorporate human baselines under both full-series and single-episode conditions, plus artifact checks (e.g., model performance when given only local context). These changes will make it possible to evaluate whether observed differences indeed reflect long-range reasoning capabilities. revision: yes

Circularity Check

0 steps flagged

No significant circularity; benchmark introduction with empirical evaluation only.

full rationale

The paper introduces SagaQA as a new benchmark and reports empirical comparisons of planner types (Parallel, Sequential, Hybrid) on it. No equations, derivations, fitted parameters, or self-citation chains appear in the provided text. Claims rest on experimental results rather than any reduction of outputs to inputs by construction. The central assumption about question difficulty is a validity concern, not a circularity issue per the defined patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are introduced or relied upon; the paper is an empirical benchmark creation effort.

pith-pipeline@v0.9.1-grok · 5701 in / 951 out tokens · 24893 ms · 2026-06-28T10:13:04.767492+00:00 · methodology

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Reference graph

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    lead to danger for her children, (Hop 5) and what was her decision about the business, given her past with an alcoholic father and the observable proof of the incident? Episodes to Answer E ∗: 9 th, 20th, February and 4th March 2009 AnswerA: (Hop 1) Carly, initially excited about her Mid- night Sun vodka venture with Craig, (Hop

    2009

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    (Hop 3) Her children, Parker and Liberty, mistook these for soda, drank them, and became drunk

    found herself in a dangerous situation when Craig brought unlabeled product sam- ples to her home. (Hop 3) Her children, Parker and Liberty, mistook these for soda, drank them, and became drunk. (Hop 4) This led to them ditching school and leaving a pizza in the oven, causing a fire and leaving her kitchen with blackened appliances and counters. (Hop 5) U...

    2009

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    Multimodal Multi-Episode Multi-Hop Question around character <CHARAC- TER> involving multiple visual elements or actions (Do not provide the answer within the question, Keep your question clear and simple):

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    Compact Question (Write the same ques- tion in about 30 words):

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    Justification why the question is Multi- Hop. For each hop, explain how it relates to the previous hops and which reasoning type is involved (Bridge Entities (Com- positional), Comparison & Intersection, Causal & Counterfactual, Temporal Rea- soning, Multi-Step Quantitative Reason- ing): (a) First Hop: (b) Second Hop (Justify how it relies on previous hop...

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    Justification why the question is Multi- modal

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    Justification why the question is Multi- Episode:

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    Example 6 January 2003, ...:

    List of Episode involved (separated by comma). Example 6 January 2003, ...:

    2003

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    Answer to the above question in 200 words maximum:

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    List of Characters involved in the ques- tion (separated by comma): B.1.3 Example Output

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    Midnight Sun

    Multimodal Multi-Episode Multi-Hop Question: Considering Carly’s initial excitement about the "Midnight Sun" vodka venture with Craig, how did the visual presence of the product samples at her home lead to a dangerous situation for her children, and what was her subse- quent emotional realization and decision regarding the business, particularly in light ...

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    Midnight Sun

    Compact Question: How did "Midnight Sun" vodka samples at Carly’s home lead to danger for her children, and what was her decision about the business, given her past with an alcoholic father and the visual evidence of the incident?

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    Mid- night Sun

    Justification why the question is Multi- Hop: (a) First Hop (Bridge Entities): Identify Carly’s involvement in the "Mid- night Sun" vodka business with Craig. This establishes the core en- tity and her initial engagement. (b) Second Hop (Causal & Counterfac- tual): Connect the vodka samples (a specific product of the business) be- ing present at her home ...

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    visual presence of the product samples

    Justification why the question is Multi- modal: The question explicitly refers to "visual presence of the product samples" (the vodka cans/bottles) and "blackened kitchen appliances and counters" as ev- idence of the incident and its impact on Carly. The act of "drinking" is also an action that would be visually depicted

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    Mid- night Sun

    Justification why the question is Multi- Episode: The events described span mul- tiple episodes: the inception of the "Mid- night Sun" vodka business (09 February), the children consuming the samples and Carly’s initial realization (20 February), and the subsequent fire and Carly’s re- newed emotional distress upon seeing the damaged kitchen (04 March)

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    List of Episode involved: 09 February 2009, 20 February 2009, 04 March 2009

    2009

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    Midnight Sun

    Answer to the above question in 200 words maximum: Carly, initially excited about her "Midnight Sun" vodka venture with Craig, found herself in a dangerous situation when Craig brought unlabeled product samples to her home. Her chil- dren, Parker and Liberty, mistook these for soda, drank them, and became drunk. This led to them ditching school and leav- ...

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    The prompt is given to Qwen3-30B- thinking

    List of Characters involved in the ques- tion: Carly Tenney, Craig Montgomery, Parker Snyder, Liberty Snyder, Johnny Donovan, Jack Snyder, Janet Snyder B.2 Multi-Hop Filtering We provide the prompt below to filter our generated QA pairs, keeping only the questions that are truly multi-hop. The prompt is given to Qwen3-30B- thinking. • <QUESTION> is the qu...

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    (b) The Hops can be part of the same narrative You can compare the pros and cons and pro- vide your final justification and answer

    Requirements (a) Inference required: Answers cannot be copied verbatim; they demand logical connection. (b) The Hops can be part of the same narrative You can compare the pros and cons and pro- vide your final justification and answer. Question: <QUESTION> B.3 Video Planners Prompts We provide the prompts below for the video plan- ners we tested on SAGAQA...

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    Group the keywords together

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    This title is later used as a query to the Vide- oRAG

    d)Subquestion Generation for each Group We generate a title for each group of keywords. This title is later used as a query to the Vide- oRAG. Propose a title for each group in a few words. Always include the group keywords into the title. Always refer to the name of the characters in your title. Group List: ** Group 1 Title: ... ** ** Group 2 Title: ... ...

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    Queries should be very short and clear

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    Rank your queries from easier to harder Output Format Do not include answers, summaries, or explanations — only the planned queries. Your output must be in the same format as in the examples below Example: Question: What visual actions directly resulted in the definitive exposure of Meg’s deception about taking her medication, and who was the primary witn...

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    I need to break down the last query into subqueries as the last query is too com- plex)

    BREAK DOWN the last query (e.g. I need to break down the last query into subqueries as the last query is too com- plex)

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    the answer to the last query is satisfying and we can move on to the next query or we decide to move on after too many un- successful attempts to answer the last query)

    MOVE_ON to the next query (e.g. the answer to the last query is satisfying and we can move on to the next query or we decide to move on after too many un- successful attempts to answer the last query). Justify which action you want to perform. Given your action, what is the next query. Produce the next query only. Querying Rules

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    Altogether, the queries should eventually help you to build your answer to Query 1

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    The Queries should be very short and clear

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    Answer in the following way: Justification:

    Multiple BREAK DOWNS can be per- formed as long as they are justified. Answer in the following way: Justification: ... Action performed: ... Query i: ... B.3.4 Hybrid Planner The hybrid planner is a combination of both the parallel and sequential planners. We leverage the prompts of both methods in the implementation of the hybrid approach. At any given s...

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