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Fostering Video Reasoning via Next-Event Prediction

T0 review · 3 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper claims that training video-language models to predict unseen future events, using automatically generated captions as supervision, improves their temporal reasoning without degrading general video understanding.

desk verdict Useful new task and dataset, but the headline causal claim rests on a confounded comparison and a partially circular benchmark. read the letter →

arxiv 2505.22457 v1 pith:ZRT7EDYY submitted 2025-05-28 cs.CV cs.AIcs.CL

Haonan Wang , Hongfu Liu , Xiangyan Liu , Chao Du , Kenji Kawaguchi , Ye Wang , Tianyu Pang This is my paper
classification cs.CVcs.AIcs.CL
keywords next-eventpredictiontemporalreasoningmultimodallargelanguagemodelsvideoinstructiontuningself-supervisedlearningfutureeventunderstandingBench
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper proposes next-event prediction (NEP) as a self-supervised training task for multimodal large language models: split a video at a chosen moment, give the model only the earlier frames, and train it to write a text summary of what happens in the later frames. The authors argue that this task forces the model to combine what it sees with commonsense knowledge about causes, effects, and human behavior, rather than merely describing visible content. To support the task, they build V1-33K, about 33,000 automatically curated video segments paired with captions of their continuations, and introduce FutureBench, a multiple-choice benchmark for predicting unseen future events at one, two, and three inferential hops. Experiments on Qwen2.5-VL models report that NEP training raises temporal-benchmark scores while leaving general video understanding roughly unchanged. If the result holds, captions of future frames are a cheap and scalable supervision signal for building temporal reasoning into video AI.

What carries the argument

The load-bearing object is the NEP task formulation: with a video split into past frames and future frames, the model sees only the past and must write a summary of the future. The task is made trainable by a four-stage pipeline — vision-language captioning of the full video, LLM-based scene and causal analysis to pick a split point, segmentation into input and target parts, and reasoning plus critique to generate and filter predictions — that yields V1-33K. FutureBench operationalizes the same skill by asking models to fill one to three missing future events (extrapolation) or to bridge between anchor events (interpolation). The reasoning-and-critique stage matters because predicted continuations are checked against the actual later caption, so training rewards futures that really happened, not merely plausible ones.

What would settle it

Train a control model on the same V1-33K segments but with the full video visible, asking it to caption the later part; if FutureBench and temporal-benchmark gains match NEP's, the temporal improvement comes from the extra caption data rather than from hiding the future. Conversely, randomizing past/future splits so the target is not a true continuation should show whether genuine causal ordering is what matters.

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Extended reading notes

Core claim

The central claim is that next-event prediction is a better learning task than captioning or video question answering for teaching temporal reasoning, because it hides the answer from the model. Training on NEP requires the model to infer likely continuations from visual cues and stored world knowledge instead of extracting facts from visible frames; the paper reports that this deduction-style objective outperforms equal-size training on captioning, multiple-choice QA, open-ended QA, and previous-event prediction across TemporalBench, TempCompass, SeedBench-R1, and the new FutureBench. NEP training also preserves average performance on the general benchmarks VideoMME, MVBench, and LongVideoBench. The paper frames NEP as the video analogue of next-token prediction: a task whose supervision is automatically available, yet rich enough to require multi-step causal inference.

Load-bearing premise

That the gains come from the prediction task itself, not from differences between the V1-33K data pipeline and the LLaVA-Video-178K baselines — an assumption made fragile by the fact that FutureBench is built from the same pipeline as the training data.

Editorial extensions

If this is right

  • NEP can be added to video instruction tuning as a drop-in task: on Qwen2.5-VL-7B it lifts the temporal-benchmark average from 49.7 to 53.5 while the general average stays at 60.9.
  • Deduction (predicting the next event) beats induction (full-video QA) and abduction (previous-event prediction) at equal training size on temporal benchmarks, suggesting predictive extrapolation is a stronger reasoning exercise.
  • Plain supervised fine-tuning on NEP gives most of the benefit; mixing SFT with critique and distillation data yields the best temporal average, but scaling beyond about 5K samples does not consistently improve and can degrade performance.
  • Applying GRPO reinforcement learning to NEP-style multiple-choice questions improves in-distribution and out-of-distribution future-event prediction, including 3-hop and interpolation tasks, but lowers general-video scores and shows reward hacking, so SFT remains the more balanced recipe.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • FutureBench is generated with the same caption-splitting pipeline as V1-33K, so part of NEP's edge may reflect train/evaluation distribution overlap rather than a general leap in temporal reasoning; an independent, human-annotated future-event benchmark would settle that.
  • The supervision ceiling is set by the model that writes the captions, so NEP inherits whatever biases that model has; the paper's limitation section acknowledges this and suggests diversifying annotation sources.
  • A natural extension the paper tests separately but not jointly is combining next-event prediction with previous-event prediction, which would train both forward and backward causal inference in one model.
  • The non-monotonic scaling curves imply NEP is best used as one component inside a larger instruction-mixture, not as a standalone large-scale objective.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper proposes next-event prediction (NEP) as a learning task for video MLLMs: a video is split into past and future segments, and the model is trained to generate a caption-like summary of the unobserved future segment from the past frames. The authors introduce V1-33K, a roughly 33,000-sample dataset built with a four-stage automatic pipeline (fact translation, analysis, segmentation, reasoning/critique), and FutureBench, a 1,056-question multiple-choice benchmark with 1/2/3-hop extrapolation and interpolation settings. Experiments compare NEP against captioning, MCQA, and OEQA instruction tuning; compare SFT/CFT/Distill/Mix training strategies; and add a GRPO reinforcement-learning variant. The paper concludes that NEP significantly enhances temporal reasoning while preserving general video understanding.

Significance. The proposal is interesting, and the artifact release (code, dataset, and benchmark) is a useful community contribution. If the causal claim were cleanly established, NEP would be an attractive scalable alternative to human- or stronger-MLLM-annotated video reasoning data. The benchmark design with varying hop distances is thoughtful, and the GRPO experiment reports out-of-distribution transfer to 3-hop and interpolation tasks, as well as a candid discussion of reward hacking. The main weakness is that the central comparison (Table 1) confounds learning task with training data source, and FutureBench partially shares its construction pipeline and distribution with the NEP training data; the headline causal attribution is therefore not yet supported.

major comments (3)
  1. [§4.1, Table 1] The comparison in Table 1 is not matched: NEP is trained on V1-33K, while Captioning, MCQA, and OEQA are trained on LLaVA-Video-178K, with only sample count held equal (3K). The reported gains on TemporalBench, TempCompass, SeedBench-R1, and FutureBench therefore conflate the learning task with the training data source, annotation style, and the fact that baselines see the full video while NEP sees only the first part. A matched comparison—for example, captioning/MCQA/OEQA tasks derived from V1-33K, or NEP trained on LLaVA-Video-178K examples—is required to support the causal claim in the abstract and in Section 5. Without it, the central attribution that incorporating NEP significantly enhances temporal reasoning is not established.
  2. [§3.2, §B.2] FutureBench is generated with the same four-stage pipeline, the same captioning models, and the same video-source topology as V1-33K, and §3.1 explicitly describes the 1-hop extrapolation task as a standard NEP. Consequently, NEP-trained models are evaluated largely in-distribution on FutureBench, whereas baseline models trained on LLaVA-Video-178K are not. The large FutureBench gap (61.3 vs 52.6 for Instruct, Table 1) may reflect format and template familiarity rather than improved temporal coherence. Please report averages excluding FutureBench and add at least one independently constructed (ideally human-authored) benchmark before interpreting FutureBench as neutral evidence. Appendix F lists other limitations but not this evaluation-loop risk.
  3. [§4.3, Table 4] The GRPO experiment is the strongest piece of generalization evidence in the paper: 3-hop and interpolation gains over SFT show that the trained policy transfers beyond the 1-hop training task. However, the RL training set is itself a FutureBench-style QA set derived from V1-33K via the same pipeline, so the OOD claim is relative to hop count, not to the broader distribution shared with FutureBench. Moreover, the reward hacking reported in Section 4.3 shows that MCQA-format training can exploit lexical shortcuts; this cautions against reading the FutureBench numbers in Tables 1–3 as pure measures of temporal reasoning.
minor comments (6)
  1. [§3.2] In the sentence reporting the o4-mini text-only result, 'suggestion' should be 'suggesting'.
  2. [Appendix B.1, Appendix F] There are typos: 'pecifically' should be 'specifically' in Appendix B.1, and 'urrent state-of-the-art' should be 'current state-of-the-art' in Appendix F.
  3. [References] References [6] and [7] are identical (the SeedBench-R1 technical report); please merge or disambiguate them.
  4. [References] Reference [42] is cited for LLaVA-Video-178K, but the listed title is 'Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding'; this appears to be a citation mismatch.
  5. [Figure 6] Figure 6 is hard to parse: the arrows for abduction and deduction both point to the same 'Answer SpanObservation' box, and the what-happened-before versus what-happens-next distinction is not visually clear; consider a clearer diagram of the past/future input partition.
  6. [Section 2.4, Appendix C] The training target is described in Section 2.1 as a summary of future frames, but Appendix C distinguishes SFT (ground-truth captions), CFT (critiques), and Distill (reasoning traces); please state explicitly which target each strategy uses for the language-modeling loss.

Circularity Check

1 steps flagged · score 6.0 of 10

FutureBench is built by the same pipeline as the NEP training set and its 1-hop task is explicitly 'a standard NEP', so the headline FutureBench gains are partly in-distribution; independent external benchmarks still provide partial support.

  1. self definitional [Section 3.1 (Future Event Prediction—Extrapolation), Section 3.2 (Question-Answer Generation), Appendix B.2]
    "1-Hop: The model predicts a single future event that directly links the observed scenes to the final one. This corresponds to a standard NEP. ... we construct another distinct video dataset from V1-33K, following the same processing pipeline illustrated in Figure 4. ... the videos used in FutureBench have no overlap with V1-33K to ensure fair evaluation despite the same curation pipeline."

    The NEP training target is exactly a textual summary of the future segment given the past segment. FutureBench 1-hop asks the model to fill in the single missing future event between the observed segment and the final scene, and the paper itself says this 'corresponds to a standard NEP.' FutureBench was additionally built from V1-33K videos using the same four-stage caption/segmentation/reasoning pipeline as the V1-33K training set (Figure 4), so its question format, caption style, and event decomposition match the training distribution. Thus the large FutureBench improvement (e.g., 61.3 vs 52.6 for Instruct) is to a substantial degree a held-out test of the very task on which the model was trained, not an independent probe of temporal-reasoning generalization.

full rationale

The paper's main causal claim is that NEP itself improves temporal reasoning. The evidence is mixed. FutureBench is a self-built benchmark, and Section 3.1 states that the 1-hop task 'corresponds to a standard NEP'; Section 3.2 states that FutureBench was built 'following the same processing pipeline' as V1-33K. Consequently, gains on FutureBench partly measure mastery of the same task and annotation distribution used in training, not purely transferable temporal reasoning. Appendix B.2 controls only video overlap, not task/distribution overlap. The GRPO experiment's 3-hop and interpolation results are genuine OOD tests, and the external temporal benchmarks (TemporalBench, TempCompass, SeedBench-R1) are not built by the authors, which provides some independent support. However, Table 1 compares NEP trained on V1-33K to other tasks trained on LLaVA-Video-178K, confounding task with data source; this is a methodological confound rather than a definitional circularity. We therefore flag one partial evaluation loop (score 6) but no fully circular derivation, and no load-bearing self-citation.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new physical entities, fitted constants, or invented mechanisms. Its load-bearing assumptions are about data quality, split point validity, and benchmark validity.

assumptions (3)
  • domain assumption Automatically generated captions for future video segments are accurate enough to serve as ground truth for training and evaluation.
    The entire V1-33K supervision and FutureBench answers derive from a VLM captioning model and LLM reasoning outputs, not from human verification of event correctness; see Section 2.3, Section 3.2, and Appendix F.
  • domain assumption The LLM-selected split point marks a boundary where the future is causally predictable from the past.
    If split points are placed where outcomes are arbitrary, training targets become noise and NEP cannot teach temporal reasoning; see the Analysis stage in Section 2.3.
  • domain assumption Temporal benchmark gains are caused by the task formulation rather than by data distribution and annotation style.
    Baseline tasks use LLaVA-Video-178K while NEP uses V1-33K, and FutureBench uses the same pipeline as V1-33K, so task and data effects are entangled; see Section 4.1 and Section 3.2.

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Cite this review

Pith. "Pith review of Fostering Video Reasoning via Next-Event Prediction." pith.science (2026). https://pith.science/paper/ZRT7EDYY

@misc{pith2026250522457,
  author       = {Pith},
  title        = {Pith review of: Fostering Video Reasoning via Next-Event Prediction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZRT7EDYY}},
  note         = {Machine review of arXiv:2505.22457}
}
read the original abstract

Next-token prediction serves as the foundational learning task enabling reasoning in LLMs. But what should the learning task be when aiming to equip MLLMs with temporal reasoning capabilities over video inputs? Existing tasks such as video question answering often rely on annotations from humans or much stronger MLLMs, while video captioning tends to entangle temporal reasoning with spatial information. To address this gap, we propose next-event prediction (NEP), a learning task that harnesses future video segments as a rich, self-supervised signal to foster temporal reasoning. We segment each video into past and future frames: the MLLM takes the past frames as input and predicts a summary of events derived from the future frames, thereby encouraging the model to reason temporally in order to complete the task. To support this task, we curate V1-33K, a dataset comprising 33,000 automatically extracted video segments spanning diverse real-world scenarios. We further explore a range of video instruction-tuning strategies to study their effects on temporal reasoning. To evaluate progress, we introduce FutureBench to assess coherence in predicting unseen future events. Experiments validate that NEP offers a scalable and effective training paradigm for fostering temporal reasoning in MLLMs.

Figures

Figures reproduced from arXiv: 2505.22457 by the authors.

Figure 1
Figure 1. Comparison of Video Instruction Tuning tasks. (1) Video Q&A: Extracting answers from a single key frame; (2) Captioning: Summarizing from frame-by-frame visual perception of observed videos; (3) Next-Event Prediction: Predicting the summary of future frames by visual perception of observed past frames and temporal reasoning with commonsense knowledge. As the example in the given first part video, after a defensive s… view at source ↗
Figure 2
Figure 2. Reasoning structure underlying NEP. Each node is a potential event or action derived from visual cues, branching into alterna￾tive scenarios such as failing to defend or being pushed in transition. The red line highlights ac￾tual event sequence observed in the video. Com￾ments provide reasoning for less likely scenarios [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 4
Figure 4. Overview of the four-stage V1-33K construction pipeline: Fact Translation, Analysis, Segmentation, and Reasoning & Critique. video future prediction involves implicitly considering various potential future states informed by current observations and pre-learned commonsense knowledge. Even if the exact future diverges, the underlying reasoning process teaches generalizable patterns, for instance, predicting likely re… view at source ↗
Figures from the paper (4 more)
Figure 5
Figure 5. Figure 5: Task demonstration of FutureBench. This figure presents two paradigms for future event prediction: Extrapolation and Interpolation. In the Extrapolation task (Top), the model observes the initial video (Current Event) and is required to sequentially predict a series of…
Figure 6
Figure 6. Figure 6: Three types of logic reasoning in video instruction tuning tasks. (1) Induction (Video Q&A): The model watches entire video sequences and learns common event patterns and temporal relationships, building an internal “engine” of how visual events unfold over time. (2) D…
Figure 7
Figure 7. Figure 7: Performance comparison of different data scales for SFT, CFT, Distill, and Mix tuning on Qwen2.5-VL-7B-Instruct. The top showcases the curves for general benchmarks, and the bottom showcases the curves for temporal benchmarks [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Data Construction Pipeline. B Appendix: Detailed Data Construction Pipeline B.1 V1-33K Construction Fact Translation. In this initial stage, visual content is translated into a textual format to serve as the foundation for further processing. For every video, we use a …

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Cited by 1 Pith paper

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    events

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    Analyze the causal r e l a t i o n s h i p s among these events

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    suitable

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    d e s c r i p t i o n

    ** Replace r e f e r e n c e s to " d e s c r i p t i o n " or " caption "** with wording that r e f e r e n c e s **" the video ."** - For example , " The d e s c r i p t i o n says ..." could become " The video shows ..." - " The caption suggests ..." could become " The vide...

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    d e s c r i p t i o n

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    d e s c r i p t i o n

    ** Replace r e f e r e n c e s to " d e s c r i p t i o n " or " caption "** with wording that r e f e r e n c e s **" the video ."** - For example , " The d e s c r i p t i o n says ..." could become " The video shows ..." - " The caption suggests ..." could become " The vide...

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    Note that a c c u r a t e l y p r e d i c t i n g future events is i n h e r e n t l y c h a l l e n g i n g ; allow for minor d i s c r e p a n c i e s and avoid overly strict j udg me nt s

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    Conclude your analysis by stating either " C o n c l u s i o n : right " if the p r e d i c t i o n aligns well , or " C o n c l u s i o n : wrong " if it does not . Output : Return your analysis in a valid JSON format exactly as shown below ( do not include any extra text ) :...

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    Question

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    Question

    Question Content : - Given the video with observed scenes ( scene 1 to k ) , the question should force the test model to predict future events ( scene k +1 to scene n ) and ask what i n t e r m e d i a t e events would be s up po si ng scene n is given and scene n is the p ot ...

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    - Ensure only one correct answer and that the r em ai nin g three options are wrong

    Question Format : - Create one multiple - choice question with four answer options : A , B , C , and D . - Ensure only one correct answer and that the r em ai nin g three options are wrong . - Only output required question - answer pairs shown in the output s tru ct ur e . Out...

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    Question

    Question Content : - Given the video with observed scenes ( scene 1 to k ) , the question should force the test model to predict future events ( scene k +1 to scene n ) and ask what i n t e r m e d i a t e events would be s up po si ng scene n is given and scene n is the p ot ...

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    - Ensure only one correct answer and that the r em ai nin g three options are wrong

    Question Format : - Create one multiple - choice question with four answer options : A , B , C , and D . - Ensure only one correct answer and that the r em ai nin g three options are wrong . - Only output required question - answer pairs shown in the output s tru ct ur e . Out...

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    Question

    Question Content : - Given the video with observed scenes ( scene 1 to k ) , the question should force the test model to predict future events ( scene k +1 to scene n ) and ask what i n t e r m e d i a t e events would be s up po si ng ( scene k + i and scene k + j are given ,...

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    - Answer options should be built upon the scenes after the observed scenes and before the last scene

    Question Format : - Create one multiple - choice question with four answer options : A , B , C , and D . - Answer options should be built upon the scenes after the observed scenes and before the last scene . - Ensure only one correct answer and that the r em ai nin g three opt...

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.