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Map the Flow: Revealing Hidden Pathways of Information in VideoLLMs

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 7.0Download Report PDF
Video Large Language ModelsInformation Flow AnalysisVideo Question Answering
Abstract:

Video Large Language Models (VideoLLMs) extend the capabilities of vision-language models to spatiotemporal inputs, enabling tasks such as video question answering (VideoQA). Despite recent advances in VideoLLMs, their internal mechanisms on where and how they extract and propagate video and textual information remain less explored. In this study, we investigate the internal information flow of VideoLLMs using mechanistic interpretability techniques. Our analysis reveals consistent patterns across diverse VideoQA tasks: (1) temporal reasoning in VideoLLMs initiates with active cross-frame interactions in early-to-middle layers, (2) followed by progressive video-language integration in middle layers. This is facilitated by alignment between video representations and linguistic embeddings containing temporal concepts. (3) Upon completion of this integration, the model is ready to generate correct answers in middle-to-late layers. (4) Based on our analysis, we show that VideoLLMs can retain their VideoQA performance by selecting these effective information pathways while suppressing substantial amount of attention edges, e.g., 58% in LLaVA-NeXT-7B-Video-FT. These findings provide a blueprint on how VideoLLMs perform temporal reasoning and offer practical insights for improving model interpretability and downstream generalization.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper investigates internal information flow in VideoLLMs using mechanistic interpretability techniques, focusing on how video and textual information propagate through model layers during VideoQA tasks. It resides in the 'Vision-Language Information Flow Analysis' leaf, which contains only three papers total, indicating a relatively sparse research direction within the broader taxonomy. This leaf sits under 'Cross-Modal Information Integration and Alignment', distinguishing it from architectural design work by emphasizing empirical analysis of existing model internals rather than proposing new fusion mechanisms.

The taxonomy reveals neighboring research directions that contextualize this work. The sibling leaf 'Cross-Modal Fusion Architectures' focuses on designing integration modules, while 'Spatial-Temporal Disentanglement' addresses explicit separation of content and dynamics. Adjacent branches cover 'Temporal Information Processing' (encoding schemes and causal reasoning) and 'Token Efficiency' (compression methods). The paper's mechanistic lens connects it to 'Model Evaluation and Interpretability' but diverges by tracing token-level pathways rather than behavioral benchmarking, positioning it at the intersection of interpretability and cross-modal understanding.

Among thirty candidates examined across three contributions, none were found to clearly refute the paper's claims. The first contribution (mechanistic analysis of information flow) examined ten candidates with zero refutable matches, as did the second (identification of effective pathways) and third (blueprint of temporal reasoning stages). This suggests that within the limited search scope, the specific combination of mechanistic interpretability techniques applied to VideoLLM information flow patterns represents relatively unexplored territory, though the analysis does not claim exhaustive coverage of all potentially relevant prior work.

Based on the top-thirty semantic matches examined, the work appears to occupy a niche intersection between interpretability methods and video-language models. The sparse population of its taxonomy leaf and absence of direct refutations within the search scope suggest novelty in applying mechanistic analysis to trace cross-frame interactions and video-language integration stages. However, the limited search scale means potentially relevant work in broader interpretability or vision-language literature may exist outside this candidate set.

Taxonomy

Core-task Taxonomy Papers
40
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: internal information flow mechanisms in video large language models. The field has organized itself around several complementary challenges. Temporal Information Processing and Representation addresses how models capture motion and temporal dynamics across frames, while Cross-Modal Information Integration and Alignment focuses on bridging visual and linguistic modalities through attention mechanisms and feature fusion strategies. Token Efficiency and Long Video Compression tackles the computational bottleneck of processing extended sequences, and Long-Sequence and Multi-Image Understanding explores architectures that scale to hours of footage or large image collections. Unified Multimodal Frameworks and Pretraining examines end-to-end architectures like mplug-owl3[1] that jointly learn across modalities, whereas Embodied and Interactive Video Understanding considers grounded scenarios where agents must act on visual input. Model Evaluation and Interpretability provides diagnostic tools, and Specialized Applications and Extensions covers domain-specific adaptations. Recent work reveals a tension between architectural efficiency and interpretability of information flow. Studies like Video-LLMs answer questions[10] and Cross-modal Information Flow[18] investigate how visual tokens propagate through transformer layers and influence language generation, while approaches such as Slow-fast architecture[2] and Video coding meets multimodal[3] optimize temporal representations to reduce redundancy. Map the Flow[0] sits squarely within the Cross-Modal Information Integration and Alignment branch, specifically analyzing vision-language information flow. It shares thematic ground with Cross-modal Information Flow[18], which also examines how modalities interact internally, but Map the Flow[0] emphasizes tracing token-level pathways through the network rather than broader architectural patterns. This contrasts with works like Video-LLMs answer questions[10], which focus more on behavioral evaluation than mechanistic analysis, highlighting an emerging interest in opening the black box of multimodal transformers to understand where and how visual semantics merge with linguistic reasoning.

Claimed Contributions

Mechanistic analysis of information flow in VideoLLMs

The authors apply mechanistic interpretability methods to reverse-engineer how VideoLLMs process spatiotemporal and textual information. They reveal consistent patterns across VideoQA tasks, including cross-frame interactions, video-language integration, and answer generation stages.

10 retrieved papers
Identification of effective information pathways sufficient for VideoQA

The authors demonstrate that VideoLLMs maintain VideoQA performance when only critical information pathways are retained, even when suppressing up to 58% of attention edges. This validates that the identified pathways are sufficient for temporal reasoning tasks.

10 retrieved papers
Blueprint of temporal reasoning stages in VideoLLMs

The authors provide a systematic characterization of how VideoLLMs perform temporal reasoning, decomposing the process into distinct stages: cross-frame interactions, video-language integration via temporal keywords, and answer generation readiness in middle-to-late layers.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Mechanistic analysis of information flow in VideoLLMs

The authors apply mechanistic interpretability methods to reverse-engineer how VideoLLMs process spatiotemporal and textual information. They reveal consistent patterns across VideoQA tasks, including cross-frame interactions, video-language integration, and answer generation stages.

Contribution

Identification of effective information pathways sufficient for VideoQA

The authors demonstrate that VideoLLMs maintain VideoQA performance when only critical information pathways are retained, even when suppressing up to 58% of attention edges. This validates that the identified pathways are sufficient for temporal reasoning tasks.

Contribution

Blueprint of temporal reasoning stages in VideoLLMs

The authors provide a systematic characterization of how VideoLLMs perform temporal reasoning, decomposing the process into distinct stages: cross-frame interactions, video-language integration via temporal keywords, and answer generation readiness in middle-to-late layers.

Table of Contents