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Video-LLMs struggle with temporal information flow, researchers find

By PulseAugur Editorial · [1 sources] ·

Researchers have identified a significant bottleneck in how Video Large Language Models (Video-LLMs) process temporal information, hindering their ability to understand the direction of video playback. While video-centric encoders can effectively capture temporal signals, standard Video-LLM architectures often fail to transfer this information reliably. The study highlights that the projection layer is a critical component, with certain designs disrupting temporal data, whereas a time-preserved MLP projection improves information flow. By optimizing the encoder, projector, and incorporating specific supervision, a new Video-LLM achieved near-human accuracy on temporal reasoning tasks. AI

IMPACT Identifies key architectural limitations in Video-LLMs for temporal reasoning, suggesting pathways for improved performance on video understanding tasks.

RANK_REASON Academic paper detailing a new method for diagnosing and improving temporal information flow in Video-LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Video-LLMs struggle with temporal information flow, researchers find

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Academic paper detailing a new method for diagnosing and improving temporal information flow in Video-LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Shigeru Kitazawa ·

    Tracing the Arrow of Time: Diagnosing Temporal Information Flow in Video-LLMs

    The Arrow-of-Time (AoT) task, determining whether a video plays forward or backward by recognizing temporal irreversibility, is one humans solve with near-perfect accuracy, yet frontier Video Large Language Models (Video-LLMs) perform only modestly above chance. This gap raises a…

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