Map the Flow: Revealing Hidden Pathways of Information in VideoLLMs
Authors: name: Minji Kim, name: Taekyung Kim, name: Bohyung Han

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 a 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.
Our project page with the source code is available at
https://map-the-flow.github.io