Map the Flow: Revealing Hidden Pathways of Information in VideoLLMs
Authors: Minji Kim
Taekyung Kim
Bohyung Han
Venue: ICLR 2026 Poster (2026)
Keywords: Video Large Language Models
Information Flow Analysis
Video Question Answering
Primary area: foundation or frontier models, including LLMs
TL;DR: This paper presents a systematic analysis of where and how information flows in VideoLLMs for temporal reasoning in VideoQA, revealing key patterns and effective pathways.

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 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 for how VideoLLMs perform temporal reasoning and offer practical insights for improving model interpretability and downstream generalization.

Published as a conference paper at ICLR 2026
MAP THE FLOW: REVEALING HIDDEN PATHWAYS OF INFORMATION IN VIDEOLLMS
Minji Kim
Taekyung Kim
Bohyung Han
ECE & IIPAL, Scoul National University
NAVER AI Lab
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 VideoL.MMs, their internal mech- anisms on where and how they extract and propagate video and textual information remain less explored. 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 Vide- oLLMs initiates with active cross-frame interactions in carly-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 analy- sis, we show that VideoLLMs retain their VideoQA performance by selecting these effective information pathways while suppressing substantial amount of attention edges, e.g., 58% in LLa - --------Vidd--F These findings provide a blueprint for how VideoLLMMs 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: / /mapp/aapa//p the- low g thub.
INTRODUCTION
Multimodal large language models (MLLMs) (Chen et al., 2024b;c;a; Liu et al., 2023; 2024a; Wang al., 2024a) have achieved remarkable success in languae tasks by combining powerful auto-regressive language models with vision encoders. Building upon the success of MLLMs, recent efforts have extended these architectures to videos, giving rise to video large language models (VideoLLMMs) (Maaz et al., 2024b; Lin et al., 2024; Xu et al., 2024; Wang et al., 2024c) that process spatiotemporal information alongside text. These models have shown promising results on video question answering (VideoQA) tasks, which demand temporal reasoning over multiple frames.
Most prior studies on VideoLLMs have focused on external designs of the models, such as scaling video instruction tuning datasets (Li et al., 2023b; Maaz et al., 2024b;a; Li et al., 2024a), key frame selection (Tan et al., 2024; Korbar et al., 2024; Wang et al., 2024b), and compression of input video tokens (Li et al., 2024b; Du et al., 2025; Xu et al., 2024: Zhang et al., 2025b; Jin et al., 2024; Weng et al., 2024; Shen et al., 2025). However, little is known about the internal mechanisms of where and how these models extract relevant temporal information from given videos and propagate it through text tokens to generate final answers. Although recent studies on image-based MLLMs (Neo et al., 2025; Zhang al., 2025c) have identified their structured behaviors for image-text inputs, it remains unclear whether these findings hold in VideoLLMs and what novel capabilities are acquired through video-text alignment beyond image-text pretraining.
In this study, we aim to provide complete blueprint that reveals the systematic behaviors of VideoLLMs on temporal reasoning tasks, with a focus on the information flow across different layers and modalities. To understand how VideoL.Ms generate an answer from a given (video, question)
'These authors contributed equally this work.

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Published as a conference paper at ICLR 2026
[1) Cross-frane Intoractions within Video Tokema
VideoLLM
frame interactions
Viceo Guestion
Vikaon
Scongge Proboolly
Question = Last
Video
Layer
Video Language Integration Temporal Keywords
(b) Attention Knockout Result
Slack sopear li voloof
True
Question
beginneng
Opikona
the
coeginiing
[3] Answer
Arsawer
Generation
Deg ri
Early
Middle
13
20
20
30
(a) Overview of Information Flow Pathways in VideoLLMM
(c) Answer Probability Change
Figure 1: Summary of our findings on VideoLLMs' information flow. (a) Temporal reasoning
begins with cross-frame interactions within video tokens at early-middle layers [green], followed by video-language integration into temporal keywords in the question [purple]. This information is conveyed to the last token at middle-late layers [orange], where answer generation occurs [yellow]. (b) These effective pathways are identified via Attention Knockout, which disconnects attention pairs and tracks the drop in probability of the final answer to quantify their impact. (c) Layer-wise answer probability rises immediately after video- languae integration, indicating that the model is ready to predict correct answers after the middle layers.
pair, decompose the temporal reasoning process into several stages and investigate the following key questions: (1) How do VideoLLMs encode spatiotemporal information from the given flattened sequence of video tokens? (2) How are the temporal concepts in the question extracted from video tokens and propagated to text tokens? (3) At what stage does the model become ready to generate an answer? (4) Can identify effective information flow pathways sufficient to solve VideoQA?
To answer these questions, we take a mechanistic interpretability perspective (Rai et al., 2024; Nanda al., 2023; Geva al., 2023) and reverse-engineer the internal computations of VideoI..Ms. Our analysis reveals consistent patterns in how VideoLLMs process video- video-language information across various VideoQA tasks. Our key findings are summarized as follows:
Active temporal interaction within video tokens in early-to-miden layers ($3.2): Temporal reasoning begins by building spatiotemporal representations from video tokens through focused cross-frame attention in carly-to-middle layers. Our analysis using Attention Knockout (Geva al., 2023), which selectively disconnects attention edges quantify their impact, shows VideoQA instruction tuning from base ImageLLMs distinctly induces this capability.
Video-language integration on temporal keywords in middle layers ($3.3): Analyzing semantic concepts in video tokens through Logit Lens (nostalgebraist, 2020) shows that temporal concepts are emergent among video tokens. Alignment between these representations and temporal keyword embeddings facilitates selective rideo-language integration over relevant question tokens in early-to-middle layers, which is followed by information converging to the last token in middle-to-late layers.
Answer generation at middle-to-late layers ($3.4): Tracing layer-wis answer probability at the last token reveals that the model is prepared to generate a correct answer immediately after the video-language integration concludes from the middle layers.
Effective information flow pathways for solving VideoQA tasks ($3.5): To validate the above findings, disable all information pathways except those identified as critical. Evaluation on VideoQA benchmarks shows that the models retain performance comparable to baselines, demonstrating that these effective pathways suffice for accurate answer generation.
Our findings provide a first step in understanding the internal mechanisms of VideoLLMs for temporal reasoning. Code and data are available at https: wam-the--e-eeeee f1 Tlo. www.gitub.... io.
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2 PRELIMINARY
2.1 VIDEO LARGE LANGUAGE MODELS (VIDEOLLMS)
Video and instruction tokenization. Given input video V E IRIHH*Wx,, where T and H W denote the number frames and the spatial resolution, we patchify each frame into non- overlapping patches of size p X p. resulting in total of N, = # W. patches. These patches p p are processed by a vision encoder ʃ(-)) to produce sequence of video tokens {vik where V; TR. On the other hand, the instruction text t of length Nr is processed using a tokenizer of the language model component in the V.deoLLMM, which acts as a lookup table of word embeddings, Nr. resulting in sequence of text tokens [t. The video and text tokens are then combined as [V1, VN ti...., tNr] E N and fed into the VideoLLM for multimodal processing.
Causal attention. For each of f attention heads in transformer layer, d, di-ddmmnnioonll query, key, value representations are computed from the input x'-1 from the previous layer as q' x'-IWW & k.
k' = W and v' = w! where W q: W k: W E Rdddd are linear projection matrices. Since VideoLLMMs adopt causal attention to preserve the autoregressive nature of generation, the attention output for each head is computed using scaled dot-product attention, which is given by
Attention( k', vt) = softmax (T(K)
+MM
v', v
(1)
vdn
where M' is a causal mask. The outputs from all heads are concatenated and projected through W! Rdxd to form the final output of the multi-head attention module at layer l.
2.2 ATTENTION KNOCKOUT
Attention Knockout (Geva et al., 2023) selectively disables specific attention connections between tokens during inference. This technique allows us to causally trace the contributions of different modalities or frames. By ablating particular attention paths and measuring the impact predictions, we can uncover the mechanisms by which information propagates through the model, revealing knowledge localization and the functional roles of individual components.
In practice, to prevent information flow from source tokens (e.g., video inputs or earlier frames) to target tokens (e.g., later frames, question, or answer tokens), we set the value of the attention mask M' at position (s, t) to -o in Eqn. (1), where and t denote the positions of the source and target tokens, respectively. This replacement ensures that the token at position t cannot attend to the token at position s, effectively blocking specific token interactions.
In Vi,eoQA, model generates answer from given iideo-question pair (v, q), where the question may contain n options o = [01: 0y; ...; o,] The model initially predicts the answer a with the highest probability Phase at the last token position of the input sequence. We trace the relative probability change in percentage, defined as ((Penockont Prase Phase) x 100, where Pknockout is the updated probability for the same answer derived after intervention.
3 INFORMATION FLOW DYNAMICS IN VIDEOLLMS
We investigate the behaviors of VideoLLMs in VideoQA tasks. Our analyses reveal effective information flow pathways of VideoLLMs, leading to four key findings: (1) temporal interactions are primarily established among video tokens in the carly-to-middle layers ($3.2); (2) video information is selectively propagated to corresponding temporal reasoning vocabulary tokens and integrated with textual information ($3.3); and (3) answer generation emerges near the completion of the video- language integration and progresses through the mid-to-late layers ($3.4). We further discuss the impact of pruming ineffective information flow pathways of VideoLLMs on the VideoQA performance ($3.5). We present the analysis setup ($3.1) and provide detailed discussion in the following sections. Our analyses focus on multiple-choice VidcoQA samples for structured evaluation, with open-ended question extensions in the Appendix (SB).
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Table 1: Overview of tasks and data for our analyses. We adopt five tasks from TVBench (Cores et al., 2024), a multiple-choie VideoQA benchmark covering diverse temporal reasoning types.
Task
Reasoning Type
Question Example
Option Example
Action recognition
What is the action
temporally opposite actions
Antonym
Sequential ordering
being performed in the video?
g.g. Wear jacket.; Take off jacket.
Action
Action recognition
2 actions that actually happened on the video
Sequence
Temporal localization
What the person first?
e.g.. Put down the blanket.; Took the towel.
Scene
Scene recognition
What's the right option for how the scenes the video change?
From (scenel) | to (scene2). )-
Trunsition
Sequential ordering
From [scene2] [scenel].
Moving
Which direction does the gray cube move in the video?
Down and the right:; Down and the left. Up and to the right.; Up and to the left.
Moving object properties
Direction
Object
Moving object properties Temporal localization
How muny metal objects are moving
0: 1;2;
Count
when the video begins?
3.1 EXPERIMENTAL SETUP
Tasks and data. We construct our data by selecting five tasks from TVBench (Cores al., 2024), a multiple-choice VideoQA benchmark designed to evaluate temporal understanding while minimizing static-scene bias. As shown in Table 1, our data contains tasks requiring reasoning over diverse attributes under temporally challenging situations. To guarantee the validity of our causal tracing, we restrict the analysis to instances where the model provides the correct answer. This filtering step ensures our study focuses on samples where the model successfully reasons about visual and temporal content, thereby eliminating noise from random guesses or fundamental misunderstandings. Extended analyses covering open- ended QA, long-form video understanding, and spatial reasoning are provided in the Appendix.
Models. We study the behavior of MILMS that are fine-tuned with video instruction tuning. Specifically, we focus on models originally trained static image-text data and later fine-tuned on video datasets to analyze unique properties learned during the video instruction tuning procedure. To this end, we fine-tune LLaVA-NeXT-7B (Liu et al., 2024b) with VideoChat2-IT (Li al., 2024a) for 3 epochs and use this model for the analysis in our main paper. For convenience, we refer to this model as LLaVA-NeeT-77- --BB-iido-FF.. For both training and inference, we use 8-frame sampling with 144 tokens per frame. We further extend our analyses on other VideoLLMs with diverse VL--ennLLL----ie
architectures, including LLaVA-NeXT- 13I--IB-iiiee Video-FT, 2024), and Vide7LL (Zhang et al., 2025a) in the Appendix.
(Gao et al.,
3.2 TEMPORAL INTERACTION WITHIN VIDEO TOKENS
To successfully perform VideoQA, VideoL.LMs must aggregate information distributed across tempo- ral dimensions and produce the correct answer at the final token position. This subsection investigates the internal mechanisms by which VideoLLMs encode spatiotemporal features from a flattened sequence of video tokens.
Training with VideoQA data boosts cross-frame interactions in the early-to-middle layers. While ImageQA tasks, such as object identification, can often be solved by localizing specific regions at the token level, VideoQA presents distinct challenge. In VideoQA, visual information is distributed across a sequence of frames, requiring the model to integrate spatiotemporal cues to capture essential temporal concepts. To investigate how these disparate task requirements shape the internal mechanisms of MLLMs, we compare Attention Knockout results between a model trained exclusively image data (LLaVA-NeXT-7BB and its video-fine fine-tuned counterpart TLLaVA-NeXT 7B-Video-FT), Specifically, for each layer I, we inhibit vision tokens from attending to tokens in preceding frames within k(= 9) layer window! centered at l, and measure the percentage relative change in answer prediction probability. As shown in Figure 2, disrupting cross-frame interactions in
We observed that blocking too narrow a range allows information to bypass the intervention via residual connections, whereas wider windows robustly induce more significant degradation in prediction probability. Consequently, we adopt window size of l 9 following (Geva et al., 2023); further sensitivity analyses are provided in $G.6.
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No cross- cross-frame interactions LLL.VA--cXT--B-iddo
No cross-frame interactions (LLaVA-NeXT-7B)
Ctange in Pretubliity
Clange in Probablity
in Probubillty
Changge in Prebabiity
56 Claue in Peduabiltt
% "5
3
Layer
Layar
Layer
Layer
Lager
(a) Action Antonym (b) Action Sequence (c) Scene Transition (d) Moving Direction
(e) Object Count
Figure 2: Change in prediction probability when disconnecting cross-frame attention edges.
Blocking cross- cross-frame interactions in carly-to-middle layers significantly harms LLaVA-NcXT-7B- Video-FT's prediction, while LLaVA-NeXT-1 remains mostly unaffected.
Table Impact of cross-frame attention on answer generation. We block cross-frame attention in the first half of the total layers and measure the resulting accuracy drop. Answers in the third column are taken from open-ended responses from each case. Without cross-frame attention, the model generates incorrect or even opposite answers to the given videos.
Task
Acc Drop
Answer Example
Baseline: action being performed in video to stand up
Action Antonym
24.1%
Knockout: The action being performed in the vidco
chair
Baseline:
The action the person is doing first is open the plastic bag
Action Sequence
20.2%
Knockout: The action the person is doing first is to put bag in the mierowave
The scene in the vidco changes from the bedroom the street. Knockout: scene the video changes from street a different location
Baseline:
Transition
18.0%
Baseline:
The purple sphere
to the right the video.
Moving Direction
-4.8%
Knockout: The purple sphere moves to the left in the video.
Baseline: The number of moving objects is when the video begins.
Object Count
60.8%
Knockout: The number of moving objects is three when video begins.
the early-to carly-to-middle layers consistently degrades the performance the VideoLLM across all tasks. In contrast, the Imagel.LM does exhibit such layer-wise sensitivity in most scenarios. These results suggest that VideoQA fine-tuning explicitly fosters robust cross-frame dependency within the model's internal representations.
Impact of early-stage temporal interactions to which early-to-middle layer temporal interactions contribute to the final answer generation, we inhibit fross-frame attention in the first half of the model (i.e., layers 1-16) and evaluate the resulting impact on baseline performance. As shown in Table 2, this intervention results in accuracy drops of least 18% among samples that were correctly predicted under full causal attention. The qualitative analysis in the third Solumn-ddsplayinn the model's open-ended intervention leads to incorrect or even contradictory answers across tasks. These findings imply that VideoLLMs rely heavily on cross-frame interactions during the early stages of processing to effectively reason about temporal events.
generation. To investigate the extent
crenesss-reealss that
3.3 VIDEO-LANGUAGE INTEGRATION ON TEMPORAL REASONING KEYWORDS
Having demonstrated that early-layer cross-frams frame interactions facilitate the formation of spatiotempo- ral representations, we now examine how this visual information integrates with textual tokens. To establish a baseline, trace the overall video-to-language information flow in Fig. 3, which reveals that VideoLLMs operate through a structured pathway originating from video tokens, passing through question tokens, and culminating at the final token. Building this observation. we investigate the mechanisms by which VideoLLMs selectively propagate spatiotemporal information via specific temporal reasoning keywords.
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Video w
Question
Video
Last
Question vs Last
Last vs Last
Change in Probability
Changn in Probabiltty
in Probubiiiyy
Change in Probabiitt
Change in Poobdiiiit
%
Layer
Layer
Layer
Layer
Layer
(a) Action Antonym (b) Action Sequence (c) Scene Transition (d) Moving Direction
(e) Object Count
Figure 3: Overall cross-modal information flow in VideoLLMs. We analyze changes in the prediction probability when intervening on attention edges between video, question, and last token (i.e., the starting position for answer generation), following the protocol of (Zhang et al., 2025c). Information from the video tokens is conveyed to the question tokens in the early-to-middle layers, followed by the transfer of information from the question tokens the last token in the middle-to-late layers. Source + Targer indicates blocking attention edges from source tokens to the target tokens.
Spatial Keywords
Temporal Keywords
Average
"box"
"camera"
"person"
"floor"
Nomaltmal hapanyy
Nomralea Fowparyy
Nooralted Feepoce 2
Laysz
Laye
Laser
All Average
"throw"
"down"
"hold"
"up"
Nomstinel Eequetsy
Nomalted Rood
Layar
Figure 4: Normalized frequency of spatial and temporal keywords extracted from video tokens via Logit Lens. Spatial concepts start to appear in the very early layers, whereas temporal concepts develop later in the middle layers onward. The full list of keywords is shown in Table D.
Emergence of temporal concepts in video tokens. Which semantic concepts are extracted from video tokens, and how do they emerge across layers? To answer this, we employ Logit Lens (nostal- gebraist, 2020) trace the evolution of latent concepts across layers. Specifically, we project the hidden states of video tokens at all layers through the language model head to obtain logits, then count the occurrence of spatial and temporal keywords examine their distribution across layers. We use LLa VA- 13B-Video--- Video-FT on videos from the Action Sequence task, with spatial and temporal keywords parsed from the question prompts. Figure shows that both spatial and temporal concepts are captured in video tokens, but with distinct emergence patterns: spatial concepts start to appear in very early layers, while temporal concepts develop in middle layers onward.
To further validate the disparity in emergence timelines, we visualize the evolution of logit distri- butions for these key vocabularies within a video in Fig. 5. Specifically, we map the patch token positions associated with spatial and temporal keywords identified using Logit Lens across the early, middle, and late layers. Our findings reveal a sequential allocation of token positions: the model first grounds spatial concepts (e.g., salient entities or attributes) at foreground tokens in early layers, and later utilizes the remaining token positions to represent temporal dynamics, rather than overriding the already stabilized spatial representations.
Video lideo-language alignment enables selective spatiotemporal propagation. How are the emergent concepts in videos propagated through text tokens? We analyze the propagation of spatiotemporal information to question tokens and compare it against the propagation of static visual information. We qualitatively show two aspects: (1) temporal visual information is aligned with temporal concept vocabularies, and (2) such alignment emerges specifically through cross-frame interactions.

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Published as a conference paper at ICLR 2026
Time
Early Layer
Hallaan
Middle
301
Late
Layer
Spatial concepts: (Roor baper,
book, bad,
bos/le): bianntet!"" sandlwich): person')
Temporal concepts:
hold"
Figure 5: Visualization of spatial and temporal concepts extracted by Logit Lens. To investigate
the emergence timing and positional distribution of concepts, we visualize the vocabularies extracted from video tokens across early, middle, and late layers. Spatial concepts tend to localize salient regions early, and temporal concepts then emerge mainly on the remaining tokens rather than displacing already stabilized spatial tokens. The full visualization across all layers is shown in Fig. F.
Irpul Video
Input Video
Question: How objects there video
Query begins
Query begins
Question: How moving objects
Query ends
Quary Bands
(a) Baseline
(b) No cross-frame interactions
Figure Visualization of video-to-question attention maps. Queries are "begins" and "ends" question tokens; keys are video tokens. (a) With spatiotemporal interactions, each question token attends semantically relevant regions: "begins" focuses the blue sphere at the start, while "ends" focuses on the blue sphere and green square at the end. (b) When temporal interactions among video tokens are blocked, video-text alignment fails, and text tokens instead attend to positionally proximate regions rather than semantically relevant ones.
To this end, we compare question (e.g., "begins". "ends"" before and after masking cross-framd attention. As illustrated in Fig. 6(a), when the standard cross-frame frame attention is maintained, attention maps highlight the semantically relevant temporal segment the video corresponding to the temporal meanings words "begins" and "ends". This demonstrates that spatiotemporal interactions enable the selective exploitation of semantically crucial information throughout the video, allowing question tokens to focus the most relevant regions across both spatial and temporal dimensions. In contrast, when this cross-frame attention is blocked in Fig. 6(b), the model fails to associate temporal concept vocabulary with relevant video content and instead exhibits positional bias based positional proximity toward question tokens. These findings indicate that VideoLLMs implicitly learn to align spatiotemporal representations with linguistic embeddings corresponding to temporal concepts.
attention of different temporal concept words in the
Temporal keywords serve core checkpoints in video-text information flow pathways. Inter- leaving the two previous findings raises natural question of how video information is propagated to the last token via temporal reasoning keywords. However, directly tracing keyword-to-keywonl pathways is non-trivial, as the relevant keywords vary across questions. Instead, we analyze the infor- mation flow through the options, leveraging the fact that multiple-choice options consistently act temporal keywords in VideoQA. We segment the full question prompt into fine teee-graied components: the non-option question (e.g., "What is the action being performed in the video?" the true option
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Non-option question Last
True option Last
Falsc option Last
No Change in Probakiity
% Cunpp in Prebabiity
Prebaility
% Clangein in Prouabiity
% Cungee in Prebabiity
Layer
Layer
Layer
Layer
(a) Action Antonym (b) Action Sequence (c) Scene Transition (d) Moving Direction
(e) Object Count
Figure 7: Change in the prediction probability when intervening on attention edges from different parts of the question tokens to the last token. Source - Targer indicates blocking attention edges from source positions the target positions. Most of the information flowing the last token in the middlc-to-late layers derives from the true option tokens, rather than the broader context in the non-option question. Note that the observed probability rise false option last likely because removing the false option makes the task easier to solve.
Vidco v Non-option qucstion
Non-option question vs Truc option
Video v True option
% Ctange in Pretakiity
Compre in Incooniing
Clagge in Prbubiliy
Change in Probobility
Cangs in Peduhiiit
8
Layer
Layer
Layer 02
Layer
Layer
(a) Action Antonym (b) Action Sequence
(c) Scene Transition
(d) Moving Direction
(e) Object Count
Figure 8: Change in the prediction probability when intervening attention edges to the true option position. Information from video tokens consistently converges to the true option tokens in early-to-middle layers, while routing to non-option question tokens varies depending on the task.
(e.g.. "(A) Wear jacker"), and the false option (e.g.. "(B) Take off jacket"). We then examine where the last token primarily derives information from. Figure shows that non-option question tokens contribute little direct flow to the last token, whereas information from the true option propagated in the middle-to-late layers. This indicates that video-language integration completes at the option tokens containing temporal keywords.
However, the pathways by which video information reaches option tokens may be question-dependenn as temporal keywords are embedded differently across questions. To verify this, we decompose the flow toward the true option into two routes: (1) direct route (video true option) and (2) indirect route (video - non-option question - true option), and compare their layer-wise patterns across tasks. Figure reveals divergent, task-specific strategies. In Action Antonym, Action Sequence, and Scene Transition, video information is primarily transferred via the direct route (purple), with minor contributions from non-option question tokens. Conversely, in Moving Direction and Object Count, information regarding the queried target object is first routed to the non-option question (red) and then propagated to the true option (red dotted line).
These results indicate that temporal keywords function as information integration checkpoints, with their specific pathways adapting to the underlying prompt structure. Our extended analysis open- ended answer generation (see Appendix B) further supports this conclusion; we observe that the model dynamically establishes these checkpoints around temporal keywords (e-g., verbs) facilitate the propagation of video information critical for response generation.
3.4 ANSWER GENERATION BEHAVIOR AT MIDDLE-TO-LATE LAYERS
We further examine the role of layers beyond the information propagation stage. Regarding the prior study (Zhang al., 2025c) that the last layers of MLLMs primarily focus linguistic completion, we trace the evolution the answer generation process. Specifically, we probe the layer-wise hidden representations the last token position monitor the probabilities for both the true and false
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True option
False option
Probabiir
Pribabilty
Probulaliy
Probahiitty
Layer
Layer
Layer
Layer
Layer
(a) Action Antonym (b) Action Sequence (c) Scene Transition (d) Moving Direction
(e) Object Count
Figure 9: Layer-wise prediction probability for true and false options in the last token position. The probability for the true option starts to rise immediately after the middle layers.
Table 3: Impact of effective flow pathways on performance TVBench and TOMATO. The
number of attention edges is the count of valid (query, key) pairs over all attention layers. When we enable attention only for effective pathways, VideoQA performance is retained across diverse tasks and models even though suppressing substantial portion of attention edges.
Model
# Video Tokens Attention Type
Attention Edges
TVBench
TOMATO
Full causal attention
25.7M (100%)
51.5
30.2
LLaVA-NeXT-7B-Videe Video-FT
8x12x12
Effective pathways
10.8M (42%)
51.2
29.2
Random blocking
10.8M (42%)
40.1
23.1
Full causal attention
32.2M (100%)
55.1
27.2
ILaVA-NexT-/3B-Video 1x
Rffective pathways
14.3M (37%)
54.6
27.4
Random blocking
14.3M (37%)
41.5
23.8
Full causal attention
74.6M (100%)
56.0
32.2
Mini-IntemmVL-4BB--VA 1x
Effective pathways
29.0 29.6M (40%)
56.0
31.2
Random blocking
29. 6M (40%)
41.0
25.9
causal attention
19.9M (100%)
55.2
28.0
VideoLLaMA3-7B 8x12x12
Effective pathways
11.4M (58%)
57.2
28.7
Random blocking
11.4M (58%)
22.2
13.9
options. Figure 9 shows that the prediction probability for the true option rises abruptly starting
around the 20th layer, which coincides with the completion the video-to-questie information flow. Furthermore, the true option rapidly dominates the output probability rather than exhibiting gradual phase of competition among all options. This suggests that the final decision for a correct answer is contingent upon the success of the video-to-language propagation in the preceding middle layers.
3.5 DOMINANT CONTRIBUTION OF EFFECTIVE INFORMATION FLOW TO VIDEOQA
We have discovered the effective information flow pathways of the temporal reasoning process within L.d This observation raises natural question regarding the contribution of these pathways to overall VideoQA performance. To assess the impact of effective information pathways, we conduct quantitative analysis by evaluating VideoLLMs VidcoQA benchmarks after retaining only the identified effective token interactions, while disabling all others?. Table 3 summarizes the performance on TVBench (Cores al., 2024) and TOMATO (Shangguan al., 2024) benchmarks. Although restricting attention to these effective pathways prunes substantial portion of attention edges (e.g., retaining only 42% of original edges LLAVA-NeeT-77B- -e--ee-T-----iio----- Video-FT), results only marginal degradation in accuracy across both benchmarks. Conversely, randomly sparsifying the same proportion of attention edges leads to a severe performance drop. These findings validate our characterization of the effective information flow pathways and their role in VideoLLM inference.
"The layer ranges for effective pathways empirically determined based on the patterns observed in Sections 3.2-3.4. 3.4, specifically targeting intervals where blocking induced significant probability drops (see Table E for details). We enable cross-frame interactions (c.g.. 15) and video - question flows (c.g.. L6-20) in moildle layers, followed by question - last token transitions in middle-to- late layers (e.g.. L16-25) In contrast, video last and last - last connections are disabled across all layers. Furthermore, flows directed toward video and question tokens blocked in later layers they longer contribute the final output.

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Video Non optior quession
Nom option question Last
No cross-frame inferactions (Case 1)
Nott-pption question
option
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N Changeinn in Protubility
% Change in Prabability
Change Probuballit
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(a) No cross-frame Interactions
(b) Vidco ~ Question
(c) Question -+ Last
Figure 10: Information flow in failed VideoQA samples. Tracing the probability change of the wrong answer in failed samples shows that (a) errors stem from spurious cross-frame attentions (Case or static bias (Case 2) in the early layers. In contrast, (b-c) cross-modal integration from middle layers shows a similar pattern to the successful case, indicating that the failure arises from earlier spatiotemporal representation building rather than a collapse of the cross-modal integration pathways.
4 DISCUSSION
Failure case analysis. To mechanistically understand why the model makes incorrect predictions, we analyze how the prediction probability the wrong answer changes in failed VideoQA samples in the Action Antonym task. Figure 10(b-c) illustrates that the cross-modal flow patterns routing false options failed samples are similar to those routing correct options in successful samples. This suggests that failures may stem from inaccuracies established during the initial video representation stage rather than a breakdown in the subsequent integration pathways. In contrast, disabling cross- frame interaction (Fig. 10(a) reveals two distinct failure modes. In Case 1 (green), the incorrect option probability decreases upon disabling cross-frame attention, suggesting that the erroneous signal carried by these edges was a primary cause of the model's misprediction. In Case 2 (pink), however, the incorrect option probability increases, indicating a reliance on static bias where the model defaults uninformative static information cues in the absence of temporal context.
Future applications of findings. First, analysis reveals that current VideoLLMs rely on a highly concentrated set of dominant information pathways, suggesting the need for pathway regularization during training. Suppressing dominant pathways could encourage the model ex- plore alternatives (Zehui et al., 2019; Li et al., 2023a), thereby better utilizing its representational capacity. Second, our findings of concept emergence and failure cases highlight the importance of establishing temporal concepts in earlier layers while reducing static-scene bias. This implies that training objectives or architectural constraints promoting the early formation of visual representa- tions (Nikankin et al., 2025; Neo et al., 2025) may improve the robustness of temporal reasoning. Finally, our identification of effective information flow ranges indicates that token interactions beyond these ranges contribute marginally to the final decision. This discovery paves the way for early-exit strategies (Elbayad et al., 2020; Schuster et al., 2022; Bae et al., 2023) that adaptively truncate redundant computations, substantially reducing inference overhead while preserving accuracy.
CONCLUSION
We have conducted a comprehensive mechanistic analysis to understand the spatial and temporal dynamics of information extraction and propagation within VideoLLMs for VideoQA tasks. Our study reveals that temporal reasoning is initiated by the formation of spatiotemporal representations of video tokens during the aarl-ttommiddle layers, driven by active cross-frame interactions. This processed visual information then selectively transferred to semantically aligned temporal keywords within the question. Furthermore, we observe that the critical information required for final decision- making is conveyed to the final token during the middle-to-le generation. These sparse pathways prove sufficient for effectively solving VideoQA tasks. Our findings provide practical insights into the internal working mechanisms of VideoLLMs and pave the way for future research into their interpretability and architectural generalization.
layers, where it culminates in answer
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REPRODUCIBILITY STATEMENT
To ensure reproducibilityy, we provide comprehensive implementation details in Appendix H, in- cluding model architectures, training and inference strategies, and experimental configurations. Our Attention Knockout and Logit Lens analyses are implemented based on the public codebase from (Geva et al., 2023). All experiments use publicly available datasets [TVBench (Cores et al., 2024), TOMATO (Shangguan et al., 2024), VCGBench (Maaz et al., 2024b), Long VideoBench (Wu et al., 2024), Video-MME (Fu et al., 2024)] and models [I.AAV-NeXT (Liu et al., 2024b), Mini- Intern (Gao et al., 2024), Vide (Zhang et al., 2025a)]. Our complete code and models available at the project page.
ACKNOWLEDGMENTS
This work was partly supported by the National Research Foundation of Korea (NRF) grant [RS- [ and by the Institute of Information & communications Technology Planning & Evaluation (ITTP) grants ww---22222-2222222
(Part 2) Few-Shot Learning of Causal Inference in Vision and I Language for Decision Making, No.RS-2021-11 1343, Artificial Intelligence Graduate School Program (Seoul National University)] funded by the Korean government (MSIT).
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APPENDIX
AA: Related Work
SB Analysis on Open-ended VideoQA
SC: Additional Visualization of Video-to-Question Attention Maps
DD Full Visualization of Logit Lens Results
E Scalability of Our Findings (LLaVA-NeXT-I3B--iieo-
SF: Generalization of Our Findings (Mini-Intem V--44--Vido--FF Video-FT and VideoL.aMA3-7B)
S Further Analysis and Discussion
H Implementation Details
$1: The Usage Large Language Models
A RELATED WORK
Video Large Language Models (VideoLLMs). Research on video understanding has increasingly focused on leveraging image-level pre-trained MLLMs by fine-tuning them for video-language tasks such video question answering, video captioning, and video conversation. To improve the temporal reasoning ability of VideoL.LMs, the majority of the existing studies have concentrated on the external aspects of VideoLLLMs, such scaling video instruction tuning datasets (Li et al., 2023b; Maaz et al., 2024b;a; Li et al., 2024a), selecting key frames (Tan et al., 2024; Korbar et al., 2024; Wang et al., 2024b), retaining memory banks (Song et al., 2024; He et al., 2024), and compressing the input video tokens (Li et al., 2024b; Du et al., 2025; Xu et al., 2024; Zhang et al., 2025b; Jin et al., 2024; Weng al., 2024; Shen al., 2025). contrast, our study focuses on the inner mechanism of VideoLLMs and investigates how temporal reasoning occurs.
Mechanistic interpretability of multimodal models. Mechanistic interpretability (Rai et al., 2024; Nanda et al., 2023; Geva al., 2023) is an emerging area that seeks understand neural networks by renerse-engineeriig their internal computations. Recently, several studies (Palit et al., 2023; Yu & Ananiadou, 2024; Cohen et al., 2025; Basu al., 2024; Neo al., 2025; Zhang al., 2025c) have applied mechanistic interpretability techniques to MLLMs to explain their inner mechanisms. (Basu et al., 2024) focused on how information is stored and retrieved from the model parameters of MLLMs. On the other hand, (Neo et al., 2025) examined the object identification task and explored how the object-level information flows and emerges. Most recently, (Zhang et al., 2025c) systematically studied cross-mode information flow in MLLMs, revealing that many models exhibit a single-stream information transfer pattern from the visual to the linguistic modality. Building upon these methodologies, we extend attention based knockout and layer- wise logit probing techniques to VideoLLMs to understand temporal reasoning mechanisms.
Video Question Answering (VideoQA) benchmarks. VideoQA benchmarks have evolved to vali- date temporal understanding for VideoLLMs. VCGBench (Maaz et al., 2024b) and Video-MME (Fu al., 2024) provide wide coverage of aspects that appear within videos, establishing strong general- purpose baselines. TVBench (Cores et al., 2024) is designed to require true temporal reasoning and discourage single-frame shorteuts. TempCompass (Liu al., 2024c) evaluates multiple temporal aspects through varied VidcoQA formats to measure time-aware perception. Vinoground (Zhang et al., 2024) uses counterfactual short video pairs to assess fine- ain-ggainedd temporal distinctions beyond static cues. TemporalBench (Cai et al., 2024) focuses on detailed temporal dynamics such as event order frequency and change patterns. TOMATO (Shangguan et al., 2024) introduces expert-written tasks that force reasoning about event evolution across frames. MotionBench (Hong et al., 2025) targets fine- ane-grained motion comprehension using motion-centric questions. Long VideoBench (Wu et al., 2024) evaluates long- long-form interleaved video language understanding, where models must retrieve relevant moments and maintain evidence over extended contexts. In this study. we primarily focus analysis on TVBench, TOMATO, VCGBench, Long VideoBench, and Video-MME.
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No cross-frame interactions (LLaVA-NeXT-7B- -iT-o-F-V
cross-frame frame interactions (LLaVA-NeXT-7B)
% Change in Probability
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Figure A: Change in prediction probability in open-ended QA format when disconnecting cross- frame attention edges. -LaVA-NeXT-7B-- Video-FT shows a stronger correlation with fross-frame
interactions and the final answer probability compared LLaVA-NeXT-7B.
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Figure B: Change in the prediction probability in open-ended QA format when intervening on attention edges between video, non-option question, and last token. the absence explicit temporal keywords in the open-ended format, the last position itself serves as a checkpoint for video-text integration in the middle layers.
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Figure C: Layer-wise prediction probability for ground truth answers in open-ended QA format in the last token position. The probability for the ground truth starts rise immediately after the middle layers.
B ANALYSIS ON OPEN-ENDED VIDEOQA
In the main text, we demonstrated that temporal keywords in the prompt serve critical checkpoints in information flow pathways for multiple-choice QA tasks. In open-ended VideoQA, however, the prompt lacks explicit keywords that correspond to the ground-truth answer. Consequently, the information flow to the last token may differ, as the model must independently generate answer vocabularies rather than simply selecting from the given options. In this section, we investigate whether this difference in prompt format alters the established mechanisms of information propagation. We first analyze single-token generation ($B.1) and extend our investigation to multiple-token generation scenarios ($B.2).
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First anchor generation
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Figure D: Open-ended generation analysis VCGBench. We set verbs in the generated response as potential semantic anchors containing temporal vocabulary and measure a probability drop at generating each anchor when intervening attention edges from Source Target tokens.
B. SINGLE TOKEN GENERATION
Open-ended analysis setup. The input prompt formats TVBench are modified by removing the options and adopting a sentence completion style. For example: "USER: <video> USER: Question: Which direction does the gray cube move in the video? ASSISTANT: The gray cube moves to the To avoid ambiguity, we select tasks where the first tokenized sub-word of the model's possible answer relatively constrained, such as Action Antonym, Moving Direction, and Object Count. We and LLaVA-NeXT-7B for this analysis.
adopt LLa
Active temporal interaction in open-ended VideoQA. We examine the impact of temporal interaction within video tokens in open-ended VideoQA. Using the same attention-blocking setup as in previous evaluations, we observed that disabling cross-frame interactions in early-to-middle layers leads to a significant decrease in answer probability even in the open-ended questions answering problems, as shown in Fig. A. These results suggest that active temporal interaction is a general mechanism leveraged by VideoLLMs, regardless of the format of the question answering problems.
Video-language integration. Unlike multiple-choiee question answering, open- apen-ended question answering does not explicitly provide candidate answers. Consequently, the input text lacks explicit temporal reasoning keywords that directly reference temporal information in the video. We hypoth- esize that, in the absence of true option tokens, the last token itself becomes the core checkpoint for video-language integration. To this end, examine the information flow from the video and question tokens to the last token. Figure B shows two different routes: video + last (purple lines) and video + non-option question last (red lines). While video information may first pass through question tokens, the final integration converges at the last token. This behavior aligns with what we have observed in multiple-chot choice tasks, where video and language information merge core checkpoint, although this checkpoint shifts to the last token position in the open-ended case.
Answer generation. We observe that answer generation occurs middle-to-late layers also for open-ended problems. As shown in Fig. C, the prediction probability rises from around layer 20, similar the trend observed in multi-choice question answering problems.
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B.2 MULTIPLE TOKEN GENERATION
Building on our previous finding that the last token serves as the key checkpoint for video-language integration in open-ended generation, we further examine how checkpoints emerge within responses and how information propagates through these checkpoints as the model continues to generate multiple tokens.
Experimental setup. We adopt the Temporal QA subset of VCGBench (Maaz et al., 2024b), video conversation benchmark that includes diverse reasoning-basel QA examples. Specifically, we analyze how information flows among the video, question, generated response, and the last position, as the model continues to generate multiple temporal vocabularies. While automatically identifying temporal vocabulary in generated responses challenging, verbs serve as strong candidates, since they often contain action and time-related semantics crucial for solving VideoQA tasks. To extract temporal vocabulary from model responses, we employed spaCy's 's en-core- en-core-web-lg model detect verbs and used their token positions as semantic anchors for our analysis.
Then, we trace the probability change after Attention Knockout at each stage of anchor generation. For instance, given the question "Whul is happening in this video?" with baseline response "A boy swings a bat and runs to the buses..." ; the detected anchors are [swings, runs, ...]. We then analyze information flow at different generation stages: generating the first anchor with no prior context (e.g.. prompt: "USER: <video> What is happeming in this video? ASSISTANT: A boy". target: "swings") "), generating the second anchor after one (e.g., prompt: "USER: <video> What is happening in this video? ASSISTANT: A boy swings a bat and", target: "runs") "), and so on.
Results. Figure D depicts the information flow of different routes: video - question - last (a-b), video - last (c), and video - non-last response last (d-e). Our results show that, generation continues, newly produced temporal verbs in the response increasingly function as additional core checkpoints. This is evidenced by clear shift in the dominant sources feeding the last position. As the number of temporal anchors increases, the contribution of response to last consistently grows, while the relative importance of video to last and question to last decreases (Fig. D(a-c)). We also observe a structural change in the route through which video evidence reaches the final prediction. At the initial anchor generation with no given anchor, the model relies more on video to question to last (Fig. D(a-b)), whereas with larger number of anchors it increasingly depends on video to the response to last (Fig. D(d-c)).
We observe consistent monotonic trends as the number of generated anchors increases from I to 3; therefore, we expect similar patterns to hold for more anchors. Overall, open-ended generation exhibits the same checkpoint-driven information flow pattern observed in multi-choice QA. The model dynamically forms new checkpoints around temporal concepts, and effective information flow reorganizes accordingly, confirming that our core claims generalize to open ended VideoQA.
C ADDITIONAL VISUALIZATION OF TO-OOSSIOOUU ATTENTION MAPS
We extend our analysis of emergent video concepts propagated through text tokens (Fig. 6) to various VideoQA tasks to verify the generalizability of our findings. To this end, further conduct qualitative analysis of video-to video-to-question attention maps on the other tasks in TVBench.
As shown in Fig. E, the baseline model consistently exhibits attention on video tokens that are semantically aligned with the highlighted query words such as down.first first,from, and left. For example, in the Action Antonym (Fig. E(a)) and Action Sequence tasks (Fig. E(b)), the query tokens focus on frames around the critical action change, while in the Scene Transition (Fig. E(c)) and Moving Direction (Fig. E(d)) tasks, they concentrate on frames that capture the transition of the scene or the motion of the object. In contrast, when cross-frame interactions are disabled, the question tokens fail to attend to relevant temporal vocabulary, resulting in indistinguishable attention maps across frames. This observation highlights the model's inability capture temporal relationships and scene dynamics without cross-frame interaction, which supports the conclusion that our findings are broadly applicable to VideoQA models beyond specific task categories.
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Question: What the action being performed the video? Options: Standing up Sitting down.
Question: What did the person do first?
Options: Opened door. Took the book.
Baseline
Baseline
No Cross-frame Interactions
No Cross-frame frame Interactions
Action Antonym
Action Sequence
Question: What's the right option for how the the video change? Options:.. From the kitchen the roofiop.
Question: Which diraction does the blue cube video?
Options: Up and to the left.
No Cross-frame Interactions
No Cross-frame Interactions
(c) Scene Transition
(d) Moving Direction
Figure E: More visualizations of video-to-question attention maps. Queries are highlighted in yellow, and keys correspond video tokens. The baseline model attends to visually relevant tokens that align with the semantics of each query token, whereas disabling cross-frame interactions makes the model's attention less adaptive and limits ability infer the correct temporal context.
D FULL VISUALIZATION OF LOGIT LENS ANALYSIS
Figure F presents the full Logit Lens visualization referenced in Sec. 3.3. By visualizing the token positions corresponding to extracted spatial and temporal concepts, we observe distinct ordering pattern: static concepts emerge in the earlier layers, while temporal concepts appear subsequently in deeper layers. Furthermore, the emergent positions of temporal concepts spatially align with their relevant foreground regions; for instance, the concept "sit" focuses on the region surrounding seated person. Beyond this overall trend, spatial concepts tend to stabilize on salient regions early, and temporal concepts subsequently emerge primarily on the residual tokens rather than replacing already stabilized spatial tokens. We interpret this behavior as a consequence of priority in spatial localization, which explains the positioning mechanisms temporal concepts.
E SCALABILITY OF OUR FINDINGS
To verify the scalability of our findings, we investigate larger-scale VideoLLM. Specifically, fine-tune tue LLaVA-Next-133 (Liu et al., 2024b) on video instruction tuning datasets, resulting in LLaVA-NO ----T---1--Vieo show that the effective information flow pathways identified in the smaller model remain consistent scale.
In this section, analyze the information flow in the 13B model and
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Menk
Layer 1-5
TeoBooo
ank
Layer 6-10
Moot
Wooer
241
gloya
Boor
Layer 11-15
Layer 16-20
black
Oat c01
Layer 21:25
24
Layer 31-35
st:
Roor
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2rt
Stt
Spabal concepts: (foor", baper, Yood, 'bookk, bagg. 'glass', 'clohhes, 'bot/ley, blankjet). 'sandlwichý, 'personi, 'table, box, 'phone Temporal concepts: ('oaf, 'sir, hold, lupp. down')
Visualization of spatial and temporal concepts extracted by Logit Lens. Spatial concepts tend to settle on salient regions early, and temporal concepts then emerge mainly on the remaining tokens rather than replacing already stabilized spatial tokens.
Figure F:
Active temporal interaction within video tokens. As shown in Fig. G, blocking cross-frame interactions in the early-to-middle layers consistently degrades the performance of the VideoLLM across all tasks (green), different from its ImageLLM baseline (pink). Notably, LLLVA-NeX--II Video-FT exhibits similar trends to LLaVA -LaVA-NeXT-7B Video-FT. highlighting that our findings on active temporal interactions among video tokens generalize across model scales.
Video-language integration on temporal keywords. We further analyze the integration mecha- nism of video and text information in the larger-scale VideoLLM. Fig. H illustrates the impact of blocking attentions between video, question, and last position tokens. Consistent with the trends observed in the smaller-saal model, video information is not directly transmitted to the last token but instead routed through the question tokens. To further trace how video and language infor- mation reaches the option tokens, we analyze the attention flow toward the answer options. As in Fig. J, information from video tokens flows to the true option tokens either directly or indirectly via non-option question tokens. The balance between these pathways varies across tasks, suggesting task-specific patterns in cross- modal integration. These consistent results showcase that our findings on video-language integration hold across scales.
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Table A: Impact effective information flow pathways on LongVideoQA performance. The total number of attention edges is calculated by counting valid (query, key) pairs over all attention layers.
Total Number of Attention Edges
Object-referred
Object before/
Scene-referred Object Tracking
All
Event
after Object
Full causal attention Attention in effective pathways
25.7M (100%)
$2.9
44.4
46.1
10.8M (42%)
54.0
39.4
45.5
Answer generation. Figure K illustrates that generation occurs only after video and language information has been fused, primarily through the option tokens. The main difference from the smaller-scale Videol lies in the specific layer range where this transition from integration to generation occurs, while the overall pattern remains consistent.
F GENERALIZATION OF OUR FINDINGS ACROSS VIDEOLLMS
In this section, we validate the generalization of our findings to other VideoLLMs. Specifically, we employ La and Mini-InternVL--4B. -ntteemnV-----iiiee...... a VideoLLM obtained by fine-tuning Mini-Intern (Gao et al., 2024) on video instruction tuning datasets. We verify Videol.LaMA3- 7B and Vinn-InnernVV.-4BB Video-FT to validate whether our findings on effective information flow generalize across different Vid.oLLMs.
Active temporal interaction within video tokens. Fig. L and Fig. Q show that blocking the attention between video tokens leads to a greater performance drop across all tasks, indicating and VideoLLLMAA-77 also learn stronger temporal interaction through VideoQA training than its ImageLLM counterpart. Moreover, this behavior appears in the carly-to-middle layers, similar the behaviors of the LLaVA-NeXT series.
that Wiiii-nntemnVL-4--iiiieee
Video-language integration on temporal keywords. We analyze how Mini-Intern L----Video- FT and VideoLLaMA3-7B integrate video and language information in response to temporally grounded questions. As shown in Fig. MM and Fig. R. video information is transmitted to question tokens in the maidde layers, and only later transferred to the last position for answer gen- eration. Fig. and Fig. T further show that the video-language information is also gathered in the true option tokens, and the pathways toward the option tokens vary across the VideoQA tasks. These results demonstrate that our findings on video-language integration generally hold across various VideoLLMs.
Answer generation. Fig. and Fig. show that although Mini-Intern Vi-d--- tends to exhibit sharp rise in generation probability across various VideoQA tasks, its overall behavior remains consistent with that of LLaVA-bor VideoLLMs, where the probability begins to increase near the end of the video-language integration process.
G FURTHER ANALYSIS AND DISCUSSION
G.1 QUANTITATIVE VALIDATION EFFECTIVE INFORMATION FLOW PATHWAYS LONG-FORM VIDEOS
We extend the effective pathway analysis to long video question-answeriin problems. To this end, we disabled the ineffective pathways of LLaVA-NeXT--BB-Vieeee Video-FT as configured Section 3.5 and evaluate the performance LongVideo VideoBench (Wu et al., 2024). Table A showcases that LLaVA-NeXT--P7 B---7BB-VVeo--F also retains competitive performance on long-form videos understanding using only 42% of the original attention edges, with only a marginal accuracy drop of 0.6%p. This validates that our findings on the internal mechanisms of VideoLLMs generalize across various video nuestion-answering tasks.
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No cross moss-frame interactions MON-N--NNXT-------ee
No cross-frame interactions (LLLVV-NcXX--13))
%o Change in Pobsbiliyy
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Layor
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(a) Action Antonym (b) Action Sequence (c) Scene Transition (d) Moving Direction
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Figure G: Change in prediction probability when disconnecting cross-frame attention edges in
LLa WeW.-VeXT--13--iie--- and LLaVA-NeXT-133.
Video *
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Figure H: Change in the prediction probability of LLLa N when intervening
on attention edges between video, question, and last token.
Non-optien question
True option
False option Last
% Clange in Pnobobiity
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Sc in Prduubiity
N: Changein in Podubiity
N Chunge in Prebeiaity
(a) Action Antonym (b) Action Sequence
(c) Scene Transition (d) Moving Direction
(e) Object Count
Figure I: Change in the prediction probability of LLLaV--NeNT-BBB-Wieee on attention edges from different parts of the question tokens the last token.
when intervening
Video Non-option question
Non-option question
True option
Video True option
35 Change Paechilry
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+ & i
&
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Tayer
(a) Action Antonym (b) Action Sequenee
(c) Scene Transition
(d) Moving Direction
(c) Object Count
Figure J: Change in the prediction probability of LLaVA-NexTT13B--Wde. when intervening
on attention edges the true option position.
True option
False option
Probabiity
% Probabillty
% Podaaiiliyy
Lager
Layer
Lager
Layer
Layer
(a) Action Antonym (b) Action Sequence
(c) Scene Transition (d) Moving Direction
(e) Object Count
Figure K: Layer-wise prediction probability of LLaVA-NeeT-13R- Video-- for true and false
options in the last token position.
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No cross-fiine coss-ffram interactions MiriernVL--B-Viddooo
frass-frame interactions (Mini-Intern -L-eereteee--BB)
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Change in Protubliry
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(a) Action Antonym (b) Action Sequence (c) Scene Transition (d) Moving Direction
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Figure L: Change in prediction probability when disconnecting cross-frame attention edges in
Maai-nnntenVLL-BB.... Video-FFF and Minii-ntternVL-4BB
Video *
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Last
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Last + Last
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in Probubility
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is
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(d) Moving Direction
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Figure Change the prediction probability of Vini-IntennVI--B--Vieeoe Video-FT when interven- ing attention edges between video, question, and last token.
Non-option question * Last
True option "
Last
False option ~ Last
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Probubiity
% Cannge Preuabiity
15
30
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(e) Object Count
Figure N: Change in the prediction probability of Mini-IntterWVL-4BB. Video-FFF when intervening
on attention edges from different parts of the question tokens to the last token.
Video + Non-option question
Non-option question +
True option
Video True option
Sy Change Preebiirs
25 Change Procsbillyy &
%, Clange Probshilly
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Clange in Pubabiliy &
Layer
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Action Antonym (b) Action Sequence
Scene Transition
(d) Moving Direction
(c) Object Count
Figure O: Change in the prediction probability of on attention edges to the true option position.
when intervening
True option
False option
Pibubbiity
Prttubiliyy
Uribbblity
Prolbiiit
Prfuuiiity
(a) Action Antonym (b) Action Sequence (c) Scene Transition (d) Moving Direction
(e) Object Count
Figure P: Layer-wise prediction probability of Mimi-IntermVI-44B-iideee for and false
options in the last token position.
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No cross-frame interactions (VidooLLaMA33-7)
TV Change in Provebiity
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Change Proubiiity
S Change in Provbbiity
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Scene Transition Moving Direction
Object Count
Figure Q: Change in prediction probability when disconnecting cross-frame attention edges in VideoLLaMA3-7B.
Vidco a Question
Video
Last
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Last * Last
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10
13 Layer
13
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(a) Action Antonym (b) Action Sequence (c) Scene Transition (d) Moving Direction
(c) Object Count
Figure R: Change in the prediction probability of VideoLLaMA3-7B when intervening
attention edges between video, question, and last token.
Non-option question
option
option
Change in Probchiiyy
% Change is Probbbiir
Probbbiity
% Clange in Pobuiilly
Chunge in Prolabiity
Layer
Layer
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(a) Action Antonym (b) Action Sequence (c) Scene Transition (d) Moving Direction
(c) Object Count
Figure S: Change in the prediction probability of VideoLLaMA3-7B when intervening on
attention edges from different parts of the question tokens to the last token.
Video Non-option question
Non-option question
True option
Video True option
No Changee Pobbiiliyy
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(c) Scene Transition
(d) Moving Direction
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Figure T: Change in the prediction probability of VideoLLaMA3-7B when intervening attention edges to the true option position.
True option
False option
Probealiyy
Probability
Probusiiin
Probeiiity
Probdiityy
Layer
Layez
Layer
Layer
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(a) Action Antonym
(b) Action Sequence (c) Scene Transition (d) Moving Direction
(e) Object Count
Figure U: Layer-wise prediction probability of VideoLLaMA3-7B for true and false options in
the last token position.
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Na cross frame interactions
56 Clange Probchiity
% Clange in Pubaahllyy
Campgr in Pobihiiltt
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(b) 030 (8 (8-frames)
(c) SOS (8 (o-frames
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Change Probabiiity
Change Probubility
-10
-1
(d) O2E (24-frames)
(e) 030 (24-frames)
SOS (24-frames)
Figure V: Long VideoBench: Changes in prediction probability after blocking cross-frame
attention in VideoLLaMA3-7B with 8-frames (a-c) and 24-frames (d-f) imputs. Object-referred
Event (O2E), Object before/after Object (030), and Scene-referred Object Tracking (SOS) are used.
Video
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19
(d) O2E (24-frames)
(c) 030 244-frame)
(1) SOS (24-frames)
Figure W: Long VideoBench: Changes in prediction probability when blocking attention edges
between video, question, and last token with 8-frames (a-c) and 24-frames (d-f) inputs.
G.2 GENERALIZATION OF OUR ANALYSIS TO VARIOUS BENCIIMARKS
We further investigate how the information flow changes with different forms of input videos and question types, including long video understanding Long VideoBench (Wu et al., 2024) and spatial understanding in Video-MME, (Fu et al., 2024).
Long Video Understanding. We validate VideoLLaMA3-7B Long Beng (Wu al., 2024) using the same setup in the main paper. Figure V. W, X, and Y highlight the results with 8- 8-frame and 24-frame inputs. with 12 x 12 tokens per frame. Overall, the effective layer ranges for long-form VideoQA maintain similar patterns to short video benchmarks, regardless the input frame lengths. A notable difference is that the probability drop from cross-frame attention and
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Non-option question Last
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Figure X: Long Video Prediction probability change when blocking attention edges from question tokens to the last token with 8-frames (a-c) and 24-frames (d-f) inputs. Object-referred Event (O2E), Object before/after Object (O30), and Scene-referred Object Tracking (SOS) are used.
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15
Layst
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(d) 02E (24-frames)
(c) 030 (24-frames)
(f) SOS (24-frames)
Figure Y: Long VideoBench; Changes in prediction probability when blocking attention edges to
the true option position in Vid33LL with 8-frames (a-c) and 24-frames (d-f) inputs.
video-to-question components is relatively smaller compared to short video tasks (See Fig. Q and Fig. R). We conjecture this to two factors: (1) long video benchmarks typically do not require every frame to be equally informative, reducing the need for comprehensive visual processing, and (2) questions long video benchmarks contain more descriptive information, causing the model to rely more heavily textual cues from the question rather than visual content.
Spatial Understanding. To compare the patterns driven from spatial and temporal reasoning tasks, we adopt action recognition and spatial perception tasks from Video-MME (Fu et al., 2024). Results with LLa ----eeT--BB- Videoo- the action recognition task exhibits a clear and consistent performance drop, indicating that temporal aggregation is crucial for this setting. In contrast, the spatial perception task shows much smaller
shown in Fig. Z. When block the cross-frame interaction,
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Video v Question
Video r Last
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Figure Z: Video-MME: Change in prediction probability when disabling each part of attention edges in LLa LLAVA-NeXT-7B- --d-e--o--... When cross-frame interactions are blocked, the spatial perception task shows much smaller drop, it contains questions that can be answered with static scenes. Other cross-modal information flow remains similar for both tasks.
Video-FT.
optior quession
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$
20
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(a) No cross-frame Intoractions
(b) Vidco ~ Question
(c) Question - Last
Figure AA: Information flow in failed QA samples Moving Direction task in TVBench. Tracing the probability of the wrong answer in failed samples shows that errors stem from erroneous cross- frame attention (Case 1) or static bias (Case 2) during the early stage, rather than from a collapse of the cross-modal integration pathways.
average degradation and much larger variance across samples. We conjecture that this pattern arises because some spatial perception questions incidentally benefits from temporal information, whereas many others can be answered from static scenes. Therefore, the impact of blocking cross-frame interaction ranges from almost no change to significant drop.
G.3 FAILURE CASE ANALYSIS
Continuing from Section 4, we have also conducted failure case analysis in Moving Direction task of TVBench. Specifically, we trace how the probability of a wrong answer changes in the failure samples of LLaVA-NeeT-7B Video-FT. F. As shown in Fig. AA (a), blocking cross cross-frame interactions shows two distinct patterns: (1) the model is highly confident in false options due erroneous signals from failed cross-frame attention (green), (2) the model relies on per-frame static scene information
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No cross-frame interactions (Block from early layers)
No cross-frame interactions (Block from late layers)
Change is Aecusay
% Change in Accurac
Aocurecy
Clange in Ascurac
Change in Aucuray
Layer
Layer
Layer
Layer
Lager
(a) Action Antonym (b) Action Sequence (c) Scene Transition (d) Moving Direction
(e) Object Count
Figure AB: Change in accuracy when gradually disconnecting cross-frame attention edges in each layer. The green line shows the accuracy change when gradually blocking cross- foass-frame attention from the first layer up the 1 layer, whereas the pink line shows the accuracy change when blocking from the 1th layer to the last layer.
Table B: Impact of framm attention in the second half layers of VideoLLMs on answer generation. We block cross-frame attention the first and second half of the total layers and measure the resulting accuracy drop (%). While disabling cross-frame attention in the first half layers significantly degrades accuracy, disabling it in the second half layers barely impact performance.
Action Antonym
Action Sequence
Scene Transition
Moving Direction
Object Count
Case
First half layers Second half layers
24.1
20022
-18.0
44.8
60.88
00.5
-0.7
-0.8
1.7
-1.2
(pink). The cross-mode information flow shown Fig. AA (b-c) remains similar to the successful samples, which implies that the failure mainly stems from the earlier spatiotemporal building process.
G.4 SIGNAL LEAKAGE CHECK
Our Attention Knockout setup blocks cross-frame interaction within a local layer window, which may not capture potential residual signal leakage through bypassing pathways. To address this concern, we extend the intervention in two complementary ways. Instead of blocking cross-frame interaction only in the first half of the layers, we progressively expand the knockout from the first layer up to the N-th layer for N = ,,...,32. ,2. We also conduct the reverse intervention by blocking from the last layer backward. This gradual design explicitly check whether auxiliary signals from video tokens continue to propagate beyond early-to-midde layers, which would contradict the effective information flow range identified in our analysis. As shown in Fig. AB, progressively expanding the knockout does not lead to any additional significant performance drop unless the intervention overlaps with early-to-middle layers, the effective fross-frame interaction range that we identified. These results indicate that the cross-frame interaction actively occurs in the early-to sarly-to-middle layer, supporting the validity of our findings beyond a specific knockout configuration.
G.5 IMPACT OF BLOCKING CROSS-FRAME ATTENTION IN THE SECOND HALF LAYERS
We further examine the impact of blocking cross-frame attention in the second half layers. In Table B, the accuracy drop is marginal when temporal interactions are blocked in the latter layers, compared the earlier layers. This supports our claim that active temporal interaction within video tokens occurs in carly-to-midee layers.
G.6 ROBUSTNESS OF OUR ANALYSES ON THE CHOICE OF WINDOW SIZE k
We observed the robustness our analyses when using window sizes with sufficient width, and therefore chose to follow the & value of used in (Geva et al., 2023). Specifically, to examine the robustness of our analyses to the window sizes, we conducted an extended analyses by varying the window sizes in 1, 5, 9, 13. As shown in Table AC, when the window size is extremely small (e.g.. k==),, the narrow attention block is easily bypassed and VideoLLMs can still transmit information through remaining effective information pathways. This leads only marginal probability drops
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Video
Question
Question Last
% Clange Probabsiity
Change Probability
W
%/ Changge Probability
Change Probabsiltt
1 5
-30
Layer
Layer
Layer
k
(b) l:
(d) A: 13
Figure AC: Impact of window size k on Attention Knockout. Following Geva et al. (2023), we take our default choice.
Table C: Coverage of models. We adopt MLLMs with diverse sizes, base vision encoders, and base LLMs ensure generalizable experimental validations.
Model
Size
Base Vision Encoder
Buse LLM
Mini Moni-IntemVLL. VL-4B
4B
Intern ViT-300M
Phi-3-ming 128k-instruct Vicuna-7B-vll 1auna-13B-v1.5 Qwen2.5-7B
LLaVA-NeXT-7B
7B
CLIP CLIP-ViT-L- L-P-ViI---336xx
LLaVA-NeXT-133
13B
CLIP-VIT-L-336pX
LaM3-7B
7B
siglip-so4000m-pucchh443333
across the layers. In contrast, with wider windows (k=5,9,13). we observe significant probability drops, which validates the robustness of our analyses across various choice of window sizes.
G.7 DISCUSSION ON THE PROBABILITY INCREASES IN THE LAST LAYERS
We observe an increase in the true option probability in some cases when attention from the question the last position is knocked out in later layers (e.g., g.. Fig. 1(b)). Interleaving the attention knockout analysis from question to last (Fig. 3 and H) and the layer-wise prediction probability analysis (Fig. 9 and K) suggests that propagating information from question tokens to the last token boosts the true option, as this flow consolidates evidence in the middle layers. Therefore, the belief of VideoLLMs already stabilized, probabilities for both true and false options. Thus, blocking the pathway only at the final layers preserves earlier propagated evidence for the true option in the hidden states while preventing further amplification of false options.
keeping this pathway open mainly acts a broad amplifier, increasing
H IMPLEMENTATION DETAILS
We describe the implementation details for the VideoLLMs and their training setup. Table C shows details of the VideoLLMs.
Training setup. Our video instruction tuning data is derived from VideoChat2-IT (Li al., 2024a), comprising 874k samples covering tasks such as VideoQA, captioning, reasoning, classification, and conversation. These samples from diverse video understanding benchmarks, including VideoCharGPT-100g (Maaz et al., 2024b), VideoChat- 1k (Li et al., 2023b), Webvid (Bain et al., 2021), YouCook2 (Zhou al.. 2018). TextVR (Wu al., 2025), NExT-QA (Xiao et al., 2021), CLEVRER (Yi et al., 2019), TGIF (Li et al., 2016). Ego4D (Grauman et al., 2022), Kinetics-710 (Kay al., 2017), and Something Something V2 (Goyal al., 2017). We freeze the vision encoder while fully fine-tuning the MLP projector and LLM backbone. Our experiments are conducted with NVIDIA A6000 GPUs.
LLAVA-NeXT-TAN Video-FT. During training, we initialize the model with LLaVA-NeXX 7B (Liu et al., 2024b), which employs CLIP-VIT-L-336px (Radford et al., 2021) as the vision encoder and Vicuna-7B -B-vl--B-vv.5 (Zheng et al., 2023) as the language model. We utilize a batch size of 128 and train for 3 epochs. The base learning rate is initially set to 2e-5 and
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Table D: List of vocabularies used for semantic concept extraction. Keywords are parsed from Action Sequence question prompis and converted to lowercase and present tense to avoid interference from linguistic completion in later layers.
Spatial Keywords
bug, bed, blank, book, box, cabinet, camera, clothes, cup. door. floor, food, glass, laptop, puper. person. phone, sandwich, table
Temporal Keywords
close, down, drink, eat, hold, on, open, put, sit, take, throw, tidy, up
Table E:
Effective pathway layer ranges for different VideoLLMs. (a) Layer ranges for effective pathways across different models, determined by selecting 5-layer intervals with significant probabil- ity drops from Attention Knockout analysis. (b) Detailed knockout results showing probability drops across layer intervals in Action Antonym task. Significant drops (< 55) are highlighted in gray; N/A indicates unavailable layers.
(a) Effective pathway layer ranges
Model
Cross-frane Interactions
Video-to Video-to-Question
Question-to to-Last
LLaVA-NeXT-7BB- Video-FF
L6-15
L6-20
L16-25
LLaVA-NeXT-I3B-VAN Fi
L6-15
L6-20
LI6-30
Ba-tnnennnL-----iieeee VideoLL
L6-15
L6-20
L11-30
L1-15
1.6200
1,21-28
(b) Attention Knockout results in Action Antonym
Model
Interaction
L1-5
16-10
L11-15
L16-20
1,21-25
1,26-00
L31-35
L33-40
Cross-frame
-42
-11.1
-6.3
-0,2
0
-022
-0.2
N/A
LLAVA-NeXT-7B-Videe
Video-to-Question
-3,9
-15.1
-21.5
-5.6
-0.2
0
N/A
Question-to-Lass
-0.3
-1.2
-4.5
-19.3
-15.1
0.7
1.1
Cross-frame
-0.7
-11.2
-11.1
-2.1
-0.2
-0.2
-0.2
-0.3
LLaVA-NcKT-13B-VidooFF
Vidco-to-Qucstion
-1.2
-16.7
-29.1
-9.2
-0.3
-0,2
-0.2
-0.2
Question-to-Lass
-1.8
-2.0
-4.6
-21.7
-28.4
-5.7
-0.1
-1.9
Cross-frame
-2.3
-11.1
-11.5
-3.3
0
0.2
0
N/A
Enn--neemVLL-B--ideeeo
Video-to- QueQusttonn
-2.4
-24.4
-35.0
-15,9
-1.3
-0.3
-0.2
N/A
Question-to-Last
0
-1.3
-5.9
-30.8
-46.8
-1411
-3.2
N/A
Cross-frame
65.1
-61.4
-14.9
-5.0
0
0.2
N/A
N/A
VideoLLaMA3-7B
4.3
-7.2
-18.5
-16.3
-2.2
N/A
N/A
Question-to-Lee
2.0
3.7
1.9
-2.7
-16.2
19.1
N/A
N/A
is decayed to 5e-6 using cosine scheduler, with warmup ratio of 0.2. For both training and inference, we uniformly sample 8 frames as input and resize each frame into 336x 336 pixels. These frames are then processed through vision encoder extract 8x24x24 patch embeddings. Next, we use an MIP projector to project these embeddings, followed by average spatial pooling to generate 8x 12x 12 video tokens.
LLaVA-NeXT-I3B- Video-F.. Similarly, we initialize the model with LLaVA-NeXT- 13B (Liu et al., 2024b), which utilizes CLIP-ViT-L-336px (Radford al., 2021) as the vision encoder and Vicuna-13B-vi (Zheng et al., 2023) for the language model component. The model is trained for 1 epoch using the same training recipe and video token sampling strategy as LLaVA-NeXTT-BB-Vin Video-FFF.
Mini-Intern VI.-4B- Viddo-F- We start with Mini-Intern VL.-BB (Gao et al., 2024), which adopts Intern VTT-300M-444535 as the vision encoder and Phi-3-mini (Abdin et al., 2024) as the LLM backbone. We use batch size of 128 with a learning rate of 4e-5, which decays to zero following cosine schedule with a warmup ratio of 0.03 for total of epoch. For both training and inference, we uniformly sample frames as input and resize each frame into 448 x 448 pixels. These frames are passed through the vision encoder, producing 32x332 patch embeddings. After applying the MLP projection, we put 8x 16x 16 video tokens as the input of the language model.
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VideoLLaMA3-7B. VideoLLaMA3-7B (Zhang al., 2025a) uses SigLIP (Zhai et al., 2023) as a vision encoder and Qwen2.5-7B (Yang et al., 2024) as a LLM backbone. We directly use VideoLLaMA3-7B without fine-tuning and put 8x 12x 12 video tokens as the input.
Implementation details for Attention Knockout. In VideoQA, model generates an answer from given video-question pair (v,9). where the question may contain n number of options [g::0:... We employ Attention Knockout (Geva et al., 2023) to measure the information flow between different input parts. Specifically, the model initially predicts the answer a with the highest probability Phase at the last token position of the input sequence. After applying Attention Knockout as explained in $ 2.2, we trace the relative change in probability RPetaange ((Knookoout Phase Phase)/Ponse) X 100, where Pknockout is the updated probability for the same answer derived after intervention. Unless otherwise stated, we apply Attention Knockout within a window size of k = layers around the 1t layer of MILMs, and trace the probability change for the first tokenized subword of the complete answer.
Implementation details for Logit Lens. To quantify emergence of spatial and temporal semantic concepts in video tokens, we employ Logit Lens (nostalgebraist, 2020). We trace top-1 logits by projecting intermediate representations of all video tokens across layers using the language model head. We use Action Sequence videas with LLaVA-NeXT-II 13B- 1---------- For the vocabulary pool, we parse spatial and temporal keywords from Action Sequence question prompts (Table D). To trace initial concept emergence, we convert parsed words to lowercase present tense, as linguistic completion occurs in later layers and can impact the analysis.
Implementation details for effective pathway analysis. To identify effective pathways, Attention Knockout results from Action Antonym tasks (Table E). We divide layers into 5-layer intervals, calculate average probability drops, and select intervals with significant drops (< -5%) as effective layers. We then enable cross-frame. interactions, video + question, and question + last flows only within these effective layers while disabling video last and last last connections across all layers. Additionally, flows to video and question tokens are blocked in late layers as these tokens are no longer needed (e.g., after layers 20 and respectively in M...VA--NeT---- Video-FT).
THE USAGE OF LARGE LANGUAGE MODELS
In this work, LLMs were used only polish manuscript clarity, fix grammatical errors, and enhance readability. Specifically, all initial writing was done by the authors, with LLMs used afterwards for sentence-level polishing in part the manuscript. LLMs were not involved in research ideation and experimental design. All core contributions, methodologies, and findings are the result of the authors' original work.
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Reviews:
[{"review_id": "JZF4gtGhd6", "reviewer": "Reviewer_NrDF", "summary": "This paper presents a mechanistic interpretability study of Video Large Language Models (VideoLLMs) to understand how they perform temporal reasoning for video question answering (VideoQA). Using techniques like Attention Knockout and Logit Lens, the authors trace the flow of information through the model's layers. They identify a consistent, multi-stage process where the model does temporal encoding, followed by video-language integration and finally answer formulation. \nTo validate their findings, the authors demonstrate that by preserving only these identified \"effective information pathways\" and pruning a substantial portion of the attention edges (e.g., 58% in LLaVA-NeXT-7B), the model's performance on VideoQA benchmarks remains largely intact, whereas random pruning of the same number of edges causes a significant performance drop. The study's findings are shown to be consistent across different model architectures and scales.", "questions": "* The analysis convincingly shows that the \"true option\" token is a key integration point in multiple-choice QA. In a purely open-ended generation task without explicit options, where does this critical video-language integration converge? The paper suggests it shifts to the last token, but is the process as effective and localized without the explicit semantic anchors provided by the options?\n* The pruning experiment is performed post-hoc on a trained model. Do you think these findings could be leveraged during training? For instance, could a regularization technique that encourages this sparse, efficient information flow lead to more robust, generalizable, or computationally efficient VideoLLMs? For example, if visual integration is important to occur early (as also observed in [3]) and is useful, can the embeddings be fed back in earlier? \n* How do you hypothesize these pathways would adapt to videos where the crucial temporal information is not uniformly distributed, such as in \"needle-in-a-haystack\" tasks common in long-video understanding? Would the cross-frame interactions in early layers become more dynamic or would the model rely on different mechanisms entirely?\n* The study focuses on temporal reasoning. Did you perform any analysis on tasks that are purely spatial (e.g., \"what color is the object on the left throughout the video?\") to see if the information pathways differ significantly, perhaps by bypassing the cross-frame interaction stage (see above comment on spatial distribution)?\n* In Figure 1(b), why does Question -/-> Last have an increase in probability for the later layers?\n\n[3] Nikankin et. al., Same Task, Different Circuits: Disentangling Modality-Specific Mechanisms in VLMs. arXiv 2025.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "**Important and Timely Problem**: While VideoLLMs are advancing rapidly, their internal reasoning mechanisms are poorly understood. There is a large body of work done on LLMs, and somewhat less so for Vision Language Models (VLMs) (although recently has gathered much attention). This paper addresses a critical gap by providing one of the first mechanistic analyses of temporal reasoning in these models, moving beyond the more common studies on static image-based VLMs.\n\n**Rigorous and Causal Methodology**: The authors employ established and powerful interpretability techniques (Attention Knockout, Logit Lens). The use of Attention Knockout is particularly strong, as it allows for causal interventions, disabling specific information pathways and measuring the direct impact on output probabilities. This provides much stronger evidence than purely observational methods like attention visualization.\n\n**Clear Narrative and Strong Empirical Support**: The paper presents a compelling and easy-to-follow \"blueprint\" of information flow. Each stage of the proposed process is backed by targeted experiments. For instance, the comparison between image- and video-tuned models clearly demonstrates the emergence of cross-frame reasoning (Figure 2), and the analysis of information flow to question vs. option tokens provides a nuanced view of integration points (Figures 6 & 7).\n\n**Thoroughness and Generalization**: The authors validate their findings across multiple modern VideoLLMs (LLaVA-NeXT 7B & 13B, Mini-InternVL-4B, VideoLLaMA3-7B) and datasets (TVBench, TOMATO). This demonstrates that the identified information flow patterns are a general property of current VideoLLMs rather than an idiosyncrasy of a single model.", "weaknesses": "**Reliance on Attention Knockout**: The primary evidence for this model of information flow is based on attention knockout, which is conceptually sound. However, this makes the takeaways prone to method-specific idiosyncrasies, so having an experiment to validate the conclusions which isn’t knocking out pathways for effective flow would strengthen the paper significantly. \n\nFor example, complex reasoning might involve more iterative processes, where later layers revisit or re-contextualize information from earlier layers (e.g., text representations directing further attention to specific video frames). The current knockout methodology, which blocks entire pathways across layers, may not capture such dynamic or recursive patterns. Blocking i->j for some layers but not all risks leaking alternative pathways of obtaining such information, which captures a flawed picture of what is happening. One example of this is Table 2, where a total cross-frame attention knockout from L1-16 could be extended to looking at the L1-N, N=1,...,32, to see when the integration is important until. \n\n**Limited Scope of Tasks Format for Reasoning**: The analysis is primarily focused on multiple-choice VideoQA on the TVBench benchmark, which features relatively short videos and structured questions. It is unclear if this clean, sequential \"blueprint\" holds for more complex, long-form video tasks such as summarization, script generation, or open-ended dialogue, where temporal relationships are more complex and reasoning is less anchored to specific keywords. How are current pathways measured for multiple output token generations? Could such a method hold up for longer generations?\n\n**Coarse-Grained Analysis of Representations**: The paper successfully identifies where and when information flows (i.e., which layers and between which token groups) but offers less insight into the substance of the information itself. For example, while Logit Lens shows the emergence of \"temporal concepts\" as vocabulary items, it doesn't reveal what features in the hidden states (e.g., motion, object state change, event boundaries) constitute these concepts. The analysis remains at the level of token interactions rather than diving into the underlying neural representations. The logit lens analysis could also be used to understand the spatial distribution of how the model is distributing attention, much like previous VLM works [1,2]. Something to this effect as an addition to this paper would be significant. \n\n[1] Jiang et. al. Interpreting and Editing Vision-language Representations to Mitigate Hallucinations, ICLR 2025. \n\n[2] Chen et. al., Why Is Spatial Reasoning Hard for VLMs? An Attention Mechanism Perspective on Focus Areas, ICML 2025.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "fjDkKivJyG", "reviewer": "Reviewer_wzb7", "summary": "This paper focuses on uncovering the internal information flow mechanisms of VideoLLMs for VideoQA tasks. Using interpretability techniques (e.g., Attention Knockout), it analyzes how VideoLLMs extract and propagate spatiotemporal and textual information across layers and modalities. It identifies consistent temporal reasoning patterns across diverse VideoQA tasks and validates the sufficiency of \"effective information pathways\" for maintaining model performance.", "questions": "See weaknesses.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 2, "strengths": "Overall, this paper is well written.\n\n1. This paper pioneers using mechanistic interpretability (Attention Knockout, Logit Lens) to unpack internal information flow, which is an interesting and novel topic.\n\n2. Multiple models (LLaVA-NeXT-7B/13B, Mini-InternVL-4B, VideoLLaMA3-7B) and benchmarks (TVBench, TOMATO, LongVideoBench) are tested, with validations for scalability and generalization, ensuring reliability.", "weaknesses": "The paper observes that spatial concepts emerge in very early layers and temporal concepts in middle layers (via Logit Lens analysis on video tokens) but fails to probe the underlying causes. For instance, it lacks ablations on critical factors directly tied to this phenomenon: visual encoder design (e.g., CLIP-ViT-L-336px in LLaVA-NeXT models vs. SigLIP in VideoLLaMA3-7B, as noted in Table C) and the temporal information density of video fine-tuning data—both of which could explain why concept emergence timelines differ. Deeper investigation into these root factors would significantly strengthen the persuasiveness of its findings on concept emergence patterns.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ZXBCsoehTC", "reviewer": "Reviewer_trVM", "summary": "This paper investigates the internal mechanisms of Video Large Language Models (VideoLLMs) on VideoQA tasks using mechanistic interpretability techniques. By applying tools such as Attention Knockout and Logit Lens, the authors identify a consistent three-stage information flow: (1) cross-frame interactions among video tokens in early-to-middle layers, (2) video-language integration onto temporal keywords in the middle layers, and (3) answer generation in the middle-to-late layers. The authors validate these \"effective pathways\" by demonstrating that a pruned model, which retains only these key connections (e.g., 42% of edges), largely maintains its baseline performance.", "questions": "See weaknesses above.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper addresses a critical and under-explored area. As VideoLLMs grow in capability, understanding how they perform temporal reasoning (their internal mechanism) is as important as if they can (their performance). Applying established interpretability techniques from text/images to the spatiotemporal domain is a valuable and logical contribution.\n2. The paper's main contribution is a clear \"blueprint\" of the temporal reasoning process in VideoLLMs (Fig. 1). The identified 3-stage pathway—cross-frame interaction, integration, and generation—is intuitive and provides a strong conceptual framework for the research community.", "weaknesses": "1. Selection bias in analysis: The analysis is exclusively performed on samples where the model outputs the correct answer. This introduces a selection bias, as the study only explains the mechanism of successful reasoning. A complete mechanistic understanding should also include a failure-mode analysis. It is unclear if model errors are due to a breakdown in these same pathways (e.g., failed video-language integration) or if they follow entirely different, pathological pathways.\n2. Limited Temporal Scope of Inputs: The entire analysis is conducted on models processing only 8 frames of video. This is a short temporal window, and the findings may not generalize to more complex, long-form video reasoning.\n3. Reliance on Multiple-Choice Format: The paper's core findings—such as the convergence of information onto the \"true option\" tokens —may be an artifact of the multiple-choice task format itself. This format steers the model toward a \"verification\" task (i.e., selecting the best option) rather than a \"generative\" one. This makes it unclear if the identified pathway is fundamental to temporal reasoning or just a learned shortcut for solving MC-QA. While the Appendix (SC) attempts to address this with an open-ended analysis, this analysis is limited, as it only includes three of the five tasks, potentially omitting more complex reasoning scenarios.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "lUWrlY0BVo", "reviewer": "Reviewer_5oZh", "summary": "This paper conducts a mechanistic interpretability study to uncover how Video Large Language Models (VideoLLMs) internally process temporal information for video question answering (VideoQA). Using techniques such as Attention Knockout and Logit Lens, the authors trace how video and text signals propagate across model layers. They identify a consistent three-stage information flow pattern: (1) temporal reasoning begins with active cross-frame interactions among video tokens in early-to-middle layers; (2) video-language integration emerges in middle layers as video representations align with linguistic embeddings tied to temporal concepts; and (3) answer generation occurs in middle-to-late layers once integration is complete. Additionally, the authors show that retaining only the identified “effective information pathways” preserves VideoQA performance while suppressing up to 58% of attention edges in LLaVA-NeXT-7B-Video-FT, demonstrating that these pathways are sufficient for temporal reasoning. The study further validates that these patterns generalize across model sizes, architectures, and both multiple-choice and open-ended question settings.", "questions": "- If we perform the same analysis on non-temporal video understanding tasks (e.g., \"What is the main object presented in the video?\"), what would the information flow be like?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "- This paper tackles an important yet under-explored problem: how spatiotemporal information is internally processed and propagated across layers in VideoLLMs.\n- The experimental design is rigorous and well thought out, making effective use of mechanistic interpretability techniques such as Attention Knockout, Logit Lens, and attention map visualizations.\n- The study successfully identifies the “effective information pathways” that underpin temporal reasoning in VideoLLMs, providing valuable insights into their inner workings.\n- The paper is clearly written, logically structured, and easy to follow.", "weaknesses": "- The paper does not clearly articulate how its findings can be leveraged to guide the future development or improvement of VideoLLMs, limiting the practical implications of the insights.\n- Most analyses are conducted on a single benchmark (TVBench), which may constrain the generalizability of the conclusions. Incorporating additional video temporal reasoning benchmarks (e.g., [1,2,3,4,5]) would strengthen the robustness of the results.\n- The attention map visualizations in Fig. 5 are only presented for a single task. Including examples from all five task types listed in Table 1 would provide a more comprehensive understanding of the observed behaviors.\n- The paper overlooks several relevant studies on video temporal reasoning benchmarks [1,2,3,4,5] and related methodological advances [6,7].\n\n[1] Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis.\n\n[2] TempCompass: Do Video LLMs Really Understand Videos?\n\n[3] MotionBench: Benchmarking and Improving Fine-grained Video Motion Understanding for Vision Language Models.\n\n[4] Vinoground: Scrutinizing LMMs over Dense Temporal Reasoning with Short Videos.\n\n[5] TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models.\n\n[6] Temporal Preference Optimization of Large Multimodal Models.\n\n[7] Temporal Reasoning Transfer from Text to Video.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)