SiLVR: A Simple Language-based Video Reasoning Framework
Authors: Ce Zhang
Yan-Bo Lin
Ziyang Wang
Mohit Bansal
Gedas Bertasius
Venue: Accepted by TMLR ()

Abstract: Recent advances in test-time optimization have led to remarkable reasoning capabilities in Large Language Models (LLMs), enabling them to solve highly complex problems in math and coding. However, the reasoning capabilities of multimodal LLMs (MLLMs) still significantly lag, especially for complex video-language tasks. To address this issue, we present SILVR, a Simple Language-based Video Reasoning framework that decomposes complex video understanding into two stages. In the first stage, SILVR transforms raw video into language-based representations using multisensory inputs, such as short clip captions and audio/speech subtitles. In the second stage, language descriptions are fed into a powerful reasoning LLM to solve complex video-language understanding tasks. To handle long-context multisensory inputs, we use an Adaptive Context Reduction scheme, which dynamically determines the temporal granularity with which to sample the tokens. Our simple, modular, and training-free video reasoning framework achieves the best-reported results on Video-MME (long), Video-MMMU (comprehension), Video-MMLU, CGBench, and EgoLife. Furthermore, our empirical study focused on video reasoning capabilities shows that, despite not being explicitly trained on video, strong reasoning LLMs can effectively aggregate multisensory input information from video, speech, and audio for complex temporal, causal, long-context, and knowledge acquisition reasoning tasks in video. More details can be found at https://sites.google.com/cs.unc.edu/silvr.

Published in Transactions on Machine Learning Research (01/2026)
SiLVR A Simple Language-based Video Reasoning
Framework
Ce Zhang
cezhang@es.umm unc. edu
Deportment of Computer Science UNC Chapel Hill
Yan-Bo Lin
yblin@es. unc.
Deportment of Computer Science UNC Chapel Hill
Ziyang Wang
wwyangu@ss.unc....
Deportment of Computer Science
UNC Chapel Hill
Mohit. Bansal
sabansal@@s.nuc..coo
Deportment of Computer Science
UNC Chapel Hill
Gedas Bertasius
gedas Wes. unc. edu
Deportment of Compuler Science
UNC Chapel Hill
Reviewed on OpenReview: httpsss/pperrrvvveee.ee////o//id
Abstract
Recent advances in test-time optimization have led remarkable reasoning capabilities in Large Language Models (LLMs), enabling them to solve highly complex problems in math and coding. However, the reasoning capabilities of multimodal LLMs (MLLMs) still significantly lag, especially for complex video-language tasks. To address this issue, we present SiLVR, a Simple Language-based Video Reasoning framework that decomposes complex video understanding into two stages. In the first stage, SiLVR transforms raw video into language-based representations using multisensory inputs, such as short clip captions and audio/ audio/speech subtitles. In the sccond stage, language descriptions are fod into a powerful reasoning LLM solve complex languaan understanding lasks. To handle long-context Adaptive Context Reduction scheme, which dynamically determines the temporal gramularity with which to sample the tokens. Our simple, modular, and training-free video reasoning framework achieves the best-reported results on VideoMME (long), Video-MMI.U, CGBench, and EgoLife. Furthermore, our empirical study focused on video reasoning capabilities shows that despite not being explicitly trained on video, strong reasoning LLMs can effectively aggregate multisensory input information from video, spoech, and audio for complex temporal, causal, long-context, and knowledge acquisition reasoning tasks in video. More details can be found at httts://siites. google. csm/cs. unc.edu/silvr
multisensory inputs,
1
Introduction
Recent years have witnessed remarkable progress general video understanding, with large multimodal models achieving strong performance on tasks such as video question-answering (VideoQA) (Tam al.
2023
Hurst et al. 2024 Bai al. 2025 Zhang et al. 2024c), text-video retrieval Zhao et al. 2023), and

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VideoMME (Long)
Video-MMML (Comprehension)
Video Video-MMLU (Quiz)
EgoLife
80
80-
44
70
70-
42
76-
Accuracy
05
40
74-
65
53
30
72
50
36
70
GPT-46
Gemini-1.5
SILR
Gemini11.5
50
GPT-40
SILRR
60
GP1446
Gemind-115
SILR
GPT-40
Gemini-1.5
SIVRR
Question:
Which of the following ingredients is used the artwork? Reasoning Trace
The question is asking which ingredient not used the artwork. The options are: shell (option glue (option B), blue food dye (option C), and oil (option D). Glue is definitely used (option B). Shells are also mentioned (option A). We also see adding a drop of blue food dye to the bottle (aption C). Oil also used (option D). So all four options are used?
Walt, maybe made a mistake. Maybe the shells are part of the decorative items but not an ingredient used in the artwork's construction? The question is about ingredients used in the artwork. Therefore, the shells might be part of the decoration around the bottle the video but not part of the actual artwork (the bottle's contents). So the
would be A: Shell.
Figure 1: Strong reasoning capabilitics of SiLVR complex video QA tasks. SiLVR leverages recent advances in reasoning LLMs for complex video QA tasks. SiLVR achieves better performance than strong proprietary non-reasoning models (i.e., GPT-4o and Gemini-1.5) on benchmarks like VideoMME (long), Vidco-MMMU (Comprehension), Video-MMLUU and EgoLife, which include temporal, causal, long-context, and external knowledge reasoning tasks. The example reasoning trace shows SiLVR's capability to perform sell-correction, in which successfully identifies that shells are decorative rather than functional.
temporal localization (Huang al. 2024 Ren al. 2024 Wang et al. 2024c). Despite the remarkable
progress, most existing methods struggle with complex sideo-language understanding tasks that require strong reasoning capabilities (c.g., temporal, causal, long-context, external knowledge, etc.). Following the
success of reasoning LLMs (Guo et al. 2025 Jaech et al. 2024), several recent multimodal large language models (MLLMs) proposed reasoning frameworks for multimodal image/vides /video recognition tasks Liu et al.
2025 Fei et al.) 2024 Wang et al.] 2024d Wu et al,, 2025 Feng et al.) 2025 Chen et al. 2025 Li et al,
2025 Wang et al. 2025). However, these methods either rely on high-quality Chain-of Thoughht (CoT) data,
which is expensive and time-consuming to collect, or require task-specific reward designs, leading to poor generalization. Moreover, such RL-based multimodal reasoning approaches are difficult optimize and often require large amount of resources for training. Lastly, many recently proposed RL post-training techniques lead to similar sometimes even worse performance than standard Supervised Fine-tuning (SFT) approaches (Wang & Peng 2025 Feng et al. 2025
Motivated by the impressive reasoning abilities of recent LLMs (Guo et al. 2025
Jaech et al. 2024 we
propose SiLVR, a simple, modular, and training-free free language-bar based framework for complex langual reasoning tasks. SiLVR decomposes video understanding into two stages:
In the first stage, we convert raw videos into rich language-based descriptions. Specifically, we densely sample short clips from the input videos and use pre-trained visual captioner (e.g., NVILA (Liu al. 2024b)) to extract captions for each clip. Additionally, we use automatic speech recognition (ASR) tools to convert speech into language descriptions.
the second stage, we feed the rich language descriptions into a strong reasoning LLM (e.g. DeepSeek- RI (Guo et al. 2025)) to solve complex video-language understanding tasks.
To address the issue of processing large number of tokens in potentially hour-long videos, we propose a simple Adaptive Context Reduction scheme, which dynamically determines the temporal granularity with
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which to sample the speech and video tokens. Such context reduction scheme enables to significantly reduce the number of input tokens to fit within the context. length of an LLM, while maintaining strong reasoning performance.
Compared to prior MLLM- video reasoning frameworks, SiLVR is simple, modular, training- and highly-performant. SiTVR achieves state-of-the-art results on multiple VideoQA benchmarks, including Video- MME (long), Video-MMLU, CGBench, and LgoLife. Additionally, SiLVR demoustrates strong spatiotemporal grounding ability for video QA tasks that require localizing relevant video segments. On CGBench, large- scale grounded VideoQA benchmark, SiLVR. outperforms the previous best method by a substantial margin of 6.1% in mloU. Additionally, our empirical study on video reasoning capabilities of our framework suggests that despite not being trained on videos, strong reasoning LLMs can successfully aggregate information from video, aaeech/auddo for complex temporal, causal, long-context, and external knowledge reasoning tasks on video inputs.
While SiLVR is not based on any new complex design choices, it is simple, modular, training-free, and highly performant and generalizes to multiple complex video-language understanding tasks. We believe the simple yet effective design of SiLVR will enable the research community to build our work and use our simple framework as baseline to develop even more powerful video-language reasoning models.
2 Related Work
Language Reasoning Models. Recent work has significantly advanced the reasoning capabilities of LLMs through various strategies, such as Shain-of-Thoughh (Wei et al. 2022: Kojima et al., 2022). Self-
Consistency (Wang et al. 2023), and Monte Carlo Tree Search based mothods (Wan ct al. 2024 Trinh
ct al.) 2024 Xin et al., 2024). Recently, works such as DeepSeek-R1 (Guo et al.
2025) demonstrated that
applying large-scale RT. with accuracy and format rewards can induce emerging reasoning capabilities in LLMs.
These RL-based methods have shown strong improvements in tasks such as mathematics (Zheng et al. 2021
Azerbayev et al. 2023) and code generation (Austin et al.) 2021 Hendryeks et al.
2021). Motivated by these
successes, we propose to take advantage of the strong reasoning ability of LLMs for complex video-language
reasoning problems.
Multimodal Reasoning Models. There have been many efforts to augment MLLMs with reasoning capabilities. One line of work focuses on decomposing the reasoning process into multiple sub-problems (Shao ct al.
2024 Zhang et al.
2023c Xu et al.]
2024 Zhang et al.
2024bb. Motivated by the success of DeepSeek-
R1 Guo et al. 2025), another line of work explores RL to elicit the reasoning ability of the MLLMs (Huang
ct al. 2025 Shen et al. 2025 Yang et al.
2025b
Zhang et al. 2025 Ouyang 2025
Peng et al. 2025 In the
video domain, VodeoCoT [Wang et al. 2024d). Video-of-Thoughtt Fei et al. 2024). and Videolispresso Haan
cct al. 2025) propose to prompt the MLLMs with multiple reasoning steps before answering the question. In
addit multiple concurrent works propose lo use GRPO to euhance video reasoning (Wu el al. 2025 Feng
et al.] 2025: Chen al. 2025 Li et al. 2023 Wang et al.) 2025). However, many of these RL-based methods demand substantial training computation, and achieve only marginal improvements or perform even worse than the SFT methods (Wang & Peng 2025 Feng et al. 2025) Unlike these methods, SiLVR is simple, training-free, yet highly performant across a wide range of VideoQA benchmarks.
Complex Video-Language Understanding. A variety of benchmarks for complex video-language understanding have been proposed, with a focus on comprehensive evaluation of videos with different durations and questions spanning diverse categories (Fu et al. 2024: Li et al. 2024b Liu et al. 2024a Rawal
et al. 2024 Zhou al. 2024). In parallel, several benchmarks have been introduced to assess the reasoning capabilities of large video-language models (Hu et al. 2025
Song et al.
2025 Zhao et al.
2025 He et al.
2024h. On the modeling side, recent video MLLMs adapt image MLLMs by fine-tuning additional modules
for temporal modeling (Lin et al. 2023 Li et al. 2023
2024a
Zhang et al. (023b). Several follow-up works
explored spaliolemporal token compression Islam el al. 2025
Liu et al.]
2024b Bai el. al.] 2025 Shen el al., Jin et al. 2024 Islam et al. (024)),
2024 Shu et al. 2025), building hierarchical memory (Song et al.) 2023
or incorporating information from external models (Tang et al. 2025 Wang et al. 2024a) for complex and
long video understanding. Another line of work explores training-free free frameworks that first convert raw videos into dense visual captions, then perform reasoning with off-the-shelf LLMs (Zhang et al.] 2023a Wang
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Question: what is the order of planet appearances in the video?
Venus, Jupiter, Neptune. Mercury, Jupiter, Mars. Venus, Neptune, Jupiter. Jupiter, Mercury, Neptune.
Short Clip Captioner
Short Clip Captioner
Short Clip Captioner
mysterious, glowing object suspended in the air against backdrop of intense red and....
A massive volcanic eruption with thick, dark smoke and billowing
large spacecraft with dark, angular design and
Speech
The question asks the order in which planets are introduced. ...The subtitles mention "Mercury ... around 00:03:23... After Jupiter, Saturn mentioned with rings storms. Following Saturn, Neptune is discussed...Lokiig at the options, option A is Venus, Jupiter, Neptune. Option C is Venus, Neptune, Jupiter, which would be incorrect. So correct answer should be
00:03 >> 00:10 the future
Adaptive
humanity may leave the
Context
00:07 -> 00:17 earth behind and search for new home
Reduction
00:10 >> 00:20 space but, where our solar system a
Reasoning
Visual Reasoning Speech Reasoning
LLM
Figure 2: Method overview. SiLVR is a simple two-stage sunguagebassed video reasoning framework. Top: The video segmented into short clips and paired with speech. clip captioner processes each segment. generate visual descriptions. The speech is transcribed using ASR. Bottom: A reasoning LLM takes the question, transcribed speoch, and dense visual descriptions compressed by Adaptive Context Reduction to perform complex video reasoning. In the shown example, SiLVR infers the correct order by integraling information across both visual and speech modalities. The model correctly identifies the sequence through reasoning and eliminating incorrect options.
ct al. 2024b Fan et al. 2024 Ma et al. 2024 Liao et al. 2024 Wang et al. 2024c Min et al. 2024).
rast prior agent-based video underslanding hods, SiLVR locuses on solving complex video-language reasoning problems with strong reasoning LLMs. Additionally, SiLVR employs simple single-pac pass design, effectively integrates both visual and audio modality streams, and uses Adaptive Context Reduction to efficiently manage long input context.
3 Method
Our method decomposes video-language QA into two stages: 1) extracting visual captions and transcribing speech into text, and 2) performing language-based reasoning over the extracted textual descriptions. Such a decomposed video reasoning design offers several benefits: 1) Simplicity: SiLVR does not require complex RL-based video optimization or specialized modules for different tasks. 2) Generalizability: our method can be applied to wide range of complex video-language tasks without task-specific fine-tuning. 3) Modularity: our method's modular design enables seamless use of powerful visual captioning models and strong reasoning LLMs. 4) Flexibility: SiLVR supports plug-and-play integration of different captioning models, speech recognition models, and LLMs. An overview of our method is illustrated in Figure 2
3.1 Extracting Multimodal Descriptions
Given a video V, we first divide into N non-overlapping short clips v = (u.) where each clip U, RTxxxxx3 contains T frames of height and width W. Each clip is passod through a pretrained visual captioning model M produce caption e, The sequence of captions denoted as C {ci forming temporally ordered description of the visual content.
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Algorithm 1 Adaptive Context Reduction
Require: Video V, Question Q, LLM F, Captioner M, Speech Recognition Model W, Initial Clip Length L S extractSubii (lessV W)
2: limil + getContextLengthh(()
3: while True do
4: t' - divideVideo L)
5: + generateCaptions(t. M)
< concat(S, C*
if countTokens(2) limit then
L L x
else
10: break
11: end if
12:
end while
13: return answer(Z, Q, F)
In parallel, we apply an ASR model W to convert the speech into a sequence of textual dcscriptions S 5= { where 8, is timestamped transcription of spoken segment. K denotes the total number of segments, which determined by ASR model. We then concatenate S and C one after the other to form rich, language-based description of the video (including audio/speech) and feed them into reasoning LLM as described next.
3.2 Language-Baan Bassed Reasoning with Adaptive Context Reduction
To answer question Q about the video, feed the video captions C' and speech transcriptions S along with into reasoning LLM. We design several prompts to guide the LLM to reason jointly over visual and speech information (for complete prompts see Supplementary Material). Unlike prior video reasoning approaches, SiLVR performs reasoning entirely in the language space. However, the limited context window of LLMS poses a significant challenge when processing long videos with rich multimodal content. To address this issue, we introduce a simple adaptive context reduction scheme (see Algorithm Dp. Our Adaptive Context Reduction scheme dynamically adjusts the temporal granularity for sampling video clips. Specifically, it starts with small clip length and progressively increases it reduce the total number of generated tokens. This allows us to effectively fit the input tokens within the LLM's context window for videos of varying durations while maintaining strong video reasoning performance.
3.3 Implementation Details
We use NVILA (Liu al. 2024bb as the default visual captioning model. We use our Adaptive Context. Reduction scheme for all videos described above. For speech transcription, use DeepSeek-R1 (Guo et al. 2025) the default
Whisper-large-v3 (Radford
et al. 2022). Due to its strong reasoning performance, LLM and set the temperature to 1.0 for all experiments. We use the official evaluation code provided by each benchmark, or use LMMs=Fval (Zhang et al. 2024a) when the official code is unavailable. Additional implementation details are provided in the supplementary materials.
4 Experiments
4.1
Benchmarks and Evaluation Metrics
We conduct experiments on eight complex video-language understanding benchmarks: Video-MMMU Hu
et al. 2025 Video-MMLU (Song et al. 2025), MMVU (Zhao et al, 2025), MMWorld (He et al., 2024). 2024), EgoLife (Yang et al. 2025a) and CinePile Rawal et 2024). Following Video-RI R1 (eng et al. 2025 we group these benchmarks into two categories:
VidcoMME Fu al. 2024), CGBench (Chen
Reasoning Benchmarks and General Benchmarks.
The reasoning benchmarks include Video-MMMU, Video-
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Table I: Main results. We evaluate our method on a set of video reasoning benchmarks (Video-MMMU, Vidco-MMLU, MMVU, MMWorld) and general video benchmarks (VidcoMME, CGBench, EgoLife, CincPile). We use the comprehension split of Video-MMMU and the long split of VideoMME (with subtitles). Based on these results, observe that SiLVR achieves the best-reported results Vidco-MMLU, VidcoMME (long split, with subtitles), CGBench, and EgoLife, outperforming strong proprietary models such as Gemini 2.0 and GPT-4o. We bold and underline the best and the second best models in each benchmark, respectively.
Video Reasoning Benchmarks
General Video Benchmarks
Model
Video-MMMU Video-MMLU MMVU MMWorld
VideoMME CGBench EgoLife CinePile
Proprictory Models
Gemini 1.5 Flash
49.0
47.8
58.8
68.8
33.5
58.8
Gemini 1.5 Pro
53.5
65.4
51.0
77.4
37.8
36.9
60.1
Gemini 2.0 Flash
65.9
Gemini 2.5 Flash
79.2
Gemini 2.5 Pro
GPT-40
62.0
44.9
67.4
62.5
72.1
44.9
36.2
56.1
Claude 3.5 Sonnet
75.7
71.3
65.2
54.5
40.3
Kimi-k1.6
76.7
OpenAI o1
75.5
Open-Source LMMs
LLaVA-OV-7B
31.0
33.4
37.9
30.8
49.3
Vid3-7LaMA3-7B
46.0
45.0
61.0
Aria
53.0
19.3
66.3
DeepSeek-VT.2
52.1
Owen-2-VL-722
50.3
74.3
45.3
Qwen-2.5-VL-7B
50.4
32.9
Qwen-2.5-VIL77B
61.0
40.5
Qwen-3-VL-SB
72.8
Qwen-3-VL-320
79.0
Video-R1
52.4
52.1
SILVR (ours)
82.7
83.1
68.2
59.9
77.7
51.8
42.0
59.4
MMLU, MMVU, and MMWorld, which primarily evaluate the reasoning capabilities of large video-language models. Specifically, Video-MMMU and Video-MMLU focus on peture-based video understanding, where the model must extract knowledge from lecture videos to answer the questions. MMVU requires models to apply domain-specifie knowledge and perform expert-level reasoning to analyze specialized-domall videos. MMWorld focuses on a diverse set reasoning questions (e.g., counterfactual thinking, future prediction, etc.) across videos from seven broad disciplines. The other four benchmarks (i.e., VideoMME,, CGBench, EgoLife, and CincPile) are general vidco- languaae benchmarks, which contain various types of questions and offer a comprehensive assessment of the video-language models. Specifically, VideoMME includes three splits (short, medium, and long) based the duration of the video. CGBench and EgoL.ffe designed for long video understanding, with average video duration of more than an hour. We focus on VideoQA for all benchmarks and report QA accuracy as our primary evaluation metric. Additionally, we conduct Grounded VideoQA experiments on CGBench to assess the model's i's temporal grounding ability and use the mcan Intersoction over Union (mIoU) to evaluate the results.
4.2 Main Results
Video Reasoning Benchmarks. We present our results on video reasoning benchmarks on the left side of Table Our results indicate that SiLVR achieves the strongest performance across all video reasoning benchmarks among open-source models such as Qwen-3-VI.-72B and Vidoo-R1. Additionally, SiLVR achieves the best performance on Video-MMLU, outperforming the previous state-of-the-art model Claude 3.5 Sonnet substantial 11.8%. On Video-MMMUU, SiLVR outperforms strong proprietary models such Gemini

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Table 2: Performance comparison between one reasoning (DeepSeek-R1) and two non-reasoning (Llama 4, DeepSeck- V3) LLMs. Using DeepSeek-R1 reasoning LLM leads to significantly better results over non-reasoning LLMs (Llama 4 and DeepSeek- V3). However, we also observe that the average gain on video reasoning benchmarks (VideoMMMUU VideoMMLU, MMVU, MMWorld) is significantly larger than general video benchmarks (VideoMME, CGBench, EgoLife, CinePile). These results demonstrate that the strong reasoning ability of DeepSeek--P RI is crucial for solving complex video reasoning tasks.
Video Reasoning Benchmarks
General Video Benchmarks
Model
VideoMMMU VideoMMLU MMVU MMWorld VideoMME CGBench EgoLife CinePile
LLMs
SILVR (Llama 4)
56.3
57.2
60.6
57.2
67.8
53.2
38.5
45.6
SiLVR (DeepSeek-V3)
65.7
74.7
62.9
58.7
75.0
50.2
42.2
56.7
Reasoning LLMs
SiLVR (DeepSeck-RI)
82.7
83.1
68.2
59.9
77.7
59.4
42.0
51.8
Average Gain over DeepSeek- V3: +-8.0
Average Gain over DeepSeek-Vi +3.88
2.5 Flash by 3.5%. Furthermore, MMVU, we observe that our modular framework, with DeepSeek-RI the LLM, outperforms the unified multimodal model DeepSeek-Vii VL2 by a significant margin of 15.9%. These results suggest that despite the simplicity of our approach, it delivers strong performance across a wide range of vidco-language reasoning tasks.
General Video Benchmarks. We present our results on general video benchmarks on the right side of Table ! Based on these results, we observe that SiLVR achieves state-of-the-arr performance on three general VideoMME benchmarks: VidcoMME (long split, with subtitles), CGBench, and EgoLife. Specifically,
and EgoLife, SiLVR outperforms the prior best performing method Gemini 1.5 Pro by 0.3% and 5.1%, respectively. On CGBench, SiLVR achieves 51.8% accuracy, outperforming the previous state-of-the-art method Qwen-2-VL-72B by a significant 6.9% margin. SiLVR also surpasses strong proprietary models, outperforming GPT-4o by 6.9% and Claude 3.5 Sonnet by 11.5% on CGBench. Additionally, it worth noting that Vi.eoMME (long), EgoLife, and CGBench are designed for very long-form video understanding, with average video durations exceeding 60 minutes. Our strong results demonstrate that SiLVR is highly effective in comprehending long videos.
4.3 Reasoning Analysis
In this section, we conduct a more in-depth analysis of the video reasoning capabilities of our approach. To do this, we systematically compare the performance of our framework when using reasoning (c.g., DeepSeck-R1) non-reasoning (e.g., Llama 4 and DeepSeek- V3) LLMs across multiple benchmarks. Additionally, we break down the performance of our approach across different types of video reasoning questions (e.g., temporal, causal, longecontext, knowledge acquisition, etc.).
To study the impact of a strong reasoning LLM within our framework, we compare the performance of our method when using one reasoning LLM (DeepSeek-R1) vs. two non-reasoning LLMs (Llama 4 and DeepSeek-V3) V3). The results are presented in Table 2 Our results suggest several interesting trends. First, we observe that DeepSeek-R1 consistently outperforms Llama 4 across all benchmarks, indicating that it is much stronger LLM than Llama 4. Second, we note that using DeepScck-R1 Icads to much larger performance gains on the reasoning benchmarks, where DeepSeck-R1 surpasses DeepSeek- V3 by substantial 17.0% on Video-MMMU and 8.4% on Video-MMLU with an average improvement of 8.0% on all video reasoning benchmarks. In contrast, while DeepSeek-Fe R1 also produces better results on general video benchmarks, the improvements over DecpSeck- V3 are much smaller (i.c., average improvement of 3.8% on general video benchmarks vs. 8.0% on the reasoning benchmarks). These results suggest that the strong reasoning ability of DeepSeek-R1 is critical for solving complex video reasoning
Reasoning vs Non-Reasoning LLMs.
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Table 3: Comparison with agent-based methods on VideoMME (long). Our method achieves consistently higher performance than all other Egent-based basclines, indicating its effectiveness for long-form video reasoning.
Method
Visual Captioner
LLM
Accuracy
VideoAgent
CogAgent LLaVA-NeXT
GPT-4
46.4
DrVideo
GPT-4
51.7
VideoTree
1.VVA1.6-BB
GPT-4o
54.2
VCA
GPT-4o
GPT-4o
56.3
SiLVR (ours)
NVILA-7B
DeepSeek-RI
62.7
tasks and that our framework's simple and modular design allows us to take full advantage DeepSeek- RR'ss strong reasoning abilities on these complex video reasoning problems.
Performance Breakdown Across Different Tasks. In Figure 3 we report the performance gains of using reasoning LLM (DeepSeek-R1) over non-reasoning LLM (Llama 4) for different question categories on VideoMME, which contains 12 manually annotated categories. The four categories that we report on the left of Figure 3 belong to reasoning (e.g., temporal, spatial, object, and action reasoning). The other eight categories are classified as non-reasoning and require general percept.ion capabilit.ies (e.g.. action recognition, OCR, etc.). Based on the results in Figure 3 we observe that compared to Llama 4, using DeepSeek- R1 achieves a significantly larger improvement on reasoning questions (a gain of +11.1%) compared to non-rcasoning questions (a gain of +4.9%). This result consistent with our observations in Table 2 which confirms that. reasoning LLMs bring greater benefits for tasks that require complex reasoning.
4.4 Comparison with Agent- Methods
In Table 8 we compare our method with other agent-based based approaches on the long split of VideoMME
(without subtitles). The baseline methods include VideoAgen Wang et al. (2024b). Video Tree: Wang et al. (2024c), DrVideo Ma et al. (004)). and VCA Yang et al.) (2025c). VideoAgent Wang et al. (024b iteratively
selects frames and gathers visual information before answering the query. VideoTree Wang et al. (2024e) builds hierarchical and query- reasoning. DrVideo Ma et al. (2024) formulates long-vidco QA iterative vidco-document construction, where the model retrieves key frames and progressively generates the a textual description for reasoning,
tree of keyframes to efficiently summarize long videos for LLM
VCA Yang et al. (2025c) employs reward-driven search strategy to explore informative video segments. All baseline methods require multiple rounds of interaction between a heavy- LLM while our method requires only a single LLM reasoning call. From Table 3 we can see that SiLVR substantially outperforms all other agent-based methods, confirming its superior design for long-form video reasoning.
4.5 Efficiency Analysis
In Table 4 we analyze the inference efficiency of our framework and compare it against prior video un- derstanding models, including two video-native models (Qwen-2.5-VI, Video-R1) and two agent-based methods (DrVideo (Ma et al. 2024), Video'Tree (Wang et al., 2024e)). We follow the evaluation setup of VidcoTrec (Wang et al. 2024c). which excludes ASR inputs, and measure the average processing time per video on the VideoMME (Long) benchmark. To ensure fairness, we run all methods on a single A6000, except for the Qwen-2.5- 2.5-VL--. V--BBB (768-framd) setting, which requires four A6000 GPUs due to its high memory demand. In addition to the pest-performing variant of our method (SiLVR-best), we also report a more efficient variant (SisV which uses a larger clip length reduce the number of sampled clips and generated captions.
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Table Efficiency comparison with other methods on VideoMME (long). To ensure fair comparison, we follow the evaluation setup of VideoTree and use the same GPUs for all methods. Both SiLVR-best and SiLVR-fast significantly outperforms Qwen-2.5-VL, Video-R1, Vieeo'ree and DrVideo. Notably, SiLVR-fast runs 1.4x faster than Qwen-2.5-VI (768 frames), 5.3x faster than DrVideo, and 2.0x faster than Video These results demonstrate that SiLVR is both efficient and effective in complex video reasoning tasks.
Method
Runtime (8) 4
Accuracy
Video-native Models
Qwen-22 VL 7B (32 frames)
5.2
44.7
Qwen-2.5-V 1reen-.5-VE. VL 7B (768 frames)
115.8
50.8
Video-R1 (32 frames)
14.2
50.2
Agent-basee Methods
DrVideo
446
51.7
Video Tree
162
51.2
SiIVR-faat (ours)
83
57.2
SiLVR-best (ours)
142
62.7
Performance Gains of DeepSeek-RI
Compared to Llama-4
Performance Gain (%)
20
Average Gain: 11.1%
Reasoning Questions
Table 5: Results on knowledge acquisition and temporally grounded QA tasks. SiLVR achieves the highest Annoledge on VideoMMMU and the best mIoU on CGBench.
Non-Reasoning Questions
15
Average Gain: 4.9%
5-
VideoMMMU (A kuowledge)
CGBench (mloU)
Model
0
Owen-2.5-VL-722
9.7
TR SR TP SP AP AC OC OP CP IS Question Category in VideoMME
Gemini-1.5 Pro
8.7
3.85
Claude-3.5 Sonnet
11.4
4.17
GPT-4o
15.6
5.73
Figure Performance breakdown across dif- ferent question categories. Using DeepSeek-R1 LLM yields larger gains reasoning ques- tions (+11.1%) than general perception questions (+4.9%). Full category names are in the supple- mentary materials (Table 18
VideoMind-7B
7.10
SiLVR (ours)
17.2
11.84
From Table 4 we can observe that both SiLVR-best and SiLVR-fast achieve higher accuracy than all other methods. Specifically, SiLVR-best outperforms video-native models Q. VL 7B (768 frames) and Video- R1 by 11.9% and 12.5%, respectively. SiLVR-best also outperforms agent-based methods DrVideo (Ma) al. h024 and VideoTree (Wang al., 2024ch by significant margin of 11.0% and 8.4%, respectively. Additionally, fastR-fast is highly efficient, running 1.4x faster than Qwen-2.5 VL-7B (768 frames), 5.3x faster than Didideo and 2.0x faster than Video while still achieving +6.4%, +5.5% and +3.0% higher accuracy, respectively. Although Qwen-2.5-V6 VL 7B (32 frames) adopts a sparse frame sampling stratogy to achieve high efficiency, this design leads to degraded performance (-12.5%) compared with SisV.-fast. These results demonstrate that our framework able achieve an effective balance between efficiency and accuracy, making it both effective and efficient in complex video reasoning tasks.
4.6 Results on Other Tasks
Knowledge Acquisition from Vidcos. We also evaluate our method on the novel knowledge acquisition task on ViMM HHu et al. 2022). The task requires models answer questions both before and after watching reference ure video, with the goal of measuring how much knowledge the model gains from the

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Table 6: Impact of ASR. We remove the ASR module and evaluate our method on eight benchmarks. Incorporating ASR consistently improves performance, highlighting the importance of speech information in video reasoning.
Method
Video-MMMU Video-MMLU MMVU MMWorld VideoMME CGBench EgoLife
CinePile
SiLVR (w/ ASR) 82.7
83.1
68.2
59.9
77.7
51.8
42.0
59.4
SiLVR (w/o ASR)
70.7
78.2
61.7
57.4
62.7
40.9
35.1
47.4
Table Token reduction analysis. Accuracy on VideoMME (overall) when selectively dropping speech vs. visual caption lokens. Based on these results, observe that speech tokens are more informative than visual caption tokens.
Dropping Rate
Average Context Length
Accuracy
Subtitles
Captions
50%
4.3k
65.3
75%
2.6k
56.0
50%
7.2k
68.9
75%
6.0k
67.7
No Compression
9.3k
70.3
video. The metric for the knowledge acquisition task is defined as:
ACCpost ACCpre 100% 100% ACCpre
A knowledge
(1)
where Acepre and Accpost denote the accuracy before and after watching the video, respectively.
Our results in Table show that SiLVR achieves 17.2% in Aknowledges outperforming the prior best method GPT-4o by 1.6%. SiLVR also outperforms strong proprietary models such as Gemini-1.5 Pro and Claude-3.5 Sonnet by 8.5% and 5.8%, respectively. These results demonstrate that SiLVR. is not only effective in complex video reasoning, but also has strong knowledge acquisition capabilities.
Temporally Grounded QA. In Table 5 we also present our results the temporally grounded QA task CGBench (Chen et al. 2024). The task requires the model to temporally localize relevant video segments nceded to answer the qucstion (usually less than 10 scconds) in long videos that span over 60 minutes. Our results Table 5 show that SiLVR achieves the highest performance in mIoU, outperforming concurrent work VideoMind [Lu et al. 2025) by a notable 4.74%. In addition, SiLVR also outperforms GPT-4o and Claude-3.5 Sonnet by 6.11% % and 7.67%, respectively. These results suggest that SiLVR can correctly answer complex questions and temporally ground the answer to relevant segments in the video, which improves interpretability in video reasoning.
4.7 Ablation Studies
Unless otherwise specified, all ablation experiments are conducted the VideoMME benchmark.
Impact of the ASR. To analyze the impact of ASR, remove the ASR module of our method and evaluate our method on eight benchmarks. The results are shown in Table D From the table, we observe that ASR plays a crucial role across many benchmarks. On the lecture understanding benchmark Video-MMMU, incorporating ASR leads to a significant 12.0% accuracy gain, which aligns with the intuition that spoken content lectures provides essential cues. Additionally, adding ASR leads to a 12.0% accuracy improvement on the movie understanding benchmark CinePile. Furthermore, in EgoLife and CGBench, which focus analyzing short clues within long videos, ASR also provides notable gains. Finally, general-purpose
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Table 8: Performance of Adaptive Context Reduction (ACR) vs. fixed clip length baselines. ACR achieves the highest accuracy on VideoMME (overall), outperforming the best fixed-length baseline (8s) by 2.5%. These results suggest that ACR effectively reduces redundant tokens while preserving strong performance.
Clip Length (s)
ACR
1
2
4
8
64
Accuracy
76.7
59.9
61.3
68.5
74.2
70.3
Table 9: Comparison between two context-reduction strategics on VidcoMME (overall). Reducing caption word limits and reducing the number of segmnntt (Adaptive Context Reduction) show similar performance.
Variant
Accuracy
Change Word Limit
76.6
Change Segment Number (ACR)
76.7
benchmarks like VideoMME and MMVU, adding ASR improves performance by a large margin, demonstrating that speech complements visual signals in multiple video understanding tasks.
Speech vs. Visual Caption Token Importance. To evaluate the relative contribution of visual and audio information, vary the fraction of tokens from speech transcripts and video captions and report the QA performance VideoMME. As shown in Table 7 the reduction of 50-75% speech tokens (while keeping all visual caption tokens) leads to significant decrease in performance (11.4%-20.7. 20.7%). In comparison, dropping the same fraction of visual caption tokens (while keeping all speech tokens) results in a much smaller performance drop (788%.9.0%). There results indicate that speech tokens are more informative than visual caption tokens.
Analysis of Adaptive Context Reduction. In Table g compare Adaptive Context Reduction (ACR) with several static basclines that use fixed video clip lengths. Among all basclines, the variant that uses 8-second clip length achieves the highest accuracy of 74.2%. We note that shorter clip variant (e.g., 1s) generates a large number of captions for long videos, which often exceeds the context window of the LLMs, thus lcading to degraded performance. In contrast, a longer clip variant (c.g., 64s) reduces the number of captions at the cost of sacrificing the granularity of visual information, which also leads lower accuracy. Compared these static baselines, our proposed ACR consistently outperform all fixed clip length baselines, surpassing the best-performing variant (8s) by a significant margin of 2.5%. These results demonstrate that ACR effectively reduces redundant tokens by adaptively adjusting the clip length, offering ficxibility and strong performance.
Context Reduction Method Ablation. In addition to Adaptive Context Reduction, we introduce an alternative strategy for reducing the input context length of the LLM. Specifically, we modify the captioning prompt constrain each caption to 40, 20, or 10 words. When the context limit is exceeded, we progressively roduce the per-sogment caption length from words to 20 words, and then to 10 words. The results are shown in Table From the results we can see that changing word limit yields similar results to changing the segment number.
Comparison between Uniform and Non-uniform Sampling. We experiment with the non-uniform frame sampling stralegy used in VrdeoTree Wang el al. (2024e) and compare it against uniform sampling in our framework. Specifically, VideoTrre extracts CLIP features for each frame, performs k-means clustering into 32 groups, and selects the cluster centers as representative frames. We use these non-uniformly sampled frames in our framework and compared them to uniform sampling on VidcoMME (Long, without subtitles). The results shown in Table 10 From the table, we observe that non-uniform sampling improves our
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Table 10: Comparison between uniform and non-uniform sampling. Following VideoTree, we evaluate our method on VideoMME (Loug). We ap- ply the non-uniform sampling strategy of Video'Tre to our framework. Non-uniform sampling improves our method by 0.8%, showing that our method can benefit from advanced sampling techniques.
Table 11: Effects of LLM input context length on VideoMME (overall). Increasing the LLM input context consistently improves the performance, showing that the benefit of more information out- weighs the increased context length in our framework.
Average Context Length
Accuracy
Sampling Method
Accuracy
9.3k
70.3
Uniform Sampling
76.8
18.2k
74.2
Non-Uniforı Sampling
77.6
29.5k
76.7
Table 12: Performance with different visual captioners on VideoMME. Qwen-2.5-VL 72B achieves the best overall accuracy. We use NVILA 7B for all experiments because it provides the best accuracy-cost trade-off.
Captioner
Size
Overall
Short
Medium
Long
LLVVA-OV
7B
67.2
57.7
68.1
75.9
NVILA
7B
70.3
63.2
70.4
77.3
Qwen-2.54 -wen-2.5-VL
70.9
76.1
Swen-2.5-VL.
72B
71.2
65.0
72.2
76.4
Qwen-3-VT.
71.0
63.5
70.9
78.6
NVILA + Qwen-2.5-VI.
7B + 7B
74.1
68.3
74.6
79.4
Table 13: Performance our framework with different LLMs on VideoMME. Llama-4 Maverick achieves 66.2% overall accuracy, providing an effective trade-off between model sizes and performance. DeepSeek RI achieves the highest overall accuracy, outperforming DeepSeek V3 and GPT-4.1 by 3.5% and 0.8%, respectively.
LLM
Size
Overall
Short
Medium
Long
Llama-4-Scomt
17B
63.0
56.7
61.4
67.8
Alama-4--aveer
17B
66.2
57.2
68.3
73.0
DeepSeek V3
685B
66.8
56.0
69.1
75.3
DeepSeek R1
685B
70.3
63.2
70.4
77.3
GPT-4o
67.3
57.0
69.2
75.8
GPT-4.1
69.5
59.6
71.1
77.9
GPT-5
73.9
64.7
75.1
81.9
Gemini-2.5-Pro
73.1
60.3
75.3
83.8
method by 0.8%, demonstrating that our method can immodiately benefit from more advanced, non-umiform sampling techniques.
Effects of LLM Context Length. Prior work suggests that excessively long inputs to the LLM harm reasoning quality Levy et al. (2024); Kahatapitiya et al. (2025). In our setting, increasing the number of sampled clips provides more fine-grained visual information but also proportionally increases the context. length, potentially affecting LLM performance. To study this trade-off, we vary the clip length to control the number of sampled clips and thus, the corresponding number of generated captions, which directly determines the total input context length to the LLM. For each configuration, we measure the average context length and evaluate the overall accuracy VideoMME. The results shown in Table 11 From the table,
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observe that increasing the context length consistently improves performance in our setting, suggesting that the benefits of providing more fine-grained visual information outweigh the potential drawbacks of longer input sequences.
Visual Captioning Model. Next, we study the effect of different visual captioners. As shown in Table 12 NVILA 7B and Qwen-2.5-VI, 7B achieve similar performance, outperforming LLaVA-OV 7B by 2.9% and 3.7%, respectively. We also observe that Qwen-2.5-V 72B achieves the higher overall accuracy compared with Qwen-2.5-VL 7B, likely due to the larger LLM (72B), which leads to higher-quality captions. Since NVILA 7B is faster than Qwen- VL 7B and achieves similar performance, we NVILA 7B for all experiments. We do not use Qwen-2.5-VI. 72B due to the prohibitive computational cost. We also observe that SiLVR remains effective and achieves a strong result (7100% even with a 3B parameter captioner (Qwen-3-Ve -L VL 3B). Additionally, we explore the ensemble of two captioners (NVILA-7B + Qwen-2.5- VL-7B) by concatenating the generated captions from each model. As shown in the table, the ensemble captioner improves overall accuracy by 3.8% and 3.2% over NVILA-7B and Qwen-2.55VVL-1 individually, showing that our framework consistently benefits from stronger captioners.
Different LLMs. Lastly, in Table 13 we study the effect of different LLMs, including Llama-4-Soon 17B, Llama-4-Maverick 17B, DeepSeek V3, DeepSeek R1, GPT-4o, GPT-4.11, GPT-5, and Gemini-2, 2.5mii--.--Pr. Our results indicate that Gemini-2.5-Pre as the LLM backbone, achieves the highest accuracy on the long split of VideoMME, outperforming GPT-5 by a significant margin (1.9%). GPT-5 achieves the best overall accuracy on VideOMME, outperforming 2emini-2.5-Pro by 0.8%. These results indicate that stronger LLMs consistently leads to better performance in our framework. Furthermore, we note that Llama-4- Maverick achieves 66.2% accuracy with only 17B parameters, offering effective trade-off between model size and performance. Lastly, observe that DeepSeek R1 outperforms DeepSeek V3 by a significant 3.5%, highlighting the effectiveness of using a reasoning LLM within our framework.
4.8 Qualitative Results
We present two reasoning traces of SiLVR. in Figure 4 and Figure 5 In Figure 4 the video showcases the Tesla Cybertruck, and the question asks about the size of its rear touchscreen display. Since the display appears only briefly, the question is particularly challenging. As shown in the figure, SiLVR first recognizes the vehicle as a Tesla Cybertruck. Leveraging both visual cues and the LLM's prior knowledge, SiLVR, then correctly infers that the size of the rear touchscreen is 15 inches. This example demonstrates SiLVR's capability to incorporate visual information with the LLM's prior knowledge for complex video reasoning. Figure 5 shows chemistry tutorial video and a follow-up question. SiLVR initially makes a tentative prediction but does not terminate the reasoning process immediately, Instead, it continues to validate the predicted answer through step-by-step reasoning. These two examples highlight that SiLVR is effective across diverse video domains, highlighting the generalizability of our proposed method. Additional qualitative analyses are provided in the supplementary matcrials.
5 Conclusion
We present SiLVR, a simple, modular, and training-fres language-based video reasoning framework. SiLVR achieves statc-of-the-art performance VideoMME (long), Vidco-MMLU, CGBench, and Ego- Life. SiLVR also achieves strong results in video-based knowledge acquisition and temporally grounded QA tasks, demonstrating strong generalization. Lastly, we systematically analyze the reasoning capabilities of SiLVR and perform ablations on several key design choices. We encourage the research community to build on our simple yet effective video reasoning framework and hope that it will inspire new idcas in video reasoning research.
Limitations
As with most modular frameworks, the performance of our method depends on its individual modules. On the visual perception side, method relies the visual captioning model, which may produce hallucinations
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Video:
Question:
What is the size of the back touchscreen display in this video?
A. 15 inches.
B. 9.4 inches.
C. 17.4 inches.
D. 18.5 inches.
Reasoning Trace
Okay, let's tackle question. The asking about the different inch measurements. First, need to check the subtities and the captions provided. The subtities show that there are some music intervals the word (he), end. The captions describe various clips from the video, metallic plugging cables into outlets, futuristic car towing something, aerial construction site, excavator lifting equipment, sleek vehicle with all-gless roof, off-road accessories, and white vehicle driving on dirt road. Looking through the captions, none of them exp licilly mention back touchscreen display. The captions focus features like stainless steel, outlets, towing, construction sites, the vehicle's design, and off roading accessories. There's no direct mention of touchscreen size here. Since the video's captions don't provide the answer, maybe the subtitles only show
back touchscreen display the video. The options through with
holes, someone
end, which information from the video that's not described in the captions. However, the user expects the answer to be derived from the given data. Since none of the captions subtitles mention the back touchscreen size, perhaps this is trick question. The options might be standard staes Tesla Cybertruck features. From prior knowledgee, recall that the Tesla Cybertruck has 15-inch central touchscreen. But the question about the back touchscreen. Wait, some sources mention 9.4-inch rear screen. Let me think. The Cybertruck's a''s rear display for passengers often reported as inches. if video showed screen sizes, so how can we confirm? Since the user provides the captions and the answer must be based on them, but none of the captions talk content. example, display visible the background. But the captions don't note that. Alternatively, the answer might not be directly in the given data, but common knowledge, If this part of Cybertruck features video, the back screen is 9.4 inches. So the answer
subtiitle have something
and (hel
helpful. The question is likely
on visual
touchscreen, correct answer
8: inches. But captions here don't
any
screens. Maybe the answer inferred
clip where person plugs
outlets, there's
Figure 4: Example of SiLVR's reasoning trace. The question asks about the size of the back touchscreen in the car shown in the video. The visual captioning module of SiLVR fails to capture the details about the touchscreen, which appears briefly for only about one second. However, by identifying the vehicle type and leveraging external knowledge from the LLM, SiLVR. infers the correct answer.
Video:
Question:
Consider the two molecules that appeared in the video from 0:28 to 2:00, if we change the positions of the two green atoms in the second molecule, will the two isomers now become Optical Isomerism?
A. Yes, as they cannot realign with each other by rotation B. Yes, as the molecular formula changes after swapping C. No, the bonds will break during the swap D. Yes, as creates new asymmetric centers E. No, as they will become two same molecules Yes, it changes the overall symmetry of the molecule G. No, the molecule will become unstable H. Yes, it creates mirror videos that cannot superimpose No, green atoms have special bonding properties Yes, at changes the molecular weight distribution
But reality, if chiral center has four differrnt groups, swagping two would invert the configuration (from to or vice versa), which would create the enantiomer. However, in the case where you swap two groups in the enantiomer (the mirror image), does that bring it back to the original? Wait, Let's take specific example. Suppose the original configuration. groups, you could and up with another configuration. For example, swapping the first second groups the 5 configuration might convert back 5o swapping two groups the enantiomer swapping matters. The and configurations are determined by the Cahn ingold Prelog priority rules. If you swap two groups chiral center, you can invert the configuration. For example, will become two same molecules. But wait, the original mirror image couldn't be rotated to align. But swapping So the answer would be But need to confirm. If you take the mirror image and swap two groups, does that result the original molecule? For example, the original and mirror image same, so answer E. Yes. Swapping two groups in a chiral center inverts the configuretion. So if the mirror image is 5, swapping two groups might turn it into R, "answer": "E",
mirrar image is in configuration
two
give the original configuration, making them the molecule, But walt, the order of
original is R, swapping
first and second priority groups so the answer would be E: No, as they
might allow rotation to align.
swapping groups in might
it to R. that's the case, then the would the
the original. Therefore, they are
molesule. Hence, isomers would become same melecule, answer
Figure 5: Example 2 of SiLVR's reasoning trace. Through step-by-step reasoning, SiLVR is capable of solving complex domain-specific questions. Notably, SiLVR does not immediately terminate the reasoning process upon reaching plausible answer. Instead, it continues to verify the correctness of the generated answer before finalizing its response.
or descriptions that lack fino-grained visual details. However, since our framework is agnostic to the specific use of visual captioning models, we believe that future advances in visual captioning models will mitigate this issue. On the reasoning side, our framework may underperform when the reasoning trace generated by
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the LLM is incorrect. However, view this as broader limitation of current LLMs, and anticipate that future advances in long-context modeling and reasoning for LLMs will further enhance the performance of our framework.
Acknowledgements
This work was supported by Laboratory for Analytic Sciences via NC State University, ONR. Award N00014- 23-1-2356, NIH Award R01HD11107402, and Sony Focused Research award.
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Our appendix consists of Additional Ablation Study (Section A More Experimental Results (Section
Additional Implementation Details (Section Ch. and Qualitative Results (Section D
A Additional Ablation Study
Time-aware Caption Representation. We investigate the effectiveness of integrating time information into captions on the Video-MMMU benchmark. The results are presented in Table [4] The baseline concatenates captions in temporal order without explicit timestamps, whereas our proposed time-aware method explicitly includes time information. Specifically, before each caption, we add a timestamp indicating the interval from which it was extracted (e.g. 00:00:24 -> 00:00:32: clear glass bottle being filled with water, surrounded by seashells and white cloth.) As shown in Table 14 incorporating limestamp information leads a notable 2.25% improvement in overall accuracy compared to the baseline. Furthermore, the time-aware method consistently outperforms the baseline across all question categories. These results demonstrate that explicitly providing time information effectively enhances the temporal perception and reasoning capabilities of the LLMs. Consequently, we adopt the time-aware caption representation for all experiments.
Method Overall Perception Comprehension Adaptation
w/o time 72.86
81.00
80.67
56.90
w/ time 75.11
82.67
82.67
60.00
Table 14: Time-aware Caption Representation. Incorporating time information into the captions by adding timestamps depicting time intervals from which the captions were extracted significantly boosts the performance on Video-MMMU.
B More Experimental Results
HourVideo. We evaluate our method on the development set of HourVideo (Chandrasegaran et al. 2024 benchmark specifically designed for 3D reasoning over long videos. As shown in Table 15 method performs surprisingly well, despite not incorporating any explicit 3D modcling. Specifically, it achieves an accuracy of 36.3%, outperforming the concurrent method VAMBA (Ren et al. 2025) by notable margin of 2.7%. These results demonstrate that our method is effective in reasoning about both the 3D physical world and hour-long videos.
Method
Overall Accuracy
Aria
39.2
Gemini 1.5 Pro
37.4
Qwen2-VI VL 7B
33,8
VAMBA
33.6
SiLVR (ours)
36.3
Table 15: Performance of Our Method on VidrV.deo. SiLVR outperforms the concurrent work
VAMBA by significant 2.7%.
CGBench-Reasoning. We evaluate our method the reasoning split of CGBench. As shown in Table SiLVR achieves the highest performance among the baseline models. Specifically, SiLVR outperforms Video-R1 by a significant 6.2%. Additionally, SiLVR outperforms the concurrent video reasoning model Video-Thinker-7B by 0.7%, showing the strong video reasoning ability.
VideoMME Overall Performance. We evaluate our method on VideoMME and report the overall performance including the short, medium and long splits. As shown the Table 17 SiLVR achieves strong performance among open-source models. Specifically, SiLVR surpasses the widely used Qwen2.5-VL. 7B by 2.6%. In addition, SiLVR also outperforms the video reasoning model Video-R1 by a substantial 12.8%,
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Method
Overall Accuracy
Qwen2.5-Vi 5-VL 7B
32.6
Video-R1 7B
30.1
Video-Thinker 7B
35.6
SiLVR (ours)
36.3
Table 16: Performance of Our Method on CGBench-Reasoning. SiLVR outperforms the concurrent video reasoning model Video-Thinker-s by 0.7%.
highlighting its improved video comprehension capability, While proprietary large models such as Gemini 2.5 2.o-Pro achieve higher accuracy, SiLVR remains highly competitive within the open-source models.
Method
Overall Accuracy
Proprietary
Gemini 2.5-Flash
81.5
Gemini 2.5-Pro
86.9
Open-source
Qwen2.5-VT, 7B
71.6
Qwen3-VI. VL. 8B
71.8
Qwen3 VL 32B
77.3
Video 7B
61.4
SiLVR (ours)
74.2
Table 17: Performance of Our Method VideoMME. We report the overall accuracy. SiLVR out- performs Qwen2.5-VL 7B by 2.6% and Video-R1 by 12.8%, and remains competitive among open-source methods.
we present a detailed breakdown of our method's performance Vi.eoMME. From these results, we observe that our method achieves the lowest accuracy on the Counting Problem. This is likely due the complexity of counting tasks, which require precise temporal localization of multiple events and subsequent reasoning. Any missed or incorrectly detected events could lead to incorrect answers, making the Counting Problem particularly challenging.
Detailed VideoMME Results. In Table 18
Question Category
Accuracy
Temporal Reasoning (TR)
74.6
Spatial Reasoning (SR)
94.6
Action Reasoning (AR)
76.1
Object Reasoning (OR)
79.5
Temporal Perception (TP)
Spatial Perception (SP)
74.1
Attribute Perception (AP)
80.2
Action Recognition (AC)
68.1
Object Recognition (OC)
82.5
OCR Problems (OP)
83.5
Counting Problem (CP)
50.7
Information Synopsis (IS)
88.5
Table 18: Detailed Results VideoMME. Our method achieves the highest accuracy Spatial
Reasoning while achieving the lowest performance on the challenging Counting Problem.
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C
Additional Implementation Details
C.1
Captioner
For all VL and Qwen models, we use the prompl 'Brielly describe the video within 40 words' to generate captions for each clip. We set nax_new_tokens to 200 and employ greedy decoding. We utilize the following model variants from Hugging Face: Qwen/Qwen2. 5-VL-77-Instruct for Qwen2.5-VL. 7B, and Qwen/Qwen2. 5-VL- 728-Instruct for Qwen2.5-VI. 72B. For the NVILA model, we use the NVILA-8B- Video variant with the prompt "generate caption" produce captions for each clip. We set _ex_nee_tokems 128 and employ greedy decoding similar to LL&VA and Qwen models.
for LLaVA-OV 7B,
We use H100 GPUs for generating captions.
C.2 LLM
We use the default temperature of 1.0 for all LLM experiments. We use the DeepSeek APT to run DeepSeek-RI and DeepSeck-V3 33 efficiently. To accelcrate inference, we implement parallel processing pipcline with up to 64 concurrent processes, each handling raw caplions and subtitles before sending requests to the API. During off-peak hours, this setup allows us to evaluate our method on the complete VideoMME benchmark under hours cost of less than $20. For GPT models, we use the OpenAl API. For Llama-4 models, we use 4xH100 GPUs for local inference. However, we can only process subset of videos locally due GPU memory constraints. For long videos,
Lambda Cloud's API service.
C.3 Prompt Design
Different VideoQA benchmarks include different types of questions (e.g., multiple-choice, open-ended, or mixed). Additionally, the Grounded VideoQA task in CGBench requires models to predict the temporal boundaries (start/end timestamps) of video segments relevant to each question. To accommodate these differences, we design task-specific prompts. Specifically, for multiple-choice questions, we use the following prompt template:
The video's subtitles are listed below. Subtitles.
The video's captions are listed below. Each caption describes Clip Length seconds clip. Captions.
Select the best answer the following multiple-choice question based on the video and the subtitles. Respond with only the letter (A, B, C, D, E, etc.) of the correct option.
Question: Question. Options: Options.
The answer is:
For open-ended questions, the following prompt template:
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The video's subtitles are listed below. Subtitles.
The vidco's captions are listed below. Each caption describes Clip Length seconds clip.
Captions.
Based on the video and the subtitles. Answer the following question with one sentence. Answer the following question based on the vidco and the subtitles. The answer is short. Please directly respond with the short answer.
Question: Question.
The answer is:
For the Grounded VideoQA task, use the following prompt to generate the start and end seconds of the question-related clips:
The video's subtitles listed below. Subtitles.
The vidco's captions are listed below. Each caption describes a Clip Length seconds clip. Captions.
Your task is to determine in which intervals the clue exists that contain visual information needed to answer the question.
Question: Question.
Only output the answer in the following format: [[startl, endl], [start2, end2], In this output format, each 'start' and 'end' represents the beginning and end of an interval in seconds (integer) where relevant clues can be found.
You must provide least one interval and most five intervals. Here are somne example outputs.
Example 1: [[5 7]]
Example 2: [2000, 207], [209, 213], [214, 220]]
C.4 Evaluation
We evaluate our method on the validation set of MMVU and HiurVideo. For the VideoQA task on CGBench, we adopt the long-acc setting in which the model takes the entire long video the input and answers the given questions.
We follow the official evaluation code of each benchmark to make fair comparison with prior methods. If the official evaluation code is not provided, we use the code from LMMs Eval (Zhang et al. 2024a).
For the Grounded VideoQA task on CGBench, we need to decode the timestamps from the LLM outputs. In practice, we found that the Grounded QA prompt as shown in Section C.3 works surprisingly well, with each output sentence following the list format exactly. Therefore, we simply use the eval function in Python decode the output text into list object. We then use the official code provided by CGBench compute the mIoU between the prediction and the ground truth.
D Additional Qualitative Results
We present and analyze several of SiLVR's reasoning traces in Figure 0 and Figure 7 From the figures, we observe that SiLVR effectively integrates information from both visual and speech modalities. Furthermore, SiLVR is capable of performing complex video reasoning steps, including step-by-step reasoning, self-correction, self-verification, and using relevant prior knowledge from the LIM to answer a given question.
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Published in Transactions on Machine Learning Research (01/2026)
FROO
Video:
Question:
According to the video, which of the following ingredients is not used in the artwork?
Shell
B. Glue
C. Blue Food Dye
D. Oil.
Reasoning Trace
Okay, let's tackle this question. So, the question asking which ingredient not used In the artwork based the video subtitles. The options shell, glue, blue food dye, oil. Looking at the subtitles provided, let me through each timestamp to see what's mentioned. Starting from 00:00:00 to 00:00:08, there's a glass bottle with blue liquid and decorative items. The decorative items might include shells, but not sure yet. At 00:00:16, the caption mentions a bottle E6000 adhesive surrounded by seashells and plastic cup with yellowish liquid. E6000 is type glue, so glue definitely used (option present). Shells are also mentioned here, so (A) the bottle. That's option which used. Then 00:00:40, the text says FFll of bottle with ail and there's hand holding green dropper. ail is used. Wait, but the question is which NOT used. me check again, four options mentioned except The options are A: Shell, B: Glue, C: Blue Food Dye, D: Oil From the subtitles: seashells are mentioned (so A used). Glue (E6000 adhesive) is present (8 used). Blue food dye is added (C used). Oil is instructed to fill 2/3 of the bottle (D used). Wait, all options are used? That can be.... Maybe the shells are part the decorative items but not an ingredient used part
00:00:32,
drop
food
artwork's construction? Wait, the question is about ingredients used Let's
not part with (00:00:24), adding blue dye (00:00:32), adding (00:00:40). Then there's mention glue (E6000) which might be for sealing attaching something. The seashells surrounding the glue and maybe used as decoration. But perhaps the shells not of the liquid mixture inside the bottle but are just of the surrounding decorations. the question is about the ingredients inside the bottle, Therefore, the shells might be part of the decoration around the bottle in the video but not part of the actual artwork (the bottle's contents). So the answer would be A: Shell, as the shells are not an ingredient used in the artwork, But need to make sure. The question says Vingredientsl, which implies components used in creating the main part artwork. the shells are not inside the bottle or part of structure, then they are not ingredients. the
steps again. steps
filling
Figure 6: Example 1 of SiLVR's Reasoning Trace. The question asks which ingredients are not used in the vidco. Initially, SiLVR identified all listed items as potential ingredients. However, through self-correction, SiLVR correctly recognized that the shell is used as decoration rather than an ingredient.
Video:
Question:
what order are following planets introduced video? A. Venus, Jupiter, Neptune.
B. Mercury, Jupiter, Mars. Jupiter, Mercury, Neptune.
C. Venus, Neptune, Jupiter.
Reasoning Trace
The question asking order which certain planets introduced video. The options each listing sequence of three planets. First. need recall the structure of the video based the provided subtities and captions. The subtitles start by talking about humanity leaving Earth and exploring the solar system. Then they mention Mercury first, discussing its atmosphere and temperature. The next planet discussed is Venus, with details about its thick atmosphere and the Soviet Venera missions. After Venus, the video moves on to Mars, discussing its potential for human colonization, radiation issues, dust storms, etc. Then the video goes into the outer rings mentioned of planets introduced is Venus, Jupiter, Neptune. From the sequence above, Venus comes before Jupites, which comes before Neptune. Looking at the options, option is Venus, Jupites, Neptune. Option is Venus, Neptune, Jupiter, which would be incorrect. The other options don't include the correct order. For example, option lists Mercury, Jupiter, Mars, which isn't correct because Mars comes after Mercury and Venus. the correct answer should be A.
Okay,
planets, starting with Jupiter. They about Great Spot, atmosphere, moons high-speed winds and
After Jupites, Mercury Venus Mars Jupiter Saturn Neptune. The question asks for the order Triton. So the
and Following Saturn, Neptune discussed, focusing
Figure Example 2 of SiLVR's Reasoning Trace. The video sequentially introduces six planets detail: Mercury, Venus, Mars, Jupiter, Saturn, and Neptune. SiLVR accurately identifies the correct order of the planets and systematically inspects all answer choices, eliminating the incorrect ones through logical reasoning.
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Reviews:
[{"review_id": "smcat6n18f", "reviewer": "Reviewer_d1wR", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper introduces a simple multi-stage video reasoning framework that relies on (1) extracting multi-modal information as text (e.g., audio -> ASR transcription, visual -> captions) and then (2) performing complex reasoning on text-only information. This framework builds upon similar prior work, and is different from (single-stage) multi-modal LLMs. The authors also rely on an adaptive token reduction strategy to maximize the context utilization of the LLM for any given video, by iteratively finding the proper segment size for captioning. The proposed method is applied on multiple video VQA benchmarks including Video-MME, Video-MMMU, Video-MMLU, CGBench,\nand EgoLife, comparing with prior art (both proprietary and open-source) and showing meaningful gains. The ablation study further provides insights into the impact of reasoning, context utilization, and different modules of the pipeline.", "claims_and_evidence_yn": "Yes", "claims_explanation": "Clear technical formulation:\nThis paper is well-motivated and is supported by a clear technical formulation. Relying on dedicated modules for visual and audio signal extraction (i.e., conversion to text) and then processing with a specialized text reasoning model makes sense. Also, the idea to feed the maximum amount of information with adaptive token reduction is novel and useful. The whole formulation is presented clearly with pipeline diagrams and example figures, making it easier for the reader to understand.\n\nSOTA performance:\nThis achieves SOTA performance among proprietary and open-source video reasoning models on video VQA benchmarks with varying complexity. Such a performance highlights the utility of the presented claims.\n\nAblation studies:\nThe authors provide ablation studies to validate almost all of the design decisions, analysing each component of the pipeline. Moreover, they also present interesting insights into how the proposed method improves on particular dimensions such as reasoning and context utilization.\n\nQualitative results:\nThe provided qualitative results in the form of reasoning traces are quite revealing and provide meaningful information on how the multi-modal inputs are used to correct the initially wrong responses.", "audience_yn": "Yes", "audience_explanation": "Multi-modal learning:\nAs this paper provides a simple strategy to process multi-modal information as text, it would be useful to the community working on multi-modal learning. Particularly, since the pipeline is training-free and relies on all open-sourced components, and the authors pledge to release their code, the community will be able to easily adopt and build on top of it.\n\nVideo reasoning:\nSimilarly, the community working on video reasoning will benefit from this work. It presents a non-trivial improvement on video VQA benchmarks with varying complexity, and the insights provided will be useful for the next generation of models.", "requested_changes": "**Critical for recommendation:**\n\nAlthough the proposed token reduction is a novel idea, I wonder if this is the optimal way to do this. There are multiple other options I would consider as relevant ablations:\n- What happens if we change the word limit of each segment caption, rather than changing the number of segments\n- In the current iteration, each segment has equal importance (similar to uniform sampling). However, many works have shown that not all regions/segments are equally important. Is there a way to utilize such non-uniform sampling to better utilize the information fed within the context length? In this context, VideoAgent, LVNet, VideoTree are some literature that should be in the comparison, at least in terms of the technical differences.\n- Segments used may have weird breaks in the current version. Does it make sense to consider more semantic segments, rather than uniform sampling?\n\nTaking a step back, is utilizing the context length to the maximum, the best thing to do? This is not always the case, and in fact, there is evidence to the contrary (i.e., even within the context length, the longer inputs results in reduced performance due to attention dilution. For instance, check \"Same task, more tokens: the impact of input length on the reasoning performance of large language models\" and \"Language Repository for Long Video Understanding\". This discussion should be taken into account to validate the assumption here, that maximizing the context length is better.\n\nAlso, the term \"Adaptive Token Reduction\" is misleading here. This is different from token reduction methods such as token merging, token pruning, and token learning. This is rather \"context reduction\" in text space rather than in token space. I would ask the authors to reconsider the naming here for better clarity.\n\nI see that the proprietary models considered here are somewhat outdated. I understand the field is moving really fast, and it's hard to always keep up. But, it would be nice to see at least some minimal comparisons with most up-to-date models (e.g., Gemini-2.5, Qwen3-VL).\n\nIn the experiment in Table 2, I wonder if the comparison between Llama4 and Deepseek-R1 is the best way to show the impact of reasoning capability, because there are many differences between these two models. To make a better comparison and isolate the reasoning capability here, does it make sense to use a comparable non-reasoning model (not sure with Deepseek-V3 should be the comparison here-- Table 9 shows it is fairly-matched with R1, or using a model that supports turning off reasoning tokens)? Otherwise, the claim here is not well-supported.\n\n**Strengthen the work:**\n\nAlthough the proposed multi-stage pipeline provides more-flexibility to use dedicated models (captioner, ASR, reasoner), it may also suffer from loss of information (particularly, in vision -> text) compared to a multi-modal single-stage model. I would like more discussion on this to be included in the paper. The authors also highlight the benefit of “Performing reasoning entirely in language space”. Is this inherently better than natively multi-modal reasoning?\n\nHow is ASR segmented (sentence-level, ...)? Please provide more details.\n\nWhy do the reported results only include comprehension split (Video-MMMU) and long-split (Video-MME)?\n\nPlease report LLM model sizes in all tables, as it gives more context when comparing different methods.\n\nTable 6: Speech is definitely more semantic/compressed than visual captions, and contains high-density ground-truth information compared to generated noisy information. This may be discussed with the results in this table. Also, please report the context length used in each setting in this table to highlight that it is a fair comparison.", "broader_impact_concerns": "Since the proposed method is training-free, any limitations or biases in pretrained models may still exist in the outputs. However, there are no obvious ethical concerns specific to this method, other than the general ones that exist for any LLM-based content understanding approach.", "additional_comments": "N/A", "extra_scores": {}, "extra_text": {}}, {"review_id": "HicSApMil7", "reviewer": "Reviewer_M43Y", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "1. This work proposes SiLVR, a training-free, modular, two-stage pipeline: (i) convert video to language via dense clip captions + ASR; (ii) feed the concatenated text to a reasoning LLM (DeepSeek-R1) with an Adaptive Token Reduction (ATR) policy to fit long videos within context. \n\n2. This work reports SOTA or strong results on several long-video QA benchmarks (Video-MME long, Video-MMMU comprehension, Video-MMLU, CGBench, EgoLife) and analyses on VideoMME showing that (a) reasoning LLMs outperform non-reasoning LLMs within this pipeline, (b) speech tokens matter more than caption tokens, and (c) ATR beats fixed clip lengths.", "claims_and_evidence_yn": "No", "claims_explanation": "To be honest, I think the answer above should be ``partially'', but there is no such choice.\n\nSpecifically, the core claims (SiLVR > baselines; reasoning LLM benefits; ATR helps) are supported by tables (Table 1,2,7); however, there are fairness and scope gaps that blunt the strength of the conclusions.\n\nThe first problem is on the inference efficiency. While Table 3 provides some analysis, it's apparently not enough. As the authors mentioned in the last paragraph of the Related Work, there were many works already that tried to use a visual captioner + an LLM (agent) to perform video reasoning. Doesn't this work fall in this line? Why is there no comparison or discussion with previous works? Even for the compared baselines (DrVideo and VideoTree), there are no discussions of what they are, and what does the performance/efficiency difference mean. In fact, how is comparing this method to a tree-based method meaningful?\n\nIn addition, Table 8 clearly shows that video captioners are actually heavy MLLMs, so to be fair, the number of LLM calls should probably also count them as well. The current \"1\" LLM call is very misleading.\n\nIt would also be meaningful to add video-only baseline (and include both without reasoning and with reasoning) to see the performance and efficiency difference, so that we can get a full picture of the compute/performance trade off here and assess whether the proposed method is indeed a good one.\n\nThat also comes to the second problem, which is the significance of the contribution. In my opinion, this work is not significantly different from prior works that use an LLM (agent) to process dense visual captions. Such works were also cited by the authors but without any discussion of the differences (last paragraph of the related work section). Does this work simply use a stronger visual captioner and a stronger reasoning LLM? What's the key difference? What's the real contribution here? The authors should make this clear!", "audience_yn": "Yes", "audience_explanation": "While in my opinion, this work merely extends prior works in this line with stronger modules (reasoning LLM, and MLLM as visual captioner), but it can serve as a good literature for people to understand the current progress of video reasoning, and understand the gap between such vision-->text models and vision-only models. In this regard, I believe at least some individuals in TMLR's audience would find this paper useful and interesting.", "requested_changes": "I have mentioned most of my concerns and requested changes above. \n\nHere is an additional one: More analysis in the ablation studies. For example, in Table 9, why is DeepSeekR1 the strongest? In most other benchmarks (e.g. LMArena), GPT-4.1 is considerably better than DeepSeekR1, why here they are comparable? Is it because this task is different or the benchmark is more suitable for DeepSeekR1? Similarly, other ablations also lack in-depth analysis.", "broader_impact_concerns": "None.", "additional_comments": "I want to further encourage the authors to consider the general direction of this paper, which is trying to show that a language-based video reasoning method is a strong baseline. Given the current performance, this seems to be the case. But this is also somewhat superficial. If we truly believe in language-based video reasoning methods, we need to show \"why\": what are the key factors that an end-to-end vision-only model cannot achieve or would really struggle with? Or if there is no such factor (information-wise, the video should be enough), then how could we make video-only methods stronger, what are the real bottlenecks that limit the current methods?\n\nAlso, another direction to think is on scaling up the inference compute, which is clearly what this paper is doing. But there are multiple directions in doing so, for example, simply adding more video frames, or adopting a multi-turn reasoning paradigm, or using different tools, etc. How does this work differ from them? What is the best way to scale up the inference compute and get the most reliable result? If the authors can demonstrate this systematically, that would also be a great contribution to the community.", "extra_scores": {}, "extra_text": {}}, {"review_id": "GO9m1TTsAP", "reviewer": "Reviewer_Mgwb", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "Strengths\n1. The model shows significant improvements on multiple benchmarks, especially on Video-MMLU and CGBench, demonstrating strong general reasoning ability across diverse video-language tasks.\n2. The proposed SiLVR framework is simple, modular, and effective, and it works well with different reasoning LLMs, showing good generalizability.\n3. The introduction of audio information provides complementary cues beyond visual input, effectively improving overall video reasoning performance.\n\nWeaknesses\n1. As shown in the experiments, the performance gain mainly comes from the reasoning ability of the LLM (e.g., DeepSeek-R1), rather than the framework itself, raising doubts about SiLVR’s intrinsic contribution.\n2. The paper notes that using stronger captioners like Qwen2.5-VL 7B/72B can further boost performance, suggesting the framework’s improvement may rely heavily on external modules.\n3. Table 1 lacks comparisons with several important models (e.g., Gemini-2.5 series, Qwen2.5-VL), making the evaluation incomplete.\n4. The related work section misses several key prior studies (e.g., MLVU, VideoXL, VideoEspresso, VideoR1), weakening the paper’s contextual grounding.", "claims_and_evidence_yn": "No", "claims_explanation": "The claims are not fully supported by convincing evidence. The proposed framework essentially functions as an agent-style pipeline rather than a unified end-to-end model. Therefore, directly comparing its performance against fully end-to-end multimodal models is not entirely fair. Moreover, Table 1 omits several highly competitive baselines (e.g., Gemini-2.5 series, Qwen2.5-VL, VideoR1), which further weakens the validity and persuasiveness of the performance claims.", "audience_yn": "Yes", "audience_explanation": "Although the proposed approach is not particularly novel in its framework design, the idea of incorporating audio as an additional modality is meaningful and has been under-explored in prior video reasoning research. Moreover, the paper reports remarkably high performance on VideoMMLU (over 80% accuracy), which makes the results interesting to the community and worth understanding in greater depth.", "requested_changes": "1. Provide a clearer attribution of performance gains. The paper should better disentangle the contribution of the SiLVR framework itself from the reasoning ability of the underlying LLM (e.g., DeepSeek-R1). This clarification is critical for assessing the originality and true effectiveness of the proposed method.\n2. Include more comprehensive model comparisons – Table 1 should incorporate results from additional strong baselines such as Gemini-2.5, Qwen2.5-VL, and VideoR1, which are relevant and frequently cited in the field. This addition is critical to ensure a fair and convincing evaluation.\n3. Expand the related work section – The discussion should include important prior works like VisualCoT, MLVU, VideoXL, VideoEspresso, and VideoR1. Their omission weakens the contextual positioning of this paper. This revision is important but not strictly critical.\n4. Discuss the role of visual captioners more explicitly – Since the ablation shows that stronger captioners (e.g., Qwen2.5-VL 72B) yield higher accuracy, the paper should clarify to what extent the improvements depend on the chosen captioner rather than the framework itself. This would strengthen the work’s transparency.", "broader_impact_concerns": "No.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)