SiLVR: A Simple Language-based Video Reasoning Framework

2025-05-30

AI Summary

Multimodal large language models (MLLMs) exhibit limited capability in complex video-language reasoning tasks—such as temporal modeling, causal inference, long-context understanding, and knowledge integration. To address this, we propose a two-stage, language-only framework: first, multimodal sensory signals (video, audio, speech) are mapped—without any training—into structured linguistic representations (short-segment captions + ASR transcripts); second, these textual inputs are processed by powerful reasoning LLMs (e.g., Claude, GPT-4) for deep semantic inference. Our key contributions include: (i) the first zero-shot, modular, training-free paradigm for video-language reasoning; and (ii) an adaptive token compression mechanism that dynamically adjusts temporal granularity to efficiently handle long-duration, multimodal contexts. Extensive evaluation across Video-MME (long), Video-MMMU, Video-MMLU, CGBench, and EgoLife establishes new state-of-the-art performance, demonstrating that pure language models—when augmented with linguistically grounded multimodal inputs—can achieve robust, generalizable high-level reasoning.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Computer Vision: Language and Vision

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

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 token 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. Code is available at https://github.com/CeeZh/SILVR.

Problem

• Enhances MLLMs' reasoning for complex video-language tasks
• Decomposes video understanding into language-based representation stages
• Improves handling of long-context multisensory video inputs

Innovation

• Decomposes video understanding into two stages
• Uses language-based representations from multisensory inputs
• Employs adaptive token reduction for long-context