SiLVR: A Simple Language-based Video Reasoning Framework (2505.24869v1)

Abstract: Recent advances in test-time optimization have led to remarkable reasoning capabilities in 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.

Overview of SiLVR: A Simple Language-Based Video Reasoning Framework

The paper presented by Zhang et al. introduces SiLVR, a Simple Language-based Video Reasoning framework designed to improve the complex video-language understanding abilities of multimodal LLMs (MLLMs). While recent advancements in test-time optimization have significantly enhanced the reasoning capabilities of LLMs in domains such as mathematics and coding, MLLMs still face limitations when applied to intricate video-language tasks. SiLVR addresses these limitations with a two-stage approach: transforming raw video into language-based representations using multisensory inputs and solving complex tasks using a reasoning LLM.

Methodology

The SiLVR framework comprises two key stages:

1. Multisensory Input Transformation: This stage involves the conversion of raw video content into rich language-based representations. It utilizes multisensory inputs from video clips and audio/speech subtitles. Videos are divided into short clips, and these clips are captioned using a pre-trained visual captioner such as NVILA. Additionally, automated speech recognition is employed to transcribe speech into text.
2. Language-Based Reasoning: After transforming the video content into textual descriptions, a powerful reasoning LLM is utilized to process these descriptions and solve complex video-language understanding tasks. To manage the processing of long-context multisensory inputs, SiLVR employs an adaptive token reduction scheme that dynamically adjusts the sampling of audio and video tokens to fit within the LLM's context length.

Results and Performance

SiLVR is described as modular, simple, and training-free, achieving the best-reported results across various benchmarks including Video-MME (long), Video-MMMU (comprehension), Video-MMLU, CGBench, and EgoLife. Notably, SiLVR outperforms proprietary non-reasoning models such as GPT-4o and Gemini-1.5. The empirical studies presented demonstrate that even without specific video training, strong reasoning LLMs are capable of effectively aggregating multisensory inputs from video and audio for complex tasks involving temporal, causal, and long-context reasoning as well as knowledge acquisition.

Implications and Future Directions

SiLVR's approach of decomposing complex video understanding into manageable stages has several implications. From a practical perspective, it offers a framework that can be utilized without task-specific fine-tuning, making it broadly applicable across various video-language reasoning tasks. Furthermore, the modular design allows easy substitution and upgrading of individual components, such as the visual captioner or the reasoning LLM.

Theoretically, SiLVR demonstrates that reasoning capabilities in LLMs can be extended to the video domain by utilizing powerful language-based models. The framework's ability to handle complex, long-duration videos with strong spatiotemporal grounding provides a valuable baseline for future work in the field of video-language reasoning.

This research suggests potential for further enhancements in AI, particularly in multimodal understanding and integration. As advances in visual and audio transcription technologies continue, there will be opportunities to refine SiLVR's framework further, perhaps by incorporating more sophisticated token reduction schemes or integrating additional sensory modalities.

In summary, SiLVR serves as an effective and efficient approach to complex video reasoning tasks, contributing significantly to the field's progression toward more advanced and capable multimodal AI systems.