Title: V-LynX: Token Interface Alignment for Video+X LLMs

Original: arXiv:2606.00508v1 Announce Type: cross Abstract: This study introduces an intriguing phenomenon in Video LLMs: rather than merely translating frames into textual embeddings, Video LLMs establish a continuous manifold, token interface, allowing visual tokens to operate as standalone entities within the architecture. Exploiting this discovery, we propose V-LynX, a scalable framework that integrates novel modalities into Video LLMs by repurposing the internalized interface. Departing from conventional paradigms that necessitate heavy modality-specific encoders or paired supervision, V-LynX employs a lightweight auxiliary pathway in parallel with the frozen vision encoder. Our method integrates new sensory inputs with intrinsic video priors by aligning both attention responses and statistical distributions using unpaired unimodal data sets. This ensures manifold compatibility while preserving the integrity of the Video LLMs. Extensive benchmarks demonstrate that V-LynX achieves SOTA and efficiency across audio-visual QA, 3D reasoning, high-frame-rate, and multi-view video understanding. The code is available at https://github.com/park-jungin/lynx.

Rewrite:

Abstract: This paper highlights a significant characteristic of Video Large Language Models (LLMs): instead of simply converting video frames into text-based embeddings, these models construct a continuous manifold known as a token interface. This mechanism enables visual tokens to function as independent components within the model’s architecture. Leveraging this insight, we present V-LynX, a scalable approach designed to incorporate new modalities into Video LLMs by utilizing this existing internal interface. Unlike traditional methods that rely on complex, modality-specific encoders or require paired supervision, V-LynX introduces a lightweight auxiliary pathway that runs concurrently with a frozen vision encoder. By aligning both attention mechanisms and statistical distributions through unpaired unimodal datasets, our technique effectively merges fresh sensory data with the model’s inherent video priors. This process maintains manifold compatibility without compromising the structural integrity of the Video LLM. Comprehensive evaluations show that V-LynX delivers state-of-the-art performance and efficiency in tasks such as audio-visual question answering, 3D reasoning, high-frame-rate processing, and multi-view video analysis. The source code can be accessed at https://github.com/park-jungin/lynx.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC