X-Stream: Exploring MLLMs as Multiplexers for Multi-Stream Understanding
Summary
X-Stream introduces the first benchmark for multi-stream video understanding, evaluating MLLMs as multiplexers across multiple concurrent streams. The study reveals that current MLLMs achieve only about 50% accuracy, exposing significant limitations in handling multiple streams.
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Paper page - X-Stream: Exploring MLLMs as Multiplexers for Multi-Stream Understanding
Source: https://huggingface.co/papers/2606.02482 Authors:
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Abstract
X-Stream introduces the first benchmark for multi-stream streaming understanding, revealing significant limitations of current MLLMs in handling concurrent streams.
While video streaming understanding has made significant strides, real-world applications, such as live sports broadcasting, autonomous driving, and multi-screen collaboration, inherently demand continuous, multi-stream interactions. However, existing benchmarks are confined to single-stream paradigms, leaving a critical gap in evaluating online, cross-stream reasoning. To bridge this, we introduce X-Stream, the first benchmark dedicated to multi-stream streaming understanding. Comprising 4,220 rigorously curated QA pairs across 932 videos, X-Stream evaluates 11 subtasks acrossmulti-window,multi-view, andmulti-device scenarios. Crucially, our dataset is constructed using a noveldual-verification pipelinethat prevents over-reliance on a single stream. Furthermore, we pioneer the conceptualization ofmulti-modal large language models(MLLMs) as naive multiplexers, systematically evaluating their performance through the lens ofSignal Multiplexing Theory. Our extensive online inference experiments reveal a stark reality: state-of-the-art MLLMs struggle significantly withconcurrent streams, achieving only about 50% score and exhibiting poor proactive ability. Ultimately, X-Stream exposes the trade-off of current multiplexing schemes, providing both a practical evaluation protocol and empirical guidance for next-generation multi-stream agents.
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