arXiv:2605.25842v1 Announce Type: cross Abstract: Vision-language models (VLMs) increasingly rely on chain-of-thought (CoT) reasoning to solve complex multimodal tasks, but their large parameter sizes make deployment expensive. Structured pruning offers a natural solution; however, existing methods fail to preserve CoT reasoning accuracy in VLMs. We identify two key reasons: (1) CoT consistency depends on sparse transition points (pivot tokens) in the generation trajectory, while existing pruning methods are CoT-agnostic; and (2) pruning methods designed for unimodal LLMs do not account for ac
The increasing complexity and parameter size of vision-language models necessitate more efficient deployment solutions, driving innovation in pruning techniques specifically tailored for multimodal CoT reasoning.
This research addresses a critical bottleneck in VLM deployment, potentially making advanced AI more accessible and scalable by reducing computational costs without sacrificing reasoning capabilities.
Existing pruning methods, primarily designed for unimodal LLMs or ignoring CoT consistency, will be superseded by approaches that preserve intricate multimodal reasoning, making pruned VLMs more robust.
- · AI compute providers
- · Developers deploying large AI models
- · Sectors using VLMs (e.g., robotics, autonomous systems)
- · Edge AI hardware manufacturers
- · Inefficient VLM architectures
- · Cloud providers reliant on high-cost VLM inference
More efficient and cost-effective deployment of complex multimodal AI models becomes feasible.
The reduced computational overhead allows for wider adoption of VLMs in resource-constrained environments or with higher throughput requirements.
Accelerated development and application of advanced AI agents capable of sophisticated multimodal understanding and reasoning at scale.
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Read at arXiv cs.CL