arXiv:2605.28115v1 Announce Type: new Abstract: Vision-Language Models (VLMs) face severe memory and latency bottlenecks due to high-resolution visual tokens. While current token reduction methods theoretically save FLOPs, post-hoc pruning introduces structural overhead, failing to yield proportional wall-clock acceleration. However, enforcing a contiguous compact pathway risks geometric disorientation and loss of fine-grained localization. To overcome these barriers, this paper introduces CIVIC, a path-consistent compact visual inference framework. By maintaining compact sequence representati
The proliferation of Vision-Language Models (VLMs) and their computational demands is driving immediate urgency for efficiency innovations.
Improving VLM efficiency can significantly reduce the computational and energy costs associated with advanced AI, broadening accessibility and deployment.
This research introduces a novel method to compact visual sequences in VLMs, directly addressing memory and latency bottlenecks without significant performance degradation.
- · AI developers
- · Cloud providers
- · GPU manufacturers
- · SaaS companies leveraging VLMs
- · Inefficient VLM architectures
VLMs become more efficient and cost-effective to train and deploy.
This efficiency enables the use of more complex VLM architectures or broader VLM applications in resource-constrained environments.
Lower inference costs for powerful VLMs could accelerate the development and adoption of AI agents and other advanced AI systems.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI