arXiv:2602.06566v3 Announce Type: replace-cross Abstract: Despite recent successes, test-time scaling -- i.e., dynamically expanding the token budget during inference as needed -- remains brittle for vision-language models (VLMs). Unstructured visual reasoning chains entangle perception and reasoning, leading to long, disorganized contexts where small perceptual mistakes may cascade into completely wrong answers. Reasoning also requires expensive reinforcement learning with hand-crafted rewards. Here, we introduce SPARC (Separating Perception And Reasoning Circuits), a modular framework that e
The continuous growth in complexity and scale of Vision-Language Models necessitates more efficient and robust architectures to handle increasing token budgets and prevent cascading errors.
This research addresses fundamental limitations in VLM scalability and reliability, potentially unlocking new applications requiring sophisticated visual reasoning and reducing the compute cost of advanced AI.
VLMs could become significantly more reliable and scalable at test-time, enabling more complex agentic behaviors and broader implementation in real-world, dynamic environments.
- · AI developers
- · Robotics companies
- · Generative AI platforms
- · Cloud compute providers
- · Companies with inefficient VLM architectures
- · AI training and inference budget overspenders
Improved VLM performance across a wider range of complex visual reasoning tasks.
Accelerated development and deployment of more capable AI agents and automated systems.
Enhanced human-computer interaction and a broader societal integration of AI into daily tasks and critical infrastructure due to increased reliability.
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Read at arXiv cs.CL