LOCI: A Locator-Critic with Refinement Loop
Quick summary
arXiv:2608.30959v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) still struggle on tasks requiring complex visual understanding. We argue that the core issue is not high-level reasoning, but instead failing to locate critical details in the image. Due to this shortcoming, VLMs generate often plausible but incorrect reasoning based on flawed perceptual grounding. To address this, we propose Locator-Critic (LOCI), a training-free framework that decouples visual search from evidence verification. LOCI employs a Locator agent to propose candidate visual evidence and a separate Criti
Key takeaways
- arXiv:2608.30959v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) still struggle on tasks requiring complex visual understanding.
- We argue that the core issue is not high-level reasoning, but instead failing to locate critical details in the image.
- Due to this shortcoming, VLMs generate often plausible but incorrect reasoning based on flawed perceptual grounding.
Why it matters
“LOCI: A Locator-Critic with Refinement Loop” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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