2 papers
cs.AI2026
Let's Think in Two Steps: Mitigating Agreement Bias in MLLMs with Self-Grounded Verification
Moises Andrade, Joonhyuk Cha, Brandon Ho +3
Verifiers--functions assigning rewards to agent behavior--have been key to AI progress in math, code, and games. However, extending gains to domains without clear-cut success crite…
cs.CV2025
Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models
Chengyue Huang, Yuchen Zhu, Sichen Zhu +4
Vision-language models (VLMs) are widely assumed to exhibit in-context learning (ICL), a property similar to that of their language-only counterparts. While recent work suggests VL…