vision language models posts and papers

In-Context Robot Learning with VLM Agents arxiv.org

GPT‑Policy is a framework that lets a large vision‑language model (e.g. GPT‑6 Astra) perform in‑context robot learning: a context compiler extracts visual transitions from demos, the VLM proposes tool actions, and a constrained controller verifies and executes them. Real‑robot experiments show that raw video demos improve success rates even without explicit action labels, and that providing align…

Hugging Face Daily Papersarxiv.orgDongzhou Cheng, Taoran Yi, Ye Fang6d1 minpaper

E2A-Bench: Benchmarking Evidence-to-Action Reliability in Financial Chart Reasoning arxiv.org

E2A‑Bench is a 969‑query benchmark for financial chart reasoning that evaluates vision‑language models across a full evidence‑to‑action chain using four metrics (UCR, RCI, ECI, NDR). Experiments on 20 VLMs expose hidden failures: low‑UCR models have only 6.4 % directional coverage, oracle‑aided verification cuts unsupported claims but can kill coverage, and fine‑tuning inflates BUY:SELL ratios by…

Hugging Face Daily Papersarxiv.orgXiaoya Wang, Yutong Xu, Junjie Wang9d1 minpaper

vision language models

In-Context Robot Learning with VLM Agentsarxiv.org

E2A-Bench: Benchmarking Evidence-to-Action Reliability in Financial Chart Reasoningarxiv.org

In-Context Robot Learning with VLM Agents arxiv.org

E2A-Bench: Benchmarking Evidence-to-Action Reliability in Financial Chart Reasoning arxiv.org