arXiv:2610.41358v1 Announce Type: new Abstract: Existing visual preference evaluation relies on costly human annotations or preference reward models, whereas online marketplace transaction prices are...
arXiv:2609.15100v1 Announce Type: cross Abstract: An image tool can change its underlying generator while retaining its public name, making version attribution from online posts ambiguous. We study t...
arXiv:2609.13279v1 Announce Type: new Abstract: Fashion attribute extraction is evaluated inconsistently: results are reported as single aggregate numbers across image types that pose different probl...
arXiv:2609.13308v1 Announce Type: cross Abstract: A companion evaluation found that naming the target part in a manipulation prompt increased action accuracy by 0.32-0.63 across eight vision-language...
DICL-Qwen3VL is a decoupled, interpretable vision‑language model that localizes upper‑body clothing and identifies workwear in indoor construction sites. It uses low‑rank adaptation of Qwen3‑VL, separates visual and reasoning confidence estimates, and employs token entropy and image perturbation sensitivity to supervise visual confidence. Experiments on the Indoor Construction Site Workwear Dataset and the web‑collected Indoor Construction Workwear Dataset achieve mAP@50 scores of 0.730 and 0.753, outperforming baseline VLMs and providing explicit evidence and confidence scores for industrial safety monitoring.
arXiv:2610.27192v1 Announce Type: cross Abstract: Multimodal large language models (LLMs) are increasingly used for structured extraction from financial documents, but their behavior under visual amb...
The paper investigates whether panels of vision‑language models (VLMs) can reliably judge image aesthetics. It shows that a panel of holistic judges rarely outperforms its best member, but when each model scores images on five rubric‑defined dimensions and these dimension scores are fused across model families, the panel consistently beats the best single VLM on two datasets (EVA and PARA). The study demonstrates that the value of a panel depends on the type of input it receives, and that dimension‑based fusion yields measurable gains at the cost of additional labeling and API usage.
The paper demonstrates that vision‑language models’ refusal behavior is heavily influenced by whether an image is attached to a request, even when the image is blank or unreadable. Attaching such an image shifts refusal scores by large margins for borderline‑benign prompts while leaving genuinely neutral instructions largely unchanged. This effect varies with image properties, persists across checkpoints, and is not mitigated by explicit instructions to ignore the image.
arXiv:2609.22910v1 Announce Type: new Abstract: Vision-language agents that crop and zoom are trained with rewards that credit a successful tool call, yet a successful call does not show that the mod...
The paper compares a non‑generative verifier (Jev) with a frontier LLM (Astra) on 495 human‑labelled claims from LLM‑AggreFact. Both models achieve statistically indistinguishable accuracy, yet Jev is 187‑fold cheaper and admits more unsupported claims. A confidence‑gated cascade that escalates uncertain claims improves cost‑efficiency, though the benefit comes mainly from the escalation share rather than the probability estimates.
arXiv:2603. 04419v2 Announce Type: replace-cross Abstract: We characterize the phenomenon of context-dependent affordance computation in vision-language models (VLMs).
arXiv:2609.13617v1 Announce Type: new Abstract: We evaluate whether the improvements advertised for ChatGPT Images 2.5 translate into better performance on forgery tasks with predetermined answers. W...