The paper introduces EmoNet‑Face‑HQ, a benchmark of 2,500 AI‑generated portraits each evaluated by four psychology experts across 40 emotion categories. It shows that most vision‑language models (VLMs) perform no better than random guessing unless fine‑tuned, but that changing the prompt to a simple yes/no per category can raise a VLM’s agreement with experts from chance to near expert levels. When evaluated using probability‑of‑yes scores on the five most‑agreed‑upon categories, eleven open‑weight VLMs achieve a quadratic‑weighted κ of 0.425, below the 0.468 two‑expert agreement, yet their average probability‑of‑yes score (0.553) matches the benchmark’s fine‑tuned model and outperforms it in three cases without any training.
By Tobias Hallmen, Fabian Deuser, Robin-Nico Kampa, Norbert Oswald, Elisabeth Andr\'e
The paper introduces EmoNet‑Face‑HQ, a fine‑grained emotion recognition benchmark that uses generated portraits and a 40‑category taxonomy to evaluate vision‑language models (VLMs). It finds that VLMs perform poorly when asked to generate responses but can match or surpass a fine‑tuned model (Empathic‑Insight‑Face) when their logits are read directly as binary queries. The study shows that the benchmark’s difficulty lies in the readout process rather than in perception, and that graded probability outputs yield better performance than simple yes/no questions.
By Tobias Hallmen, Fabian Deuser, Robin-Nico Kampa, Norbert Oswald, Elisabeth Andr\'e
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.
By Amit Jadhav, Shaurya Beriwala, Beomjin Kim
The paper presents an unsupervised multimodal reward model built on a frozen Qwen3‑VL backbone. By adding a linear head to an intermediate layer and training it to rank a web caption higher than a perturbed version of itself, the authors avoid human preference labels and achieve competitive performance—outperforming GPT‑4o on several benchmarks with far fewer parameters and compute. The approach demonstrates that a carefully chosen intermediate layer, simple perturbation-based training, and verifier‑driven fine‑tuning can yield effective vision‑language rewards without costly annotation.
By Yueying Li, Jingxuan Fan, Kiant\'e Brantley, Sham M. Kakade, Hanlin Zhang
arXiv:2611.05004v2 Announce Type: replace
Abstract: Requested-value control is stricter than continuous steering: given $c$ on a human-rating scale, the generated image should attain that value from...
By Jianbo Tang, Keita Ishii, Hiroki Sakamoto, Manabu Kikuchi, Katsuma Kitamura, Chiho Semba, Kenta Nishiyama, Teppei Mori, Yukihiro Kiwaki, Takeo Kato
The paper introduces Bayesian Pairwise Merging (BPM), a method that personalizes a shared expert library by inferring reward trade‑off weights from users’ pairwise choices. BPM treats these weights as latent variables, learns a posterior over them, and uses the posterior mean as merge coefficients, thereby converting natural pairwise feedback into actionable model personalization. Experiments on radiology summarization, image captioning, and story generation show BPM outperforming uniform merging and achieving high win rates with limited feedback, while also quantifying uncertainty in the inferred trade‑offs.
By Yaling Shen, Tongtong Wu, Siyuan Yan, Gholamreza Haffari
arXiv:2606. 07810v1 Announce Type: cross Abstract: Large language models (LLMs) are widely used as judges for evaluating model outputs, but their high cost, latency, and opacity limit scalability.
By Anish Laddha, Nitesh Pradhan, Gaurav Srivastava
arXiv:2605. 20731v2 Announce Type: replace-cross Abstract: Text-to-image models now generate graphic design at production scale, yet their supervision still comes primarily from photo-style preference datasets with a single overall verdict per comparison.
By Haonan Zhu, Elad Hirsch, Alexandria Minetti, Allison Nulty, Purvanshi Mehta
PortraitAes: Intent-Conditioned Structured Portrait Aesthetic Assessment introduces a new benchmark, PortraitAes-Bench, with 11,000 images and a detailed rubric covering nine photographic intents, six first-level dimensions, and 22 secondary criteria. The model predicts aesthetic scores conditioned on photographic intent, using multi-task supervision and Gaussian score calibration to improve comparability. On the benchmark, PortraitAes achieves high Pearson (0.924) and Spearman (0.934) correlations, outperforming general-purpose MLLMs and specialized baselines.
By Junzhou Xie, Haozhong Xiong, Xunyun Tian, Kaile Du, Tianchen Yu, Qiang Li, Wei Liu, Jiaming Liu, Ruihua Huang, Yang Shi, Guangcan Liu
arXiv:2608.29590v1 Announce Type: new
Abstract: We propose a societal bias evaluation method for large vision-language models (LVLMs) in the era of strong safety guardrails. Existing benchmarks rely...
By Yusuke Hirota, Michael Ross Boone, Arun George Zachariah, Jibin Rajan Varghese, Yu-Chiang Frank Wang, Boyi Li, Ryo Hachiuma
arXiv:2606. 18451v1 Announce Type: new Abstract: Single-image-to-3D generators are improving quickly, but there is no agreed, human-free way to tell whether one generated mesh is better than another.
By Ali Asaria, Tony Salomone, Deep Gandhi
arXiv:2606. 08841v1 Announce Type: new Abstract: Text-to-image diffusion models are increasingly deployed in open-ended creative contexts, yet their outputs remain impersonal, optimized for aggregate aesthetics rather than individual taste.
By Harini SI, Somesh Singh, Yaman Kumar Singla, David Doermann, Rajiv Ratn Shah