arXiv AI
Jul 20
An Exam for Active Observers
arXiv:2607. 16165v1 Announce Type: cross Abstract: Human vision is a closed loop: gaze is continuously redirected by intermediate hypotheses rather than a single snapshot.
Frontier vision-language models have overtaken young adults at detecting AI-generated portraits -- but not their calibration
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arXiv Computer Vision
2d ago
Guardrail-Agnostic Societal Bias Evaluation in Large Vision-Language Models
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...
arXiv AI
Jul 28
AI-generated Images Challenge Visual Trust in High-risk Scenarios
arXiv:2607. 22745v1 Announce Type: cross Abstract: Rapid advances in image generation are eroding the evidentiary value of visual content in settings where authenticity can affect public safety and personal reputation.
arXiv AI
Jun 18
When Cars Have Stereotypes: Auditing Demographic Bias in Objects from Text-to-Image Models
arXiv:2508. 03483v3 Announce Type: replace-cross Abstract: While prior research on text-to-image generation has predominantly focused on biases in human depictions, demographic bias in generated objects remains relatively underexplored.
arXiv Computation and Language
Aug 27
GGSS: Geodesic-Gated Spherical Steering for Inference-Time Debiasing of Generative Vision-Language Models
The paper introduces GGSS (Geodesic‑Gated Spherical Steering), a norm‑preserving method for inference‑time debiasing of generative vision‑language models. GGSS identifies a counterfactual bias subspace on the unit hypersphere, steers visual tokens along geodesic arcs, and applies an adaptive gate to target tokens with strong demographic signals. Experiments on four generative VLMs show that GGSS achieves the lowest average bias across multiple tests while maintaining visual‑language performance within ±0.6 pp of the baseline.
arXiv AI
Jul 2
MindEdit-Bench: Benchmarking Object-Level Counterfactual Spatial Reasoning in VLMs from In-the-Wild Photos
arXiv:2607. 00491v1 Announce Type: cross Abstract: Benchmarks for vision-language models (VLMs) mostly test observational spatial reasoning: models describe relations already visible in the input.
arXiv Machine Learning
Jun 15
Gaze Heads: How VLMs Look at What They Describe
arXiv:2606. 14703v1 Announce Type: cross Abstract: How a vision-language model internally solves the task of describing an image is far from obvious.
arXiv AI
Jun 16
Mitigating Object Hallucinations in LVLMs via Attention Imbalance Rectification
arXiv:2603. 24058v2 Announce Type: replace-cross Abstract: Object hallucination in Large Vision-Language Models (LVLMs) severely compromises their reliability in real-world applications, posing a critical barrier to their deployment in high-stakes scenarios such as autonomous driving and medical image analysis.
arXiv AI
Aug 14
How Do VLMs Behave When Blind or Misled? Behavioral Evaluation of VLMs on Scientific Figures
arXiv:2608. 13267v1 Announce Type: cross Abstract: Existing vision-language model (VLM) benchmarks emphasize perception and reasoning accuracy (how well VLMs describe and reason about what they see in an image), with limited attention to behavioral reliability under uncertainty (how they behave when visual evidence is missing or misleading).
arXiv AI
Jul 13
Evolution of Accuracy and Visual-Cognitive Errors in a Decade of Vision-Language AI Models
arXiv:2607. 09654v1 Announce Type: cross Abstract: Vision language models (VLMs) have made remarkable progress in visual reasoning during the last decade.
arXiv Computation and Language
Aug 27
Overview of SHROOM-Visions 2026: A Shared Task on Hallucination Detection in Large Vision-Language Models
In 2026, the SHROOM-Visions shared task was launched at the UncertaiNLP Workshop co‑located with EMNLP to address hallucinations in large vision‑language models. The task builds on the SHEEP dataset and asks participants to detect and classify fine‑grained hallucination spans in image‑conditioned text generation across four languages (Chinese, English, French, Italian) using a five‑class taxonomy. The competition attracted 27 teams and over 600 system submissions, with top systems achieving character‑level, label‑conditioned, and IoU scores of 0.58, 0.46, and 0.51 respectively, surpassing baselines by 30‑40 points.
arXiv AI
Aug 18
Matched Outcomes, Divergent Gaze: How Foveated MLLMs Search Compared to Humans
arXiv:2608. 16514v1 Announce Type: cross Abstract: Human visual search is serial: the fovea must land on a candidate to confirm it, and those landings form a scanpath.