arXiv:2610.35017v1 Announce Type: new Abstract: Text-steerable visual encoders condition a frozen Vision Transformer on a natural-language prompt and point at the matching image region, producing pro...
FairLens is a benchmark and evaluation framework that measures fairness and validity of vision‑language models (VLMs) in high‑stakes domains such as hiring, legal, and healthcare. It uses over 100,000 face‑image and question pairs covering gender, race, and age, and assesses responses through demographic parity, soundness, demographic association, and bias in free‑text generation. The study finds that VLMs often make unwarranted inferences from faces rather than abstaining, especially in legal and healthcare contexts, and that small parity gaps can still hide unsafe treatment across groups.
The paper introduces an explainable pedestrian routing method that estimates perceived safety by first generating a natural‑language caption from street‑level images and then deriving risk scores solely from structured features of that caption. Benchmarking nine captioning setups against a CLIP image‑embedding baseline shows comparable performance, and the system was deployed on over 650,000 images across 36 wards in Manchester and Huddersfield. Field validation with 3,669 ratings from 70 participants revealed a modest but statistically significant correlation (r = 0.262) with human judgments, while a stronger supervised benchmark did not translate into better real‑world performance.
The paper introduces a perception interface that separates vision from language in vision‑language models. A frozen perception stack detects objects, a deterministic semantic serializer converts the perceived state into text, and a standard text‑only large language model (LLM) answers questions. Experiments on a campus‑robot benchmark show that this serialized interface outperforms a zero‑shot VLM of the same language‑model size, especially as the language model shrinks, and that the advantage persists under paraphrase and different supervision regimes.
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:2608.21415v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have achieved remarkable performance across a wide range of tasks; however, they often inherit social biases fro...
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...
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:2608.16081v2 Announce Type: replace Abstract: Open-weight and frontier vision-language models (VLMs) perform well on general image understanding, but their ability to interpret fine-grained han...
arXiv:2608. 05050v1 Announce Type: cross Abstract: Against the backdrop of violence in police interactions with the U.
The paper introduces Shift‑Reliance Alignment (SRA), a method for ranking vision‑language models (VLMs) based on how much they rely on specific cues such as scene context or text. By editing source images to mislead these cues and measuring model responses, SRA predicts which VLMs will be misled by physical sticker attacks and which will hallucinate on unedited photographs. In pre‑registered tests, SRA selects models that reduce error on challenging datasets like Objects365 and OpenImages, outperforming selection by source error and model confidence on the CueDial benchmark.
arXiv:2608.28609v1 Announce Type: cross Abstract: A personalized agent needs a user memory: a persistent model of who its user is. Today it is almost always text -- transcripts and captions retrieved...