arXiv AI
ChatGPT Images 2.5 in the Wild: A Launch-Period Dataset and Detector Evaluation
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arXiv Computer Vision
Sep 15
From Advertised Improvements to Measured Capabilities: Evaluating ChatGPT Images 2.5 on Forgery Tasks
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...
arXiv Computer Vision
Sep 17
ChatGPT Images 2.5 on Forgery Tasks: Testing Advertised Improvements Against Known Answers
arXiv:2609.13617v2 Announce Type: replace Abstract: OpenAI released ChatGPT Images 2.5 on 8 September 2026, advertising more precise local edits, better consistency across edits, more faithful refere...
arXiv Computation and Language
Sep 22
The Copy Ceiling: An Input-Exposure Control for Ontology-Grounded Generation over Curated Corpora
arXiv:2609.24885v1 Announce Type: new Abstract: When a language model answers from a curated corpus via graph-based retrieval, a large grounding uplift does not establish reasoning over the retrieved...
arXiv Computer Vision
Oct 27
More Memory, Better Videos? A Controlled Study of Visual Memory in Long-Video Generation
arXiv:2610.35001v1 Announce Type: new Abstract: Long-video generators increasingly retrieve past visual observations to prevent identity drift. The intuition is compelling: more access to history sho...
arXiv AI
Jul 31
Forensic Reproducibility Audit of a Radiology Vision-Language Model Benchmark: From Intended Protocol to Released Artifact
arXiv:2607. 25589v1 Announce Type: cross Abstract: Medical-imaging AI benchmarks combine datasets, DICOM rendering, prompts, provider APIs, automated labels, statistical code, manuscripts, and repository releases.
arXiv Computation and Language
Oct 19
Not Every Mention Is a Claim: Rethinking Object Hallucination Detection in Vision-Language Models
The paper argues that many vision‑language model (VLM) hallucination metrics mistakenly treat every object mentioned in a caption as a claim that the object is present. It introduces NAME‑Bench, a dataset of 25,497 non‑assertive mentions labeled as depictive, purpose, negation, or hedged, and shows that current detectors flag these as hallucinations at high rates. Adding non‑assertive mentions as negatives improves detection on LLaVA‑1.5 but requires much more data for newer VLMs, indicating that conflating mention with assertion is a persistent problem.