collaborators

12 papers

cs.CL2026

Vision-Default, Prior-Override: Causal Mechanisms of Perception-Knowledge Conflict in Vision-Language Models

Niclas Lietzow, Danielle Bitterman, Carsten Eickhoff +2

Vision-language models must reconcile visual evidence with memorized world knowledge when the two conflict. How they resolve this conflict shapes the reliability of multimodal syst…

cs.CL2026

UbuntuGuard: A Culturally-Grounded Policy Benchmark for Equitable AI Safety in African Languages

Tassallah Abdullahi, Macton Mgonzo, Mardiyyah Oduwole +4

Current guardian models are predominantly Western-centric and optimized for high-resource languages, leaving low-resource African languages vulnerable to evolving harms, cross-ling…

cs.CV2026

PubMed-Ophtha: An open resource for training ophthalmology vision-language models on scientific literature

Verena Jasmin Hallitschke, Carsten Eickhoff, Philipp Berens

Vision-language models hold considerable promise for ophthalmology, but their development depends on large-scale, high-quality image-text datasets that remain scarce. We present Pu…

cs.CV2026

Mechanisms of Prompt-Induced Hallucination in Vision-Language Models

William Rudman, Michal Golovanevsky, Dana Arad +4

Large vision-language models (VLMs) are highly capable, yet often hallucinate by favoring textual prompts over visual evidence. We study this failure mode in a controlled object-co…

cs.CV2026

Is There Knowledge Left to Extract? Evidence of Fragility in Medically Fine-Tuned Vision-Language Models

Oliver McLaughlin, Daniel Shubin, Carsten Eickhoff +3

Vision-language models (VLMs) are increasingly adapted through domain-specific fine-tuning, yet it remains unclear whether this improves reasoning beyond superficial visual cues, p…

cs.LG2025

APP: Accelerated Path Patching with Task-Specific Pruning

Frauke Andersen, William Rudman, Ruochen Zhang +1

Circuit discovery is a key step in many mechanistic interpretability pipelines. Current methods, such as Path Patching, are computationally expensive and have limited in-depth circ…