collaborators

11 papers

cs.LG2026

A Geometric View of Counterfactual Behavior: Interaction of Boundary Proximity and Local Support

Ioanna Gemou, Matteo Gamba, Randall Balestriero +1

Counterfactual explanations seek small, semantically meaningful changes to an input that alter a model's prediction, and are widely used to interpret and audit machine learning sys…

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.LG2026

Handling and Interpreting Missing Modalities in Patient Clinical Trajectories via Autoregressive Sequence Modeling

Andrew Wang, Ellie Pavlick, Ritambhara Singh

An active challenge in developing multimodal machine learning (ML) models for healthcare is handling missing modalities during training and deployment. As clinical datasets are inh…

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.AI2026

The Persona Paradox: Medical Personas as Behavioral Priors in Clinical Language Models

Tassallah Abdullahi, Shrestha Ghosh, Hamish S Fraser +5

Persona conditioning can be viewed as a behavioral prior for large language models (LLMs) and is often assumed to confer expertise and improve safety in a monotonic manner. However…