12 papers
Composition, Not Conversation: VLMs Lose the Scene, Not the Thread
L. D. M. S. Sai Teja, Ufaq Khan, N. Siva Gopala Krishna +5
Vision-language models (VLMs) increasingly reason over visual evidence that is cropped, segmented, retrieved, or revealed over time. Yet most VQA benchmarks present the complete im…
MVC-Bench: Benchmarking Calibration of Medical Vision-Language Models
Ashshak Sharifdeen, Shihab Aaqil Ahamed, Ufaq Khan +6
Reliable evaluation of vision-language models (VLMs) and medical vision-language models (Medical-VLMs) requires calibrated confidence, particularly under realistic clinical conditi…
On the Robustness of Temporal Vision-Language Models for Surgical Endoscopy Videos
Darakshan Rashid, Raza Imam, Ufaq Khan +9
Temporal vision-language models (TVLMs) offer a reusable, prompt-based interface for surgical video understanding, yet, their robustness under clinically realistic acquisition arti…
An Empirical Analysis of Continual Learning for Heterogeneous Medical Visual Question Answering
Mai A. Shaaban, Tausifa Jan Saleem, Alaa Mohamed +3
Deploying medical visual question answering (MedVQA) systems in real-world clinical settings requires models that adapt to new clinical tasks without forgetting previously acquired…
MMClima: A Framework for Multimodal Climate Science Data and Evaluation
Muhammad Umer Sheikh, Hassan Abid, Khawar Shehzad +2
Climate change research increasingly requires AI systems that reason across text, dynamic visual content, and scientific figures, yet existing climate QA benchmarks are small, most…
PathWISE: Multi-Agent Cancer Pathway Triaging Ontology Learning from Clinical Flowcharts
Sofiat Abioye, Ufaq Khan, Shazad Ashraf +6
Clinical pathways are disseminated as visual flowcharts where spatial topology, arrow direction, colour coding, and font weight encode critical triage logic that remains inaccessib…
RAPTOR+: A Visually Grounded Vision-Language Framework to Improve Clinical Trust and Auditability in Automated Cancer Referral Processing
Sofiat Abioye, Ufaq Khan, Shazad Ashraf +4
Urgent suspected colorectal cancer (CRC) referrals create operational bottlenecks because semi-structured clinical documents often require manual review and transcription. The orig…
PolyAlign: Conditional Human-Distribution Alignment
L. D. M. S. Sai Teja, Ufaq Khan, Sathira Silva +2
Post-training methods such as supervised fine-tuning (SFT) and preference optimization typically align language models toward a single global assistant behavior. While effective fo…
MedObvious: Exposing the Medical Moravec's Paradox in VLMs via Clinical Triage
Ufaq Khan, Umair Nawaz, L D M S S Teja +5
Vision Language Models (VLMs) are increasingly used for tasks like medical report generation and visual question answering. However, fluent diagnostic text does not guarantee safe…
AURORA: Adaptive Unified Representation for Robust Ultrasound Analysis
Ufaq Khan, L. D. M. S. Sai Teja, Ayuba Shakiru +4
Ultrasound images vary widely across scanners, operators, and anatomical targets, which often causes models trained in one setting to generalize poorly to new hospitals and clinica…
DAMASHA: Detecting AI in Mixed Adversarial Texts via Segmentation with Human-interpretable Attribution
L. D. M. S. Sai Teja, N. Siva Gopala Krishna, Ufaq Khan +2
In the age of advanced large language models (LLMs), the boundaries between human and AI-generated text are becoming increasingly blurred. We address the challenge of segmenting mi…
SelfAI: A self-directed framework for long-horizon scientific discovery
Xiao Wu, Ting-Zhu Huang, Liang-Jian Deng +9
Scientific discovery increasingly entails long-horizon exploration of complex hypothesis spaces, yet most existing approaches emphasize final performance while offering limited ins…