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cs.CV2026
Spatially Grounded Concept Bottleneck Models via Part-Factorized Attention
Dhanesh Ramachandram
Concept bottleneck models (CBMs) predict a layer of human-named attributes before predicting a class, which makes their decisions auditable. On fine-grained recognition tasks, thou…
cs.AI2026
CaVe-VLM-CoT: An Interpretable Vision-Language Model Framework
Sneha Rao, Shaina Raza, Dhanesh Ramachandram
Vision-Language Models (VLMs) remain prone to hallucinations, producing fluent but visually unfaithful outputs. Existing chain-of-thought and retrieval-augmented methods only parti…
cs.AI2026
From Features to Actions: Explainability in Traditional and Agentic AI Systems
Sindhuja Chaduvula, Jessee Ho, Kina Kim +6
Over the last decade, Explainable AI has primarily focused on interpreting individual model predictions, producing post-hoc explanations that relate inputs to outputs under a fixed…