9 papers
When RL Suppresses Its Own Vocabulary: Recovering Reasoning Diversity in Puzzle-to-Math Transfer
Mayug Maniparambil, Arjun Karuvally, Terrence Sejnowski +1
Reinforcement learning using verifiable rewards (RLVR) improves LLM reasoning, but the conditions under which it transfers across domains -- and why it does so -- remain under-expl…
Are Natural-Domain Foundation Models Effective for Accelerated Cardiac MRI Reconstruction?
Anam Hashmi, Mayug Maniparambil, Julia Dietlmeier +2
The emergence of large-scale pretrained foundation models has transformed computer vision, enabling strong performance across diverse downstream tasks. However, their potential for…
TopoBench: Benchmarking LLMs on Hard Topological Reasoning
Mayug Maniparambil, Nils Hoehing, Janak Kapuriya +5
Solving topological grid puzzles requires reasoning over global spatial invariants such as connectivity, loop closure, and region symmetry and remains challenging for even the most…
Ensemble Learning with Sparse Hypercolumns
Julia Dietlmeier, Vayangi Ganepola, Oluwabukola G. Adegboro +3
Directly inspired by findings in biological vision, high-dimensional hypercolumns are feature vectors built by concatenating multi-scale activations of convolutional neural network…
Underrepresented in Foundation Model Pretraining Data? A One-Shot Probe
Chris Vorster, Mayug Maniparambil, Noel E. O'Connor +2
Large-scale Vision-Language Foundation Models (VLFMs), such as CLIP, now underpin a wide range of computer vision research and applications. VLFMs are often adapted to various doma…
Hold-One-Shot-Out (HOSO) for Validation-Free Few-Shot CLIP Adapters
Chris Vorster, Mayug Maniparambil, Noel E. O'Connor +2
In many CLIP adaptation methods, a blending ratio hyperparameter controls the trade-off between general pretrained CLIP knowledge and the limited, dataset-specific supervision from…