3 papers
cs.CV2026
Fewer Tokens, More Answers: Verifiable-Reward Tuning of an Ornith-Style VLM for Multi-View Driving QA
T N Rickesh, Pranav Vinodh, Sowmya Kamath S
Reasoning-enabled model derivatives must turn intermediate deliberation into a usable answer within a deployment budget. We study that transition in an Ornith checkpoint adapted to…
cs.CV2026
Answer Correction with Verifiable Rewards: A Gemma-Family Study of Multi-View Driving QA
T N Rickesh, Pranav Vinodh, Sowmya Kamath S
Multi-camera driving question answering provides a discrete test of vision-language models, but post-training gains can reflect changed selected answers or improved answer extracti…
cs.CV2026
Bridging the Synthetic-to-Real Gap for Few-Shot Cryo-ET Classification
Siddhant Bharadwaj, Ashish Vashist, Rashi Singh +5
Subtomogram classification in cryo-electron tomography (cryo-ET) is a challenging problem due to the scarcity of labeled examples. While cryo-ET simulators can be adopted to genera…