P. Taghavi

ActionSplice is introduced, an inference framework that formulates this problem as Counterfactual State Transport (CST), a lightweight corrector that transports the interrupted backbone-native representation toward the matched state induced by the revised action at the same solver step.

Grounding AI coding agents in the existing codebase during spec-driven development significantly improves code quality and reduces hallucinations, proving that context is king even for LLMs.

Distilling foundation models with a novel instance-aware contrastive loss yields smaller segmentation models that surprisingly outperform their larger teachers, even with limited labeled data.

Decoupling reasoning from action generation in autonomous driving VLMs lets you beat larger end-to-end models while slashing training costs.