1 citations · 1 across the 6 of their papers we have counts for
6 papers
Coding Agents are Strong Prompt Optimizers
Agamdeep Singh, Srishti Gautam, Priyanshu Gupta +3
Search-based prompt optimizers improve prompts through iterative search: they propose edits, execute fresh rollouts, score the resulting trajectories, and retain only edits that im…
CricRAG: Retrieval Augmented Vision-Language Models for Personalized Cricket Coaching
Agamdeep Singh, Sujit PB, Mayank Vatsa
Vision-Language Models (VLMs) offer promising capabilities for automated sports coaching but face a fundamental limitation: they implicitly compare against professional standards,…
Reason Wide, Not Deep: Amortizing the Reasoning Premium into Distilled Skills
Agamdeep Singh, Srishti Gautam, Priyanshu Gupta +3
Reasoning modes of language models outperform their non-reasoning counterparts on multi-step agentic tasks, but pay a 3-6x premium in output tokens on every episode -- much of it s…
Distributions In, Distributions Out: The Case for Soft-Label Training
Agamdeep Singh, Ashish Tiwari, Hosein Hasanbeig +1
Supervised classifiers output a distribution over classes but are typically trained against a single label obtained by collapsing multiple annotators into a majority vote. On tasks…
AnyTraverse: An off-road traversability framework with VLM and human operator in the loop
Sattwik Sahu, Agamdeep Singh, Karthik Nambiar +2
Off-road traversability segmentation enables autonomous navigation with applications in search-and-rescue, military operations, wildlife exploration, and agriculture. Current frame…
Poze: Sports Technique Feedback under Data Constraints
Agamdeep Singh, Sujit PB, Mayank Vatsa
Access to expert coaching is essential for developing technique in sports, yet economic barriers often place it out of reach for many enthusiasts. To bridge this gap, we introduce…