2 citations · 2 across the 4 of their papers we have counts for
4 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…
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…
SkillAxe: Sharpening LLM-Authored Agent Skills Through Evaluation-Guided Self-Refinement
Srishti Gautam, Arjun Radhakrishna, Sumit Gulwani
Skill documents, structured natural-language instructions that guide Large Language Model (LLM) agents, are critical to modern agent frameworks, yet LLMs struggle to write skills t…
Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications
Yanchen Liu, Srishti Gautam, Jiaqi Ma +1
Recent literature has suggested the potential of using large language models (LLMs) to make classifications for tabular tasks. However, LLMs have been shown to exhibit harmful soci…