activity
20242026
collaborators

7 papers

cs.SE2026

RubricCraft: Where Solutions Diverge, Criteria Emerge

Hosein Hasanbeig, Priyanshu Gupta, Ashish Tiwari +2

Evaluating language agents on open-ended, artifact-producing tasks is difficult: model-based graders can be inconsistent, while exhaustive programmatic checks are often impractical…

cs.AI2026

Whose turn is it? Context-guided model routing for stateful agents

Anikesh Parashar, Avik Dutta, Vashu Chauhan +6

Model routing can reduce the cost of LLM applications by assigning simpler requests to cheaper models without sacrificing the quality of the response. However, most routers treat r…

cs.AI2026

Dude, Where's My State? Execution Information Requirements for Stateful Agents

Nikita Mehrotra, Ashish Tiwari, Priyanshu Gupta +1

Long-running agents must preserve information that later steps depend on. We introduce the Execution Information Requirement (EIR), a lower bound on the information that must remai…

cs.AI2026

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…

cs.AI2026

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…

cs.AI2025

Improving Language Agents through BREW: Bootstrapping expeRientially-learned Environmental knoWledge

Shashank Kirtania, Param Biyani, Priyanshu Gupta +4

Large Language Model (LLM)-based agents are increasingly capable of complex, multi-step tasks such as GUI automation, tool use, and data manipulation, yet they cannot learn from ex…

cs.LO2024

LOGIC-LM++: Multi-Step Refinement for Symbolic Formulations

Shashank Kirtania, Priyanshu Gupta, Arjun Radhakirshna

In this paper we examine the limitations of Large Language Models (LLMs) for complex reasoning tasks. Although recent works have started to employ formal languages as an intermedia…