Skip to content

Author

Sumit Gulwani

We have 7 of 283 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

#artificial intelligence Preprint Sep 2026

On Device Agentic Operation Caches -- Classifier-Centric NL-to-Action Generation

This paper introduces a novel means of converting the NL-to-Action problem from a generative one into a classification-centric formulation via on-device operation caches via on-device operation caches that allow an agentic system to handle frequently occurring classes of actions completely on-device -- reducing latency...

Moghis Fereidouni, A. Arnold, Sumit Gulwani et al. · 0 citations
#artificial intelligence Preprint Sep 2026

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

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 remain accessible for correct completion under specified task and access conditions. We develop LACUNA, a framework that generates tasks with kno...

N. Mehrotra, Ashish Tiwari, Priyanshu Gupta et al. · 0 citations
#artificial intelligence Preprint Aug 2026

Coding Agents are Strong Prompt Optimizers

Search-based prompt optimizers improve prompts through iterative search: they propose edits, execute fresh rollouts, score the resulting trajectories, and retain only edits that improve a validation metric. We show that this optimization loop is unnecessary. Given only a static corpus of agent trajectories, an off-the-...

Agamdeep Singh, Srishti Gautam, Priyanshu Gupta et al. · 0 citations
Preprint Aug 2026

Reason Wide, Not Deep: Amortizing the Reasoning Premium into Distilled Skills

This work shows that reasoning traces are not a prerequisite: skills distilled from non-reasoning trajectories alone remain competitive with skills distilled from paired reasoning/non-reasoning corpora, with domain-dependent differences between the two sources.

Agamdeep Singh, Srishti Gautam, Priyanshu Gupta et al. · 0 citations
Open access 2026

Asking language models how to represent data for fine-tuning

It is shown that format choice remains important even after fine-tuning; models learn more efficiently with specific formats rather than adapting to any format; this finding allows format selection to be done via inference alone, avoiding costly trial-and-error fine-tuning runs.

Usneek Singh, Ananya Singha, Abhijeet Awasthi et al. · 0 citations
Preprint Jul 2026

Plans Work in Mysterious Ways: Evaluating a Plan Mode for Spreadsheet Agents

A prototype of a Plan Mode for spreadsheet programming is built and evaluated against a non-planning baseline and it is found that using Plan Mode led to a reduction in refinement and a better perception of the tool across dimensions of creativity support and human-machine collaboration.

Aayush Kumar, Avik Dutta, Sumit Gulwani et al. · 0 citations

Towards Autonomous Software Development

A three-level taxonomy inspired by autonomous driving that distinguishes degrees of autonomy along a roadmap from today’s AI-assisted development workflows to fully autonomous software development in which AI systems autonomously identify demands and design, implement, verify, and maintain software without human oversi...

Hao Wang, Ruijie Meng, Zhe Ye et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.