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Priyanshu Gupta

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#artificial intelligence Preprint Sep 2026

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

The EIR is introduced, a lower bound on the information that must remain accessible for correct completion under specified task and access conditions and LACUNA is developed, a framework that generates tasks with known dependencies and varies information demand, retention, and recovery separately from the difficulty of...

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

Coding Agents are Strong Prompt Optimizers

Results suggest that corpus-scale statistical reflection is a viable alternative to iterative search for prompt optimization, and given only a static corpus of agent trajectories, an off-the-shelf coding agent can directly synthesize an optimized prompt, requiring neither environment access nor validation data.

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

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