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
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
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
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
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.· Proceedings of the First Wor...· 0 citations
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
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
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