1 citations · 1 across the 5 of their papers we have counts for
6 papers
Learning Strategies to Break Judges
Guruprerana Shabadi, Aaditya Naik, Rajeev Alur +1
As AI agents surpass human performance, it becomes exceedingly hard for system designers to evaluate them directly and understand their failure modes. Consequently, agents themselv…
Auction-Based Online Policy Adaptation for Evolving Objectives
Guruprerana Shabadi, Kaushik Mallik
We consider multi-objective reinforcement learning problems where objectives come from an identical family -- such as the class of reachability objectives -- and may appear or disa…
Do We Need Frontier Models to Verify Mathematical Proofs?
Aaditya Naik, Guruprerana Shabadi, Rajeev Alur +1
Advances in training, post-training, and inference-time methods have enabled frontier reasoning models to win gold medals in math competitions and settle challenging open problems.…
Optimization Modulo Integer Linear-Exponential Programs
S Hitarth, Alessio Mansutti, Guruprerana Shabadi
This paper presents the first study of the complexity of the optimization problem for integer linear-exponential programs which extend classical integer linear programs with the ex…
Risk-Sensitive Agent Compositions
Guruprerana Shabadi, Rajeev Alur
From software development to robot control, modern agentic systems decompose complex objectives into a sequence of subtasks and choose a set of specialized AI agents to complete th…
Programmatic Reinforcement Learning: Navigating Gridworlds
Guruprerana Shabadi, Nathanaël Fijalkow, Théo Matricon
The field of reinforcement learning (RL) is concerned with algorithms for learning optimal policies in unknown stochastic environments. Programmatic RL studies representations of p…