3 citations · 3 across the 11 of their papers we have counts for
14 papers
Training Small Language Models for Transferable Tool Selection
Zhaoyi Zhou, Akshayaa Magesh, Jeongyeol Kwon +4
As LLM agents operate over corpora of more than ten thousand tools, selecting the right tool for a complex multi-step user instruction becomes a central bottleneck. Tool descriptio…
BayesBench: Evaluating LLM Belief Trajectories Under Multi-Turn Evidence Accumulation
Ankur Samanta, Akshayaa Magesh, Tal Lancewicki +7
Large language models (LLMs) are typically deployed in multi-turn conversations, where each turn provides new evidence that should reduce epistemic uncertainty about their environm…
Credit Assignment with Resets in Language Model Reasoning
Ankur Samanta, Akshayaa Magesh, Ayush Jain +7
Contemporary reinforcement learning with verifiable reward methods post-train language models on multi-step reasoning by assigning a single outcome reward uniformly across all toke…
Structure Enables Effective Self-Localization of Errors in LLMs
Ankur Samanta, Akshayaa Magesh, Ayush Jain +8
Self-correction in language models remains elusive. In this work, we explore whether language models can explicitly localize errors in incorrect reasoning, as a path toward buildin…
Self-Improvement of Language Models by Post-Training on Multi-Agent Debate
Ankur Samanta, Akshayaa Magesh, Runzhe Wu +7
Self-improvement, where models improve beyond their current performance without external supervision, remains a challenge. The core difficulty is sourcing a training signal stronge…
Principled Detection of Hallucinations in Large Language Models via Multiple Testing
Jiawei Li, Akshayaa Magesh, Venugopal V. Veeravalli
While Large Language Models (LLMs) have emerged as powerful foundational models to solve a variety of tasks, they have also been shown to be prone to hallucinations, i.e., generati…
Heterogeneous Multi-Player Multi-Armed Bandits Robust To Adversarial Attacks
Akshayaa Magesh, Venugopal V. Veeravalli
We consider a multi-player multi-armed bandit setting in the presence of adversaries that attempt to negatively affect the rewards received by the players in the system. The reward…
Robust Multi-Hypothesis Testing with Moment Constrained Uncertainty Sets
Akshayaa Magesh, Zhongchang Sun, Venugopal V. Veeravalli +1
The problem of robust binary hypothesis testing is studied. Under both hypotheses, the data-generating distributions are assumed to belong to uncertainty sets constructed through m…
Adaptive Step-Size Methods for Compressed SGD
Adarsh M. Subramaniam, Akshayaa Magesh, Venugopal V. Veeravalli
Compressed Stochastic Gradient Descent (SGD) algorithms have been recently proposed to address the communication bottleneck in distributed and decentralized optimization problems,…
Multiple Testing Framework for Out-of-Distribution Detection
Akshayaa Magesh, Venugopal V. Veeravalli, Anirban Roy +1
We study the problem of Out-of-Distribution (OOD) detection, that is, detecting whether a learning algorithm's output can be trusted at inference time. While a number of tests for…
Autoequivariant Network Search via Group Decomposition
Sourya Basu, Akshayaa Magesh, Harshit Yadav +1
Recent works show that group equivariance as an inductive bias improves neural network performance for both classification and generation. However, designing group-equivariant neur…
Dynamic Spectrum Access using Stochastic Multi-User Bandits
Meghana Bande, Akshayaa Magesh, Venugopal V. Veeravalli
A stochastic multi-user multi-armed bandit framework is used to develop algorithms for uncoordinated spectrum access. In contrast to prior work, it is assumed that rewards can be n…
Multi-User MABs with User Dependent Rewards for Uncoordinated Spectrum Access
Akshayaa Magesh, Venugopal V. Veeravalli
Multi-user multi-armed bandits have emerged as a good model for uncoordinated spectrum access problems. In this paper we consider the scenario where users cannot communicate with e…
Decentralized Heterogeneous Multi-Player Multi-Armed Bandits with Non-Zero Rewards on Collisions
Akshayaa Magesh, Venugopal V. Veeravalli
We consider a fully decentralized multi-player stochastic multi-armed bandit setting where the players cannot communicate with each other and can observe only their own actions and…