collaborators

6 papers

cs.AI2026

The Language of Bargaining: Linguistic Effects in LLM Negotiations

Stuti Sinha, Himanshu Kumar, Aryan Raju Mandapati +2

Negotiation is a core component of social intelligence, requiring agents to balance strategic reasoning, cooperation, and social norms. Recent work shows that LLMs can engage in mu…

cs.CR2026

Do You Really Need a GPU to Guard Your LLM? CPU-Class Classifiers and Multi-Stage Pipelines for Safety Enforcement at Scale

Vasudev Majhi, Dhruv Gupta, Advait Singh +2

Safety classifiers that screen LLM inputs for jailbreak attempts have become standard deployment components, yet almost all production systems rely on GPU-based models: fine-tuned…

cs.AI2026

neuralFOMO: Can LLMs Handle Being Second Best? Measuring Envy-Like Preferences in Multi-Agent Settings

Arnav Ramamoorthy, Shrey Dhorajiya, Ojas Pungalia +6

Envy shapes competitiveness and cooperation in human groups, yet its role in large language model interactions remains largely unexplored. As LLMs increasingly operate in multi-age…

cs.LG2026

TabPFN Through The Looking Glass: An interpretability study of TabPFN and its internal representations

Aviral Gupta, Armaan Sethi, Dhruv Kumar

Tabular foundational models are pre-trained models designed for a wide range of tabular data tasks. They have shown strong performance across domains, yet their internal representa…

cs.CL2025

Evaluating Novelty in AI-Generated Research Plans Using Multi-Workflow LLM Pipelines

Devesh Saraogi, Rohit Singhee, Dhruv Kumar

The integration of Large Language Models (LLMs) into the scientific ecosystem raises fundamental questions about the creativity and originality of AI-generated research. Recent wor…

cs.CL2025

Evaluating LLMs for Zeolite Synthesis Event Extraction (ZSEE): A Systematic Analysis of Prompting Strategies

Charan Prakash Rathore, Saumi Ray, Dhruv Kumar

Extracting structured information from zeolite synthesis experimental procedures is critical for materials discovery, yet existing methods have not systematically evaluated Large L…