From the 1 of 10 linked papers with an AI index.
10 papers
Flout at Your Own Risk: LLMs Struggle with Pragmatic Cooperativity Under Epistemic Asymmetry
Hannah VanderHoeven, Abhijnan Nath, Nikhil Krishnaswamy
The paper investigates how large language models handle pragmatic cooperation when they have less information than their collaborators, evaluating their ability to follow Grice's m…
ECHO: Learning Epistemically Adaptive Language Agents with Turn-Level Credit
Abhijnan Nath, Nikhil Krishnaswamy
What does it mean for a language agent to be adaptive? Effective multi-turn agents must decide what information to seek, how to use new evidence, and when they are certain enough t…
Owen-Shapley Policy Optimization: A Principled RL Algorithm for Generative Search LLMs
Abhijnan Nath, Alireza Bagheri Garakani, Tianchen Zhou +3
Large language models are increasingly trained via reinforcement learning for personalized recommendation tasks, but standard methods like GRPO rely on sparse, sequence-level rewar…
CRAFT: Grounded Multi-Agent Coordination Under Partial Information
Abhijnan Nath, Hannah VanderHoeven, Nikhil Krishnaswamy
We introduce CRAFT, a multi-agent benchmark for evaluating pragmatic communication in large language models under strict partial information. In this setting, multiple agents with…
Collaborate, Deliberate, Evaluate: How LLM Alignment Affects Coordinated Multi-Agent Outcomes
Abhijnan Nath, Carine Graff, Nikhil Krishnaswamy
As Large Language Models (LLMs) get integrated into diverse workflows, they are increasingly being regarded as "collaborators" with humans, and required to work in coordination wit…
Learning "Partner-Aware" Collaborators in Multi-Party Collaboration
Abhijnan Nath, Nikhil Krishnaswamy
Large Language Models (LLMs) are increasingly being deployed in agentic settings where they act as collaborators with humans. Therefore, it is increasingly important to be able to…