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Flout at Your Own Risk: LLMs Struggle with Pragmatic Cooperativity Under Epistemic Asymmetry
Hannah VanderHoeven, Abhijnan Nath, Nikhil Krishnaswamy
Fruitful collaborations rely on cooperative communications, including of contextual cues to incorporate into reasoning. The increasing use of LLMs in collaborative and agentic pipe…
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…
Dynamic Epistemic Friction in Dialogue
Timothy Obiso, Kenneth Lai, Abhijnan Nath +2
Recent developments in aligning Large Language Models (LLMs) with human preferences have significantly enhanced their utility in human-AI collaborative scenarios. However, such app…
Frictional Agent Alignment Framework: Slow Down and Don't Break Things
Abhijnan Nath, Carine Graff, Andrei Bachinin +1
AI support of collaborative interactions entails mediating potential misalignment between interlocutor beliefs. Common preference alignment methods like DPO excel in static setting…
Any Other Thoughts, Hedgehog? Linking Deliberation Chains in Collaborative Dialogues
Abhijnan Nath, Videep Venkatesha, Mariah Bradford +5
Question-asking in collaborative dialogue has long been established as key to knowledge construction, both in internal and collaborative problem solving. In this work, we examine p…