7 papers
SBCO: Self-Supervised, Verifier-Grounded Harness Optimization For Planning Agents
Vivek Kulkarni, Sudipta Paul, Aounon Kumar +2
Self-improving agents seek to reduce the human engineering effort behind AI systems by enabling them to evolve and self-improve their performance over time. Recently, methods like…
Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering
Aounon Kumar, Sudipta Paul, Vivek Kulkarni +2
The effective use of search engines by large language models (LLMs) remains a significant challenge, particularly in complex, multi-hop question-answering (MHQA) tasks. These tasks…
PROGRESS: Coverage-guided RL to Train Search-augmented LLM Agent
Sudipta Paul, Vijay Srinivasan, Vivek Kulkarni +4
Existing search-augmented LLM agents are trained using Reinforcement Learning to boost its reasoning capabilities. However, these approaches primarily rely on outcome-level rewards…
TRUSTMEM: Learning Trustworthy Memory Consolidation for LLM Agents with Long-Term Memory
Tianyu Yang, Sudipta Paul, Vijay Srinivasan +2
Large language model (LLM) agents rely on long-term memory to support extended interactions and personalized assistance beyond finite context windows. Existing memory agents active…
VOYAGER: A Training Free Approach for Generating Diverse Datasets using LLMs
Avinash Amballa, Yashas Malur Saidutta, Chi-Heng Lin +2
Large language models (LLMs) are increasingly being used to generate synthetic datasets for the evaluation and training of downstream models. However, prior work has noted that suc…
From sunblock to softblock: Analyzing the correlates of neology in published writing and on social media
Maria Ryskina, Matthew R. Gormley, Kyle Mahowald +3
Living languages are shaped by a host of conflicting internal and external evolutionary pressures. While some of these pressures are universal across languages and cultures, others…