3 citations · 5 across the 10 of their papers we have counts for
4 papers · 2 filters
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…