3 papers
cs.CL2026
Towards Scalable Customization and Deployment of Multi-Agent Systems for Enterprise Applications
Paresh Dashore, Shreyas Kulkarni, Uttam Gurram +5
Large language model (LLM)-based multi-agent systems demonstrate strong performance on complex reasoning and task execution, enabling broad enterprise applications. However, produc…
cs.CL2026
T1-Bench: Benchmarking Multi-Scenario Agents in Real-World Domains
Genta Indra Winata, Amartya Chakraborty, Yuzhen Lin +12
Recent advances in reasoning and tool-calling capabilities of large language models (LLMs) have enabled increasingly capable agentic systems. However, existing benchmarks remain li…
cs.CL2025
T1: A Tool-Oriented Conversational Dataset for Multi-Turn Agentic Planning
Amartya Chakraborty, Paresh Dashore, Nadia Bathaee +6
Large Language Models (LLMs) have demonstrated impressive capabilities as intelligent agents capable of solving complex problems. However, effective planning in scenarios involving…