activity
20192026
most citedAutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

178 citations · 259 across the 19 of their papers we have counts for

collaborators
Showing 2025Show all

7 papers · 1 filter

cs.LG2025

NVIDIA Nemotron Nano V2 VL

NVIDIA, :, Amala Sanjay Deshmukh +121

We introduce Nemotron Nano V2 VL, the latest model of the Nemotron vision-language series designed for strong real-world document understanding, long video comprehension, and reaso…

cs.AI2025★ 3 cited

A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence

Huan-ang Gao, Jiayi Geng, Wenyue Hua +24

Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks but remain fundamentally static, unable to adapt their internal parameters to novel task…

cs.LG2025

BEST-Route: Adaptive LLM Routing with Test-Time Optimal Compute

Dujian Ding, Ankur Mallick, Shaokun Zhang +7

Large language models (LLMs) are powerful tools but are often expensive to deploy at scale. LLM query routing mitigates this by dynamically assigning queries to models of varying c…

cs.MA2025★ 1 cited

Which Agent Causes Task Failures and When? On Automated Failure Attribution of LLM Multi-Agent Systems

Shaokun Zhang, Ming Yin, Jieyu Zhang +8

Failure attribution in LLM multi-agent systems-identifying the agent and step responsible for task failures-provides crucial clues for systems debugging but remains underexplored a…

cs.CL2025★ 1 cited

Nemotron-Research-Tool-N1: Exploring Tool-Using Language Models with Reinforced Reasoning

Shaokun Zhang, Yi Dong, Jieyu Zhang +6

Enabling large language models with external tools has become a pivotal strategy for extending their functionality beyond text space. To enhance LLMs' tool-calling abilities, previ…

cs.CL2025

Divide, Optimize, Merge: Fine-Grained LLM Agent Optimization at Scale

Jiale Liu, Yifan Zeng, Shaokun Zhang +5

LLM-based optimization has shown remarkable potential in enhancing agentic systems. However, the conventional approach of prompting LLM optimizer with the whole training trajectori…

cs.AI2025★ 16 cited

Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems

Bang Liu, Xinfeng Li, Jiayi Zhang +45

The advent of large language models (LLMs) has catalyzed a transformative shift in artificial intelligence, paving the way for advanced intelligent agents capable of sophisticated…