2 citations · 4 across the 7 of their papers we have counts for
7 papers
LOCA-bench: Benchmarking Language Agents Under Controllable and Extreme Context Growth
Weihao Zeng, Yuzhen Huang, Junxian He
Large language models (LLMs) are increasingly capable of carrying out long-running, real-world tasks. However, as the amount of context grows, their reliability often deteriorates,…
Kimi K2.5: Visual Agentic Intelligence
Kimi Team, Tongtong Bai, Yifan Bai +333
We introduce Kimi K2.5, an open-source multimodal agentic model designed to advance general agentic intelligence. K2.5 emphasizes the joint optimization of text and vision so that…
Pushing Test-Time Scaling Limits of Deep Search with Asymmetric Verification
Weihao Zeng, Keqing He, Chuqiao Kuang +2
Test-time compute can be scaled both sequentially and in parallel. Sequential scaling involves lengthening the generation process, while parallel scaling involves verifying and sel…
The Tool Decathlon: Benchmarking Language Agents for Diverse, Realistic, and Long-Horizon Task Execution
Junlong Li, Wenshuo Zhao, Jian Zhao +18
Real-world language agents must handle complex, multi-step workflows across diverse Apps. For instance, an agent may manage emails by coordinating with calendars and file systems,…
Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectives
Wei Zeng, Hengshu Zhu, Chuan Qin +8
The ongoing evolution of AI paradigms has propelled AI research into the agentic AI stage. Consequently, the focus of research has shifted from single agents and simple application…
From Accuracy to Robustness: A Study of Rule- and Model-based Verifiers in Mathematical Reasoning
Yuzhen Huang, Weihao Zeng, Xingshan Zeng +2
Trustworthy verifiers are essential for the success of reinforcement learning with verifiable reward (RLVR), which is the core methodology behind various large reasoning models suc…
Multi-Agent Transfer Learning via Temporal Contrastive Learning
Weihao Zeng, Joseph Campbell, Simon Stepputtis +1
This paper introduces a novel transfer learning framework for deep multi-agent reinforcement learning. The approach automatically combines goal-conditioned policies with temporal c…