30 citations · 108 across the 57 of their papers we have counts for
9 papers · 1 filter
SciResearcher: Scaling Deep Research Agents for Frontier Scientific Reasoning
Tianshi Zheng, Rui Wang, Xiyun Li +5
Frontier scientific reasoning is rapidly emerging as a key foundation for advancing AI agents in automated scientific discovery. Deep research agents offer a promising approach to…
Training LLM Agents for Spontaneous, Reward-Free Self-Evolution via World Knowledge Exploration
Qifan Zhang, Dongyang Ma, Tianqing Fang +5
Most agents today ``self-evolve'' by following rewards and rules defined by humans. However, this process remains fundamentally dependent on external supervision; without human gui…
World-Model-Augmented Web Agents with Action Correction
Zhouzhou Shen, Xueyu Hu, Xiyun Li +3
Web agents based on large language models have demonstrated promising capability in automating web tasks. However, current web agents struggle to reason out sensible actions due to…
Inference-Time Scaling of Verification: Self-Evolving Deep Research Agents via Test-Time Rubric-Guided Verification
Yuxuan Wan, Tianqing Fang, Zaitang Li +5
Recent advances in Deep Research Agents (DRAs) are transforming automated knowledge discovery and problem-solving. While the majority of existing efforts focus on enhancing policy…
Guided Self-Evolving LLMs with Minimal Human Supervision
Wenhao Yu, Zhenwen Liang, Chengsong Huang +4
AI self-evolution has long been envisioned as a path toward superintelligence, where models autonomously acquire, refine, and internalize knowledge from their own learning experien…
The Cognitive Bandwidth Bottleneck: Shifting Long-Horizon Agent from Planning with Actions to Planning with Schemas
Baixuan Xu, Tianshi Zheng, Zhaowei Wang +4
Enabling LLMs to effectively operate long-horizon task which requires long-term planning and multiple interactions is essential for open-world autonomy. Conventional methods adopt…
NewtonBench: Benchmarking Generalizable Scientific Law Discovery in LLM Agents
Tianshi Zheng, Kelvin Kiu-Wai Tam, Newt Hue-Nam K. Nguyen +10
Large language models are emerging as powerful tools for scientific law discovery, a foundational challenge in AI-driven science. However, existing benchmarks for this task suffer…
Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent Foundation Models Training
Tianqing Fang, Zhisong Zhang, Xiaoyang Wang +16
General AI Agents are increasingly recognized as foundational frameworks for the next generation of artificial intelligence, enabling complex reasoning, web interaction, coding, an…
ASER: Towards Large-scale Commonsense Knowledge Acquisition via Higher-order Selectional Preference over Eventualities
Hongming Zhang, Xin Liu, Haojie Pan +4
Commonsense knowledge acquisition and reasoning have long been a core artificial intelligence problem. However, in the past, there has been a lack of scalable methods to collect co…