17 citations · 25 across the 27 of their papers we have counts for
33 papers · 1 filter
Before the Action: Benchmarking LLMs on Prospective Hypothesis Discovery
Tianyun Zhong, Wangyi Jiang, Wei Wang +15
Large language models (LLMs) excel at answering pre-specified questions, yet their ability to navigate the open-ended, pre-conclusion stage of discovery remains largely unmeasured.…
EvoRoute: Experience-Driven Self-Routing LLM Agent Systems
Guibin Zhang, Haiyang Yu, Kaiming Yang +4
Complex agentic AI systems, powered by a coordinated ensemble of Large Language Models (LLMs), tool and memory modules, have demonstrated remarkable capabilities on intricate, mult…
ExpSeek: Self-Triggered Experience Seeking for Web Agents
Wenyuan Zhang, Xinghua Zhang, Haiyang Yu +5
Experience intervention in web agents emerges as a promising technical paradigm, enhancing agent interaction capabilities by providing valuable insights from accumulated experience…
Selective Weak-to-Strong Generalization
Hao Lang, Fei Huang, Yongbin Li
Future superhuman models will surpass the ability of humans and humans will only be able to \textit{weakly} supervise superhuman models. To alleviate the issue of lacking high-qual…
EIFBENCH: Extremely Complex Instruction Following Benchmark for Large Language Models
Tao Zou, Xinghua Zhang, Haiyang Yu +3
With the development and widespread application of large language models (LLMs), the new paradigm of "Model as Product" is rapidly evolving, and demands higher capabilities to addr…
Reverse Preference Optimization for Complex Instruction Following
Xiang Huang, Ting-En Lin, Feiteng Fang +5
Instruction following (IF) is a critical capability for large language models (LLMs). However, handling complex instructions with multiple constraints remains challenging. Previous…