4 citations · 8 across the 22 of their papers we have counts for
4 papers · 2 filters
EvoTest: Evolutionary Test-Time Learning for Self-Improving Agentic Systems
Yufei He, Juncheng Liu, Yue Liu +5
A fundamental limitation of current AI agents is their inability to learn complex skills on the fly at test time, often behaving like "clever but clueless interns" in novel environ…
FlowReasoner: Reinforcing Query-Level Meta-Agents
Hongcheng Gao, Yue Liu, Yufei He +6
This paper proposes a query-level meta-agent named FlowReasoner to automate the design of query-level multi-agent systems, i.e., one system per user query. Our core idea is to ince…
Evaluating the Paperclip Maximizer: Are RL-Based Language Models More Likely to Pursue Instrumental Goals?
Yufei He, Yuexin Li, Jiaying Wu +3
As large language models (LLMs) continue to evolve, ensuring their alignment with human goals and values remains a pressing challenge. A key concern is \textit{instrumental converg…
Meta-Reasoner: Dynamic Guidance for Optimized Inference-time Reasoning in Large Language Models
Yuan Sui, Yufei He, Tri Cao +3
Large Language Models (LLMs) often struggle with computational efficiency and error propagation in multi-step reasoning tasks. While recent advancements on prompting and post-train…