1 citations · 1 across the 16 of their papers we have counts for
19 papers
AREX-2: Advancing Self-Improving Agents through Long-Horizon Reflective Tasks
Hongjin Qian, Chaofan Li, Kun Luo +11
We present AREX-2, an effort to advance the self-improving capability of LLM agents, which we define as the ability to iteratively refine a solution at test time. This ability rest…
Just-In-Time Agent Memory with Runtime Agentic Research
Bingyu Yan, Chaofan Li, Hongjin Qian +3
Memory is critical for AI agents. Many existing agent-memory systems follow an Ahead-of-Time (AOT) design, constructing memory before a specific request arrives. While this reduces…
Repo-To-Skill: Distilling GitHub Repositories Into AI4AI Skills
Jianlyu Chen, Yuyang Hu, Hongjin Qian +8
Autonomous agents are beginning to carry out machine-learning (ML) research end to end. These agents combine a model backbone with a harness for planning, execution, memory, and ve…
AREX: Towards a Recursively Self-Improving Agent for Deep Research
Shuqi Lu, Chaofan Li, Kun Luo +21
Deep research requires agents to find answers that jointly satisfy multiple constraints. Discovering such answers is costly, whereas verifying a candidate can often be decomposed i…
IntElicit: Eliciting and Assessing Contextualized Creativity via Dialogue Policy Optimization
Mingjia Li, Jin Wu, Hong Qian +7
Contextualized assessment offers high ecological validity for evaluating creativity but introduces a critical challenge: observed performance may be confounded with cognitive profi…
Toward Generalist Autonomous Research via Hypothesis-Tree Refinement
Jiajie Jin, Yuyang Hu, Kai Qiu +15
Scientific progress depends on a repeated loop of exploration, experimentation, and abstraction. Researchers test candidate directions, interpret the evidence, and carry the result…
PlanningBench: Generating Scalable and Verifiable Planning Data for Evaluating and Training Large Language Models
Ziliang Zhao, Zenan Xu, Shuting Wang +7
Planning is a fundamental capability for large language models (LLMs) because such complex tasks require models to coordinate goals, constraints, resources, and long-term consequen…
AgentFugue: Agent Scaling for Long-Horizon Tasks through Collective Reasoning
Yuyang Hu, Hongjin Qian, Shuting Wang +5
Recent progress on long-horizon agentic tasks has been driven largely by scaling up individual agents through stronger models, better tools, and more effective scaffolding. In cont…
SAM: State-Adaptive Memory for Long-Horizon Reasoning Agent
Yuyang Hu, Hongjin Qian, Shuting Wang +5
Long-horizon agentic reasoning requires large language models to act over long interaction histories containing thoughts, tool calls, observations, and partial conclusions. The cha…
AutoResearchBench: Benchmarking AI Agents on Complex Scientific Literature Discovery
Lei Xiong, Kun Luo, Ziyi Xia +15
Autonomous scientific research is significantly advanced thanks to the development of AI agents. One key step in this process is finding the right scientific literature, whether to…
AgentFactory: A Self-Evolving Framework Through Executable Subagent Accumulation and Reuse
Zhang Zhang, Shuqi Lu, Hongjin Qian +2
Building LLM-based agents has become increasingly important. Recent works on LLM-based agent self-evolution primarily record successful experiences as textual prompts or reflection…
DeepXiv-SDK: An Agentic Data Interface for Scientific Literature
Hongjin Qian, Ziyi Xia, Ze Liu +11
LLM-agents are increasingly used to accelerate the progress of scientific research. Yet a persistent bottleneck is data access: agents not only lack readily available tools for ret…
MemoBrain: Executive Memory as an Agentic Brain for Reasoning
Hongjin Qian, Zhao Cao, Zheng Liu
Complex reasoning in tool-augmented agent frameworks is inherently long-horizon, causing reasoning traces and transient tool artifacts to accumulate and strain the bounded working…
General Agentic Memory Via Deep Research
B. Y. Yan, Chaofan Li, Hongjin Qian +2
Memory is critical for AI agents, yet the widely-adopted static memory, aiming to create readily available memory in advance, is inevitably subject to severe information loss. To a…
InfoFlow: Reinforcing Search Agent Via Reward Density Optimization
Kun Luo, Hongjin Qian, Zheng Liu +5
Reinforcement Learning with Verifiable Rewards (RLVR) is a promising approach for enhancing agentic deep search. However, its application is often hindered by low \textbf{Reward De…
Model-Document Protocol for AI Search
Hongjin Qian, Zheng Liu
AI search depends on linking large language models (LLMs) with vast external knowledge sources. Yet web pages, PDF files, and other raw documents are not inherently LLM-ready: they…
MetaAgent: Toward Self-Evolving Agent via Tool Meta-Learning
Hongjin Qian, Zheng Liu
In this work, we propose MetaAgent, an agentic paradigm inspired by the principle of learning-by-doing, where expertise is developed through hands-on practice and continual self-im…
Open Data Synthesis For Deep Research
Ziyi Xia, Kun Luo, Hongjin Qian +1
Large language models (LLMs) are increasingly expected to go beyond simple factual queries toward Deep Research-tasks that require decomposing questions into sub-problems, coordina…
LiveLongBench: Tackling Long-Context Understanding for Spoken Texts from Live Streams
Yongxuan Wu, Runyu Chen, Peiyu Liu +1
Long-context understanding poses significant challenges in natural language processing, particularly for real-world dialogues characterized by speech-based elements, high redundanc…