3 citations · 7 across the 33 of their papers we have counts for
40 papers
Evolving Together, Failing Apart: The Skill-Policy Co-evolution Trap in Self-Improving Agents
Yujie Feng, Xiaobin Wang, Jinluan Yang +5
Skill-policy co-evolution, in which an agent's policy model and its skill bank improve each other during training, is a promising paradigm for self-improving agents. Current evalua…
Dr.Credit: Rubric-Grounded Process Credit Assignment for Deep Research Agents
Yingjian Zhu, Zhenyi Wang, Jiaxin Guo +6
Rubric-based tasks are increasingly addressed through reinforcement learning (RL), with rubric scores used as training rewards. However, these rewards typically supervise final ans…
MemoryWalker: Stop Training Agents on Contexts They Never Saw
Zinco J, Xunjie Zhu, Shen Huang +3
Production agent harnesses such as Claude Code and Qwen-Agent compress context during rollout, but training under compression creates a conditioning problem: every eviction branche…
SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response
Lehan Wang, Boli Chen, Ruixue Ding +7
Large Language Model (LLM) agents are increasingly adopted in real-world security operations with access to host artifacts and command-line interfaces (CLIs), making it critical to…
SkillComposer: Learning to Evolve Agent Skills for Specification and Generalization
Qi Zhang, Zhaopeng Feng, Xiaonan Shi +8
Agent skills, which consist of reusable strategies that guide agent reasoning and action, have shown strong potential for improving model capability at inference time. However, cur…
EvoRubric: Self-Evolving Rubric-Driven RL for Open-Ended Generation
Xin Guan, Xiaomeng Hu, Shen Huang +6
Reinforcement Learning (RL) has advanced Large Language Models (LLMs) in verifiable domains, while open-ended generation remains challenging due to the absence of definitive reward…
DeltaMem: Towards Agentic Memory Management via Reinforcement Learning
Qi Zhang, Shen Huang, Chu Liu +4
Recent advances in persona-centric memory have revealed the powerful capability of multi-agent systems in managing persona memory, especially in conversational scenarios. However,…
AgentSwing: Adaptive Parallel Context Management Routing for Long-Horizon Web Agents
Zhaopeng Feng, Liangcai Su, Zhen Zhang +16
As large language models (LLMs) evolve into autonomous agents for long-horizon information-seeking, managing finite context capacity has become a critical bottleneck. Existing cont…
Efficient Multimodal Planning Agent for Visual Question-Answering
Zhuo Chen, Xinyu Geng, Xinyu Wang +4
Visual Question-Answering (VQA) is a challenging multimodal task that requires integrating visual and textual information to generate accurate responses. While multimodal Retrieval…
WebAnchor: Anchoring Agent Planning to Stabilize Long-Horizon Web Reasoning
Xinmiao Yu, Liwen Zhang, Xiaocheng Feng +4
Large Language Model(LLM)-based agents have shown strong capabilities in web information seeking, with reinforcement learning (RL) becoming a key optimization paradigm. However, pl…
Evidence-Augmented Policy Optimization with Reward Co-Evolution for Long-Context Reasoning
Xin Guan, Zijian Li, Shen Huang +3
While Reinforcement Learning (RL) has advanced LLM reasoning, applying it to long-context scenarios is hindered by sparsity of outcome rewards. This limitation fails to penalize un…
Nested Browser-Use Learning for Agentic Information Seeking
Baixuan Li, Jialong Wu, Wenbiao Yin +8
Information-seeking (IS) agents have achieved strong performance across a range of wide and deep search tasks, yet their tool use remains largely restricted to API-level snippet re…
AutoForge: Automated Environment Synthesis for Agentic Reinforcement Learning
Shihao Cai, Runnan Fang, Jialong Wu +10
Conducting reinforcement learning (RL) in simulated environments offers a cost-effective and highly scalable way to enhance language-based agents. However, previous work has been l…
EcomBench: Towards Holistic Evaluation of Foundation Agents in E-commerce
Rui Min, Zile Qiao, Ze Xu +18
Foundation agents have rapidly advanced in their ability to reason and interact with real environments, making the evaluation of their core capabilities increasingly important. Whi…
IterResearch: Rethinking Long-Horizon Agents with Interaction Scaling
Guoxin Chen, Zile Qiao, Xuanzhong Chen +13
Recent advances in deep-research agents have shown promise for autonomous knowledge construction through dynamic reasoning over external sources. However, existing approaches rely…
AgentFold: Long-Horizon Web Agents with Proactive Context Management
Rui Ye, Zhongwang Zhang, Kuan Li +12
LLM-based web agents show immense promise for information seeking, yet their effectiveness on long-horizon tasks is hindered by a fundamental trade-off in context management. Preva…
ParallelMuse: Agentic Parallel Thinking for Deep Information Seeking
Baixuan Li, Dingchu Zhang, Jialong Wu +9
Parallel thinking expands exploration breadth, complementing the deep exploration of information-seeking (IS) agents to further enhance problem-solving capability. However, convent…
WebLeaper: Empowering Efficiency and Efficacy in WebAgent via Enabling Info-Rich Seeking
Zhengwei Tao, Haiyang Shen, Baixuan Li +11
Large Language Model (LLM)-based agents have emerged as a transformative approach for open-ended problem solving, with information seeking (IS) being a core capability that enables…
AgentFrontier: Expanding the Capability Frontier of LLM Agents with ZPD-Guided Data Synthesis
Xuanzhong Chen, Zile Qiao, Guoxin Chen +7
Training large language model agents on tasks at the frontier of their capabilities is key to unlocking advanced reasoning. We introduce a data synthesis approach inspired by the e…
BrowseConf: Confidence-Guided Test-Time Scaling for Web Agents
Litu Ou, Kuan Li, Huifeng Yin +8
Confidence in LLMs is a useful indicator of model uncertainty and answer reliability. Existing work mainly focused on single-turn scenarios, while research on confidence in complex…
WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research
Zijian Li, Xin Guan, Bo Zhang +9
This paper tackles \textbf{open-ended deep research (OEDR)}, a complex challenge where AI agents must synthesize vast web-scale information into insightful reports. Current approac…
Tongyi DeepResearch Technical Report
Tongyi DeepResearch Team, Baixuan Li, Bo Zhang +54
We present Tongyi DeepResearch, an agentic large language model, which is specifically designed for long-horizon, deep information-seeking research tasks. To incentivize autonomous…
Repurposing Synthetic Data for Fine-grained Search Agent Supervision
Yida Zhao, Kuan Li, Xixi Wu +11
LLM-based search agents are increasingly trained on entity-centric synthetic data to solve complex, knowledge-intensive tasks. However, prevailing training methods like Group Relat…
DecoupleSearch: Decouple Planning and Search via Hierarchical Reward Modeling
Hao Sun, Zile Qiao, Bo Wang +6
Retrieval-Augmented Generation (RAG) systems have emerged as a pivotal methodology for enhancing Large Language Models (LLMs) through the dynamic integration of external knowledge.…