4 citations · 4 across the 16 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
WebResearcher: Unleashing unbounded reasoning capability in Long-Horizon Agents
Zile Qiao, Guoxin Chen, Xuanzhong Chen +13
Recent advances in deep-research systems have demonstrated the potential for AI agents to autonomously discover and synthesize knowledge from external sources. In this paper, we in…
Scaling Agents via Continual Pre-training
Liangcai Su, Zhen Zhang, Guangyu Li +19
Large language models (LLMs) have evolved into agentic systems capable of autonomous tool use and multi-step reasoning for complex problem-solving. However, post-training approache…
ZeroSearch: Incentivize the Search Capability of LLMs without Searching
Hao Sun, Zile Qiao, Jiayan Guo +7
Effective information searching is essential for enhancing the reasoning and generation capabilities of large language models (LLMs). Recent research has explored using reinforceme…
C-3PO: Compact Plug-and-Play Proxy Optimization to Achieve Human-like Retrieval-Augmented Generation
Guoxin Chen, Minpeng Liao, Peiying Yu +5
Retrieval-augmented generation (RAG) systems face a fundamental challenge in aligning independently developed retrievers and large language models (LLMs). Existing approaches typic…
Supportiveness-based Knowledge Rewriting for Retrieval-augmented Language Modeling
Zile Qiao, Wei Ye, Yong Jiang +5
Retrieval-augmented language models (RALMs) have recently shown great potential in mitigating the limitations of implicit knowledge in LLMs, such as untimely updating of the latest…
Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering Over Knowledge Graphs
Zile Qiao, Wei Ye, Tong Zhang +3
Answering natural language questions on knowledge graphs (KGQA) remains a great challenge in terms of understanding complex questions via multi-hop reasoning. Previous efforts usua…