19 citations · 67 across the 31 of their papers we have counts for
34 papers
MobileMem: Learning from a Year of Mobile Experiences
Xinle Deng, Yida Xue, Xiangyuan Ru +16
The next generation of AI agents is increasingly moving beyond systems that answer isolated questions toward persistent personal assistants that can understand, remember, and conti…
Search More, Think Less: Rethinking Long-Horizon Agentic Search for Efficiency and Generalization
Qianben Chen, Tianrui Qin, King Zhu +21
Recent deep research agents primarily improve performance by scaling reasoning depth, but this leads to high inference cost and latency in search-intensive scenarios. Moreover, gen…
EcoGym: Evaluating LLMs for Long-Horizon Plan-and-Execute in Interactive Economies
Xavier Hu, Jinxiang Xia, Shengze Xu +13
Long-horizon planning is widely recognized as a core capability of autonomous LLM-based agents; however, current evaluation frameworks suffer from being largely episodic, domain-sp…
How Far Are We from Genuinely Useful Deep Research Agents?
Dingling Zhang, He Zhu, Jincheng Ren +15
Deep Research Agents (DRAs) aim to automatically produce analyst-level reports through iterative information retrieval and synthesis. However, most existing DRAs were validated on…
O-Mem: Omni Memory System for Personalized, Long Horizon, Self-Evolving Agents
Piaohong Wang, Motong Tian, Jiaxian Li +8
Recent advancements in LLM-powered agents have demonstrated significant potential in generating human-like responses; however, they continue to face challenges in maintaining long-…
Towards Faithful and Controllable Personalization via Critique-Post-Edit Reinforcement Learning
Chenghao Zhu, Meiling Tao, Tiannan Wang +3
Faithfully personalizing large language models (LLMs) to align with individual user preferences is a critical but challenging task. While supervised fine-tuning (SFT) quickly reach…
AFM: An Adaptive Agent Foundation Model for Tool-Aware Hybrid Reasoning
Qianben Chen, Jingyi Cao, Jiayu Zhang +12
Large language models split into two families: reasoning-centric LLMs, which strengthen internal chain-of-thought reasoning but cannot invoke external tools, and agentic LLMs, whic…
ACADREASON: Exploring the Limits of Reasoning Models with Academic Research Problems
Xin Gui, King Zhu, JinCheng Ren +17
In recent years, the research focus of large language models (LLMs) and agents has shifted increasingly from demonstrating novel capabilities to complex reasoning and tackling chal…
Flash-Searcher: Fast and Effective Web Agents via DAG-Based Parallel Execution
Tianrui Qin, Qianben Chen, Sinuo Wang +10
Large language models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks when equipped with external tools. However, current frameworks predominantly rely…
Towards Personalized Deep Research: Benchmarks and Evaluations
Yuan Liang, Jiaxian Li, Yuqing Wang +11
Deep Research Agents (DRAs) can autonomously conduct complex investigations and generate comprehensive reports, demonstrating strong real-world potential. However, existing evaluat…
Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL
Weizhen Li, Jianbo Lin, Zhuosong Jiang +27
Recent advances in large language models (LLMs) and multi-agent systems have demonstrated remarkable capabilities in complex problem-solving tasks such as deep research, vibe codin…
Efficient Agents: Building Effective Agents While Reducing Cost
Ningning Wang, Xavier Hu, Pai Liu +11
The remarkable capabilities of Large Language Model (LLM)-driven agents have enabled sophisticated systems to tackle complex, multi-step tasks, but their escalating costs threaten…
MiCoTA: Bridging the Learnability Gap with Intermediate CoT and Teacher Assistants
Dongyi Ding, Tiannan Wang, Chenghao Zhu +3
Large language models (LLMs) excel at reasoning tasks requiring long thought sequences for planning, reflection, and refinement. However, their substantial model size and high comp…
OAgents: An Empirical Study of Building Effective Agents
He Zhu, Tianrui Qin, King Zhu +21
Recently, Agentic AI has become an increasingly popular research field. However, we argue that current agent research practices lack standardization and scientific rigor, making it…
TaskCraft: Automated Generation of Agentic Tasks
Dingfeng Shi, Jingyi Cao, Qianben Chen +14
Agentic tasks, which require multi-step problem solving with autonomy, tool use, and adaptive reasoning, are becoming increasingly central to the advancement of NLP and AI. However…
Scaling Test-time Compute for LLM Agents
King Zhu, Hanhao Li, Siwei Wu +12
Scaling test time compute has shown remarkable success in improving the reasoning abilities of large language models (LLMs). In this work, we conduct the first systematic explorati…
PersonaFeedback: A Large-scale Human-annotated Benchmark For Personalization
Meiling Tao, Chenghao Zhu, Dongyi Ding +3
With the rapid improvement in the general capabilities of LLMs, LLM personalization, i.e., how to build LLM systems that can generate personalized responses or services that are ta…
AI PERSONA: Towards Life-long Personalization of LLMs
Tiannan Wang, Meiling Tao, Ruoyu Fang +4
In this work, we introduce the task of life-long personalization of large language models. While recent mainstream efforts in the LLM community mainly focus on scaling data and com…
Symbolic Learning Enables Self-Evolving Agents
Wangchunshu Zhou, Yixin Ou, Shengwei Ding +9
The AI community has been exploring a pathway to artificial general intelligence (AGI) by developing "language agents", which are complex large language models (LLMs) pipelines inv…
Weaver: Foundation Models for Creative Writing
Tiannan Wang, Jiamin Chen, Qingrui Jia +43
This work introduces Weaver, our first family of large language models (LLMs) dedicated to content creation. Weaver is pre-trained on a carefully selected corpus that focuses on im…
AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning
Shuofei Qiao, Ningyu Zhang, Runnan Fang +5
Language agents have achieved considerable performance on various complex question-answering tasks by planning with external tools. Despite the incessant exploration in this field,…
Binary Classification with Confidence Difference
Wei Wang, Lei Feng, Yuchen Jiang +3
Recently, learning with soft labels has been shown to achieve better performance than learning with hard labels in terms of model generalization, calibration, and robustness. Howev…
Agents: An Open-source Framework for Autonomous Language Agents
Wangchunshu Zhou, Yuchen Eleanor Jiang, Long Li +15
Recent advances on large language models (LLMs) enable researchers and developers to build autonomous language agents that can automatically solve various tasks and interact with e…
RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text
Wangchunshu Zhou, Yuchen Eleanor Jiang, Peng Cui +5
The fixed-size context of Transformer makes GPT models incapable of generating arbitrarily long text. In this paper, we introduce RecurrentGPT, a language-based simulacrum of the r…