1 citations · 2 across the 37 of their papers we have counts for
40 papers
Data World: Scaling Data Environments from Scratch
Zhanli Li, Zeyuan Xu, Chaoxu Pang +16
Data agents use code to process heterogeneous files and derive analytical insights. Scaling their training faces costly environment curation, privacy and intellectual-property rest…
LongCat-DeepResearch Technical Report
Meituan LongCat Team, He Zhu, Yue Xu +28
We present LongCat-DeepResearch, a deep research system that combines an enhanced LongCat model with a multi-agent workflow for producing comprehensive, evidence-grounded reports.…
Beyond Final Scores: A Systematic Evaluation of Agents for Long-Horizon AI Research and Development
Yiwei Li, Wanli Yang, Hexiang Tan +10
Autonomous agents are increasingly capable of improving models, systems, and other technical artifacts through long-horizon experimentation. To understand the current state of this…
SAF-OPD: Stable Advantage Fusion for On-Policy Distillation
Yifan Ding, Xincheng Wei, Yoshua Y. Li +7
Reinforcement learning with verifiable rewards (RLVR) broadcasts a single response-level reward to every token, while on-policy distillation (OPD) scores each token against a stron…
ClawTrack: Towards Trace-Level Evaluation and Improvement of Real-World Autonomous Agents
Xingjian Wu, Xuhang Zhu, Xingchen Liu +6
As LLM-based agents are deployed in complex, multi-step workflows, a critical evaluation gap has emerged: most existing benchmarks judge only final outcomes, unable to distinguish…
Contrastive Reinforced Policy Optimization via Privileged Self-Distillation
Xingjian Wu, Junlin Liu, Xingchen Liu +6
Recent advances in post-training Large Language Models (LLMs) increasingly rely on Reinforcement Learning with Verifiable Rewards (RLVR) or On-Policy Self-Distillation (OPSD). Whil…
LoHoSearch: Benchmarking Long-Horizon Search Agents Beyond the Human Difficulty Ceiling
Jiarui Zhao, Rongzhi Zhang, Lingchuan Liu +3
Search agent benchmarks exemplified by BrowseComp have rapidly saturated over the past year, with the strongest models surpassing 90% accuracy. Since these benchmarks are predomina…
DailyReport: An Open-ended Benchmark for Evaluating Search Agents on Daily Search Tasks
Jingxuan Han, Wei Liu, Mingyang Zhu +8
Search Agents (SAs) typically leverage large language models (LLMs) to support complex information-seeking tasks by autonomously exploring web sources and synthesizing information…
Asuka-Bench: Benchmarking Code Agents on Underspecified User Intent and Multi-Round Refinement
Xin Wang, Liangtai Sun, Yaoming Zhu +8
Existing code-generation benchmarks score a single mapping from a complete prompt to a one-shot output. However, real web development is different. Users seldom write a full spec a…
SAGE: A Quantitative Evaluation of Socialized Evolution in Agent Ecosystems
Linyue Pan, Yaoming Zhu, Lin Qiu +2
Self-improving language agents are typically evaluated in isolation: an agent attempts a task, receives feedback, and iteratively refines its own behavior. Yet agents increasingly…
GUI-CIDER: Mid-training GUI Agents via Causal Internalization and Density-aware Exemplar Reselection
Zheng Wu, Chengcheng Han, Zhengxi Lu +5
Despite the rapid progress of multimodal large language models in building Graphical User Interface (GUI) agents, their real-world task completion is fundamentally bottlenecked by…
Skill0.5: Joint Skill Internalization and Utilization for Out-of-Distribution Generalization in Agentic Reinforcement Learning
Jiapeng Zhu, Jianxiang Yu, Yibo Zhao +5
Equipping large language models with explicit skills has emerged as a promising paradigm for enabling autonomous agents to solve complex tasks. Agent skills can be inherently divid…
ATLAS: All-round Testing of Long-context Abilities across Scales
Deli Huang, Cunguang Wang, Hongyin Tang +15
Long-context language models now advertise context windows up to millions of tokens, yet evaluations typically report a single length or a narrow task family, masking two failure m…
VitaBench 2.0: Evaluating Personalized and Proactive Agents in Long-Term User Interactions
Yuxin Chen, Yi Zhang, Zhengzhou Cai +11
Large language models (LLMs) have evolved into interactive agents that collaborate with users in real-world tasks. Effective collaboration in such settings increasingly depends on…
MONA: Muon Optimizer with Nesterov Acceleration for Scalable Language Model Training
Jiacheng Li, Jianchao Tan, Hongtao Xu +5
The Muon optimizer has recently offered a promising alternative to AdamW for large language model training, leveraging matrix orthogonalization to produce geometry-aware updates. H…
WBench: A Comprehensive Multi-turn Benchmark for Interactive Video World Model Evaluation
Kaining Ying, Hengrui Hu, Siyu Ren +6
Interactive world models are advancing rapidly, yet existing benchmarks cover only part of the required competencies, leaving no unified standard for systematic evaluation. To fill…
AgentEscapeBench: Evaluating Out-of-Domain Tool-Grounded Reasoning in LLM Agents
Zhengkang Guo, Yiyang Li, Lin Qiu +7
As LLM-based agents increasingly rely on external tools, it is important to evaluate their ability to sustain tool-grounded reasoning beyond familiar workflows and short-range inte…
HeavySkill: Heavy Thinking as the Inner Skill in Agentic Harness
Jianing Wang, Linsen Guo, Zhengyu Chen +8
Recent advances in agentic harness with orchestration frameworks that coordinate multiple agents with memory, skills, and tool use have achieved remarkable success in complex reaso…
Doctor-RAG: A Failure-Aware Repair Framework for Agentic Retrieval-Augmented Generation
Shuguang Jiao, Chengkai Huang, Shuhan Qi +6
Agentic Retrieval-Augmented Generation interleaves retrieval and reasoning for multi-hop QA and complex knowledge tasks. As reasoning trajectories lengthen, failures become more fr…
A Survey on LLM Mid-Training
Chengying Tu, Xuemiao Zhang, Rongxiang Weng +6
Recent advances in foundation models have highlighted the significant benefits of multi-stage training, with a particular emphasis on the emergence of mid-training as a vital stage…
AMO-Bench: Large Language Models Still Struggle in High School Math Competitions
Shengnan An, Xunliang Cai, Xuezhi Cao +8
We present AMO-Bench, an Advanced Mathematical reasoning benchmark with Olympiad level or even higher difficulty, comprising 50 human-crafted problems. Existing benchmarks have wid…
UNO-Bench: A Unified Benchmark for Exploring the Compositional Law Between Uni-modal and Omni-modal in Omni Models
Chen Chen, ZeYang Hu, Fengjiao Chen +6
Multimodal Large Languages models have been progressing from uni-modal understanding toward unifying visual, audio and language modalities, collectively termed omni models. However…
R-Horizon: How Far Can Your Large Reasoning Model Really Go in Breadth and Depth?
Yi Lu, Jianing Wang, Linsen Guo +7
Recent trends in test-time scaling for reasoning models (e.g., OpenAI o1, DeepSeek-R1) have led to remarkable improvements through long Chain-of-Thought (CoT). However, existing be…
VitaBench: Benchmarking LLM Agents with Versatile Interactive Tasks in Real-world Applications
Wei He, Yueqing Sun, Hongyan Hao +13
As LLM-based agents are increasingly deployed in real-life scenarios, existing benchmarks fail to capture their inherent complexity of handling extensive information, leveraging di…