7 citations · 14 across the 22 of their papers we have counts for
28 papers
ProductWebBench: Evaluating Website Continuity Beyond Task Completion
Jiajun Wu, Jian Yang, Yaxin Du +9
Frontend coding agents are usually graded on one question: did the requested target get built. Real engineering changes carry a second obligation, because the rest of the site has…
IQuest-Q1: Advancing Foundation Capabilities for Agentic CLI Systems
A. Jian Yang, An Huang, Bo Dan +173
We present IQuest-Q1, a sparse Mixture-of-Experts (MoE) agentic foundation model with 320B total parameters (15B activated). IQuest-Q1 is developed through a three-stage process: (…
AutoDataBench: A Data-centric Testbed for Accelerating Auto Research
Ruifeng Yuan, Yizhi Li, Yaxin Du +9
Existing auto-research benchmarks often entangle multiple sources of improvement, including training frameworks, hyperparameters, compute budgets, and data, making it difficult to…
RSI-Master: Structuring Experiments to Guide Autonomous Model Improvement
Yaxin Du, Xiyuan Yang, Zhifan Zhou +10
Recursive self-improvement (RSI) seeks to enable AI systems to participate in improving their own capabilities. A concrete pathway is autonomous model development, where agents ite…
WebWorld: The Browser as a World Model for Self-Improving Web Code
Jiajun Wu, Jian Yang, Yaxin Du +7
VLM-driven self-improvement of web code has a structural flaw: the model that proposes the repair is the model that judges it, and visual plausibility under that judge is a poor pr…
LoopCoder-v2: Only Loop Once for Efficient Test-Time Computation Scaling
Jian Yang, Shawn Guo, Wei Zhang +16
Looped Transformers scale latent computation by repeatedly applying shared blocks, but sequential looping increases latency and KV-cache memory with the loop count. Parallel loop T…
HyperTool: Beyond Step-Wise Tool Calls for Tool-Augmented Agents
Yaxin Du, Yifan Zhou, Yujie Ge +7
Tool-augmented LLM agents commonly rely on step-wise atomic tool calls, where each invocation, observation, and value transfer is exposed in the main reasoning trace. This creates…
MCP-Persona: Benchmarking LLM Agents on Real-World Personal Applications via Environment Simulation
Wenhao Wang, Peizhi Niu, Gongyi Zou +9
The Model Context Protocol (MCP) has emerged as a transformative standard for connecting large language models (LLMs) with external data sources and tools, and has been rapidly ado…
MIRA: Mid-training Rubric Anchoring for Source-Aware Data Selection
Haowen Wang, Yaxin Du, Jian Yang +9
Mid-training has become an important stage in modern LLM development, using large-scale curated mixtures to strengthen capabilities before final post-training. Its data selection p…
DataMaster: Data-Centric Autonomous AI Research
Yaxin Du, Xiyuan Yang, Zhifan Zhou +12
As model families, training recipes, and compute budgets become increasingly standardized, further gains in machine learning systems depend increasingly on data. Yet data engineeri…
InCoder-32B-Thinking: Industrial Code World Model for Thinking
Jian Yang, Wei Zhang, Jiajun Wu +22
Industrial software development across chip design, GPU optimization, and embedded systems lacks expert reasoning traces showing how engineers reason about hardware constraints and…
EvoMaster: A Foundational Evolving Agent Framework for Agentic Science at Scale
Xinyu Zhu, Yuzhu Cai, Zexi Liu +20
The convergence of large language models and agents is catalyzing a new era of scientific discovery: Agentic Science. However, common agent infrastructure is repeatedly rebuilt acr…
InCoder-32B: Code Foundation Model for Industrial Scenarios
Jian Yang, Wei Zhang, Jiajun Wu +25
Recent code large language models have achieved remarkable progress on general programming tasks. Nevertheless, their performance degrades significantly in industrial scenarios tha…
-Reader: Dual Evolving Graphs for Multimodal Document QA
Yaxin Du, Junru Song, Yifan Zhou +8
Retrieval-augmented generation is a practical paradigm for question answering over long documents, but it remains brittle for multimodal reading where text, tables, and figures are…
Mining Useful General Data for Low-Resource Domain Adaptation
Pingjie Wang, Hongcheng Liu, Yusheng Liao +5
Adapting large language models (LLMs) to low-resource domains remains challenging due to the scarcity of domain-specific data. While in-domain data is limited, there exists a vast…
InfoMosaic-Bench: Evaluating Multi-Source Information Seeking in Tool-Augmented Agents
Yaxin Du, Yuanshuo Zhang, Xiyuan Yang +10
Information seeking is a fundamental requirement for humans. However, existing LLM agents rely heavily on open-web search, which exposes two fundamental weaknesses: online content…
MCP-Flow: Facilitating LLM Agents to Master Real-World, Diverse and Scaling MCP Tools
Wenhao Wang, Peizhi Niu, Zhao Xu +8
Large Language Models (LLMs) increasingly rely on external tools to perform complex, realistic tasks, yet their ability to utilize the rapidly expanding Model Contextual Protocol (…
BrowseMaster: Towards Scalable Web Browsing via Tool-Augmented Programmatic Agent Pair
Xianghe Pang, Shuo Tang, Rui Ye +3
Effective information seeking in the vast and ever-growing digital landscape requires balancing expansive search with strategic reasoning. Current large language model (LLM)-based…
SWE-Dev: Evaluating and Training Autonomous Feature-Driven Software Development
Yaxin Du, Yuzhu Cai, Yifan Zhou +6
Large Language Models (LLMs) have shown strong capability in diverse software engineering tasks. However, feature-driven development, a highly prevalent real-world task that involv…
VLMGuard-R1: Proactive Safety Alignment for VLMs via Reasoning-Driven Prompt Optimization
Menglan Chen, Xianghe Pang, Jingjing Dong +3
Aligning Vision-Language Models (VLMs) with safety standards is essential to mitigate risks arising from their multimodal complexity, where integrating vision and language unveils…
MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systems
Rui Ye, Shuo Tang, Rui Ge +4
LLM-based multi-agent systems (MAS) have shown significant potential in tackling diverse tasks. However, to design effective MAS, existing approaches heavily rely on manual configu…
Self-Evolving Multi-Agent Collaboration Networks for Software Development
Yue Hu, Yuzhu Cai, Yaxin Du +6
LLM-driven multi-agent collaboration (MAC) systems have demonstrated impressive capabilities in automatic software development at the function level. However, their heavy reliance…
Data Quality Control in Federated Instruction-tuning of Large Language Models
Yaxin Du, Rui Ye, Fengting Yuchi +4
Federated Learning (FL) enables privacy-preserving collaborative instruction tuning of large language models (LLMs) by leveraging massively distributed data. However, the decentral…
Optimizing Cross-Client Domain Coverage for Federated Instruction Tuning of Large Language Models
Zezhou Wang, Yaxin Du, Xingjun Ma +3
Federated domain-specific instruction tuning (FedDIT) for large language models (LLMs) aims to enhance performance in specialized domains using distributed private and limited data…