4 citations · 10 across the 16 of their papers we have counts for
17 papers
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: (…
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…
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…
HTMLCure: Turning Browser Experience into State Guided Repair for Interactive HTML
Jiajun Wu, Jian Yang, Tuney Zheng +4
LLMs can now produce full HTML pages, but many of those pages are only superficially correct: they render once, then fail under scroll, hover, click, resize, or gameplay. Evaluatio…
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…
IQuest-Coder-V1 Technical Report
Jian Yang, Wei Zhang, Shawn Guo +35
In this report, we introduce the IQuest-Coder-V1 series-(7B/14B/40B/40B-Loop), a new family of code large language models (LLMs). Moving beyond static code representations, we prop…
CodeSimpleQA: Scaling Factuality in Code Large Language Models
Jian Yang, Wei Zhang, Yizhi Li +8
Large language models (LLMs) have made significant strides in code generation, achieving impressive capabilities in synthesizing code snippets from natural language instructions. H…
From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence
Jian Yang, Xianglong Liu, Weifeng Lv +68
Large language models (LLMs) have fundamentally transformed automated software development by enabling direct translation of natural language descriptions into functional code, dri…
EHR-R1: A Reasoning-Enhanced Foundational Language Model for Electronic Health Record Analysis
Yusheng Liao, Chaoyi Wu, Junwei Liu +12
Electronic Health Records (EHRs) contain rich yet complex information, and their automated analysis is critical for clinical decision-making. Despite recent advances of large langu…
Evolving Interactive Diagnostic Agents in a Virtual Clinical Environment
Pengcheng Qiu, Chaoyi Wu, Junwei Liu +11
We present a framework for training large language models (LLMs) as diagnostic agents with reinforcement learning, enabling them to manage multi-turn interactive diagnostic process…
Self-Rewarding Rubric-Based Reinforcement Learning for Open-Ended Reasoning
Zhiling Ye, Yun Yue, Haowen Wang +11
Open-ended evaluation is essential for deploying large language models in real-world settings. In studying HealthBench, we observe that using the model itself as a grader and gener…
Learning to Align, Aligning to Learn: A Unified Approach for Self-Optimized Alignment
Haowen Wang, Yun Yue, Zhiling Ye +12
Alignment methodologies have emerged as a critical pathway for enhancing language model alignment capabilities. While SFT (supervised fine-tuning) accelerates convergence through d…
RJUA-MedDQA: A Multimodal Benchmark for Medical Document Question Answering and Clinical Reasoning
Congyun Jin, Ming Zhang, Xiaowei Ma +13
Recent advancements in Large Language Models (LLMs) and Large Multi-modal Models (LMMs) have shown potential in various medical applications, such as Intelligent Medical Diagnosis.…
OrchMoE: Efficient Multi-Adapter Learning with Task-Skill Synergy
Haowen Wang, Tao Sun, Kaixiang Ji +3
We advance the field of Parameter-Efficient Fine-Tuning (PEFT) with our novel multi-adapter method, OrchMoE, which capitalizes on modular skill architecture for enhanced forward tr…
GACE: Learning Graph-Based Cross-Page Ads Embedding For Click-Through Rate Prediction
Haowen Wang, Yuliang Du, Congyun Jin +5
Predicting click-through rate (CTR) is the core task of many ads online recommendation systems, which helps improve user experience and increase platform revenue. In this type of r…
From Beginner to Expert: Modeling Medical Knowledge into General LLMs
Qiang Li, Xiaoyan Yang, Haowen Wang +14
Recently, large language model (LLM) based artificial intelligence (AI) systems have demonstrated remarkable capabilities in natural language understanding and generation. However,…
Customizable Combination of Parameter-Efficient Modules for Multi-Task Learning
Haowen Wang, Tao Sun, Cong Fan +1
Modular and composable transfer learning is an emerging direction in the field of Parameter Efficient Fine-Tuning, as it enables neural networks to better organize various aspects…