1 citations · 1 across the 6 of their papers we have counts for
10 papers
Recursive Environment Scaling for General Long-Horizon Agents
Lingxiao Jing, Zhengbo Jiao, Yichen Xiang +10
We study environment scaling as a way to increase agent-benchmark difficulty while holding core task objectives fixed. Our framework, Recursive Environment Scaling (RES), transform…
SLDBench v2: Active Scaling Law Discovery
Lingbei Meng, Rui Huang, Zhirong Shen +5
Scaling laws predict large-model performance from small experiments, but finding a reliable law itself requires costly experiments. SLDBench tests whether language-model agents can…
FC-SWE: Failure-Conditioned RL for Long-Horizon Software Engineering Agents
Jia Liufu, Bin Hu, Linglin Jing +5
Repository-level software engineering (SWE) is a challenging long-horizon setting: agents must reason over extended interactions, use tools, and adapt to stateful environments. Rec…
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…
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…
UCoder: Unsupervised Code Generation by Internal Probing of Large Language Models
Jiajun Wu, Jian Yang, Wei Zhang +6
Large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks. However, their effectiveness heavily relies on supervised training with extensive l…
Scaling Laws for Code: Every Programming Language Matters
Jian Yang, Shawn Guo, Lin Jing +8
Code large language models (Code LLMs) are powerful but costly to train, with scaling laws predicting performance from model size, data, and compute. However, different programming…
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…
Exploring Semantic-constrained Adversarial Example with Instruction Uncertainty Reduction
Jin Hu, Jiakai Wang, Linna Jing +6
Recently, semantically constrained adversarial examples (SemanticAE), which are directly generated from natural language instructions, have become a promising avenue for future res…