2 citations · 3 across the 11 of their papers we have counts for
12 papers
CyberFactory: Scaling Cyber Security Capabilities with Instances from the Wild
Jian Yang, Haau-Sing Li, Shawn Guo +10
As large language models (LLMs) continue to advance in coding capabilities, their potential in cybersecurity has drawn increasing research attention, with closed-source LLMs (e.g.,…
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…
OProver: A Unified Framework for Agentic Formal Theorem Proving
David Ma, Kaijing Ma, Shawn Guo +7
Recent progress in formal theorem proving has benefited from large-scale proof generation and verifier-aware training, but agentic proving is rarely integrated into prover training…
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…
Close the Loop: Synthesizing Infinite Tool-Use Data via Multi-Agent Role-Playing
Yuwen Li, Wei Zhang, Zelong Huang +8
Enabling Large Language Models (LLMs) to reliably invoke external tools remains a critical bottleneck for autonomous agents. Existing approaches suffer from three fundamental chall…
AGRO-SQL: Agentic Group-Relative Optimization with High-Fidelity Data Synthesis
Cehua Yang, Dongyu Xiao, Junming Lin +7
The advancement of Text-to-SQL systems is currently hindered by the scarcity of high-quality training data and the limited reasoning capabilities of models in complex scenarios. In…
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…
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…
V-GameGym: Visual Game Generation for Code Large Language Models
Wei Zhang, Jack Yang, Renshuai Tao +9
Code large language models have demonstrated remarkable capabilities in programming tasks, yet current benchmarks primarily focus on single modality rather than visual game develop…
MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series
Ge Zhang, Scott Qu, Jiaheng Liu +42
Large Language Models (LLMs) have made great strides in recent years to achieve unprecedented performance across different tasks. However, due to commercial interest, the most comp…