most citedFrom Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence

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cs.CL2026

NestedKV: Nested Memory Routing for Long-Context KV Cache Compression

Hong Chen, Xiang Liu, Yubo Gao +5

Long-context language models are limited by the memory footprint of the key-value (KV) cache. Existing training-free KV compression methods usually rank tokens by one importance si…

cs.CL2025

M2G-Eval: Enhancing and Evaluating Multi-granularity Multilingual Code Generation

Fanglin Xu, Wei Zhang, Jian Yang +5

The rapid advancement of code large language models (LLMs) has sparked significant research interest in systematically evaluating their code generation capabilities, yet existing b…

cs.CL2025

Context as a Tool: Context Management for Long-Horizon SWE-Agents

Shukai Liu, Jian Yang, Bo Jiang +4

Agents based on large language models have recently shown strong potential on real-world software engineering (SWE) tasks that require long-horizon interaction with repository-scal…

cs.CL2025

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…

cs.CL2025

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…

cs.CL2025

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…