most citedStructEval: Benchmarking LLMs' Capabilities to Generate Structural Outputs

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cs.SE2026

SpecPath: Testing Coding Agents Across Contract-Equivalent Specification Histories

Yangfan Wu, Haozhe Wang, Huanyu Yang +2

Modern coding agents increasingly appear capable of following complex software requirements, yet their success leaves a critical ambiguity: do they resolve the active specification…

cs.SE20261 cited

StructEval: Benchmarking LLMs' Capabilities to Generate Structural Outputs

Jialin Yang, Dongfu Jiang, Lipeng He +17

As Large Language Models (LLMs) become integral to software development workflows, their ability to generate structured outputs has become critically important. We introduce Struct…

cs.SE2026

Understanding by Reconstruction: Reversing the Software Development Process for LLM Pretraining

Zhiyuan Zeng, Yichi Zhang, Yong Shan +11

While Large Language Models (LLMs) have achieved remarkable success in code generation, they often struggle with the deep, long-horizon reasoning required for complex software engi…

cs.SE2026

SWE-QA-Pro: A Representative Benchmark and Scalable Training Recipe for Repository-Level Code Understanding

Songcheng Cai, Zhiheng Lyu, Yuansheng Ni +13

Agentic repository-level code understanding is essential for automating complex software engineering tasks, yet the field lacks reliable benchmarks. Existing evaluations often over…

cs.SE2025

ACECODER: Acing Coder RL via Automated Test-Case Synthesis

Huaye Zeng, Dongfu Jiang, Haozhe Wang +3

Most progress in recent coder models has been driven by supervised fine-tuning (SFT), while the potential of reinforcement learning (RL) remains largely unexplored, primarily due t…