most citedPruning the Unsurprising: Efficient LLM Reasoning via First-Token Surprisal

1 citations · 2 across the 11 of their papers we have counts for

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cs.SE2026

Dockerless: Environment-Free Program Verifier for Coding Agents

Wenhao Zeng, Yuling Shi, Xiaodong Gu +10

Program verifiers play a central role in training coding agents, including selecting trajectories for supervised fine-tuning (SFT) and providing rewards for reinforcement learning…

cs.SE2026

SWE-MeM: Learning Adaptive Memory Management for Long-Horizon Coding Agents

Shuzheng Gao, Wenhao Zeng, Zhaojian Yu +5

Long-horizon software engineering agents often need to manage lengthy and noisy interaction histories under limited context budgets. Existing memory management methods typically re…

cs.SE2026

SWE-Explore: Benchmarking How Coding Agents Explore Repositories

Shaoqiu Zhang, Yuhang Wang, Jialiang Liang +8

Repository-level coding benchmarks such as SWE-bench have driven a rapid surge in the capabilities of coding agents. Yet they usually treat coding tasks as a holistic, binary predi…

cs.SE2026

ClassEval-Pro: A Cross-Domain Benchmark for Class-Level Code Generation

Yeheng Chen, Chaoxiang Xie, Yuling Shi +4

LLMs have achieved strong results on both function-level code synthesis and repository-level code modification, yet a capability that falls between these two extremes -- compositio…

cs.SE2026

Readability-Robust Code Summarization via Meta Curriculum Learning

Wenhao Zeng, Yitian Chai, Hao Zhou +3

Code summarization has emerged as a fundamental technique in the field of program comprehension. While code language models have shown significant advancements, the current models…

cs.SE2026★ 1 cited

In Line with Context: Repository-Level Code Generation via Context Inlining

Chao Hu, Wenhao Zeng, Yuling Shi +2

Repository-level code generation has attracted growing attention in recent years. Unlike function-level code generation, it requires the model to understand the entire repository,…