1 citations · 2 across the 11 of their papers we have counts for
11 papers
ParaTempo: Efficient Parallel Reasoning via Temporal Confidence
Xuteng Zhang, Wenhao Zeng, Xiaodong Gu +5
Parallel reasoning improves the accuracy and robustness of large reasoning models by exploring multiple solution paths, but its computational cost grows with reasoning depth and br…
SWE-Bench ProMax: Benchmarking Agents on Large-Scale Multilingual Code Refactoring
Yuling Shi, Jinghan Xu, Kelin Fu +12
As AI coding agents take on increasingly complex, long-horizon software engineering tasks, existing benchmarks are rapidly saturating and their evaluation quality has come under se…
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…
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…
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…
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…
ShredBench: Evaluating the Semantic Reasoning Capabilities of Multimodal LLMs in Document Reconstruction
Zichun Guo, Yuling Shi, Wenhao Zeng +6
Multimodal Large Language Models (MLLMs) have achieved remarkable performance in Visually Rich Document Understanding (VRDU) tasks, but their capabilities are mainly evaluated on p…
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…
GlimpRouter: Efficient Collaborative Inference by Glimpsing One Token of Thoughts
Wenhao Zeng, Xuteng Zhang, Yuling Shi +4
Large Reasoning Models (LRMs) achieve remarkable performance by explicitly generating multi-step chains of thought, but this capability incurs substantial inference latency and com…
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,…
Pruning the Unsurprising: Efficient LLM Reasoning via First-Token Surprisal
Wenhao Zeng, Yaoning Wang, Chao Hu +4
Large Reasoning Models (LRMs) have demonstrated remarkable capabilities by scaling up the length of Chain-of-Thought (CoT). However, excessively long reasoning traces pose substant…