1 citations · 1 across the 10 of their papers we have counts for
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A Unified Issue Resolution Benchmark for Requirement Clarification, Planning, and Code Generation for Coding Agents
Xin Zhou, Chun Yong Chong, Kisub Kim +11
Large language model-powered coding agents are increasingly used to modify existing code repositories, for example, by adding features or fixing bugs. Yet existing repository-level…
GEB-Bench: Abstract Structures Told in Many Voices
Tong Zhang, Zhiyuan Shi, Yun Peng +1
Analogy allows models to apply relational knowledge from one situation to another. Benchmarks of equivalent representations test different ways of expressing the same underlying pr…
When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs
Tong Zhang, Zexin Li, Simin Chen +1
Jailbreak defenses are essential for protecting large language models (LLMs), but they can also introduce secondary costs that weaken model utility. We present a systematic study o…
When Does Recurrence Become an Algorithm? Convergence Selection in Weight-Tied Looped Transformers
Tong Zhang, Junhao Hu, Yun Peng +1
When does a weight-tied looped transformer -- one block applied T times -- implement an actual algorithm? We answer with four findings from controlled populations on group word pro…
What Resolve Rate Hides: Trajectory Structure Diagnostics for Coding Agents
Rui Shu, Chun Yong Chong, Xin Zhou +6
Coding agents are ranked almost entirely by resolve rate: whether their final patch passes the target tests. Yet two agents can reach the same outcome through very different proces…
Retrieval-Infused Reasoning Sandbox: A Benchmark for Decoupling Retrieval and Reasoning Capabilities
Shuangshuang Ying, Zheyu Wang, Yunjian Peng +16
Despite strong performance on existing benchmarks, it remains unclear whether large language models can reason over genuinely novel scientific information. Most evaluations score e…
MulVul: Retrieval-augmented Multi-Agent Code Vulnerability Detection via Cross-Model Prompt Evolution
Zihan Wu, Jie Xu, Yun Peng +2
Large Language Models (LLMs) struggle to automate real-world vulnerability detection due to two key limitations: the heterogeneity of vulnerability patterns undermines the effectiv…