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Chun-Yong Chong

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#artificial intelligence Review Oct 2026

SWE-Critix: Post-Training Generalist Verifiers for Assessing Coding-Agent Trajectories

Coding agents are increasingly deployed for end-to-end software engineering tasks, where a readily available oracle evaluator that defines the success criteria is often absent. This motivates verifications for coding agents that can reliably judge and explain their behavior. However, verification is non-trivial as codi...

Jia-Jun Hu, Chun-Yong Chong · 0 citations
#natural language process... Preprint Sep 2026

LoLBench: Evaluating Coding Agents with Long-Horizon Proposals on Large Software Systems

Modern coding agents can deliver increasingly large repository-level changes, and recent benchmarks reflect this by emphasizing long-horizon tasks with large reference implementations. Many benchmarks evaluate coding agents'implementation capability to produce correct code edits from detailed specifications. However, p...

Yun Peng, Zi-Han Wu, Ze-Yang Zhuang et al. · 0 citations
Review Jul 2026

What Resolve Rate Hides: Trajectory Structure Diagnostics for Coding Agents

TraceProbe is presented, a trajectory-diagnostic framework that recovers what resolve rate hides and adds auditable diagnostic context to outcomes by localizing inspection targets, suggesting failure hypotheses, and prioritizing runs for review.

Rui Shu, C. Chong, Xin Zhou et al. · 1 citation
Preprint Aug 2026

A Unified Issue Resolution Benchmark for Requirement Clarification, Planning, and Code Generation for Coding Agents

SWE-RPG is introduced, a repository-level benchmark that combines executable patch evaluation with validated ground-truth references (GTs) for Requirement Clarification and Implementation Planning, and suggests implicit-requirement recovery as a key candidate direction for improving coding agents.

Xin Zhou, Chun-Yong Chong, Kisub Kim et al. · 0 citations

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