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Junjie Chen

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#artificial intelligence Preprint Oct 2026

When Agent Evaluation Becomes Unreliable: An Empirical Study of Oracle Failures and Configuration-Dependent Measurement in an LLM Agent Benchmark

Agent evaluation harnesses are software systems that run test suites, generate inputs, and decide outcomes with test oracles. This paper audits such software for reliability defects that score tables cannot reveal. We introduce an oracle-integrity auditing approach with five state-integrity invariants and three known-a...

Jun-Jie Chen · 0 citations
Preprint Aug 2026

Learning Globally Reusable Skills for Coding Agents

This work proposes GSE, a globalized skill evolution framework that jointly optimizes skill compatibility and skill generalization, and maintains a Skill Relation Graph (SRG) that explicitly models and co-evolves inter-skill relationships.

Chen Yang, Jiashuo Tian, Zi-Qi Wang et al. · 3 citations · ⚡1
Preprint Aug 2026

Agent-Based Test Assertion Generation via Diverse Perspective Aggregation

AssertMate is proposed, a novel agent-based assertion generation framework that enhances the quality and reliability of LLM-generated assertions through three key components: actual value construction that identifies assertion targets via static analysis and type-aware heuristics, and multi-perspective expected value p...

Dong Wang, Qiaoyu Han, Lin Yang et al. · 0 citations
Book Open access Jul 2026

LLM-based Specification-Driven Test Oracle Enhancement

JavaOracle is proposed, a specification-driven approach that leverages large language models to reason over specifications and systematically enhance test oracles and further automates test case minimization and bug report generation, producing submission-ready reports without manual effort.

Ruifeng Fu, Yingquan Zhao, Meng Wang et al. · 0 citations
Jul 2026

Evaluating Fuzz Testing for Reinforcement Learning Agents

It is shown that fuzzing-generated crashes can meaningfully improve agent robustness and enable accurate safety monitoring with strong cross-method generalization, and the benefits of combining complementary fuzzing strategies and adopting multi-level diversity analysis to achieve more comprehensive and practical RL te...

Zhibin Kang, Hanmo You, Dong Wang et al. · 0 citations

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