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Wenjing Wu

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Preprint Aug 2026

What Aggregate Scores Miss: Measuring Item-Level Regressions in Commercial LLM API Migrations

Context: Software systems that depend on commercial large language model APIs must migrate to successor versions when vendors deprecate older models. Migration decisions typically rely on aggregate benchmark scores, which compress heterogeneous item-level behaviour into a single net figure. Objective: We measure what t...

Xiaonan Xu, Wen-Jing Wu · 1 citation
#artificial intelligence Preprint Sep 2026

MCP Error Messages Written for Developers Hurt the Most Capable Agents Most

Many Model Context Protocol (MCP) servers wrap web APIs built for human developers, and their error messages tell the reader to run a command, edit a configuration, open a web page or wait. Many agents that read them can only call the server's tools. In 150 widely used MCP servers, 949 of 3,001 error messages tell the...

Xiaonan Xu, Wen-Jing Wu · 0 citations
Jul 2026

Validation Evidence in LLM Repair Agents: How Much of What Passes Actually Tests the Bug?

When a repair agent runs a test and sees it pass, the result is treated as evidence about the reported defect. We measure how often that treatment is warranted. BSG-VA (buggy-state/candidate-state/gold-fix validation analysis) captures each validation command at its exact working-tree state, extracts a test-only patch,...

Xiaonan Xu, Wenjing Wu · 0 citations

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