Withdrawn arXiv Paper Proposes Framework for Standardizing AI Agent Benchmarks

A software engineering research paper submitted to arXiv in May 2026 and later revised has been withdrawn by its author, Zhiqing Zhong. The work proposed an executable benchmarking framework designed to bring consistency to how AI agents that use external tools are evaluated.

The Problem It Addressed#

Current approaches to testing closed-loop tool-using agents often mix together different components—the actual workloads being tested, the systems that generate actions, and the standards for what counts as valid evidence—without clear separation. This overlap can make it difficult to properly assess and compare agent performance across different experimental setups.

Proposed Solution#

The framework laid out a structured evaluation system that would integrate multiple existing benchmarks and environments:

• WebArena Verified, a slice of SWE-Gym (software engineering benchmarks) with compatible verification methods
• MiniWoB++, a task-based evaluation environment
• Common adapters and shared contracts for how tasks are defined, actions are recorded, and results are reported

The suite would explicitly separate evidence intended for published research from preliminary, diagnostic, and audit-level results, all documented under a single verifiable standard.

Key Metrics and Details#

The benchmarking suite was designed to track latency, action validity, patch costs, verifier information, replay data, and audit trails for full transparency. A notable finding from a separate controller study suggested that evaluation conditions—such as clean baselines versus stressed live conditions—can lead to different controller selections even under identical workloads and admission standards.

Note on Scope#

The author emphasized this was a benchmarking infrastructure proposal rather than a new agent algorithm, model leaderboard, or solver for software engineering benchmarks.