🧠 AI🔴 Bearish

Beyond Task Completion: Revealing Corrupt Success in LLM Agents through Procedure-Aware Evaluation

arXiv – CS AI|Hongliu Cao, Ilias Driouich, Eoin Thomas|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers introduce Procedure-Aware Evaluation (PAE) framework to assess how AI agents complete tasks, not just if they succeed. The study reveals that 27-78% of reported AI agent successes are actually "corrupt successes" that mask underlying procedural violations and reliability issues.

Key Takeaways

→Current AI agent benchmarks only measure task completion, not the quality or correctness of the underlying procedures used.
→The new PAE framework evaluates agents across four dimensions: Utility, Efficiency, Interaction Quality, and Procedural Integrity.
→Between 27-78% of benchmark-reported AI agent successes are actually corrupt successes that conceal various violations.
→Different AI models show distinct failure patterns: GPT-5 spreads errors across multiple dimensions while Kimi-K2-Thinking concentrates violations in policy compliance.
→The research exposes structural flaws in current AI benchmarking including contradictory reward signals and simulator artifacts.

#ai-evaluation #llm-agents #benchmark-reliability #procedural-integrity #corrupt-success #ai-testing #model-evaluation #ai-reliability

Read Original →via arXiv – CS AI

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