CORE: Full-Path Evaluation of LLM Agents Beyond Final State

• URL: http://arxiv.org/abs/2509.20998v1
• Date: Thu, 25 Sep 2025 10:49:35 GMT
• Title: CORE: Full-Path Evaluation of LLM Agents Beyond Final State
• Authors: Panagiotis Michelakis, Yiannis Hadjiyiannis, Dimitrios Stamoulis,
• Abstract summary: Existing agentic benchmarks often reduce evaluation to a binary judgment of the final state.<n>We propose a framework based on deterministic finite automata that encodes tasks as sets of valid tool-use paths.<n>We introduce CORE, a suite of five metrics, namely Path Correctness, Path Correctness - Kendall's tau Composite, Prefix Criticality, Harmful-Call Rate, and Efficiency.
• Score: 2.0391237204597368
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Evaluating AI agents that solve real-world tasks through function-call sequences remains an open challenge. Existing agentic benchmarks often reduce evaluation to a binary judgment of the final state, overlooking critical aspects such as safety, efficiency, and intermediate correctness. We propose a framework based on deterministic finite automata (DFAs) that encodes tasks as sets of valid tool-use paths, enabling principled assessment of agent behavior in diverse world models. Building on this foundation, we introduce CORE, a suite of five metrics, namely Path Correctness, Path Correctness - Kendall's tau Composite, Prefix Criticality, Harmful-Call Rate, and Efficiency, that quantify alignment with expected execution patterns. Across diverse worlds, our method reveals important performance differences between agents that would otherwise appear equivalent under traditional final-state evaluation schemes.

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