Agentified Assessment of Logical Reasoning Agents
Summary: arXiv:2603.02788v3 Announce Type: replace
Abstract
We present a framework for evaluating and benchmarking logical reasoning agents when assessment itself must be reproducible, auditable, and robust to execution failures. Building on agentified assessment, we use an assessor agent to issue tasks, enforce execution budgets, parse outputs, and record structured failure types, while the agent under test only needs to expose a standardized agent-to-agent interface.
Introduction
The growing complexity of artificial intelligence systems necessitates robust methodologies for assessing their logical reasoning capabilities. Current evaluation frameworks often lack the rigor required for reproducibility and auditing, leading to inconsistent results. To address this challenge, we propose an innovative approach known as agentified assessment.
Framework Overview
Our framework operates through a dedicated assessor agent, which is responsible for various tasks:
- Issuing Tasks: The assessor generates logical reasoning tasks that the agent under test must solve.
- Enforcing Execution Budgets: It ensures that each task is executed within a predefined budget, promoting efficiency and fairness.
- Parsing Outputs: The assessor evaluates the outputs provided by the agent, ensuring they meet the required standards.
- Recording Failure Types: Any failures in the reasoning process are meticulously documented to facilitate further analysis and improvement.
Case Study: Auto-Formalization Agent
As a case study, we benchmarked an auto-formalization agent designed for first-order logic (FOL) reasoning on a solver-verified and repaired split of FOLIO. This agent is capable of translating natural language premises and conclusions into executable Z3Py programs. It employs satisfiability modulo theories (SMT) solving to determine logical entailment effectively.
Results
On the cleaned FOLIO validation set, the auto-formalization agent achieved an impressive accuracy rate of 86.70% under the assessor protocol. This performance significantly outperformed a chain-of-thought baseline, which recorded an accuracy of only 73.89%. The results underscore the efficacy of the agentified assessment framework in robustly evaluating logical reasoning agents.
Conclusion
The agentified assessment framework presents a novel approach to evaluating logical reasoning agents, emphasizing reproducibility and robustness. By utilizing an assessor agent, we can ensure a systematic and thorough evaluation process that not only benchmarks performance but also identifies areas for improvement. The successful case study with the auto-formalization agent illustrates the potential of this framework to enhance future developments in AI reasoning capabilities.
Future Work
Moving forward, we intend to apply the agentified assessment framework to a broader range of logical reasoning agents, exploring different domains and complexity levels. Additionally, we aim to refine the assessor’s capabilities, ensuring an even more rigorous evaluation process.