Agentified Assessment of Logical Reasoning Agents
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. As a case study, we benchmark an auto-formalization agent for first-order logic (FOL) reasoning on a solver-verified and repaired split of FOLIO. The agent translates natural language premises and conclusions into executable Z3Py programs and employs satisfiability modulo theories (...
| Authors | Zhiyu Ni, Yifeng Xiao, Zheng Liang |
|---|---|
| Primary category | cs.AI |
| Published | 3 March 2026 |
| Last updated at source | 2 April 2026 |
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