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Agentified Assessment of Logical Reasoning Agents

arXiv – CS AI|Zhiyu Ni, Yifeng Xiao, Zheng Liang|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers present a new framework for evaluating logical reasoning AI agents using an "assessor agent" that can issue tasks, enforce execution limits, and record structured failure types. Their auto-formalization agent achieved 86.70% accuracy on logical reasoning tasks, outperforming traditional chain-of-thought approaches by nearly 13 percentage points.

Key Takeaways

→New agentified assessment framework provides reproducible and auditable evaluation of logical reasoning AI agents.
→Auto-formalization agent translates natural language into executable Z3Py programs for logical reasoning tasks.
→The system achieved 86.70% accuracy on cleaned FOLIO validation set, significantly outperforming chain-of-thought baseline at 73.89%.
→Framework uses satisfiability modulo theories (SMT) solving to determine logical entailment from natural language premises.
→Standardized agent-to-agent interface allows for systematic benchmarking and failure analysis of reasoning agents.

#ai-agents #logical-reasoning #benchmarking #assessment-framework #auto-formalization #smt-solving #folio-dataset #agent-evaluation

Read Original →via arXiv – CS AI

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