How to measure the reasoning skills of an AI model?
Measuring the accuracy of an AI model is easy. But what about reasoning?
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Historically we’ve mostly talked about measuring the accuracy of AI models. Like grading a paper by looking at the answers. We didn’t really care about how the AI model got there. But reasoning is turning out to be a critical capability of LLMs. We need to know how LLMs produce answers.
Classification accuracy was perfect for MNIST digits and dog-vs-cat images. One question, one ground-truth label. But LLMs build arguments, not single tokens. A 92% exact-match score can hide hallucinations, faulty logic, or brittle prompts.
Five Axes of Real Reasoning
Here the 5 axes of reasoning:
Chain-of-Thought Fidelity: Does every inference follow logically? A quick test would be to ask the model to show work. And audit each hop for validity.
Self-Consistency: Does it reach the same conclusion when asked 10 paraphrased prompts? Monte-Carlo prompting helps here. You should measure answer entropy.
Tool Use: Can it call APIs, retrieve docs, and cite verifiable sources? Give it a scratch-pad with Python + search. And score success rate.
Error Recovery: After feeding it a planted false premise, can it detect and correct? To test it, inject contradictions and look for self-correction tokens.
Meta-Reflection: Can it explain why it believes its answer? Prompt for a confidence statement and evidence ranking.
Scoring Blueprint
Here’s a quick blueprint of how we can score reasoning skills:
Rubric-Graded Traces: Human reviewers rate logic steps 0-2 (invalid → airtight). Weight later steps heavier. A bad premise early poisons the well.
Self-Consistency Index (SCI): Check this over N diverse prompts. Lower variance, higher index.
Latency-Weighted Regret: Reward shorter, correct chains. Penalize token bloat and hallucinated hops.
Automated Spot-Checks: Static analysis over traces to flag circular logic, unsupported claims, or dangling citations.
What’s next?
Use small and diverse eval suites to test reasoning skills of an LLM e.g. reasoning puzzles, open-book questions, tool-use tasks. Mix synthetic and real user prompts. Reasoning flaws surface faster under prompt variance. Track metrics per model and per prompt archetype. Reasoning can regress silently after fine-tunes.
We should learn how to audit cognition. We should know how a model thinks, not just what token it spits out. The next wave of AI products (agentic, API-driven, autonomous) will be judged by their reasoning integrity. Give them tougher exams now, before they sit in mission-critical seats.
If you're a founder or an investor who has been thinking about this, I'd love to hear from you.
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