# AI proof systems can appear convincing despite mathematical errors

DevFeed: [AI proof systems can appear convincing despite mathematical errors](<https://devfeed.tech/articles/easier-to-convince-than-to-prove-40143.md>)

Original publisher: [Read original article](<https://korbonits.com/blog/2026-06-12-easier-to-convince-than-to-prove/>)

Published: 2026-06-12T00:00:00Z

Content type: opinion

Language: en

Sources: [Alex Korbonits](<https://devfeed.tech/sources/alex-korbonits.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [math](<https://devfeed.tech/topics/math.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [analysis](<https://devfeed.tech/tags/analysis.md>), [llm](<https://devfeed.tech/tags/llm.md>), [math](<https://devfeed.tech/tags/math.md>), [model](<https://devfeed.tech/tags/model.md>), [verification](<https://devfeed.tech/tags/verification.md>)

## AI overview

The article examines MaxProof, a MiniMax system trained to generate competition-math proofs and improve them through candidate search and LLM-based verification. It reports a large gap between the training verifier's scores and independent expert judgments, while noting that the system's strongest contest results rely on extensive search rather than one-shot generation.

## Source excerpt

Two posts ago I quoted a warning: an AI will find it easier to convince you it has a proof than to write one. A middling new paper finally put a number on that gap -- 0.99 against 0.55.