MiniMax M3.1 Flash Faces Ranking Manipulation Controversy: Near Full Score on Old Questions, Almost Zero Score on New Ones
Beating AI Express News reports that large language model (LLM) evaluation blogger "Cat Boss" discovered during testing of MiniMax M3.1 Flash that the model scores nearly full marks on old questions but almost zero on new ones. On average, the model consumes tens of thousands of tokens per question, though it only uses around 10,000 tokens to answer some difficult questions correctly. This anomalous performance has sparked suspicion that the model may have memorized answers to some questions in advance.
Cat Boss maintains the private "Zhizhi" LLM leaderboard, using a non-disclosed test set focused on evaluating logic, math, and coding capabilities. The test set typically includes no more than 28 questions and 270 test cases, with partial questions updated monthly. Each question is tested three times, and both the highest and median scores are tracked.
The median score for M3.1 Flash reached 51.76, more than double the previous generation M3's 23.86, and is close to DeepSeek V4.1 Flash 1017's 56.03. Some developers suspect the model was specifically trained on old questions rather than truly mastering problem-solving methods. This practice of optimizing models solely to improve evaluation scores is known as "Benchmaxxing".
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Revolut experiences widespread service outage, affecting app and physical card payments.
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JPMorgan: CoreWeave debt sell-off poses limited risk to global banks
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