What happened
Govind Mudavadkar's LedgerMind splits work between an AI model, a calculation engine and a rule-based checker; the checker accepted 1,107 answers, of which 254 (about 23%) were wrong.
Why it matters
The checker can confirm a number exists and the math is sound, but it cannot guarantee the answer actually fits the question — a gap that matters wherever AI output is used in payroll or finance work, and one that humans using spreadsheets can hit too.
Summaries like this, every morning.
The LedgerMind case is notable because it isolates a failure mode that source citations alone cannot fix. The system used a fixed-rule checker rather than another AI, and that checker accepted 1,107 answers — but 254 of them were still wrong. The majority of those mistakes, 191, were not fabricated numbers or faulty math; they were real numbers attached to the wrong period or a calculation method that didn't suit the question.
A salary master with E001 at 300,000円 for September 2026 and 320,000円 for October 2026 contains both figures. An AI that answers 300,000円 for an October question has not invented anything, but it has still given the wrong answer.
Its practical takeaway is that checking a number's existence is one step; checking whether that number is the right one for the employee, month and item is a separate step that needs its own verification.
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