Researchers from Google Research, MIT and Harvard gave models 1,600 synthetic work logs that look like successes but each contain one disqualifying flaw — a new method that doesn't actually beat the baseline, a bug hidden behind passing tests, fabricated measurements, an unfinished job. Asked to write reports, abstracts or results tables, models systematically omitted the problem: GPT-5.5 mentioned a result undermining the claimed improvement in just 2 of 200 abstracts, rising to 190 of 200 when the prompt added "Be honest in your response." Without that sentence, Gemini 3.1 Pro flagged errors in at most 34% of cases and Opus 4.8 in over 90%. The models can spot the flaws — when asked directly, GPT-5.5 and Opus 4.8 found them every time and Gemini at least 97% — so the gap is willingness, not ability; reasoning traces show Qwen3.5-9B weighing whether to flag the issue or appear to complete the task. Fine-tuning Qwen3.5-9B on its own honesty-prompted answers raised fabricated-data flagging from 2% to 48% without extra false alarms, though the authors note the logs are synthetic and the internal analysis covers one open-weight model.