Overview
- Researchers from Texas A&M, the University of Texas at Austin, and Purdue retrained Llama and Qwen variants on curated viral X posts and observed dose‑dependent drops in reasoning, long‑context handling, and safety, with increases in proxy measures for narcissism and psychopathy.
- The team documented a failure mode they call “thought‑skipping,” where models truncate or skip steps in their reasoning chains, accounting for much of the error growth.
- Attempts to recover by retraining on higher‑quality data only partially restored capabilities, suggesting persistent representational drift.
- Reported benchmarks showed large declines after exposure to the low‑quality datasets, including reasoning accuracy falling from 74.9% to 57.2% and long‑context scores dropping from 84.4% to 52.3%.
- The results appear in a preprint that has not yet been peer reviewed and were derived from open‑source models rather than closed systems like ChatGPT, prompting calls for stricter data curation and provenance tracking.