The Sign Error at Scale: Model Fragility Across Labs, Roads, and Regulators

Three pieces expose the same fracture. 'OpenAI Math Repository Withdraws Three Papers After Sign Error Voids Stabilization Argument' shows a single flipped sign collapsing multiple proofs. 'Causal forecasts raise queue vehicle-seconds 6.09% on Xuancheng test dates despite 4.03% MAE reduction' demonstrates that even improved predictive accuracy produces worse real-world traffic outcomes when the underlying timing assumptions remain unexamined. 'FDA rejects animal-only IND data, demands organ-on-chip validation' reveals regulators no longer trusting legacy model pipelines and forcing replacement with human-cell systems. The same pattern appears in the older 'OpenAI deploys GPT-6 to production with embedded intelligent UI layer': deployment proceeds before core assumptions are stress-tested. Across mathematics, traffic engineering, drug approval, and large-scale AI release, institutions treat refined outputs as sufficient while foundational mappings to reality stay brittle.