# Five Silent Failure Modes in LLM Structured Outputs Your JSON validates but the data lies: Undetected risks in grammar-constrained generation - **Source:** [Towards Data Science](https://towardsdatascience.com) - **Published:** 2026-09-01 - **Topic:** [Data & AI](https://brifing.si/c/data-ai) - **Original article:** - **On Brifing:** https://brifing.si/s/308b310b-09be-485e-8701-f2031dce8462 ## The false safety of schema validation While constrained decoding ensures 100% valid JSON output, BAML benchmarks reveal it reduces model accuracy by 2.26%. In production, 1 in 12 runs generates plausible but completely fabricated values. Pydantic validation checks structural integrity while missing semantic correctness, creating silent data corruption risks. ## The enum hallucination problem Models randomly pick valid enum values regardless of context. A 'low/normal/high/urgent' priority scale might label routine requests as 'urgent'. Grammar guarantees value validity but not contextual appropriateness. Detecting this requires additional validation layers analyzing input-output alignment. ## Forced fabrication syndrome Free-text fields lose uncertainty expression. When BAML submitted an elephant photo as a receipt, models fabricated valid expense reports instead of refusing. Constrained decoding removes the 'I don't know' option - when schema demands a value, models invent one regardless of input validity. ## Cross-field contradictions Schema validation checks fields in isolation. Outputs can contain contradictions like 'sentiment:positive' with 'score:0.1' (should be negative). Date ranges may show 'end_date' before 'start_date'. No single-field validator catches these inter-field logical inconsistencies. ## Distributional collapse effect Models converge on safe generic values across diverse inputs. One classification pipeline showed all confidence scores frozen at 0.98 for weeks. Collin Wilkins documented cases where even gibberish inputs received 0.99 confidence. Structural validation masks these distributional anomalies. ## The empty-array avoidance trap Models strongly resist returning empty arrays. Grammar weights object paths heavier than empty arrays, causing models to fabricate entries when no matches exist. Extraction tasks falsely report findings (e.g. '3 matches') when the correct answer should be zero. ## The format accuracy tradeoff Lee et al. measured structured output formats cause 3-9% accuracy drops in open-weight models, exceeding 15% in mathematical reasoning. Tam's research shows stricter formatting rules degrade reasoning quality. The cognitive load of maintaining structure competes with answer correctness. ## Limitations of current solutions Pydantic model_validator can enforce cross-field rules (start_date < end_date). Custom checks catch known patterns like sentiment-score mismatches. However these only address anticipated failure modes, leaving systems vulnerable to novel structural-but-wrong outputs. ## Next-generation validation approaches Emerging solutions include graph-based validation modeling field relationships, statistical monitoring for distribution shifts, and hybrid approaches balancing constrained generation with free-form accuracy. Some teams run parallel constrained/unconstrained outputs to compare results, flagging discrepancies. ## Sources covering this story - https://towardsdatascience.com/your-json-is-valid-but-your-data-is-wrong-five-failure-modes-llm-structured-outputs-wont-catch/ — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._