Large Language Models (LLMs) are increasingly used to generate structured outputs—JSON objects, SQL queries, and structured records—from formal schemas. While recent advances in constrained decoding and schema-aware prompting have improved syntactic compliance, the semantic reliability of these outputs remains poorly characterized. We investigate this gap through the lens of schema drift—the inevitable evolution of database schemas in production environments through column renamings, type changes, and constraint modifications.We introduce StructHallu-Drift, a benchmark and evaluation framework for studying structured hallucinations under schema evolution. We contribute: (1) a six-category hallucination taxonomy that disentangles syntactic validity from semantic fidelity; (2) a controlled evaluation suite applying realistic schema mutations at three severity levels to established NL-to-structure datasets; and (3) a systematic evaluation of four LLMs spanning 7B to 70B parameters across three structured output tasks.Experiments on 1,200 schema–model evaluation instances reveal four key findings: (i) 39–54% of structured outputs contain at least one semantic hallucination; (ii) schema drift severity has surprisingly minimal effect on hallucination rates (∼44% across all levels, p = 0.59), suggesting imperfect schema conditioning under our prompting setup; (iii) output format is the dominant factor in generation reliability, with SQL achieving ∼85% semantic validity while schema-grounded record generation drops to 7–24%; (iv) each model exhibits a distinct hallucination fingerprint, implying that mitigation strategies must be model-specific rather than universal. We publicly release our benchmark and evaluation toolkit.

Text-to-image diffusion models learn a mapping from natural language to visual structure, but how robust is this mapping to perturbation? We use personalization—fine-tuning a model to learn a new face, object, or style—as a controlled stress test to probe the fragility of learned vision-language alignment. We find that fine-tuning for one concept systematically shifts the model’s ability to faithfully render unrelated concepts, and that this disruption follows structured, predictable patterns. To measure this fragility, we construct Concept Entanglement Maps: per-prompt, per-model disruption matrices that reveal which concepts are most affected and why. Using Stable Diffusion v1.5 as a controlled testbed, we evaluate 15 subjects across three personalization methods on 200 prompts and report three findings about the organization of vision-language alignment: (1) aggregate disruption is larger for vision-backbone and cross-attention perturbations than for text-embedding perturbations, despite the latter directly modifying the language representation; (2) abstract and compositional language is significantly more fragile than concrete, object-specific language; and (3) disruption does not follow semantic proximity—personalizing for a face does not preferentially disrupt other face-related prompts (p = 1.0), suggesting that alignment vulnerability is organized globally rather than purely by semantic category. These findings expose a structural vulnerability in current text-to-image personalization: the same cross-attention mechanism that enables compositional generalization also creates pathways through which local fine-tuning can propagate as global alignment shift.