AI RESEARCH
Orthographic Constraint Satisfaction and Human Difficulty Alignment in Large Language Models
arXiv CS.CL
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ArXi:2511.21086v2 Announce Type: replace Large language models must satisfy hard orthographic constraints during controlled text generation, yet systematic cross-family evaluation remains limited. We evaluate 39 configurations spanning three model families (Qwen3, Claude Haiku 4.5, GPT-5-mini) on 58 word puzzles requiring character-level constraint satisfaction. Cross-family differences produce substantially larger performance gaps (2.0-2.2x, F1 = 0.761 vs.