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LatticeBridge proposes a twisted sequential Monte Carlo decoder for structured sequence generation that improves constraint satisfaction by treating the problem as rare-event inference, outperforming greedy and beam baselines on CommonGen, E2E NLG, and WikiBio.
A new hybrid decoding framework called In-Writing is proposed, which delays constraint application until after a trigger token, combining free-form reasoning with structured generation for improved accuracy in classification and reasoning tasks.
This paper introduces Ishigaki-IDS-Bench, a benchmark for evaluating LLMs' ability to generate Information Delivery Specification (IDS) XML from BIM information requirements. Evaluation of 10 LLMs shows best models achieve 65.6% macro F1 for content agreement but only 27.7% pass the Content audit, indicating struggles with standard and vocabulary constraints.
DALM proposes a domain-algebraic language model that generates text under exact structural constraints derived from a domain lattice, addressing hallucination by organizing knowledge into separate domain fibers with algebraic guarantees. The model uses three-phase structured denoising (domain → relation → concept) with domain-annotated training data to prevent cross-domain contamination.