LatticeBridge: New Method for Generating Structured Text While Satisfying Multiple Constraints
Researchers introduce LatticeBridge, a system combining prefix language models, surface automata, and twisted sequential Monte Carlo decoding to improve constrained structured sequence generation. The work frames the problem as rare-event sequential inference, addressing the tendency of standard decoding methods to produce fluent but constraint-violating outputs. Evaluated on 2,610 tasks across CommonGen, E2E NLG, and WikiBio benchmarks, LatticeBridge outperforms greedy, beam-filtered, and best-of-k baselines on exact anchor satisfaction and anchor coverage.
LatticeBridge is a proposed method for structured sequence generation tasks where a model must simultaneously satisfy multiple input-derived constraints in a single output. Standard decoding approaches often assign high probability to fluent text while failing to realize all required anchors jointly — a challenge the authors frame as a rare-event sequential inference problem. The system integrates three components: a compact prefix language model, instance-compiled surface automata (requiring no manually curated lexical classes), and a twisted sequential Monte Carlo (SMC) decoder with resampling, multilevel splitting, and a source-support proposal term. Experiments on 2,610 attainable validation tasks spanning CommonGen, E2E NLG, and WikiBio show improvements over greedy, beam-filtered, and best-of-k ancestral baselines under a shared proposal model. The evaluation framework goes beyond exact anchor satisfaction to jointly report source coverage, source-intrusion diagnostics, overlap, runtime, and particle statistics, characterizing the faithfulness-overlap-latency trade-off frontier. Code and benchmark files are publicly available. The paper is 19 pages and was submitted to arXiv in April 2026.
What's missing
The paper does not report results on held-out test sets, only validation tasks, which limits assessment of generalization. It is unclear how LatticeBridge scales to longer sequences or larger language models, and no human evaluation of output quality is mentioned.
What different sources said
- arXiv cs.LGCenter
LatticeBridge: Rare-Event Sequential Inference for Faithful Structured Sequence Synthesis
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