arXiv:2606.11203v1 Announce Type: new Abstract: Structured sequence generation often requires a model to satisfy several input-derived constraints in a single output. Standard decoding methods may assign high probability to fluent continuations while placing low mass on continuations that realize all required anchors jointly. We study this regime as a rare-event sequential inference problem. LatticeBridge combines a compact prefix language model, instance-compiled surface automata, and a twisted sequential Monte Carlo (SMC) decoder with resampling, multilevel splitting, and a source-support pr
This paper addresses a core challenge in advanced AI — generating complex, constrained outputs, which is becoming critical as AI models are deployed in more sensitive and structured applications.
Improving the faithfulness and constraint satisfaction of AI-generated sequences unlocks new capabilities for AI in critical domains like code generation, scientific discovery, and decision-making.
The ability to reliably ensure AI outputs adhere to multiple 'anchors' means AI systems can move beyond fluent but unconstrained generation to more controllable and trustworthy synthesis.
- · AI developers
- · Companies using AI for complex structured tasks
- · Researchers in natural language generation
- · Systems relying on less constrained AI outputs
- · Current methods with poor constraint satisfaction
More reliable AI-generated code, medical reports, and scientific hypotheses.
Accelerated automation of highly structured white-collar tasks, particularly in engineering and legal fields.
Increased public and institutional trust in AI systems for tasks requiring precision and adherence to rules.
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Read at arXiv cs.CL