向量化Trie结构加速LLM约束解码，专为生成式检索设计，在GPU等加速器上实现高效推理。
arXiv:2602.22647v2 Announce Type: replace-cross Abstract: Generative retrieval has emerged as a powerful paradigm for LLM-based recommendation. Howeve…
扩散语言模型也能精准约束解码？这项研究用有限自动机高效搞定JSON生成，突破自回归模型局限。
arXiv:2607.07026v1 Announce Type: new Abstract: Constrained decoding is essential for serving LLMs, ensuring that generated outputs follow specific st…
如何让大模型更可靠地调用Web API？这篇论文提出RAG+约束解码双管齐下，显著削减调用错误，实战价值高。
arXiv:2607.05936v1 Announce Type: cross Abstract: Integration of web APIs is a cornerstone of modern software systems, yet writing correct web API inv…
LLM结构化生成中的约束解码存在隐藏成本：掩码和重新归一化可能扭曲生成模式，论文揭示这一关键代价。
arXiv:2603.03305v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used to generate executable outputs, JSON obje…
研究发现利用语法约束解码可诱导LLM生成恶意代码，揭示新型安全漏洞。
arXiv:2606.11817v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used for code generation, raising concerns that they m…
通过压缩词空间加速约束解码，让LLM输出精准服从语法规则，显著提升效率。
arXiv:2605.29986v1 Announce Type: new Abstract: To guarantee that an LLM's outputs conform to a specified structure, context-free grammar (CFG) decodi…
大模型对齐新方法：通过风险约束解码感知人类偏好分歧，提升模型鲁棒性。
arXiv:2603.08145v2 Announce Type: replace Abstract: Preference-based alignment methods (e.g., RLHF, DPO) typically optimize a single scalar objective,…