arXiv:2608. 12574v1 Announce Type: new Abstract: Large language models increasingly need to generate structured outputs that conform to predefined schemas, with one common constraint being selection from a finite set of valid strings.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2605.29986v2 Announce Type: replace Abstract: To guarantee that an LLM's outputs conform to a specified structure, context-free grammar (CFG) decoding engines force the selection of next tokens...
arXiv:2607. 11951v1 Announce Type: new Abstract: Large language models can write SQL, but enterprise deployment demands more than plausible text: outputs must be syntactically valid, must respect per-role and per-schema policy, must carry provable (not best-effort) guarantees, must not slow down as generations grow, and must leave a compliance-grade record of every decision.
arXiv:2607. 18357v1 Announce Type: cross Abstract: Large language models now write a growing share of the world's code, increasingly inside agents and serving systems that compile, execute, or dispatch generated code without line-by-line review.
arXiv:2607. 23089v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have enabled automated kernel generation and optimization, but most existing approaches rely on surface signals such as compilation feedback and profiling metrics.
arXiv:2511. 20849v2 Announce Type: replace-cross Abstract: We introduce a new tokenizer for language models that minimizes the average tokens per character, thereby reducing the number of tokens needed to represent text during training and to generate text during inference.
The paper presents a production streaming guardrail system that ensures exact release‑boundary equivalence for language‑model outputs. It compiles regular‑language predicates into persistent NFAs, distinguishes stable from provisional states, and applies document‑order priority to decide before each chunk release. Evaluations on over 200,000 partitioned cases show zero mismatches and demonstrate that incremental matching can outperform native regex at larger chunk sizes while remaining competitive at smaller ones.