arXiv Artificial Intelligence

Accelerating Constrained Decoding with Token Space Compression

1 hour ago 02.10.2026 07:00 👁 1 views
Accelerating Constrained Decoding with Token Space Compression

Quick summary

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 to produce strings that conform to a given CFG. Current CFG-constrained decoding engines are highly optimized, but still suffer from the inherent costs arising from their massive per-step search space---i.e. the entire token vocabulary. This results in intractably high overhead for more complex CFGs, which is precisely the situation where CFG engines are most useful. In this paper, we introduce CF

Key takeaways

  • 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 to produce strings that conform to a given CFG.
  • Current CFG-constrained decoding engines are highly optimized, but still suffer from the inherent costs arising from their massive per-step search space---i.e.
  • This results in intractably high overhead for more complex CFGs, which is precisely the situation where CFG engines are most useful.

Why it matters

“Accelerating Constrained Decoding with Token Space Compression” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗