# Constrained Decoding

Constrained decoding is often assumed to degrade LLM reasoning, but once the prompt and expected output format are aligned, causal ablations show the apparent gap comes mainly from prompt-level differences rather than decoder masking. The work splits constraints into format, validity and reasoning types and tests them across four model families and eleven open-source models; validity constraints, which remove invalid answers, give the most consistent gains, up to 17.4%, while reasoning constraints help only when the enforced trajectory is itself beneficial.

Constrained decoding is often assumed to degrade LLM reasoning, but once the prompt and expected output format are aligned, causal ablations show the apparent gap comes mainly from prompt-level differences rather than decoder masking. The work splits constraints into format, validity and reasoning types and tests them across four model families and eleven open-source models; validity constraints, which remove invalid answers, give the most consistent gains, up to 17.4%, while reasoning constraints help only when the enforced trajectory is itself beneficial.

Large language models still fabricate reasoning that contradicts domain rules in specialized scientific tasks. SciDC has a general model compile natural-language scientific knowledge into three layers of rules, top-level reasoning steps, middle-level conditional checks and bottom-level token masks, which a locally deployed domain model enforces during decoding. Across industrial formulation design, thyroid cancer TNM staging and single-step retrosynthesis, average accuracy rises 12% over vanilla generation, and ablations show removing the top or bottom layer sharply lowers formulation accuracy.

Large language models still fabricate reasoning that contradicts domain rules in specialized scientific tasks. SciDC has a general model compile natural-language scientific knowledge into three layers of rules, top-level reasoning steps, middle-level conditional checks and bottom-level token masks, which a locally deployed domain model enforces during decoding. Across industrial formulation design, thyroid cancer TNM staging and single-step retrosynthesis, average accuracy rises 12% over vanilla generation, and ablations show removing the top or bottom layer sharply lowers formulation accuracy.

Long-running agents previously made every context-management decision with a general-purpose LLM, which is slow and expensive at the volume such agents need. ScaleDown's constrained decoding models (CDMs) are trained for one task and only return answers that task allows, so they can be smaller, faster and cheaper; a new LangChain and Deep Agents integration ships three middlewares for summarization, tool-result compression and structured extraction, and the LangSmith LLM Gateway now lists ScaleDown as a direct model provider so calls are traced and count toward spend and rate limits. ScaleDown reports its models are 200x faster and 467x cheaper than frontier models on these narrow tasks.

Long-running agents previously made every context-management decision with a general-purpose LLM, which is slow and expensive at the volume such agents need. ScaleDown's constrained decoding models (CDMs) are trained for one task and only return answers that task allows, so they can be smaller, faster and cheaper; a new LangChain and Deep Agents integration ships three middlewares for summarization, tool-result compression and structured extraction, and the LangSmith LLM Gateway now lists ScaleDown as a direct model provider so calls are traced and count toward spend and rate limits. ScaleDown reports its models are 200x faster and 467x cheaper than frontier models on these narrow tasks.