Anthony @antmikinka

Universal Conditional Logic and token-efficient prompts

· 1 min read · research, ucl, prompts, local-ai

Universal Conditional Logic (arXiv:2601.00880): a formal specification approach that reduced token usage by 29.8% (p<0.001) via indicator functions, structural overhead modeling, and early binding.

Prompt quality is not only style. Under real model and token budgets—especially for local AI and constrained devices—it is an efficiency problem.

Universal Conditional Logic (UCL) is my research preprint on that problem: arXiv:2601.00880.

What UCL is trying to do

UCL treats specification as something you can structure, measure, and bind early—not only as free-form natural language.

Core ideas in the work:

  • Indicator functions for conditional structure
  • Structural overhead modeling so you can see what the prompt is paying for
  • Early binding to reduce wasteful re-description and ambiguity

In evaluation, the approach reduced token usage by 29.8% (p < 0.001) while preserving the formal intent of the specification.

That number matters for:

  • On-device / edge models with tight context windows
  • Agent loops that re-inject instructions every turn
  • Cost and latency when you scale multi-step tools

Why this connects to local AI agents

During agent work (including multi-turn interview and tool-using agents), instruction bulk competes with history and tools for the same context window.

If you can encode conditionals more densely, you free context for:

  • Conversation memory
  • Tool schemas
  • Task state / checkpoints

The preprint is the specification result. The product application is UCL tooling: a thin tool relay that keeps JSON Schema on the host. The direction is the same: make structure cheaper.

Code and paper

If you are optimizing prompts for small models, edge deployment, or multi-turn agents, UCL is the research artifact I point people to first.