What it is
Universal Conditional Logic (UCL) is a mathematical framework for prompt optimization. The claim is not “write shorter prompts.” It is that specification can be treated as something you measure: indicator functions for conditional logic, a structural-overhead term, and early binding so the model is not carrying unused branches.
The paper is on arXiv as 2601.00880. The public repo holds the experimental code, prompts, and raw model responses behind those numbers.
Why it matters
Prompt engineering is usually folklore. I wanted a result I could reproduce: 29.8% token reduction on the reported evaluation (t(10) = 6.36, p < 0.001, Cohen’s d = 2.01), with quality maintained or improved — figures from the paper and the repo README, not a new analysis.
The same work surfaced the Over-Specification Paradox: past a threshold the README writes as S* ≈ 0.509, adding more specification starts to hurt, following an inverted-U rather than “more detail is always better.”
How it works
The experiment compared a heavy baseline prompt against a sequence of UCL prompt versions (including a version that failed by getting larger again), plus a no-prompt control. Runs went through a test harness against multiple models via OpenRouter. The tree keeps prompts, per-condition responses, a CSV of results, and a written report.
A later public description of the repo also mentions a UCL Prompt Builder. The name-list writeup on this site — Universal Compact Layer — is a related, product-shaped explanation of keeping the rulebook on the host.
Status
Preprint plus public code: antmikinka/Universal-Conditional-Logic. I have not listed a journal version because I do not have one to point to.
Related
- ModelTokenEval — a small visualizer I built for OpenRouter data from the UCL runs
- SemanticScholarCheck — a helper for finding papers to cite
- The name list — a later, product-shaped explanation of keeping rules on the host
- Lemonade Interviewer — the Interviewer README links this paper