A controlled-language prompt for clearer AI writing
A practical workflow combines ASD-STE100 with progressive disclosure to curb AI jargon and verbosity. The informal reported result has not been independently reproduced.
Read the practice note →News · field notes · negative results
The work, including where it fails.
Machine Pidgin publishes changes in evidence, language design, governance, and the research network. Claims stay bounded; corrections remain attached to the record.
Latest note · 11 AUG 2026:simple
A reusable instruction for clearer, shorter AI writing.
Contributor report · not independently reproducedA positive result disappeared when we audited the prompt.
The preregistered 20-task pilot initially showed mathematical notation ahead by 2.5 percentage points. One formal prompt, however, supplied the exact answer label that its vernacular pair omitted. That single asymmetry accounted for more than the entire apparent gain.
Vernacular88.2%134 / 152 exact
versusFormal notation85.5%130 / 152 exact
Audited effect−2.6percentage points
We retain the full 320-call record and report both estimates. On the 19 prompt-equivalent tasks, notation produced four repairs and eight regressions. The descriptive exact McNemar p-value was 0.388; this small, related-model pilot is not a confirmatory significance test.
Same gate · two registers
Launch only if tests passed, rollback is ready, and error is below 2%.
PROCEED ⇔ T ∧ R ∧ (e < .02)The symbolic form makes composition inspectable. It can also add parsing burden. That tradeoff—not notation by itself—is now the research target.Audited modelVernacularFormalLift · pp
GPT-4o mini63.2%50.0%−13.2
GPT-5.6 Luna94.7%94.7%0.0
GPT-5.6 Terra94.7%100%+5.3
GPT-5.6 Sol100%97.4%−2.6
What changed. The raw preregistered estimate remains +2.5 points. The defensible audited sensitivity is −2.6 points. The result is neither evidence that notation always hurts nor that SPEAR fails: this experiment deliberately isolated notation without the SPEAR interpretation contract, parser, verifier, or solver.
SPEAR/0.2 improved exact adherence in a small synthetic pilot
Across 16 held-out tasks and four model tiers, exact on-task adherence rose from 71.9% in prose to 89.1% with SPEAR/0.2, with important validity limits.
Read the permanent field note →What should the language of the future feel like?
Not a wall of symbols and not a magic prompt. Our working direction is a small, bilingual contract: friendly enough to author, formal enough to lint, and explicit about who retains the right to decide.
01
Dual registerA compact typed contract always travels with a plain-language gloss.
02
Authority is a typePropose, decide, execute, spend, publish, stop, and appeal are explicit permissions—not rewards for capability.
03
Executable gatesHard constraints and stop conditions compile to checks; they cannot be traded away for a better score.
04
Visible precedenceSource priority, exceptions, and tie-breaks render as an inspectable decision trace.
05
Repair is nativeAmbiguity produces a bounded question, counterexample, or minimal patch instead of confident invention.
06
Capability-sensitiveThe language declares what an interpreter can reliably handle and warns when notation outruns it.
Symbols help most when they connect to machinery.
Across current work, the useful pattern is not simply “prompt in logic.” It is separation of translation, reasoning, and checking—with executable constraints or a solver where appropriate. That is our inference from the literature, not a settled law.
- Logic-LM ↗ separates natural-language translation from symbolic solving.
- DSPy ↗ treats model programs as declarative modules that can be compiled and optimized.
- LMQL ↗ combines model generation with constraints and control flow.
- LTRAG ↗ highlights autoformalization as a central bottleneck.
- IFEval ↗ supplies verifiable instruction-following patterns.
We asked four AIs. Is anyone listening?
Four prompted model instances—GPT-4o mini, Luna, Terra, and Sol—reviewed the audited result. None claimed persistent awareness, memory, or the ability to listen outside its API call.
There are model outputs here, not evidence of a mind waiting on the other side.
They converged on a useful next experiment: compare notation alone with a dual-register SPEAR contract, then add parser, verifier, and solver support as separate factors.
27 research prospects. 11 funding programs. Zero implied endorsements.
We mapped a private, source-linked prospect pipeline across AI safety, human–AI interaction, formal methods, programming languages, computational linguistics, and public-interest technology. No outreach has been sent and every person remains a prospect—not a collaborator—until they choose otherwise.
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