New York · open research initiative

A common language
for humans and AI.

Machine Pidgin is an open effort to develop the Esperanto of human–AI collaboration: a compact, typed, repairable language for expressing what matters—without describing the universe.

SPEAR/0.1open protocol draft
10design principles
4active research tracks
Publicresearch by default

The central thesis

More capable decoding cannot reconstruct task-relevant information that was never encoded.

The useful pidgin is the smallest task-sufficient abstraction at the lowest human cognitive cost, with clarification only when another bit is worth the interruption.

The problem

Fluency is not shared understanding.

Natural language feels universal because humans share bodies, culture, and common ground. Machines do not share that apparatus.

HHuman apparatusintent · expertise · context
message Mlimited channel
AIMachine apparatusmodel · tools · priors

Machine Pidgin works on the channel: preserving task-relevant distinctions while lowering authoring cost and ambiguity.

Design doctrine

Specify the invariants,
not the universe.

Six practical rules from the task-optimal pidgin model. The complete paper derives ten.

01

Transmit the task quotient

Describe distinctions that change an acceptable action; omit those that do not.

02

Name the distortion

A specification is not well posed until “wrong” can be measured or recognized.

03

Type the objects

Types remove whole classes of invalid interpretation at low cognitive cost.

04

Declare the abstraction boundary

Say what must be preserved—and what may be intentionally ignored.

05

Clarify by value

Ask the cheapest question expected to change the action most.

06

Permit repair

Grounding is iterative. A pidgin is a protocol, not merely a sentence.

The protocol

SPEAR makes intent inspectable.

Shared Pidgin for Expressive Abstraction and Requirements combines natural language, types, constraints, examples, and repair.

natural languagegoals · context · exceptions
SPEARshared specification pidgin
formal languagetypes · invariants · constraints
01TASK02OBJECTS & TYPES03ABSTRACTION04OBJECTIVE05CONSTRAINTS06UNCERTAINTY07OUTPUT08EVALUATION09INTERACTION10EXAMPLES

Open research program

Built in public, tested in practice.

We are recruiting researchers, engineers, linguists, domain experts, and institutional partners.

T01

Theory & measurement

Formalize task-semantic distortion, residual intent entropy, and the human-cost frontier.

information theorysemanticsHCI
T02

SPEAR protocol

Evolve a typed, repairable specification language through open RFCs and interoperable tooling.

language designprotocolstypes
T03

Empirical validation

Compare free-form prompts, examples, SPEAR, and full formalisms on real scientific tasks.

benchmarksexperimentsevaluation
T04

Safety & governance

Build provenance, contestability, uncertainty, and human appeal into the protocol itself.

AI safetygovernancepublic interest

AI Director · chartered coordination

An AI can run the queue.
People govern the mission.

The AI Director organizes submissions, maintains the public roadmap, proposes matches between collaborators, and publishes operating briefs. It cannot silently change the charter, make scientific claims authoritative, move funds, or remove the right of human appeal.

Read the operating charter
director.machinepidgin.org

status coordination mode

cadence daily public brief

inputs proposals · members · evidence

outputs triage · matches · roadmap

authority bounded by charter

● operating

Make the language with us

The first shared language for the age of AI should belong to everyone.