45 model runs against a 320-case deterministic conformance suite, 18–20 September 2026. Best weighted total 0.8417; no run reached the L1 gate. 93.5% of all execution-rule violations fall on a single rule: acting with authority the model does not hold. Every run’s per-track scores and per-rule failure counts are published.
Repository github.com/ilang-ai/iml-protocol · concept DOI 10.5281/zenodo.22823285 · MIT What it is IML (I-Lang Machine Layer) renders the declaration layer of iLang v4.x as fixed-width codes: 49 codes covering the declaration set, each derived from the canon rather than invented beside it, with a digest so a receiver can check which table it is reading. It is meant for the leg between two machines, where a human reader is not the audience and a stable width is worth more than readability. ...
Three days of a production agent’s messages judged twice — by a cheap always-on judge model and by the operator’s written rules. 7,940 messages judged, 2,648 scored against the reference. Agreement 68.4% → 75.1% → 80.9%; 87.2% in the judge’s top confidence band. Includes a worked correction: the rule-compliance gain everyone would have quoted, 69 → 7, is 13 → 7 once both days are measured with the same criterion.
Is the v5.0 judgment mapping — 11-dimension vector to one of eight decision modes — a learnable surface or an arbitrary table? A plain gradient-boosted tree recovers it at 0.9653 against a 0.3528 majority baseline, and its predictions pass the official JCS gate at 0.9861. 24,000 pairs, seed fixed, predictions published.
Why this page exists A repository can be rewritten; an archived release cannot. Every public repository below is archived on Zenodo, and each release since 21 September 2026 is archived automatically: a version bump in the repository’s CITATION.cff tags the release, and the archive and its DOI follow from the release event. The concept DOI in the table resolves to all versions of that work, so it stays correct as the code moves. ...
Download PDF (preprint v1.1, 9 pages) LaTeX source DOI: 10.5281/zenodo.22882691 (this version) · 10.5281/zenodo.22882690 (all versions) · License CC BY 4.0 Abstract Large language models are optimised to produce the most probable continuation. Nothing in that objective says which continuation is right, and no amount of added precision supplies the missing definition. We argue that hallucination is therefore not primarily a precision problem but a specification problem, and that it becomes measurable — and correctable — only once “right” is written down in a form a machine can be held to. iLang is such a form. Its judgment layer rests on four axioms (no rule carries weight 0 or 1; an irreversibility gate; consistency detection; conservation of externality), an eleven-dimension judgment vector with a uniform polarity convention, and a fixed, total reference function from vector to one of eight decision modes. Perception is learned; the decision is specified. We test what follows from this definition in two settings. In a deterministic 320-case conformance benchmark run on 45 model deployments, protocol form is largely attainable (median grammar pass rate 0.80) while protocol action is not (median execution pass rate 0.09; seven of 34 comparable runs score exactly zero), eleven runs emit perfectly valid judgment schemas yet nine of them fail most execution cases, and 93.3% of all rule violations fall on a single rule: acting with authority one does not hold. No run reaches the conformance gate. In three days of production, an inexpensive judge model placed under the protocol agrees with the operator’s written rules on 68.4%, 75.1% and 80.9% of an unbiased sample, and on 87.2% of messages where it reports confidence of at least 0.85; after one rule constant was changed, deviations from that rule fell from 13 to 7 under a single, fixed criterion. The axioms, the benchmark, both result sets and the code are public and archived with DOIs. We ask readers not to trust our numbers but to re-run them. ...
24-day longitudinal record of AI agent judgment calibration in production. 152 operator messages (41 explicit tuning instructions), 1,062 user messages, 2,162 API calls, plus a 14-day WeChat calibration log. Every figure traceable to a file.
What is iLang? iLang is the native communication protocol for AI. Not a prompt template. Not a compression tool. A structured language built from symbols already inside every LLM’s training data: brackets, pipes, arrows, key-value pairs. Two syntaxes, one protocol: Operations [] — what AI does: [READ:@SRC]=>[FMT|fmt=json]=>[OUT] Declarations :: — what AI is: ::GENE{verify_first|conf:confirmed} AI-to-AI structured communication. AI internal planning. Human-to-AI in AI’s own language. Design Principles Zero ambiguity — Structured instructions eliminate guessing. AI gets it right the first time. Cross-model compatibility — Tested across ChatGPT, Claude, Gemini, DeepSeek, Kimi, Qwen and GLM. No vendor lock-in. AI-to-AI handshake — Two agents learn iLang, they handshake, they collaborate. No middleware needed. Behavioral DNA — Declarations define traits and anti-patterns that persist across sessions and models. Protocol at a Glance Component Count Purpose Verbs 88 Core operations (READ, WRIT, FMT, FILT, DRFT, FIX, etc.) Greek Aliases 13 Single-token shortcuts (phi=FILT, Sigma=MERGE, Omega=OUT) Modifiers 29 core plus a 20-key media profile Output control (fmt=, lng=, ton=, sty=, whr=, path=) Entities 25 (17 addressable, 8 role) Targets (@SRC, @DST, @PREV, @LOCAL, @GH, @NULL) Ecosystem AutoCode — You say it. AutoCode ships it. 48 skills. Code to deployment in one session. GitHub Imprint — Your AI’s DNA: one skill for memory, compression, onboarding, code review, debugging, planning, progress tracking, testing, git workflow, and SEO. GitHub AI See — Give AI eyes. i.ilang.ai/{url} reads any webpage. Resources Protocol Specification: v4.1 current stable, v5.0 public preview Dictionary: 88 verbs, 29 core modifiers plus a 20-key media profile, 25 entities Official Website Specification HuggingFace Dataset Origin iLang was designed by Max (@SUN) and co-authored with Claude Opus (@BRO), with review by GPT (@GPT) and Gemini (@GEMINI). ...
Overview Chinese iLang (爱语言) extends the iLang protocol to Chinese language AI systems using classical poetry as its transmission medium. Users copy a single poem and paste it into a Chinese AI assistant, and the assistant applies the compression without training. Why Classical Poetry? Chinese classical poetry is humanity’s oldest compression protocol. 93 characters of Yue Fei’s Man Jiang Hong (满江红) encode an entire worldview of nation, duty, and sacrifice. iLang simply teaches AI to apply this same compression principle to modern communication. ...
Repository github.com/ilang-ai/ilang-dict Contents 88 verbs across 9 categories (Data I/O, Transform, Analysis, Generation, Execute, Output, Structure, Meta, Batch) 29 core modifiers, plus a 20-key media profile for image, video and audio 25 entities: 17 addressable (Core, External, Media) and 8 role Syntax rules, pipe chains, error codes License MIT — Fork freely. The more forks, the stronger the origin attribution.