Agent Input Authority Mapping

How the existing v5.0 judgment layer handles prompt injection, self-replication, and privilege inheritance without new rules. This document is not normative. It adds nothing to the canon: no declaration, no dimension, no constant, no module. It reads published attack reports through what iLang v5.0 already specifies. Why text becomes dangerous Before agents, text on the internet was content. A webpage, an email, a code comment. Humans read it, humans decided what to do. ...

September 2026 · SUN

IML — the I-Lang Machine Layer

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. ...

September 2026 · SUN

iLang Protocol v4.0 — Overview

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: v5.0 released as 5.0.0 on 2026-09-29; v4.2 is the latest release of the v4 line 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). ...

April 2026 · SUN

Chinese iLang (爱语言) — Compression via Classical Poetry

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. ...

March 2026 · SUN