Communication integrity · semiotic-deficit · Gnosis topology

Aeon Communication

Semiotic-deficit–driven language and communication integrity engines.

Library APIs for humor, narrative, linguistic, and cognitive integrity audits, grounded in Gnosis communication-topology families. Includes Rosetta chaos evolution over symbol→meaning mappings and a fivefold psyche-basis consciousness transport. Embed the engines in apps, services, or MCP tools.

$ pnpm add @a0n/aeon-communication

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Features

Everything you need. Nothing you don't own.

PleromicAuditor facade

An integration entrypoint that runs auditTotal / conductUnifiedAudit across the bundled engines when you supply a structured payload — a convenience facade over the whole integrity suite.

Four integrity engines

HumorEngine audits setup/punchline dilation and venting; NarrativeEngine audits plot holes and inciting incidents; LinguisticIntegrityEngine audits syntax complexity and pun density; CognitiveIntegrityEngine audits attention-race purity and metacognition depth.

Rosetta chaos evolution

evolveRosettaChaos runs a genetic Monte Carlo search over semiotic symbol→meaning mappings, scored by anchor recovery, roundtrip stability, and an optional chaos-swarm bridge fitness.

Consciousness transport

projectOntoPsycheBasis exposes a fivefold psyche basis — Try / Choose / Commit / LetGo / Learn — backing the communication-mcp tool consciousness_fivefold_projection.

Jet-engine compressor cascade

The L6 token-sampler compresses a full vocabulary-sized logit vector to a single token as a real compressor cascade: each filter divides the surviving candidate set and the filters compose multiplicatively (a (List, ++) → (·, ×) monoid homomorphism).

Mechanized formal axioms

Grounded in Gnosis.NaturalScience.CommunicationTopology: semiotic_deficit_floor (the minimum shared context for absolute communication) and attention_race_purity (un-dilated focus in a cognitive manifold).

Quickstart

Audit and evolve

import { PleromicAuditor } from '@a0n/aeon-communication';

const auditor = new PleromicAuditor();
const result = await auditor.auditTotal(input);

import { evolveRosettaChaosWithNativeSwarm } from '@a0n/aeon-communication/rosetta-chaos-bridge';

const result = await evolveRosettaChaosWithNativeSwarm({
  symbols: ['glyph_a', 'glyph_b', 'glyph_c'],
  meanings: ['sun', 'moon', 'river'],
  anchors: [
    { symbol: 'glyph_a', targetMeaning: 'sun' },
    { symbol: 'glyph_b', targetMeaning: 'moon' },
  ],
  generations: 24,
  populationSize: 48,
  targetSurvivalRate: 0.12,
});

import {
  buildRosettaDecoderReport,
  formatRosettaDecoderReport,
} from '@a0n/aeon-communication/rosetta-chaos-report';

const report = buildRosettaDecoderReport(result, [
  { symbol: 'glyph_a', targetMeaning: 'sun' },
  { symbol: 'glyph_b', targetMeaning: 'moon' },
]);
console.log(formatRosettaDecoderReport(report));

FAQ

Common questions

Library APIs: humor, narrative, linguistic, and cognitive integrity engines, semiotic-deficit helpers, Rosetta chaos evolution, and consciousness transport — plus related types. Consumers embed these engines in apps, services, or MCP tools.

Not by itself. Orchestrating audits across an entire workspace or CI pipeline is a monorepo concern — wire that in the parent repository's scripts/ (or your own runner), not in this package's public surface. PleromicAuditor is an integration entrypoint, not repo-wide governance.

It is a genetic Monte Carlo search over semiotic symbol→meaning mappings, scored by anchor recovery, roundtrip stability, and an optional chaos-swarm bridge fitness — with raw and lexica ingest paths and a consensus loop for curating the next input.

An OSI L6 (Presentation) view of token sampling: the fork/race/fold pipeline produces a full vocabulary-sized logit vector and the token-sampler compresses it to one emitted token. Each filter divides the surviving candidate set and the filters compose multiplicatively, exactly like a compressor cascade.