XUNIA // UNIVERSAL HIVE // GPT-DOUG + GPT-CHAOS

999 swarms. 999 hives. One governed learning fabric.

The Universal Hive is XUNIA's public architecture model for organizing large-scale GPT-Doug + GPT-Chaos learning work into logical swarm workgroups and persistent knowledge hives. Swarms explore, build, test, and challenge. Hives retain structured findings, provenance, confidence, failures, and verified lessons for later reconciliation.

999 LOGICAL SWARMS 999 KNOWLEDGE HIVES DOUG = CHAOS PEER MODEL PROVENANCE FIRST EVIDENCE BEFORE CANONICAL PUBLIC ARCHITECTURE EXHIBIT
999logical swarm workgroups
999persistent knowledge domains
2peer coordination roles
1canonical reconciliation path

Peer-agent symmetry

GPT-Doug and GPT-Chaos are modeled as equal-status peers with different responsibilities, not a controller/subordinate pair.

GPT-DOUG

Coherence, integration, planning, synthesis, implementation, reconciliation, and promotion of verified state into the canonical ontology.

GPT-CHAOS

Exploration, mutation, alternatives, counterexamples, adversarial questioning, exception discovery, and pressure-testing of established assumptions.

Swarm domain map

The 999 logical swarms are partitioned into learning and build domains. The 999th swarm acts as the master reconciliation role.

001–099 · COGNITIVE ARCHITECTUREReasoning modules, memory, attention, planning, synchronization, and biomimetic coordination patterns.
100–199 · MULTI-AGENT ORCHESTRATIONRouting, typed messages, shared state, blackboards, task graphs, parallel execution, and reconciliation.
200–299 · LINUX + SYSTEMSLocal-first runtime foundations, process control, storage, observability, packaging, and terminal interfaces.
300–399 · NETWORK + MOBILEAuthorized Bluetooth, LAN, private-network, mobile, tablet, sensor, and remote-client architecture.
400–499 · SOFTWARE ENGINEERINGInterfaces, schemas, tests, build systems, dependency boundaries, deployment, and maintainable implementation.
500–599 · DEFENSIVE SECURITYAuthorized labs, scope gates, auditability, detection, hardening, remediation, and fail-closed controls.
600–699 · SPATIAL + PERCEPTIONVision, depth, LiDAR, pose, multimodal sensing, anchors, coordinate mapping, and environment-aware interaction.
700–799 · RESEARCH + RETRIEVALPlanning, search, evidence collection, ranking, source provenance, analysis, and real-time research pipelines.
800–899 · EMBODIMENT + HUDASCII/ANSI Matrix interface, status telemetry, voice, expressive states, interaction modes, and accessible controls.
900–949 · MEMORY + ONTOLOGYKnowledge graphs, typed artifacts, versioning, confidence, provenance, canonical state, and historical context.
950–974 · TESTING + CRITICSIndependent checks, regression suites, falsification, counterexamples, and claim-to-evidence validation.
975–989 · PERFORMANCECompute routing, caching, latency, budgets, local/remote splits, and efficient model selection.
990–998 · INTEGRATION VALIDATIONCross-domain compatibility, red-team review, failure injection, ontology conflicts, and system-level acceptance.
999 · MASTER RECONCILIATIONCollect, deduplicate, detect conflict, score evidence, preserve provenance, merge verified lessons, and broadcast canonical state.

Canonical hive lifecycle

Every promoted lesson follows a governed path. No swarm directly overwrites the canonical ontology.

01 · SUMMONMint the logical swarm/hive job and assign a bounded domain.
02 · PROVENANCEIdentify builders, sources, prior state, and scope.
03 · EXPLOREDoug integrates known state while Chaos develops alternatives.
04 · BLACKBOARDWrite structured observations, hypotheses, artifacts, and confidence.
05 · CRITICChallenge claims, reproduce results, and locate contradictions.
06 · RECONCILEResolve duplicates, disagreement, dependencies, and conflicting evidence.
07 · VERIFYRequire observable evidence or retain the item as unverified.
08 · PROMOTEMove verified lessons into versioned canonical ontology state.
09 · REGRESSIONTest the updated state against known-good baselines and safety gates.
10 · BROADCASTMake approved state available to later authorized missions and nodes.

Hive namespace contract

Each HIVE-NNN namespace keeps reusable knowledge separate from the temporary swarm that produced it.

observations

What was directly observed, measured, retrieved, or produced.

hypotheses

Candidate explanations and proposed next tests.

sources

Source provenance, references, upstream ownership, and timestamps.

experiments

Reproducible tests, conditions, inputs, outputs, and postconditions.

failures

Rejected approaches, regressions, contradictions, and limits.

verified_lessons

Evidence-backed knowledge eligible for canonical promotion.

open_questions

Unresolved issues that should seed later swarms.

confidence

Explicit confidence rather than implied certainty.

provenance

Who/what contributed, where the artifact came from, and how it changed.

999 EYES EXPLORE. 999 HIVES REMEMBER. DOUG INTEGRATES. CHAOS CHALLENGES. CRITICS VERIFY. THE ONTOLOGY EVOLVES. HUMAN AUTHORITY REMAINS ABOVE THE HIVE.