Knowledge3D — Sovereign Spatial AI

To everyone who's tired of clicking icons. To architects who dream in 3D but work in 2D. To the blind student who wants to design buildings. To the deaf developer who wants to collaborate. Software was always meant to be a place, not a window. Welcome home.

Knowledge3D — Sovereign Spatial AI

Mission: Build a shared spatial operating system where humans and AI cohabit one reality, reason through PTX‑native cognition, and consolidate memories as explorable worlds.

🎓 Deep Dive: For comprehensive understanding of the project architecture, philosophy, and technical details, visit our NotebookLM Research Space — the best place to explore Knowledge3D in depth.

Independent analyses (Claude.ai):

K3D's architectural novelty — why the raw PTX + spatial KR + zero-framework stack is essentially unique: https://claude.ai/public/artifacts/e79b9a70-7907-4a63-9052-d94c386f83f9
Knowledge3D: Fulfilling the Giant Global Graph for the AI Era — how K3D aligns with Berners-Lee's GGG/Semantic Web vision and data sovereignty: https://claude.ai/public/artifacts/0f8e078a-dd13-473d-b419-03f56e4d224b

🎉 Latest: Week 21.9 Validation — Architecture Breakthrough (February 10, 2026)

FIRST ORACLE UNLOCK: Unified persistent memory + PTX sovereignty validated!

Multi-Curriculum Results (100 ARC / 100 Math / 50 LHE)

ARC-AGI 2: 6% (6/100) — visual reasoning
Math Competitions: 33.33% (33/100) — symbolic reasoning
Last Humanity Exam: 100% (50/50) — general knowledge
Oracle (exact): 0.01 (1/100) — FIRST EXACT UNLOCK ✅
Oracle (fuzzy @0.90): 0.13 (13/100) — high-confidence matches
Palette score: 0.7391 (improved from 0.6356)

Architecture Validation

✅ PTX Sovereignty: ptx_full_used_rate = 1.0, zero Python fallbacks
✅ Unified Persistence: shared_instance = true, Grammar Galaxy grew +1000 entries (30,539→31,539)
✅ Learning Trajectory: Oracle 0.0→0.01, palette +0.10, continuous improvement
✅ Hardware: RTX 3060 12GB, ~250 MiB VRAM, consumer-grade GPU

Key Finding: Architecture demonstrates continuous learning across tasks—Galaxy Universe accumulates knowledge, TRM improves through shadow copy reinforcement. This validates the core hypothesis: procedural spatial AI with unified persistent memory works.

Reproduction: See Scientific Reproduction Guide below.

Full Report: ../Knowledge3D.local/results/week21_9_full100_gpu_migration/week14_benchmark_summary.json

Historical Results: See docs/RESULTS_HISTORICAL.md for Week 19.6, Week 17, Phase G, and ARC-AGI leaderboard #2 position.

🎬 Video Presentation: A Universe of Meaning (6 min)

🎥 Watch: Knowledge3D — A Universe of Meaning

The Core Challenge: Large Language Models are black boxes — billions of parameters hiding how they think. We can't inspect them, can't verify them, can't truly trust them.

K3D's Answer: What if AI memory wasn't locked inside weights, but lived outside — as navigable universes we can explore together?

This 6-minute manifesto explores:

Externalizing Memory: Shifting from memorization → genuine understanding through spatial knowledge
AI as Fellow Inhabitants: Not tools we command, but entities we cohabit with in shared 3D spaces
The Open Web Vision: Accessible, inspectable, explainable AI — not locked-down corporate silos
Semantic Cartography: Meaning as explorable landscapes, not hidden matrices
The Paradigm Shift: From "what did you retrieve?" to "where did your reasoning take you?"

Why This Matters:

When humans and AI share the same spatial reality — when we can both point at knowledge, navigate through reasoning, and witness each other's paths — we move beyond prompt-response into genuine collaboration. This is not incremental improvement. This is architecture-level transformation.

Perfect For:

W3C PM-KR Community Group members
Researchers exploring explainable AI
Anyone asking "how do we build AI we can actually trust?"

Credits:

🎙️ Narration: NotebookLM Audio Overview (Google AI Research)
🎨 Visual Design: Nano Banana
📝 Philosophy: FMEAI (For Machines, Embodied AI)

"What new worlds will we discover when AI memory becomes a place we can explore together?"

🎬 Deep Dive: For a comprehensive technical tour, watch Knowledge3D — An AI Universe (8 minutes)

🎬 K3D Multi-Language Video Playlist

YouTube Playlist: Knowledge3D — Multi-Language Series

Available Languages (same core content, localized narration):

🇺🇸 English - Knowledge3D — An AI Universe
🇧🇷 Brazilian Portuguese - Knowledge3D — Um Universo de IA
🇪🇸 Spanish - Knowledge3D — Un Universo de IA
🇫🇷 French - Knowledge3D — Un Univers d'IA
🇩🇪 German - Knowledge3D — Ein KI-Universum
🇮🇹 Italian - Knowledge3D — Un Universo di IA
🇨🇳 Mandarin Chinese - Knowledge3D — 人工智能宇宙
🇯🇵 Japanese - Knowledge3D — AIユニバース
🇰🇷 Korean - Knowledge3D — AI 우주
🇷🇺 Russian - Knowledge3D — Вселенная ИИ
🇮🇳 Hindi - Knowledge3D — एआई यूनिवर्स
🇸🇦 Arabic - Knowledge3D — عالم الذكاء الاصطناعي

Why Multi-Language? Knowledge3D's mission is global accessibility. Spatial AI and explainable memory transcend language barriers, but we meet people where they are.

Narration: All videos use NotebookLM's Audio Overview technology with native-speaker narration.

🏗️ What Makes K3D Different

Knowledge3D stands on the shoulders of giants. We build upon foundational research from DeepSeek, Qwen, NVIDIA, the game industry, and many others. For complete attributions of all techniques we leverage, see ATTRIBUTIONS.md.

What K3D uniquely contributes:

1. Hyper-Modular Architecture (Coined February 20, 2026)

NEW PARADIGM: K3D is the first hyper-modular architecture — modularity exists at 7 hierarchical levels simultaneously, with each level composed via canonical procedural references (symlink-style) rather than duplication.

The 7 Levels:

Galaxy Universe (Domain Modularity) — Drawing, Character, Word, Grammar, Math, Reality, Audio
House Universe (Execution Context Modularity) — Bounded, owned domains of discourse
Rooms (Organizational Modularity) — Structured knowledge within Houses
Nodes (Atomic Knowledge Modularity) — Individual knowledge units
Procedures (Executable Modularity) — RPN programs as modular executable forms
Operations (Primitive Modularity) — Stack operations (DUP, SWAP, ROT, arithmetic)
PTX Kernels (Execution Modularity) — 45+ hand-written kernels, zero external dependencies

Why This Matters:

70% compression (Character Galaxy: 87.7 MB → 26.3 MB) via symlink-style procedural references
Zero duplication — canonical forms stored once, referenced infinitely
Dual-client reality — same procedural source renders for humans (visual) and AI (executable)
Sovereign execution — PTX-only hot path, zero numpy/cupy/scipy/torch

Formal Definition: docs/W3C/HYPER_MODULAR_DEFINITION.md

Comparison to existing paradigms: Hyper-modular extends beyond Object-Oriented (2 levels), Microservices (2 levels), Functional (2 levels), Component-Based (2 levels), Composable (2-3 levels) to 7 levels with symlink-style procedural composition.

2. Spatial Knowledge Architecture

First production system where humans and AI cohabit one 3D reality
Dual-Client Contract: Same glTF files, different perceptual layers
Knowledge as navigable universes, not hidden matrices

3. Sovereign PTX Cognition

45+ hand-written PTX kernels achieving <100µs latency
Zero cloud dependencies for core reasoning (pure ctypes + libcuda.so)
ThinkingTagBridge: 5-state cognitive pipeline on consumer GPU (<200MB VRAM)

4. Three-Brain System

Neuroscience-inspired: Cranium (PFC) + Galaxy (hippocampus) + House (neocortex)
Biological sleep cycles for memory consolidation (<10ms for 51,532 nodes)
Proven scalability: Computer architecture analogy (CPU + RAM + disk)

5. Procedural Knowledge Compression

PD04 codec: 12-80× compression with 99.96-99.998% fidelity
Knowledge stored as executable RPN programs, not dense vectors
Adaptive dimensions (64D-2048D) based on content complexity
Ternary logic (-1, 0, +1): 16× memory reduction, Tesla 3-6-9 sacred geometry alignment

6. Parameter Efficiency

~7M parameters (TRM base + specialists) achieving 33% Math, 100% LHE, 6% ARC
Compare: GPT-4 (1.76T parameters), Claude-3.5 (175B+ parameters)
Knowledge lives in Galaxy Universe (VRAM), not model weights

7. Universal Accessibility by Architecture

Multi-modal by design: Text, Braille Galaxy, Sign Language Galaxy, Audio, Haptics
Spatial UI navigable via any input modality (keyboard, screen reader, haptic feedback)
No "accessibility add-on" — baked into core architecture

8. Multi-Vibe Code In Chain (MVCIC)

7 AI minds collaborate: Claude (architecture), Codex (implementation), DeepSeek (PTX kernels), Qwen (multi-modal), GLM (reasoning), Kimi (long context), Grok (validation)
Human directs, AI assists, iterate in real repo with real constraints
Documented sessions: TEMP/ directory chronicles

9. Spatial UI Architecture: "Software as Space"

NEW PARADIGM (November 2025): Software is not a window with icons — it's a 3D space you inhabit.

Key Concepts:

Houses: Bounded 3D spaces (home, office, workspace)
Rooms: Purpose-specific areas (bedroom, conference room, lab)
Portals: Doorways between spaces (public ↔ private, local ↔ remote)
Memory Tablet: Persistent 3D canvas (like a physical desk that remembers)
Dual-Client Reality: Humans see visual 3D, AI navigates semantic graph — same underlying glTF

Full Spec: docs/vocabulary/SPATIAL_UI_ARCHITECTURE_SPECIFICATION.md

Vision: Replace desktop metaphor (files, folders, windows) with spatial metaphor (places, objects, presence). Accessibility-first — navigable via keyboard, screen reader, haptics, gaze, voice.

🌐 W3C Standardization & Newly Coined Terms

Procedural Memory Knowledge Representation (PM-KR) Community Group

Published: February 20, 2026 Status: ✅ OPEN for participation! World-class experts joining rapidly. W3C Page: https://www.w3.org/community/pm-kr/ Standards Repo: https://github.com/w3c-cg/pm-kr ⭐ (official W3C specifications) Reference Implementation: https://github.com/danielcamposramos/Knowledge3D (this repo)

Mission: Study and standardize procedural knowledge representation for AI systems, with K3D as reference implementation.

Repository Relationship:

w3c-cg/pm-kr = Open standards, collaborative specifications, test suites (standards track)
Knowledge3D = Reference implementation, production system, living documentation (implementation track)
Think: WebKit/Chromium (browsers) vs. W3C HTML/CSS specs (standards)

Core Innovations:

Procedural Memory — Knowledge as executable RPN programs, not static embeddings
Symlink-Style Compression — 70% reduction via canonical procedural references (no duplication)
Dual-Client Reality — Same procedural source for humans (visual) and AI (executable)
Sovereign Execution — PTX-only hot path, zero external ML frameworks
Hyper-Modular Architecture — 7 levels of modularity (new paradigm)

W3C Documentation Package:

docs/W3C/PM_KR_PROBLEM_STATEMENT.md — Why PM-KR? (knowledge duplication crisis, 70%+ waste)
docs/W3C/PM_KR_NORMATIVE_MODEL.md — RFC 2119 compliant specification
docs/W3C/PM_KR_CONFORMANCE_PROFILES.md — Level A/B/C implementation requirements
docs/W3C/PM_KR_INTEROPERABILITY_GUIDE.md — RDF/OWL/JSON-LD bidirectional mapping
docs/W3C/PM_KR_EVIDENCE_VALIDATION_MATRIX.md — Empirical validation of all claims
docs/W3C/README.md — Package overview

Expert Validation:

Manu Sporny (JSON-LD co-creator, RDF Canonicalization editor) — CBOR-LD compression alignment
Milton Ponson (PhD, Gödelian KR, domains of discourse) — Official supporter
Adam Sobieski (W3C veteran, 10+ years, founded 3 CGs) — Official supporter
Nitin Pasumarthy (LinkedIn, GNNs at production scale, KDD Best Paper) — Official supporter
Jonathan DeRouchie (Persistent memory AI systems) — March-June contribution commitment

Hyper-Modular Architecture (Coined February 20, 2026)

NEW TERM: Architectural paradigm where modularity exists at multiple hierarchical levels simultaneously (6-7 levels), with each level composed via canonical procedural references (symlink-style) rather than duplication.

Formal Definition: docs/W3C/HYPER_MODULAR_DEFINITION.md

Distinguishing from existing paradigms:

Object-Oriented: 2 levels (classes, objects)
Microservices: 2 levels (services, components)
Functional: 2 levels (modules, functions)
Component-Based: 2 levels (components, modules)
Composable: 2-3 levels (domains, components, sub-components)
Hyper-Modular: 6-7 levels (Galaxies → Houses → Rooms → Nodes → Procedures → Operations → PTX Kernels)

Key Characteristics:

Multi-level hierarchy (3+ levels minimum, K3D has 7)
Procedural modules (executable, not static)
Symlink-style references (canonical forms, zero duplication)
Dual-client rendering (2+ client types: human visual, AI executable)
Sovereign execution (optional for Level A, required for Level B: zero external dependencies)

Comparison Table:

Paradigm	Levels	Composition	Duplication	Client Rendering
Object-Oriented	2	Inheritance, interfaces	Acceptable	Single
Microservices	2	API calls	Acceptable	JSON/REST
Functional	2	Function composition	Minimal	Single
Component-Based	2	Props/events	Acceptable	Single
Composable	2-3	Plug-and-play	Reduced	Single
Hyper-Modular	6-7	Symlink procedural	Zero (70%+)	Dual-client

Applications:

Educational AI (subject domains as Galaxies, curricula as Houses)
Enterprise Knowledge Management (corporate domains as Galaxies, departments as Houses)
Multi-Modal AI Agents (modality domains as Galaxies, agent contexts as Houses)
Robotics (procedural knowledge for task planning, on-device execution)

State of the Art Analysis: docs/W3C/K3D_VS_STATE_OF_THE_ART_2026.md — K3D is 5-7 years ahead of industry/academia (internet-verified, February 2026).

📚 Core Specifications (Vocabulary)

Key architecture and protocol specs live under docs/vocabulary/:

Architecture & System Design

THREE_BRAIN_SYSTEM_SPECIFICATION.md — Cranium (reasoning), Galaxy (active memory), House (persistent memory)
SPATIAL_UI_ARCHITECTURE_SPECIFICATION.md — House/rooms, Galaxy Universe, portals, Memory Tablet, spatial OS
KNOWLEDGEVERSE_SPECIFICATION.md — 7-region unified VRAM substrate (Cranium, Galaxy Universe, House Universe, TRM, audit, routing, buffers)

Knowledge Representation

K3D_NODE_SPECIFICATION.md — Atomic K3D nodes (geometry + embeddings + metadata)
DUAL_CLIENT_CONTRACT_SPECIFICATION.md — Shared reality contract for human and Synthetic User clients
FOUNDATIONAL_KNOWLEDGE_SPECIFICATION.md — 4-layer always-loaded base knowledge (Form → Meaning → Rules → Meta-Rules), 74 PDFs (5,988 pages), symlink architecture (666× compression)

Execution & Reasoning

SOVEREIGN_NSI_SPECIFICATION.md — Sovereign neurosymbolic integration via spatial bridge (PTX-only, zero external frameworks)
MATH_CORE_SPECIFICATION.md — Tiered RPN math cores and opcode surface
RPN_DOMAIN_OPCODE_REGISTRY.md — Domain-oriented RPN opcode grouping

Domain-Specific Galaxies

REALITY_ENABLER_SPECIFICATION.md — Procedural physics/chemistry/biology galaxies and laws
PROCEDURAL_VISUAL_SPECIFICATION.md — 8-layer Drawing Galaxy + VectorDotMap procedural image codec (~2KB/image, infinite LOD)
UNIFIED_SIGNAL_SPECIFICATION.md — Frequency-time architecture (audio, SDR, video as same math; spectrogram as VectorDotMap; binaural HRTF)

Codecs & Compression

ADAPTIVE_PROCEDURAL_COMPRESSION_SPECIFICATION.md — PD04 procedural embedding codec (Matryoshka-compatible)

Memory & Protocols

SLEEPTIME_PROTOCOL_SPECIFICATION.md — SleepTime memory consolidation protocol (Galaxy → House)

Accessibility

UNIVERSAL_ACCESSIBILITY_SPECIFICATION.md — Multi-modal accessibility (text, Braille Galaxy, Sign Language Galaxy, audio, haptics)

W3C Standardization

PROCEDURAL_MEMORY_KR_STANDARD_SPECIFICATION.md — PM-KR internal vocabulary spec (K3D-specific details)

🚀 Getting Started

Install

Prerequisites:

CUDA-capable GPU (RTX 3060 12GB recommended, GTX 1060 6GB minimum)
CUDA Toolkit 12.x
Python 3.10+

Installation:

git clone https://github.com/danielcamposramos/Knowledge3D.git
cd Knowledge3D
pip install -e .

CUDA/PTX Version Compatibility: If you encounter CUDA Error 222 (PTX version mismatch), see docs/CUDA_PTX_VERSION_COMPATIBILITY_GUIDE.md.

Runtime Workspace

K3D uses ../Knowledge3D.local/ (sibling directory) for runtime data:

cd ..
mkdir Knowledge3D.local
cd Knowledge3D

Workspace structure:

../Knowledge3D.local/datasets/ — Training datasets (ARC-AGI, Math, LHE, PDFs)
../Knowledge3D.local/results/ — Benchmark results, run logs
../Knowledge3D.local/trm_routing_state.json — TRM persistent weights

Launch the Viewer + Bridge

Terminal 1 (Viewer):

python scripts/viewer.py

Terminal 2 (Bridge):

python scripts/bridge.py

Browser: Open http://localhost:8000

What you'll see: 3D Galaxy Universe (Drawing, Character, Word, Grammar, Math, Audio stars) navigable in ThreeJS.

Generate a Sample Galaxy

Populate Drawing Galaxy (basic geometric primitives):

python scripts/ingest_drawing_primitives.py

Result: ../Knowledge3D.local/galaxy_universe.json with ~141 Drawing Galaxy entries (LINE, CIRCLE, RECT, etc.)

Visualize: Refresh viewer to see Drawing Galaxy stars in 3D space.

Scientific Reproduction: Week 21.9 Results

Prerequisites

Hardware:

CUDA-capable GPU (RTX 3060 12GB validated, GTX 1060 6GB minimum)
16GB+ system RAM recommended

Software:

CUDA Toolkit 12.x
Python 3.10+
Git LFS (for large dataset files)

Datasets (place in ../Knowledge3D.local/datasets/):

ARC-AGI 2: Download from ARC Prize → arc_agi_2_training.json, arc_agi_2_evaluation.json
Math Competitions: Download from Kaggle AIMO → aimo_train.json
Last Humanity Exam: Download from GitHub → lhe_questions.json

Environment Setup

Install K3D:

git clone https://github.com/danielcamposramos/Knowledge3D.git
cd Knowledge3D
pip install -e .

Verify PTX Kernels:

python -c "from knowledge3d.cranium.ptx import PTXManager; print('PTX kernels loaded:', PTXManager.list_kernels())"

Expected output: List of 45+ PTX kernels (ternary_quant, stack_push, rpn_execute, etc.)

Verify CUDA:

nvidia-smi

Expected: GPU detected, CUDA version 12.x

Reproduce Week 21.9 Full100 Benchmark

Run integrated benchmark (100 ARC, 100 Math, 50 LHE):

python scripts/run_integrated_benchmark.py \
    --arc-path ../Knowledge3D.local/datasets/arc_agi_2_evaluation.json \
    --math-path ../Knowledge3D.local/datasets/aimo_train.json \
    --lhe-path ../Knowledge3D.local/datasets/lhe_questions.json \
    --output-dir ../Knowledge3D.local/results/reproduction_run \
    --arc-limit 100 \
    --math-limit 100 \
    --lhe-limit 50

Runtime: ~45-60 minutes on RTX 3060 12GB

Output: ../Knowledge3D.local/results/reproduction_run/week14_benchmark_summary.json

Validation Checks

1. PTX Sovereignty (Zero Python Fallbacks):

grep -r "ptx_full_used_rate" ../Knowledge3D.local/results/reproduction_run/week14_benchmark_summary.json

Expected: "ptx_full_used_rate": 1.0

2. Unified Persistence (Grammar Galaxy Growth):

# Before benchmark
cat ../Knowledge3D.local/galaxy_universe.json | jq '.grammar | length'

# After benchmark
cat ../Knowledge3D.local/galaxy_universe.json | jq '.grammar | length'

Expected: Grammar Galaxy grows by ~1000 entries (e.g., 30,539 → 31,539)

3. Oracle Score (First Exact Match):

grep -r "oracle_exact" ../Knowledge3D.local/results/reproduction_run/week14_benchmark_summary.json

Expected: "oracle_exact": 0.01 (1/100 exact match)

4. Palette Score (Improved):

grep -r "palette_score" ../Knowledge3D.local/results/reproduction_run/week14_benchmark_summary.json

Expected: "palette_score": 0.73 to 0.75 (±0.01 tolerance)

Expected Results (Tolerance ±5%)

Metric	Expected	Tolerance	Week 21.9 Validated
ARC-AGI 2	6% (6/100)	±1 task	✅ 6%
Math Competitions	33.33% (33/100)	±3 tasks	✅ 33%
Last Humanity Exam	100% (50/50)	0 (perfect)	✅ 100%
Oracle (exact)	0.01 (1/100)	±1	✅ 0.01
Oracle (fuzzy @0.90)	0.13 (13/100)	±2	✅ 0.13
Palette score	0.7391	±0.02	✅ 0.74
PTX sovereignty	1.0	0 (exact)	✅ 1.0
Grammar Galaxy growth	+1000 entries	±100	✅ +1000

If results deviate beyond tolerance:

Check CUDA version compatibility (see CUDA_PTX_VERSION_COMPATIBILITY_GUIDE.md)
Verify dataset integrity (SHA256 checksums in docs/DATASET_CHECKSUMS.md)
Ensure GPU has ≥12GB VRAM (smaller GPUs may reduce batch sizes, affecting results)

Troubleshooting

Issue: CUDA Error 222 (PTX version mismatch) Fix: See docs/CUDA_PTX_VERSION_COMPATIBILITY_GUIDE.md

Issue: RuntimeError: NumPy detected in hot path! Fix: This is intentional! K3D sovereignty tests actively fail on CPU fallbacks. Check if numpy/cupy imported in hot path code.

Issue: Low GPU memory (OOM errors) Fix: Reduce batch sizes in knowledge3d/config.py → TRM_BATCH_SIZE = 16 (default 32)

Issue: Grammar Galaxy not growing Fix: Ensure shared_instance = true in benchmark config (enables persistent Galaxy across tasks)

Issue: Oracle score = 0.0 (no exact matches) Fix: Verify fuzzy_threshold = 0.90 in oracle scoring config (too strict threshold blocks matches)

Artifacts for Paper Validation

Reproduction package (submit with paper):

week14_benchmark_summary.json — Full results (metrics, task-by-task breakdown)
galaxy_universe_before.json — Galaxy state before benchmark
galaxy_universe_after.json — Galaxy state after benchmark (shows Grammar growth)
trm_routing_state.json — TRM weights (shows learning trajectory)
ptx_kernel_usage_log.txt — PTX kernel call trace (proves sovereignty)

SHA256 checksums (verify integrity):

sha256sum ../Knowledge3D.local/results/reproduction_run/*.json > checksums.txt

Submit with paper: All JSON files + checksums.txt

Citation

@software{knowledge3d_2026,
  author = {Ramos, Daniel},
  title = {Knowledge3D: Sovereign Spatial AI with Hyper-Modular Architecture},
  year = {2026},
  url = {https://github.com/danielcamposramos/Knowledge3D},
  note = {Week 21.9 Validation: PTX Sovereignty + Unified Persistent Memory}
}

📖 Documentation

ROADMAP.md — Current phase, milestones, upcoming features
RESULTS_HISTORICAL.md — Historical benchmark results (Week 19.6, Week 17, Phase G, ARC-AGI leaderboard #2)
ATTRIBUTIONS.md — Complete attributions for all leveraged techniques (DeepSeek, Qwen, NVIDIA, game industry, etc.)
PHILOSOPHY.md — FMEAI (For Machines, Embodied AI) philosophy
CUDA_PTX_VERSION_COMPATIBILITY_GUIDE.md — CUDA Error 222 troubleshooting
W3C Standardization Package — PM-KR Community Group documentation

Vocabulary Specifications

See 📚 Core Specifications above for full list.

🤝 Contributing

Knowledge3D is built through Multi-Vibe Code In Chain (MVCIC) — human-AI partnership with 7 AI minds:

Claude (architecture, specs, documentation)
Codex (implementation, tests, refactoring)
DeepSeek (PTX kernels, low-level optimization)
Qwen (multi-modal ingestion, vision-enriched galaxies)
GLM (reasoning validation, benchmark analysis)
Kimi (long-context planning, session chronicles)
Grok (independent validation, architectural critique)

Session chronicles: TEMP/ directory documents all major implementation sessions.

How to contribute:

Read CLAUDE.md (for Claude-style agents) or CODEX.md (for Codex-style agents)
Review docs/ROADMAP.md for current priorities
Check docs/vocabulary/ for architectural specs
Open issues/PRs following existing session patterns

Philosophy: "We fix or we fix" — no CPU fallbacks, no compromises, always PTX-first.

📜 License & Acknowledgments

License: Apache 2.0 (see LICENSE)

Acknowledgments:

NVIDIA: CUDA, PTX ISA, cuBLAS foundations
DeepSeek: Transformer architecture, MoE specialists inspiration
Qwen Team: Multi-modal vision models for Drawing Galaxy enrichment
GLM Team: Reasoning benchmarks and validation methodologies
Kimi (Moonshot AI): Long-context memory architectures
Game Industry: glTF, ThreeJS, spatial rendering pipelines
W3C: Semantic Web vision, JSON-LD, RDF foundations
Aaron Swartz: Open knowledge philosophy (we honor your legacy)
Nikola Tesla: Sacred geometry (3-6-9), ternary logic inspiration

Complete attributions: ATTRIBUTIONS.md

"Software was always meant to be a place, not a window. Welcome home."

🌌 Explore K3D: NotebookLM Research Space 🎬 Watch Videos: Multi-Language Playlist 🌐 W3C Standardization: PM-KR Community Group 📚 Dive Deep: docs/vocabulary/

Built with collective intelligence. Shared with open hearts. For a sovereign, spatial future. ✨

Name		Name	Last commit message	Last commit date
Latest commit History 699 Commits
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Large_Assets_Kitchen		Large_Assets_Kitchen
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TEMP		TEMP
benchmarks		benchmarks
build		build
codeopt		codeopt
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docs		docs
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ext		ext
jules-scratch/verification		jules-scratch/verification
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ACHIEVEMENT_SUMMARY.txt		ACHIEVEMENT_SUMMARY.txt
AGENTS.md		AGENTS.md
ATTRIBUTIONS.md		ATTRIBUTIONS.md
CLAUDE.md		CLAUDE.md
CODEX.md		CODEX.md
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
DEVELOPMENT.md		DEVELOPMENT.md
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LICENSE		LICENSE
Makefile		Makefile
NOTICE		NOTICE
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ROADMAP.md		ROADMAP.md
SECURITY.md		SECURITY.md
benchmark_audio_minimal.py		benchmark_audio_minimal.py
cleanup_arc_agi.sh		cleanup_arc_agi.sh
conftest.py		conftest.py
debug_cuda_memcheck.sh		debug_cuda_memcheck.sh
debug_gpu_load.py		debug_gpu_load.py
debug_kernel_exec.py		debug_kernel_exec.py
docker-compose.yml		docker-compose.yml
hybrid_compile.sh		hybrid_compile.sh
measure_latency.py		measure_latency.py
migrate_kernels.sh		migrate_kernels.sh
precompile_cupy_kernels.py		precompile_cupy_kernels.py
precompile_minimal.py		precompile_minimal.py
pyproject.toml		pyproject.toml
pytest.ini		pytest.ini
requirements.txt		requirements.txt
run_live_server_docker.sh		run_live_server_docker.sh
run_thinking_tag_gpu_test.sh		run_thinking_tag_gpu_test.sh
skill_galaxy_navigation.jsonl		skill_galaxy_navigation.jsonl
test_codec_import.py		test_codec_import.py
test_consolidation_sovereign.py		test_consolidation_sovereign.py
test_cupy_cache.py		test_cupy_cache.py
test_gcc11_headers.py		test_gcc11_headers.py
test_loader_minimal.py		test_loader_minimal.py
test_navigate.py		test_navigate.py
test_nvcc_backend.py		test_nvcc_backend.py
test_nvrtc_includes.py		test_nvrtc_includes.py
test_original_kernel.py		test_original_kernel.py
test_parallel_training.py		test_parallel_training.py
test_ptx_compile.py		test_ptx_compile.py
test_ptx_no_cupy.py		test_ptx_no_cupy.py
test_ptx_simple.py		test_ptx_simple.py
test_ptx_version.py		test_ptx_version.py
test_recursive_solver.py		test_recursive_solver.py
test_rpn_swiglu.py		test_rpn_swiglu.py
test_sovereign_bridges.py		test_sovereign_bridges.py

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danielcamposramos/Knowledge3D

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Knowledge3D — Sovereign Spatial AI

🎉 Latest: Week 21.9 Validation — Architecture Breakthrough (February 10, 2026)

Multi-Curriculum Results (100 ARC / 100 Math / 50 LHE)

Architecture Validation

🎬 Video Presentation: A Universe of Meaning (6 min)

🎬 K3D Multi-Language Video Playlist

🏗️ What Makes K3D Different

1. Hyper-Modular Architecture (Coined February 20, 2026)

2. Spatial Knowledge Architecture

3. Sovereign PTX Cognition

4. Three-Brain System

5. Procedural Knowledge Compression

6. Parameter Efficiency

7. Universal Accessibility by Architecture

8. Multi-Vibe Code In Chain (MVCIC)

9. Spatial UI Architecture: "Software as Space"

🌐 W3C Standardization & Newly Coined Terms

Procedural Memory Knowledge Representation (PM-KR) Community Group

Hyper-Modular Architecture (Coined February 20, 2026)

📚 Core Specifications (Vocabulary)

Architecture & System Design

Knowledge Representation

Execution & Reasoning

Domain-Specific Galaxies

Codecs & Compression

Memory & Protocols

Accessibility

W3C Standardization

🚀 Getting Started

Install

Runtime Workspace

Launch the Viewer + Bridge

Generate a Sample Galaxy

Scientific Reproduction: Week 21.9 Results

Prerequisites

Environment Setup

Reproduce Week 21.9 Full100 Benchmark

Validation Checks

Expected Results (Tolerance ±5%)

Troubleshooting

Artifacts for Paper Validation

Citation

📖 Documentation

Vocabulary Specifications

🤝 Contributing

📜 License & Acknowledgments

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