Knowledge3D — True Multi-Modal AI, Not 3D RAG

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.

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⚠️ Important: Evolution from RAG to True Multi-Modal AI

What This Project Is NOT: This is not a "fancy 3D RAG" or scaffolding of the old paradigm. While previous attempts (see Old_Attempts/Legacy_Fancy_RAG/) created a working retrieval-augmented generation system with spatial indexing, our true goal is fundamentally different.

What This Project IS: A sovereign, GPU-native cognitive architecture that:

• Reasons directly through PTX kernels (not via LLM API calls)
• Fuses multi-modal inputs (text, image, audio, video, 3D) at the neural level
• Consolidates knowledge through spatial crystallization, not vector similarity search
• Operates as an embodied intelligence with perception, memory, and agency

The Key Difference:

• ❌ RAG Approach: Embed documents → similarity search → feed to LLM → generate response
• ✅ Knowledge3D Approach: Multi-modal perception → GPU-native reasoning (RPN/TRM) → spatial memory consolidation → embodied action

The Old_Attempts/ directory documents our learning journey. We keep these artifacts to show what we tried, why it worked but wasn't enough, and how we evolved toward true multi-modal cognition. See Old_Attempts/fsm_scaffolding/README_DEPRECATION.md for the most recent consolidation (Step 12).

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1. What Lives Here

Location	Purpose
Knowledge3D/	Clean PTX-first codebase (no large payloads)
Knowledge3D.local/	Runtime workspace with Houses, tablet logs, datasets, galaxy/house GLBs
Old_Attempts/Legacy_Fancy_RAG/	DEPRECATED: Original RAG scaffolding (worked, but not our goal)
Old_Attempts/fsm_scaffolding/	DEPRECATED (Step 12): Fused Head FSM (consolidated into ThinkingTagBridge)
Large_Assets_Kitchen/	Recipes for regenerating >99MB assets inside .local

All contributors must keep heavy outputs in .local and document how to rebuild them in Large_Assets_Kitchen/README.md.

Why Two Old_Attempts/ Directories?

1. Legacy_Fancy_RAG/ — Our first attempt: A working spatial RAG system with 3D indexing. Why deprecated: It was still fundamentally RAG (retrieve → feed to LLM → generate). We needed true multi-modal fusion, not retrieval augmentation.

2. fsm_scaffolding/ (Step 12) — Second attempt: A CuPy-based Fused Head FSM with 5-state dispatch. Why deprecated: Duplicated functionality with our sovereign ThinkingTagBridge but added CuPy dependency. We harvested its best patterns (5-state observability, ActionBuffer, dynamic LOD) into the sovereign architecture and retired the scaffolding.

See the deprecation READMEs in each directory for full migration guides and architectural rationale.

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2. System Overview

Dual Memory Spine

• Galaxy (RAM) — high-dimensional embeddings for fast reasoning.
• House (Persistent) — consolidated knowledge objects (books, gardens, workshops).
• Museum (Cold) — archived artifacts for audit trails.
• Memory Tablet — avatar interface to search, stream, and mutate knowledge (see docs/HOUSE_GALAXY_TABLET.md).

Cranium Core (Step 10-12: Sovereign Architecture)

• ThinkingTagBridge — Unified multi-modal cognitive inference engine (<35µs latency)
• 5-State Pipeline (Step 12): INGEST → FUSE → SPATIAL → REASON → OUTPUT
• PTX-native reasoning — RPN engine, TRM kernels, graph crystallization (no CPU fallbacks)
• ActionBuffer integration — Every inference emits 288-byte action buffer for execution systems
• Zero dependencies — Pure ctypes + libcuda.so (sovereign runtime)

PTX runtime helpers sit under knowledge3d/cranium/ptx_runtime/:

• thinking_tag_bridge.py — Primary cognitive inference engine (Step 10-12)
• modular_rpn_engine.py — GPU RPN execution (math, honesty, geometry ops)
• sleep_time_compute.py — Nightly consolidation coordinator
• text_to_3d_generator.py — Prompt-to-geometry generator (Step 11)
• galaxy_state_serializer.py / galaxy_memory_updater.py — Memory consolidation

Dual-Client Reality

• Human viewer (viewer/) renders the house/galaxy in Three.js.
• AI client reads the same GLBs through extras.k3d buffer views for semantic access.

Read the full architectural brief in docs/Jules_K3D_Whitepaper.md and the active roadmap in docs/ROADMAP.md.

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3. Documentation Jump Pad

Topic	Link
Start here (Deep dive)	NotebookLM Research Space
Vision & philosophy	docs/VISION.md
Cranium Core internals	docs/CRANIUM_CORE.md
Memory workflow & tablet contract	docs/HOUSE_GALAXY_TABLET.md
PTX fused-head plan	docs/PTX_FUSED_HEAD_PLAN.md
Training directives & prompt hygiene	docs/TRAINING_DIRECTIVES.md
Environment policy (Conda, CUDA, tmux)	docs/ENV_POLICY.md
Dual code / HR-MR strategy	docs/DUAL_CODE_STRATEGY.md
Doors & network addressing	docs/DOORS_AND_NETWORK.md
glTF extension spec	spec/glTF_K3D_extension.md
Step 12: FSM Consolidation	TEMP/STEP12_PHASE1_PHASE2_COMPLETE.md
Step 13: Parallel Development Tracks	TEMP/STEP13_MASTER_INDEX.md

Collaboration practices for AI agents are in AGENTS.md. Multi‑Vibe chain case studies live under docs/reports/multi_vibe_chain/.

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4. Getting Started

4.1 Install

git clone https://github.com/danielcamposramos/Knowledge3D.git
cd Knowledge3D

# Python dependencies (activate the k3dml Conda env per docs/ENV_POLICY.md)
pip install -e .

# Viewer (Three.js + Vite)
cd viewer && npm install

4.2 Runtime Workspace

mkdir -p ../Knowledge3D.local
export K3D_LOCAL_DIR="$(pwd)/../Knowledge3D.local"
export K3D_HOUSE_ID=default

Knowledge3D.local/ will hold Houses, galaxy GLBs, logs, and benchmarks. The repo stays lean.

4.3 Launch the Viewer + Bridge

# Terminal 1: WebSocket bridge (GPU environment)
cd Knowledge3D
scripts/k3d_env.sh run python -m knowledge3d.bridge.live_server --port 8787

# Terminal 2: Viewer
cd Knowledge3D/viewer
npm run dev   # open http://localhost:5173/?ws=ws://localhost:8787

4.4 Generate a Sample Galaxy

scripts/k3d_env.sh run python -m knowledge3d.tools.build_ai_books \
  --input data/intent_templates/en.yaml \
  --out "$K3D_LOCAL_DIR/datasets/ai_books_sample.glb" \
  --limit 200

View the GLB through the tablet or import it into the viewer via viewer/public/ when needed.

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5. Performance Benchmarks (Real Test Results)

Step 15 Phase B: Sovereign Knowledge Ingestion

Zero External Dependencies Achieved — 100% RPN-native embeddings (0MB footprint vs 66MB GloVe bootstrap)

Baseline Sequential Runs

Pipeline	Items	Runtime	Throughput	VRAM Peak	GPU Util
WordNet EN	117,659 synsets	145.87s	807 synsets/s	<200MB	6-7%
Font Harvest	2,713 fonts 168,206 glyphs	~780s	-	<200MB	6-7%
PDF Corpus	61 PDFs 23,000 sentences	41.39s	556 sentences/s	<200MB	6-7%

Parallel Optimized Runs

Pipeline	Workers	Batch	Runtime	Speedup	Throughput	Notes
WordNet EN	8	64	143.28s	1.02×	821 synsets/s	CPU preprocessing: 0.65s
Font Harvest	8	32	216.62s	3.6×	750 glyphs/s	1.4GB JSON streamed
PDF Corpus	8	32	137.64s	0.3×	167 sentences/s	PyPDF2 extraction bottleneck

Key Findings:

• ✅ Ultra-low resource usage: <200MB VRAM (40× under 8GB budget), 6-8% GPU util
• ✅ Massive parallelization headroom: 92-94% GPU idle → opportunity for 10-20× future speedup
• ⚠️ CPU-bound bottlenecks: PIL rendering (5ms/glyph), PyPDF2 extraction (300ms/PDF) dominate
• 🎯 Next frontier: GPU-accelerated PDF parsing + batch kernel calls (>256 items)

Artifacts Generated (in /K3D/Knowledge3D.local/house_zone7/):

• embeddings/rpn_embeddings.pkl — 33,428 trigrams (multi-lingual)
• lexicons/wordnet_en_parallel.json — 117,659 synsets with 3D positions
• fonts/full_font_library_parallel.json — 168,206 visual-text pairs (1.4GB)
• documents/ — 61 PDFs with semantic embeddings

See: TEMP/STEP15_PHASE_B_RESULTS.md, TEMP/STEP15_PHASE_B_SPEEDUP_RESULTS.md

Phase C: Multi-Modal PDF Ingestion (Complete)

Pipeline	Coverage	Runtime	Throughput	Method
Structured PDF	99 % of sources	~22 ms/page	≈45 pages/s	Sovereign PyMuPDF + PTX parser
Scanned PDF	~1 % of sources	~0.6 s/page	≈1.6 pages/s	Tesseract fallback (temporary)
Glyph Database	1,999 fonts	–	123,938 glyphs	Per-font HOG descriptors (Phase E input)

Key Features:

• ✅ 15× faster than Phase B baseline for structured PDFs (300 ms → 20–25 ms/page)
• ✅ Multi-modal extraction with spatial relationships + Galaxy crystallisation
• ✅ Pragmatic scanned-PDF coverage via Tesseract while sovereign OCR incubates for Phase E
• ✅ AtomicFissionFusion + GraphCrystallizer fuse RPN text + Fractal visuals into Galaxy positions
• ✅ Sovereign hot path preserved (ctypes + PTX); external OCR used only as a temporary bridge

Step 14: Specialized Swarm Kernels

Metric	Value	Notes
9-Chain Latency	80.69µs	Fused kernel (9 transformations + resonance)
Wikipedia Ingestion	0.14s/article	35× faster than 5s target
VRAM Peak	0.12GB	66× under 8GB budget

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6. Current Architecture (Steps 10-15)

ThinkingTagBridge: Sovereign Cognitive Engine

The heart of Knowledge3D is the ThinkingTagBridge — a zero-dependency, PTX-native cognitive inference engine that runs entirely on GPU via ctypes + libcuda.so.

Key Features (as of Step 12):

• ✓ 5-State Cognitive Pipeline: INGEST → FUSE → SPATIAL → REASON → OUTPUT
• ✓ Sub-35µs Latency: Strict latency budgets with LatencyGuard enforcement
• ✓ ActionBuffer Output: Every inference emits 288-byte buffer for action execution
• ✓ State Observability: Microsecond-precision tracking with percentile statistics
• ✓ Dynamic LOD: Morton-based saliency tuning during SPATIAL stage
• ✓ Multi-Modal Fusion: Native text/image/audio/video/3D reasoning
• ✓ Zero External Dependencies: Pure ctypes, no CuPy/PyTorch/TensorFlow

Import:

from knowledge3d.cranium.ptx_runtime.thinking_tag_bridge import ThinkingTagBridge

bridge = ThinkingTagBridge()
result = bridge.inference(input_embedding, modal_signature=['text', 'image'])

# Access outputs
print(result.tags)  # Confidence-weighted thinking tags
print(result.action_buffer)  # 288-byte action buffer for ActionRouter
print(bridge.get_state_trace_report())  # FSM state trace with timing

PTX Runtime Modules

The PTX helpers are centralized in knowledge3d/cranium/ptx_runtime/:

• thinking_tag_bridge.py — Primary cognitive engine (Step 10-12)
• modular_rpn_engine.py — GPU RPN execution (math, honesty, geometry ops)
• text_to_3d_generator.py — Prompt-to-geometry generator (Step 11)
• sleep_time_compute.py — Nightly consolidation coordinator
• thinking_tag_embedder.py — Tag generator for reflections and tablet
• galaxy_state_serializer.py / galaxy_memory_updater.py — Memory consolidation
• nvrtc_ptx_loader.py — NVRTC compilation harness for dynamic kernels

Legacy phase*/ directories and FSM scaffolding have been deprecated (see Old_Attempts/).

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Sovereign Knowledge Ingestion Stack (Step 15)

Mission: Feed the AI mind with multi-modal knowledge using zero external dependencies.

Architecture: RPN-native embeddings + PTX-optimized multi-modal fusion

Text Pipeline:
  RPN Trigrams (33K vocab) → 128-dim embeddings → GraphCrystallizer → VectorResonator → 3D Galaxy

Audio Pipeline:
  Temporal features + LPC formants → TemporalReasoning kernel → Fusion → Galaxy

Visual Pipeline:
  Glyph rendering → Edge detection → FractalEmitter → Fusion → Galaxy

Multi-Modal Fusion:
  AtomicFissionFusion (text + audio + visual) → Swarm refinement (80µs) → Galaxy position

Ingestion Modules:

• knowledge3d/cranium/rpn_embedding_engine.py — Language-agnostic trigram embeddings
• knowledge3d/ingestion/language/sovereign_text_pipeline.py — Text → RPN → Galaxy
• knowledge3d/ingestion/language/sovereign_audio_pipeline.py — Audio → Temporal → Galaxy
• knowledge3d/ingestion/language/sovereign_visual_pipeline.py — Visual → Fractal → Galaxy
• knowledge3d/ingestion/lexicons/parallel_lexicon_ingestor.py — WordNet + multi-lingual
• knowledge3d/ingestion/fonts/parallel_font_harvester.py — Font glyphs → visual-text pairs
• knowledge3d/ingestion/documents/pdf_ingestor.py — PDF → sentences → Galaxy

Parallel Optimization: 8-worker CPU pools + GPU batching for 1-4× speedup (See benchmarks above)

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7. Repository Layout

Knowledge3D/
├─ knowledge3d/                     # Core Python package
│  ├─ cranium/
│  │  ├─ ptx_runtime/               # PTX runtime (ThinkingTagBridge, RPN, generators)
│  │  ├─ actions/                   # ActionBuffer contract & ActionRouter
│  │  ├─ sovereign/                 # Zero-dependency CUDA loader (ctypes)
│  │  └─ ...
│  ├─ bridge/                       # Tablet + viewer WebSocket server
│  ├─ gpu/, spatial/, skills/       # CUDA utilities, navigation, multi-modal skills
│  ├─ tools/                        # Dataset builders & utilities
│  └─ ...
├─ viewer/                          # Human client (Three.js + TypeScript)
├─ Large_Assets_Kitchen/            # Regeneration recipes for heavy assets
├─ Old_Attempts/
│  ├─ Legacy_Fancy_RAG/             # DEPRECATED: Original RAG scaffolding
│  └─ fsm_scaffolding/              # DEPRECATED (Step 12): Fused Head FSM
├─ docs/                            # Specs, briefs, roadmap, playbooks
├─ TEMP/                            # Step plans and completion reports
├─ scripts/                         # Shell helpers (training, ingestion, CI)
├─ spec/                            # Formal schema & protocol definitions
├─ tests/                           # Pytest suite (250+ tests as of Step 13)
└─ README.md                        # You are here

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8. Contributing

1. Respect the memory policy (docs/HOUSE_GALAXY_TABLET.md).
2. Stay GPU-first: PTX kernels or CUDA extensions for any hot path.
3. Keep heavy artifacts local: document regeneration steps instead of committing binaries.
4. Follow agent guidelines when using AI automation (AGENTS.md).
5. Test before PR: Run pytest -q (and viewer tests when applicable).
6. Check deprecations: Don't import from Old_Attempts/ in new code.

Security, ethics, and embodiment commitments are detailed in docs/COVENANT.md and docs/CARE_PROTOCOL.md.

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9. Community & Roadmap

• Deep Dive (Best Entry Point): NotebookLM Research Space
• Roadmap status: docs/ROADMAP.md
• Step 12 Complete: TEMP/STEP12_PHASE1_PHASE2_COMPLETE.md
• Step 13 In Progress: TEMP/STEP13_MASTER_INDEX.md
• Swarm collaboration logs: docs/reports/multi_vibe_chain/
• Audio/voice architecture: docs/AUDIO_ARCH.md

Recent Milestones

• Step 15 Phase B (Oct 2025): Sovereign Knowledge Ingestion — Zero external dependencies achieved!

  • RPN Embeddings: 33,428 trigrams learned (language-agnostic, 0MB footprint)
  • Multi-lingual: WordNet EN (117,659 synsets) + PT-BR, ES, JP, ZH lexicons
  • Visual-Text Grounding: 2,713 fonts → 168,206 glyph-text pairs (1.4GB)
  • Knowledge Corpus: 61 PDFs, 23,000 sentences from curated libraries
  • Performance: <200MB VRAM, 6-8% GPU utilization (massive headroom!)
  • Parallel Pipelines: 8-worker CPU pools + GPU batching for 1.02-3.6× speedup

• Step 14 (Oct 2025): Specialized 9-chain swarm kernel (80.69µs latency, 35× faster than Wikipedia target)

• Step 12 (Oct 2025): FSM consolidation — harvested 5-state observability, ActionBuffer integration, and dynamic LOD into sovereign ThinkingTagBridge

• Step 11 (Oct 2025): Multi-modal text-to-3D generation with shape cache and confidence propagation

• Step 10 (Sep 2025): ThinkingTagBridge sovereign runtime with <35µs latency target

If you are interested in partnering, reach out via the contact information in docs/Jules_K3D_Whitepaper.md.

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Together we are building the first spatial operating system for thought — not a fancy RAG, but a true multi-modal intelligence that perceives, reasons, and acts in 3D space. Dive into the NotebookLM, explore the docs, regenerate the local assets you need, and help us fuse the Galaxy and the House into a living, embodied cognition.