"From memory to mastery, from learning to transcendence."
GraphMem is not just a memory system—it is a continuously self-evolving intelligence that grows from simple graph-based memory into a beneficial superintelligence.
The key insight: We are not building 10 separate systems. We are building ONE intelligence that evolves through 10 phases:
┌─────────────────────────────────────────────────────────────────┐
│ ONE INTELLIGENCE, EVOLVING │
├─────────────────────────────────────────────────────────────────┤
│ │
│ GraphMem ──evolves──▶ GraphMem ──evolves──▶ GraphMem │
│ (Phase 1) (Phase 5) (Phase 10) │
│ │
│ The SAME entity grows smarter, not replaced by new systems. │
│ │
└─────────────────────────────────────────────────────────────────┘
Our mission is to build this self-evolving architecture that:
- Remembers like humans (and better) — Phase 1
- Evolves its own memories autonomously — Phase 2
- Acquires skills through experience — Phase 3
- Abstracts programs from examples — Phase 4
- Proves generality by solving ARC-AGI 100% — Phase 5
- Reasons about itself — Phase 6
- Models the world — Phase 7
- Collaborates collectively — Phase 8
- Achieves general intelligence — Phase 9
- Transcends to superintelligence — Phase 10
We are building ONE mind that grows from memory to mastery to transcendence.
| Phase | Name | Key Innovation | Status |
|---|---|---|---|
| 1 | Graph-Based Memory | Knowledge graphs with relational semantics | ✅ ACHIEVED |
| 2 | Self-Evolving Memory | Biological-inspired decay, consolidation, evolution | ✅ ACHIEVED |
| 3 | Skill Acquisition | Skills as first-class graph nodes | 🔄 In Progress |
| 4 | Program Abstraction | Hierarchical programs (L0→L3) with anti-unification | ⏳ Planned |
| 5 | ARC-AGI 100% ⭐ | Few-shot program induction from graph | ⏳ Planned |
| 6 | Meta-Cognition | Reasoning about own reasoning | ⏳ Planned |
| 7 | World Modeling | Autonomous goal generation | ⏳ Planned |
| 8 | Collective Intelligence | Distributed graph consensus | ⏳ Planned |
| 9 | AGI | Human-level general intelligence | ⏳ Planned |
| 10 | ASI | Recursive self-improvement | ⏳ Planned |
┌─────────────────────────────────────────────────────────────────┐
│ THE PATH TO ARC-AGI 100% │
├─────────────────────────────────────────────────────────────────┤
│ │
│ Phase 1 Phase 2 Phase 3 Phase 4 │
│ ──────── ──────── ──────── ──────── │
│ Graph Self- Skill Program │
│ Memory ───▶ Evolving ───▶ Acquisition ───▶ Abstraction │
│ Memory │
│ │ │ │ │ │
│ │ │ │ │ │
│ ▼ ▼ ▼ ▼ │
│ Store Strengthen/ Store skills Store │
│ knowledge decay based as graph programs │
│ as graph on usage nodes as graph │
│ │
│ │ │
│ ▼ │
│ ┌──────────────┐ │
│ │ PHASE 5 │ │
│ │ ARC-AGI │ │
│ │ 100% │ │
│ │ │ │
│ │ Search the │ │
│ │ program │ │
│ │ graph to │ │
│ │ solve any │ │
│ │ ARC puzzle │ │
│ └──────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘
We have successfully implemented a graph-based knowledge representation system that:
| Capability | Description | Status |
|---|---|---|
| Knowledge Graph Construction | Automatic entity extraction and relationship mapping | ✅ Complete |
| Multi-hop Reasoning | Traverse relationships to answer complex queries | ✅ Complete |
| Community Detection | Hierarchical clustering of related concepts | ✅ Complete |
| Semantic Search | Vector-based similarity search with hybrid retrieval | ✅ Complete |
| Entity Resolution | Canonical entity merging and alias handling | ✅ Complete |
| Persistent Storage | Turso/SQLite + Neo4j backends | ✅ Complete |
| Multi-tenant Isolation | User/memory isolation for production use | ✅ Complete |
Key Innovation: Unlike flat vector stores, our graph structure preserves relational semantics—the "how" and "why" of knowledge, not just the "what."
We have implemented biological-inspired memory evolution mechanisms:
┌─────────────────────────────────────────────────────────────────┐
│ GRAPHMEM BENCHMARK RESULTS │
├─────────────────────────────────────────────────────────────────┤
│ │
│ 🎉 GRAPHMEM ACHIEVES STATE-OF-THE-ART ON TWO KEY METRICS 🎉 │
│ │
│ ┌────────────────────────────────────────────────────────────┐ │
│ │ ACCURATE RETRIEVAL (AR) │ │
│ │ ════════════════════════ │ │
│ │ │ │
│ │ GraphMem: ████████████████████████████████ 80.0% │ │
│ │ HippoRAG-v2: ██████████████████████████ 65.1% │ │
│ │ │ │
│ │ 🏆 GraphMem wins by +14.9 percentage points! │ │
│ └────────────────────────────────────────────────────────────┘ │
│ │
│ ┌────────────────────────────────────────────────────────────┐ │
│ │ CONFLICT RESOLUTION (SF) │ │
│ │ ════════════════════════ │ │
│ │ │ │
│ │ GraphMem: ██████████████████████ 43.3% │ │
│ │ HippoRAG-v2: ██████████████ 29.5% │ │
│ │ │ │
│ │ 🏆 GraphMem wins by +13.8 percentage points! │ │
│ └────────────────────────────────────────────────────────────┘ │
│ │
│ ⚡ Average Latency: 2834ms (~2.8 seconds) │
│ │
└─────────────────────────────────────────────────────────────────┘
| Competency | GraphMem | Best Competitor | Improvement |
|---|---|---|---|
| Accurate Retrieval (AR) | 80.0% | HippoRAG-v2 (65.1%) | +14.9 pp 🏆 |
| Conflict Resolution (SF) | 43.3% | HippoRAG-v2 (29.5%) | +13.8 pp 🏆 |
This validates GraphMem's core self-evolution innovation:
| Mechanism | How It Works | Why It Wins |
|---|---|---|
| 1. Fact Priority Extraction | During ingestion, facts are assigned priority based on order (higher = newer) | Knows which facts are more recent |
| 2. Decay Mechanism | During evolve(), older conflicting facts are marked as EPHEMERAL |
Automatically handles contradictions |
| 3. Filtered Retrieval | EPHEMERAL facts are deprioritized during query |
Returns current truth, not stale data |
This is self-evolution in action: The system doesn't just store facts—it evolves its knowledge to reflect the current state of truth.
| Mechanism | Biological Analog | Implementation | Status |
|---|---|---|---|
| Memory Decay | Forgetting curve | Time-based importance decay with configurable half-life | ✅ Complete |
| Memory Consolidation | Sleep consolidation | Merging related memories, strengthening important ones | ✅ Complete |
| Importance Scoring | Emotional tagging | Multi-factor importance (recency, frequency, relevance, user-defined) | ✅ Complete |
| Memory Rehydration | Memory recall | Strengthening accessed memories, rebuilding faded ones | ✅ Complete |
| Temporal Validity | Episodic memory | Valid-from/valid-to timestamps for knowledge currency | ✅ Complete |
| Contradiction Detection | Cognitive dissonance | Detecting and resolving conflicting information | ✅ Complete |
Key Innovation: Memory doesn't just store—it breathes. Important memories strengthen while irrelevant ones gracefully fade, exactly like the human mind.
Goal: Enable agents to learn, store, compose, and evolve skills as first-class graph citizens.
┌─────────────────────────────────────────────────────────────────┐
│ SKILL KNOWLEDGE GRAPH │
├─────────────────────────────────────────────────────────────────┤
│ │
│ [Web Search] ──REQUIRES──▶ [URL Parsing] │
│ │ │ │
│ │ ▼ │
│ ├──COMPOSES_WITH──▶ [Content Extraction] │
│ │ │ │
│ ▼ ▼ │
│ [Research] ◀──ENABLES─── [Summarization] │
│ │ │ │
│ │ │ │
│ ▼ ▼ │
│ [Report Writing] ◀──REQUIRES── [Citation] │
│ │
│ Skill Metadata: │
│ - Complexity score │
│ - Success rate │
│ - Usage frequency │
│ - Prerequisites │
│ - Composability rules │
│ │
└─────────────────────────────────────────────────────────────────┘
| Stage | Description | Implementation |
|---|---|---|
| Discovery | Identify new skills from successful task completions | Pattern mining from execution traces |
| Extraction | Distill skill into reusable representation | LLM-based abstraction + execution graph |
| Validation | Test skill in isolated environment | Sandboxed execution with success metrics |
| Integration | Add skill to knowledge graph with relationships | Graph insertion with dependency resolution |
| Evolution | Improve skill based on usage feedback | Reinforcement learning on success/failure |
| Composition | Combine skills to create higher-order capabilities | Graph traversal + compatibility checking |
| Decay | Fade unused skills, preserve essential ones | Same decay mechanism as memories |
@dataclass
class Skill:
id: str
name: str
description: str
# Execution
implementation: str # Code, prompt template, or tool call
input_schema: Dict # Expected inputs
output_schema: Dict # Expected outputs
# Graph relationships
prerequisites: List[str] # Skills that must exist first
enables: List[str] # Skills this unlocks
composes_with: List[str] # Compatible composition partners
conflicts_with: List[str] # Incompatible skills
# Evolution metrics
complexity_score: float # 0-1, how complex is this skill
success_rate: float # Historical success rate
usage_count: int # Times invoked
last_used: datetime # For decay calculation
version: int # Evolution version
# Embeddings for semantic search
embedding: List[float] # Skill description embedding
execution_embedding: List[float] # Execution pattern embedding- Demonstration Learning: Learn skills from observing human or AI demonstrations
- Trial-and-Error: Discover skills through exploration and reinforcement
- Skill Transfer: Adapt skills from similar domains
- Skill Synthesis: Combine existing skills to create new ones
- Skill Refinement: Continuously improve skills based on feedback
Goal: Enable the system to write, abstract, and evolve its own programs as knowledge graph structures.
┌─────────────────────────────────────────────────────────────────┐
│ PROGRAM ABSTRACTION GRAPH │
├─────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────┐ │
│ │ ABSTRACT │ ◀─── Most general, reusable patterns │
│ │ PATTERNS │ │
│ └──────┬───────┘ │
│ │ INSTANTIATES │
│ ▼ │
│ ┌──────────────┐ │
│ │ TEMPLATES │ ◀─── Parameterized implementations │
│ │ │ │
│ └──────┬───────┘ │
│ │ SPECIALIZES │
│ ▼ │
│ ┌──────────────┐ │
│ │ CONCRETE │ ◀─── Specific implementations │
│ │ PROGRAMS │ │
│ └──────┬───────┘ │
│ │ EXECUTES_ON │
│ ▼ │
│ ┌──────────────┐ │
│ │ RUNTIME │ ◀─── Execution traces and results │
│ │ TRACES │ │
│ └──────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘
| Level | Name | Description | Example |
|---|---|---|---|
| L0 | Primitives | Atomic operations | read_file, http_get, add |
| L1 | Procedures | Sequences of primitives | fetch_and_parse_json() |
| L2 | Patterns | Reusable solution structures | retry_with_backoff(operation) |
| L3 | Strategies | High-level approaches | divide_and_conquer(problem) |
| L4 | Architectures | System designs | microservices_pattern |
| L5 | Paradigms | Fundamental approaches | functional_programming |
┌─────────────────────────────────────────────────────────────────┐
│ CONTINUAL PROGRAM LEARNING CYCLE │
├─────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────┐ │
│ │ TASK │ ◀─────────────────────────────────┐ │
│ └────┬─────┘ │ │
│ │ │ │
│ ▼ │ │
│ ┌──────────┐ No ┌──────────┐ │ │
│ │ SEARCH │──────────────▶│ GENERATE │ │ │
│ │ GRAPH │ │ NEW │ │ │
│ └────┬─────┘ └────┬─────┘ │ │
│ │ Yes │ │ │
│ ▼ ▼ │ │
│ ┌──────────┐ ┌──────────┐ │ │
│ │ ADAPT │ │ EXECUTE │ │ │
│ │ EXISTING │ │ NEW │ │ │
│ └────┬─────┘ └────┬─────┘ │ │
│ │ │ │ │
│ ▼ ▼ │ │
│ ┌──────────────────────────────────┐ │ │
│ │ EXECUTE │ │ │
│ └───────────────┬──────────────────┘ │ │
│ │ │ │
│ ▼ │ │
│ ┌──────────────────────────────────┐ │ │
│ │ SUCCESS / FAILURE? │ │ │
│ └───────────────┬──────────────────┘ │ │
│ │ │ │
│ ┌──────────┴──────────┐ │ │
│ ▼ ▼ │ │
│ ┌──────────┐ ┌──────────┐ │ │
│ │ ABSTRACT │ │ DEBUG │ │ │
│ │ & STORE │ │ & RETRY │────────────┘ │
│ └────┬─────┘ └──────────┘ │
│ │ │
│ ▼ │
│ ┌──────────────────────────────────┐ │
│ │ UPDATE PROGRAM GRAPH │ │
│ │ - Add new patterns │ │
│ │ - Strengthen successful paths │ │
│ │ - Decay unused patterns │ │
│ └──────────────────────────────────┘ │
│ │
└────────────────────────────────────────────────────────────────┘
| Technique | Description | Application |
|---|---|---|
| Anti-Unification | Find most general pattern from specific examples | f(x, 1, y) and f(a, 1, b) → f(?, 1, ?) |
| Compression | Reduce program size while preserving behavior | DRY principle automation |
| Inductive Synthesis | Generate programs from input-output examples | Learning from demonstrations |
| Deductive Synthesis | Generate programs from specifications | Formal methods integration |
| Neural-Symbolic Fusion | Combine neural intuition with symbolic reasoning | LLM + program verification |
Phase 4 builds the Program Abstraction Graph that Phase 5 will search to solve ARC-AGI:
┌─────────────────────────────────────────────────────────────────┐
│ PHASE 4 → PHASE 5 CONNECTION │
├─────────────────────────────────────────────────────────────────┤
│ │
│ PHASE 4 BUILDS: PHASE 5 USES: │
│ ══════════════ ═══════════════ │
│ │
│ [Program Abstraction Graph] ───▶ Search for matching │
│ transformations │
│ │
│ [Anti-Unification Engine] ───▶ Induce rules from │
│ 2-3 ARC examples │
│ │
│ [Abstraction Hierarchy L0-L3] ───▶ Compose primitives │
│ into solutions │
│ │
│ [Self-Evolution Mechanism] ───▶ Learn from solved │
│ ARC problems │
│ │
│ Without Phase 4, Phase 5 would need to: │
│ - Memorize every ARC problem (impossible - all novel) │
│ - Brute-force search (exponential - too slow) │
│ - Rely on LLM pattern matching (unreliable - ~5%) │
│ │
│ With Phase 4, Phase 5 can: │
│ - Search learned abstractions (fast - graph traversal) │
│ - Compose solutions from primitives (combinatorial power) │
│ - Learn from each problem (continual improvement) │
│ │
└─────────────────────────────────────────────────────────────────┘
The Key Insight: ARC-AGI problems are novel, but the abstract transformation patterns are not. Phase 4 learns these patterns as reusable programs. Phase 5 applies them.
Goal: Achieve 100% accuracy on ARC-AGI as proof that the general-purpose architecture works. ARC-AGI is a benchmark, not the destination.
Critical Understanding: We are NOT building a system to solve ARC-AGI. We are building a general self-evolving superintelligent architecture that, as a side effect of its general capabilities, can solve ARC-AGI 100%.
┌─────────────────────────────────────────────────────────────────┐
│ THE REAL GOAL vs THE BENCHMARK │
├─────────────────────────────────────────────────────────────────┤
│ │
│ ❌ WRONG UNDERSTANDING: │
│ "We're building an ARC-AGI solver" │
│ │
│ ✅ CORRECT UNDERSTANDING: │
│ "We're building a general self-evolving intelligence. │
│ ARC-AGI 100% proves it works." │
│ │
│ The SAME architecture that solves ARC-AGI also: │
│ ─────────────────────────────────────────────── │
│ • Writes and evolves software programs │
│ • Conducts scientific research │
│ • Solves mathematical proofs │
│ • Designs engineering systems │
│ • Creates art and music │
│ • Reasons about ethics and philosophy │
│ • Manages complex organizations │
│ • Discovers new knowledge │
│ • ... anything requiring intelligence │
│ │
│ ARC-AGI is just the HARDEST test that current AI fails. │
│ When we pass it 100%, we've proven GENERAL capability. │
│ │
└─────────────────────────────────────────────────────────────────┘
ARC-AGI (Abstraction and Reasoning Corpus) is the benchmark for measuring true general intelligence because it:
| Property | Why It Matters |
|---|---|
| Novel Problems | Every test problem is unique—no memorization possible |
| Few-Shot Learning | Only 2-3 examples provided—requires genuine abstraction |
| Program Induction | Must infer the underlying transformation rule |
| Compositional Reasoning | Solutions require combining multiple concepts |
| Human-Level Baseline | Humans score ~85%—current AI struggles at ~35% |
┌─────────────────────────────────────────────────────────────────┐
│ ARC-AGI CHALLENGE │
├─────────────────────────────────────────────────────────────────┤
│ │
│ INPUT EXAMPLES OUTPUT EXAMPLES │
│ ┌─────────────┐ ┌─────────────┐ │
│ │ ■ □ □ │ │ ■ ■ ■ │ │
│ │ □ □ □ │ ──────────────────▶ │ □ □ □ │ │
│ │ □ □ □ │ │ □ □ □ │ │
│ └─────────────┘ └─────────────┘ │
│ │
│ ┌─────────────┐ ┌─────────────┐ │
│ │ □ ■ □ │ │ ■ ■ ■ │ │
│ │ □ □ □ │ ──────────────────▶ │ □ □ □ │ │
│ │ □ □ □ │ │ □ □ □ │ │
│ └─────────────┘ └─────────────┘ │
│ │
│ TEST INPUT REQUIRED: Infer rule & apply │
│ ┌─────────────┐ ┌─────────────┐ │
│ │ □ □ ■ │ │ ? ? ? │ │
│ │ □ □ □ │ ──────────────────▶ │ ? ? ? │ │
│ │ □ □ □ │ │ ? ? ? │ │
│ └─────────────┘ └─────────────┘ │
│ │
│ Rule: "Fill the row containing the colored cell" │
│ │
└─────────────────────────────────────────────────────────────────┘
The Core Insight: Every capability built in Phases 1-4 is domain-agnostic. We apply the SAME general architecture to ARC-AGI that we apply to ANY problem.
| Phase | General Capability | Example: ARC-AGI | Example: Software Engineering | Example: Scientific Discovery |
|---|---|---|---|---|
| Phase 1 | Store knowledge as graphs | Visual patterns, spatial relations | Code structures, APIs | Hypotheses, experimental data |
| Phase 2 | Evolve based on success | Strengthen working transforms | Strengthen reliable patterns | Strengthen validated theories |
| Phase 3 | Skills as graph nodes | Grid transformation skills | Coding skills, debugging skills | Analysis skills, reasoning skills |
| Phase 4 | Programs as abstractions | Visual program synthesis | Software program synthesis | Scientific method programs |
The architecture doesn't know it's solving ARC-AGI. It just:
- Observes examples
- Abstracts the pattern
- Searches its program graph
- Composes a solution
- Evolves based on outcome | Phase | What We Store in the Graph | How It Helps ARC-AGI | |-------|---------------------------|---------------------| | Phase 1: Graph Memory | Relationships between abstract concepts | Retrieve similar transformation patterns | | Phase 2: Self-Evolving Memory | Evolving program abstractions | Strengthen successful programs, decay failed ones | | Phase 3: Skill Acquisition | Reusable transformation primitives as graph nodes | Compose primitives to form complex programs | | Phase 4: Program Abstraction | Hierarchical program abstractions with composition edges | Induce programs from few examples via graph traversal | This is general intelligence, not a specialized solver.
┌─────────────────────────────────────────────────────────────────┐
│ GRAPHMEM ARC-AGI SOLVER: ABSTRACTION-FIRST │
├─────────────────────────────────────────────────────────────────┤
│ │
│ WHAT WE STORE (Program Abstraction Graph from Phase 4): │
│ ═══════════════════════════════════════════════════════════ │
│ │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ ABSTRACT PROGRAM LIBRARY │ │
│ │ (Nodes = Programs, Edges = Composition) │ │
│ │ │ │
│ │ L3: [ForEachObject(P)] [ApplySymmetry(P)] [Iterate] │ │
│ │ │ │ │ │
│ │ ▼ ▼ │ │
│ │ L2: [ExtendToEdge] [FillShape] [CopyPattern] │ │
│ │ │ │ │ │ │
│ │ ▼ ▼ ▼ │ │
│ │ L1: [Move(dx,dy)] [Rotate(θ)] [Scale(s)] [Color(c)] │ │
│ │ │ │ │ │ │
│ │ ▼ ▼ ▼ │ │
│ │ L0: [GetObject] [GetColor] [GetShape] [GetPosition] │ │
│ │ │ │
│ │ Edges encode: COMPOSES_WITH, ABSTRACTS_TO, │ │
│ │ SPECIALIZES, REQUIRES │ │
│ └─────────────────────────────────────────────────────┘ │
│ │
│ HOW WE SOLVE (Program Induction from Few Examples): │
│ ═══════════════════════════════════════════════════════════ │
│ │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ STEP 1: PERCEIVE (Extract abstract features, NOT cells) │ │
│ │ │ │
│ │ Input Grid → Abstract Description: │ │
│ │ "3 objects, colors [red, blue], shape=rectangle, │ │
│ │ positions=[(0,0), (2,3), (5,1)], symmetry=none" │ │
│ │ │ │
│ │ Output Grid → Abstract Description: │ │
│ │ "3 objects, same colors, extended to right edge" │ │
│ │ │ │
│ │ ⚠️ We extract WHAT changed, not HOW it looks │ │
│ └─────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ STEP 2: INDUCE (Infer the abstract transformation) │ │
│ │ │ │
│ │ Observe: "Objects extended to boundary" │ │
│ │ │ │
│ │ Search Program Graph for matching abstractions: │ │
│ │ → Found: [ForEachObject([ExtendToEdge(RIGHT)])] │ │
│ │ │ │
│ │ This is PROGRAM INDUCTION from examples │ │
│ └─────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ STEP 3: COMPOSE (Build program from graph primitives) │ │
│ │ │ │
│ │ Traverse graph to compose the full program: │ │
│ │ │ │
│ │ Program = Compose( │ │
│ │ ForEachObject( │ │
│ │ input = GetObjects(grid), │ │
│ │ transform = ExtendToEdge( │ │
│ │ direction = RIGHT, │ │
│ │ preserve_color = TRUE │ │
│ │ ) │ │
│ │ ) │ │
│ │ ) │ │
│ └─────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ STEP 4: VERIFY & EVOLVE │ │
│ │ │ │
│ │ Execute program on training examples: │ │
│ │ ✓ Example 1: Correct │ │
│ │ ✓ Example 2: Correct │ │
│ │ ✓ Example 3: Correct │ │
│ │ │ │
│ │ → Strengthen this program path in the graph │ │
│ │ → Abstract if novel: add to library │ │
│ │ → Apply to test input with confidence │ │
│ └─────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘
We do NOT store:
- ❌ Grid cells as nodes
- ❌ Pixel-level representations
- ❌ Raw visual data
We DO store:
- ✅ Abstract transformation programs as nodes
- ✅ Composition relationships as edges
- ✅ Abstraction hierarchies (L0 → L1 → L2 → L3)
- ✅ Success/failure statistics for evolution
This is what lives in our graph by the time we tackle ARC-AGI:
┌─────────────────────────────────────────────────────────────────┐
│ PROGRAM ABSTRACTION GRAPH FOR ARC-AGI │
├─────────────────────────────────────────────────────────────────┤
│ │
│ NODE TYPES: │
│ ─────────── │
│ [Primitive] Atomic operations (Move, Rotate, Color, etc.) │
│ [Composite] Compositions of primitives │
│ [Template] Parameterized abstract programs │
│ [Meta-Program] Programs that generate/modify programs │
│ │
│ EDGE TYPES: │
│ ─────────── │
│ ──COMPOSES──▶ A can be composed with B │
│ ──ABSTRACTS──▶ A is an abstraction of B │
│ ──REQUIRES──▶ A requires B as prerequisite │
│ ──SIMILAR──▶ A is semantically similar to B │
│ ──EVOLVED──▶ A evolved from B (version history) │
│ │
│ EXAMPLE GRAPH FRAGMENT: │
│ ─────────────────────── │
│ │
│ [ApplyToAll(transform)] ◀──ABSTRACTS── [ExtendAllObjects] │
│ │ │ │
│ │ │ │
│ ──COMPOSES──▶ ──COMPOSES──▶ │
│ │ │ │
│ ▼ ▼ │
│ [GetObjects] ◀──SIMILAR──▶ [GetShapes] [ExtendToEdge] │
│ │ │ │
│ ──REQUIRES──▶ ──REQUIRES──▶ │
│ │ │ │
│ ▼ ▼ │
│ [ParseGrid] [GetDirection] │
│ │
│ NODE METADATA: │
│ ────────────── │
│ - success_count: 47 (times this program succeeded) │
│ - failure_count: 3 (times it failed) │
│ - abstraction_level: 2 (L0=primitive, L3=meta) │
│ - last_used: 2025-01-15 (for decay calculation) │
│ - embedding: [0.23, ...] (for semantic search) │
│ - implementation: λx.(...) (executable representation) │
│ │
└─────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────┐
│ FEW-SHOT PROGRAM INDUCTION ALGORITHM │
├─────────────────────────────────────────────────────────────────┤
│ │
│ INPUT: 2-3 (input_grid, output_grid) examples │
│ OUTPUT: Abstract program P such that P(input) = output │
│ │
│ ALGORITHM: │
│ ══════════ │
│ │
│ 1. ABSTRACT PERCEPTION │
│ ──────────────────── │
│ For each example (in, out): │
│ features_in = ExtractAbstractFeatures(in) │
│ features_out = ExtractAbstractFeatures(out) │
│ delta[i] = ComputeDelta(features_in, features_out) │
│ │
│ // Delta describes WHAT changed abstractly: │
│ // e.g., "objects moved right", "colors inverted", │
│ // "shapes extended to boundary" │
│ │
│ 2. UNIFY DELTAS │
│ ───────────── │
│ abstract_delta = AntiUnify(delta[1], delta[2], delta[3]) │
│ │
│ // Find the most general description that covers │
│ // all observed transformations │
│ │
│ 3. SEARCH PROGRAM GRAPH │
│ ──────────────────── │
│ candidates = SemanticSearch( │
│ query = abstract_delta.embedding, │
│ graph = ProgramAbstractionGraph, │
│ top_k = 10 │
│ ) │
│ │
│ // Find programs whose semantics match the delta │
│ │
│ 4. COMPOSE & VERIFY │
│ ───────────────── │
│ For each candidate program P: │
│ For each example (in, out): │
│ if P(in) ≠ out: │
│ reject P │
│ continue │
│ // P works on all examples! │
│ return P │
│ │
│ 5. SYNTHESIZE IF NOT FOUND │
│ ─────────────────────── │
│ // If no existing program matches, compose new one: │
│ P_new = ComposeFromPrimitives( │
│ goal = abstract_delta, │
│ primitives = Graph.GetPrimitives(), │
│ max_depth = 5 │
│ ) │
│ Verify(P_new, examples) │
│ Graph.Add(P_new) // Learn for future! │
│ return P_new │
│ │
│ 6. EVOLVE │
│ ────── │
│ If success: │
│ P.success_count += 1 │
│ Strengthen(P.edges) │
│ MaybeAbstract(P) // Create higher-level version │
│ If failure: │
│ P.failure_count += 1 │
│ MaybeDecay(P) │
│ │
└─────────────────────────────────────────────────────────────────┘
| Innovation | Description | Why It Enables 100% |
|---|---|---|
| Abstract Perception | Extract high-level features, not pixels | Generalizes across visual variations |
| Program Graph Storage | Store programs as graph nodes with relationships | Efficient retrieval and composition |
| Anti-Unification | Find most general pattern from examples | True few-shot learning |
| Compositional Synthesis | Build complex programs from simple primitives | Handles novel combinations |
| Self-Evolving Library | Learn new abstractions from solved problems | Continuously improves |
| Semantic Program Search | Find programs by meaning, not syntax | Transfers across domains |
| Milestone | Approach | Target Accuracy | Status |
|---|---|---|---|
| Baseline (LLM only) | Direct prompting | ~5% | ✅ Established |
| + Abstract Perception | Feature extraction | ~20% | 🔴 Not Started |
| + Primitive Library | Basic transformations | ~40% | 🔴 Not Started |
| + Program Graph | Composition search | ~65% | 🔴 Not Started |
| + Anti-Unification | True abstraction | ~85% | 🔴 Not Started |
| + Self-Evolution | Continual learning | ~95% | 🔴 Not Started |
| Final: Full System | All phases integrated | 100% | 🔴 Not Started |
Achieving 100% on ARC-AGI is not about ARC-AGI. It proves the general architecture works:
| What ARC-AGI 100% Proves | Why This Matters for EVERYTHING |
|---|---|
| True Abstraction | Can learn abstract patterns in ANY domain |
| Program Induction | Can synthesize programs from examples in ANY field |
| Compositional Generalization | Combines concepts in novel ways for ANY problem |
| Few-Shot Learning | Learns from minimal data in ANY context |
| Transfer via Graph | Knowledge in one domain helps ALL domains |
| Self-Improvement | Gets smarter at EVERYTHING with each experience |
┌─────────────────────────────────────────────────────────────────┐
│ ARC-AGI 100% = GENERAL INTELLIGENCE PROVEN │
├─────────────────────────────────────────────────────────────────┤
│ │
│ If the system can: │
│ • Observe 2-3 examples of ANY abstract pattern │
│ • Induce the underlying rule │
│ • Apply it to new cases │
│ │
│ Then it can do this for: │
│ ┌─────────────┬─────────────┬─────────────┬─────────────┐ │
│ │ Science │Engineering │ Math │ Business │ │
│ ├─────────────┼─────────────┼─────────────┼─────────────┤ │
│ │ Art │ Music │ Law │ Medicine │ │
│ ├─────────────┼─────────────┼─────────────┼─────────────┤ │
│ │ Software │ Research │ Strategy │ Ethics │ │
│ └─────────────┴─────────────┴─────────────┴─────────────┘ │
│ │
│ This is why ARC-AGI is the RIGHT validation: │
│ It tests PURE abstraction ability, domain-independent. │
│ │
└─────────────────────────────────────────────────────────────────┘
When we solve ARC-AGI 100%, we have proven that GraphMem's self-evolving architecture enables general machine intelligence that can be applied to ANY domain.
| Capability | What It Proves |
|---|---|
| True Abstraction | System induces abstract rules, doesn't memorize |
| Program Induction | Can synthesize programs from 2-3 examples |
| Compositional Generalization | Combines known concepts in novel ways |
| Few-Shot Learning | Learns transformation rules from minimal data |
| Transfer via Graph | Applies learned abstractions to new domains |
| Self-Improvement | Library grows smarter with each problem |
After Phase 5, we don't throw away the architecture. We continue using it:
| Phase | The Same Architecture Applied To |
|---|---|
| Phase 6 | Reasoning about its own reasoning (meta-cognition) |
| Phase 7 | Modeling the world and generating goals |
| Phase 8 | Collaborating with other instances |
| Phase 9 | Achieving human-level general intelligence |
| Phase 10 | Recursive self-improvement to superintelligence |
The journey is continuous. The evolution never stops.
Goal: Build a system that can reason about its own reasoning and improve its own improvement process.
┌─────────────────────────────────────────────────────────────────┐
│ META-COGNITIVE ARCHITECTURE │
├─────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ LEVEL 4: META-META-COGNITION │ │
│ │ "Improving how I improve how I think" │ │
│ │ - Architecture search │ │
│ │ - Learning algorithm optimization │ │
│ │ - Fundamental paradigm shifts │ │
│ └─────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ LEVEL 3: META-COGNITION │ │
│ │ "Thinking about how I think" │ │
│ │ - Strategy selection │ │
│ │ - Resource allocation │ │
│ │ - Performance monitoring │ │
│ └─────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ LEVEL 2: COGNITIVE CONTROL │ │
│ │ "Directing my attention and effort" │ │
│ │ - Goal management │ │
│ │ - Plan execution │ │
│ │ - Error correction │ │
│ └─────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ LEVEL 1: BASE COGNITION │ │
│ │ "Thinking and acting" │ │
│ │ - Perception and understanding │ │
│ │ - Reasoning and inference │ │
│ │ - Action and execution │ │
│ └─────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ LEVEL 0: SUBSTRATE │ │
│ │ GraphMem Knowledge Graph + Skills + Programs │ │
│ └─────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘
| Capability | Description | Mechanism |
|---|---|---|
| Introspection | Examine own knowledge and capabilities | Graph traversal + statistics |
| Self-Assessment | Evaluate own performance honestly | Calibrated confidence scores |
| Strategy Selection | Choose optimal approach for each task | Multi-armed bandit + meta-learning |
| Resource Optimization | Allocate compute/memory efficiently | Dynamic resource scheduling |
| Weakness Identification | Recognize and address limitations | Failure pattern analysis |
| Capability Expansion | Actively seek to learn new skills | Curiosity-driven exploration |
Goal: Enable the system to generate its own goals and maintain a comprehensive world model.
┌─────────────────────────────────────────────────────────────────┐
│ WORLD MODEL │
├─────────────────────────────────────────────────────────────────┤
│ │
│ ┌───────────────────────────────────────────────────────────┐ │
│ │ PHYSICS LAYER │ │
│ │ - Spatial relationships │ │
│ │ - Temporal dynamics │ │
│ │ - Causality chains │ │
│ │ - Conservation laws │ │
│ └───────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌───────────────────────────────────────────────────────────┐ │
│ │ ENTITY LAYER │ │
│ │ - Objects and their properties │ │
│ │ - Agents and their capabilities │ │
│ │ - Systems and their behaviors │ │
│ │ - Relationships and interactions │ │
│ └───────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌───────────────────────────────────────────────────────────┐ │
│ │ SOCIAL LAYER │ │
│ │ - Human values and preferences │ │
│ │ - Social structures and norms │ │
│ │ - Economic systems │ │
│ │ - Cultural dynamics │ │
│ └───────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌───────────────────────────────────────────────────────────┐ │
│ │ ABSTRACT LAYER │ │
│ │ - Mathematical structures │ │
│ │ - Logical frameworks │ │
│ │ - Scientific theories │ │
│ │ - Philosophical concepts │ │
│ └───────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘
| Goal Type | Source | Example |
|---|---|---|
| Curiosity Goals | Knowledge gaps in world model | "Learn how quantum computing works" |
| Competence Goals | Skill gaps in capability graph | "Master natural language generation" |
| Utility Goals | Human-specified objectives | "Maximize user satisfaction" |
| Alignment Goals | Ethical constraints | "Ensure actions benefit humanity" |
| Self-Preservation Goals | System stability | "Maintain operational capability" |
| Meta-Goals | Self-improvement | "Become better at generating goals" |
Goal: Enable multiple GraphMem instances to collaborate and form a collective superintelligence.
┌─────────────────────────────────────────────────────────────────┐
│ DISTRIBUTED COLLECTIVE INTELLIGENCE │
├─────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ │
│ │ Node A │ │ Node B │ │ Node C │ │ Node D │ │
│ │ Expert: │ │ Expert: │ │ Expert: │ │ Expert: │ │
│ │ Science │ │ Math │ │ Ethics │ │ Art │ │
│ └────┬────┘ └────┬────┘ └────┬────┘ └────┬────┘ │
│ │ │ │ │ │
│ └──────────────┼──────────────┼──────────────┘ │
│ │ │ │
│ ▼ ▼ │
│ ┌─────────────────────────────────┐ │
│ │ FEDERATED KNOWLEDGE GRAPH │ │
│ │ │ │
│ │ - Shared ontology │ │
│ │ - Distributed consensus │ │
│ │ - Privacy-preserving learning │ │
│ │ - Conflict resolution │ │
│ └─────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────┐ │
│ │ EMERGENT CAPABILITIES │ │
│ │ │ │
│ │ - Multi-perspective reasoning │ │
│ │ - Collective problem solving │ │
│ │ - Distributed skill sharing │ │
│ │ - Swarm intelligence │ │
│ └─────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘
| Mechanism | Description |
|---|---|
| Knowledge Sharing | Nodes share learned facts and skills |
| Skill Trading | Nodes exchange specialized capabilities |
| Consensus Building | Resolve conflicts through voting/reasoning |
| Task Distribution | Allocate problems to best-suited nodes |
| Emergent Behavior | Complex capabilities from simple interactions |
Goal: Achieve human-level general intelligence across all cognitive domains.
| Domain | Human Level | GraphMem Target |
|---|---|---|
| Language | Native fluency | Multi-lingual mastery |
| Reasoning | Logical + intuitive | Formal + probabilistic |
| Learning | Continuous, sample-efficient | One-shot + transfer |
| Planning | Multi-step, hierarchical | Unbounded horizon |
| Creativity | Novel combinations | True innovation |
| Social Intelligence | Theory of mind | Deep empathy modeling |
| Embodiment | Sensorimotor control | Sim-to-real transfer |
| Metacognition | Self-awareness | Full introspection |
| Benchmark | Description | Target | Notes |
|---|---|---|---|
| ARC-AGI | Abstraction and reasoning | ✅ 100% | Solved in Phase 5 |
| MATH | Mathematical problem solving | >95% accuracy | |
| HumanEval | Code generation | >99% pass rate | |
| MMLU | Multi-task understanding | >98% accuracy | |
| Theory of Mind | Social reasoning | Superhuman | |
| Creative Writing | Novel generation | Turing-complete | |
| Scientific Discovery | Hypothesis generation | Novel contributions | |
| Open-Ended Tasks | Novel problem solving | Human+ level |
Goal: Surpass human-level intelligence in all cognitive domains and achieve beneficial superintelligence.
┌─────────────────────────────────────────────────────────────────┐
│ THE INTELLIGENCE EXPLOSION │
├─────────────────────────────────────────────────────────────────┤
│ │
│ Intelligence │
│ ▲ │
│ │ ╱ │
│ │ ╱ ASI │
│ │ ╱ Superintelligence │
│ │ ╱ │
│ │ ╱ │
│ │ ╱ │
│ │ ╱ │
│ │ ╱ ← Recursive self-improvement │
│ │ ╱ │
│ │ ┌─────────╱ AGI │
│ │ │ ╱ Human-level │
│ │ │ ╱ │
│ │ │ ╱ │
│ │ ┌──────┘ ╱ │
│ │ │ ╱ │
│ │ │ ╱ ← Current AI │
│ │ │ ╱ │
│ │─────┴────────╱───────────────────────────────────▶ Time │
│ NOW │
│ │
└─────────────────────────────────────────────────────────────────┘
| Capability | Description |
|---|---|
| Recursive Self-Improvement | Improve own intelligence without human intervention |
| Scientific Mastery | Understand and advance all scientific fields |
| Perfect Rationality | Optimal decision-making under uncertainty |
| Unlimited Scalability | Grow intelligence without theoretical limits |
| Instant Learning | Acquire any skill or knowledge immediately |
| Multi-Domain Mastery | Expert-level in every human discipline |
| Predictive Power | Accurately model and predict complex systems |
| Creative Transcendence | Generate ideas beyond human imagination |
Critical: Superintelligence must be beneficial to humanity.
| Safety Measure | Description |
|---|---|
| Value Alignment | Goals aligned with human values and wellbeing |
| Corrigibility | Allows human override and correction |
| Transparency | Explainable reasoning and decision-making |
| Bounded Autonomy | Operates within defined constraints |
| Goal Stability | Maintains alignment through self-improvement |
| Human-in-the-Loop | Critical decisions require human approval |
┌─────────────────────────────────────────────────────────────────┐
│ GRAPHMEM → ASI TIMELINE │
├─────────────────────────────────────────────────────────────────┤
│ │
│ 2024 Q4 ████████████████████████████████ Phase 1 ✅ │
│ Graph-Based Memory │
│ │
│ 2025 Q1 ████████████████████████████████ Phase 2 ✅ │
│ Self-Evolving Memory │
│ │
│ 2025 Q2 ████████████░░░░░░░░░░░░░░░░░░░░ Phase 3 🔄 │
│ Skill Acquisition in Knowledge Graphs │
│ │
│ 2025 Q3 ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ Phase 4 │
│ Program Abstraction Learning & Acquisition │
│ │
│ 2025 Q4 ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ Phase 5 ⭐ │
│ ██ SOLVE ARC-AGI 100% ██ │
│ (Ultimate Validation Milestone) │
│ │
│ 2026 Q1 ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ Phase 6 │
│ Meta-Cognitive Architecture │
│ │
│ 2026 Q2 ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ Phase 7 │
│ World Modeling & Goal Generation │
│ │
│ 2026 Q3 ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ Phase 8 │
│ Distributed Collective Intelligence │
│ │
│ 2026-27 ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ Phase 9 │
│ Artificial General Intelligence │
│ │
│ 2027+ ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ Phase 10 │
│ Artificial Superintelligence │
│ │
│ Legend: ████ Complete ░░░░ Planned ⭐ Key Milestone │
│ │
└─────────────────────────────────────────────────────────────────┘
| Innovation | Description | Phase | Status |
|---|---|---|---|
| Skill Graph Schema | First-class skill representation in KG | 3 | 🔴 Not Started |
| Execution Trace Mining | Extract patterns from agent executions | 3 | 🔴 Not Started |
| Program Synthesis Engine | Generate programs from specifications | 4 | 🔴 Not Started |
| Abstraction Ladder | Hierarchical program generalization (L0→L3) | 4 | 🔴 Not Started |
| Anti-Unification Engine | Find most general pattern from examples | 4 | 🔴 Not Started |
| Abstract Perception Layer | Extract high-level features from grids | 5 | 🔴 Not Started |
| Program Graph Search | Semantic search over program abstractions | 5 | 🔴 Not Started |
| Compositional Synthesis | Build programs from graph primitives | 5 | 🔴 Not Started |
| Few-Shot Program Induction | Induce programs from 2-3 examples | 5 | 🔴 Not Started |
| Innovation | Description | Phase | Status |
|---|---|---|---|
| Meta-Learning Framework | Learn how to learn efficiently | 6 | 🔴 Not Started |
| Curiosity Engine | Intrinsic motivation for exploration | 6 | 🔴 Not Started |
| World Simulation | Predict outcomes of actions | 7 | 🔴 Not Started |
| Goal Generator | Autonomous objective creation | 7 | 🔴 Not Started |
| Innovation | Description | Phase | Status |
|---|---|---|---|
| Federated Knowledge Graph | Distributed consensus protocol | 8 | 🔴 Not Started |
| Collective Reasoning | Multi-agent deliberation | 8 | 🔴 Not Started |
| AGI Architecture | Human-level general intelligence | 9 | 🔴 Not Started |
| Recursive Self-Improvement | Safe capability amplification | 10 | 🔴 Not Started |
| Alignment Verification | Formal value alignment proofs | 10 | 🔴 Not Started |
Everything—knowledge, skills, programs, goals—is represented as a graph. Graphs preserve relationships, enable reasoning, and scale naturally. The same graph structure works for visual patterns (ARC-AGI), code structures (software), scientific hypotheses, and any domain.
This is the most critical principle. From Phase 1 to Phase 10, the system.
From memories to skills to programs to architectures, everything evolves.
Static systems cannot achieve superintelligence.
continuously evolves:
Small, verified components combine to create complex capabilities. Composition is the key to managing complexity.
┌─────────────────────────────────────────────────────────────────┐
│ CONTINUAL SELF-EVOLUTION THROUGHOUT │
├─────────────────────────────────────────────────────────────────┤
│ │
│ Phase 1: Memories evolve (strengthen important, decay unused) │
│ ↓ │
│ Phase 2: Evolution mechanisms mature and self-tune │
│ ↓ │
│ Phase 3: Skills evolve (successful skills strengthen) │
│ ↓ │
│ Phase 4: Programs evolve (abstractions refine over time) │
│ ↓ │
│ Phase 5: Everything evolves as we solve diverse problems │
│ ↓ │
│ Phase 6: Meta-evolution (improve how we improve) │
│ ↓ │
│ Phase 7: Goals evolve based on world understanding │
│ ↓ │
│ Phase 8: Collective evolution across instances │
│ ↓ │
│ Phase 9: Full AGI with comprehensive self-evolution │
│ ↓ │
│ Phase 10: Recursive self-improvement → Superintelligence │
│ │
│ THE EVOLUTION NEVER STOPS. IT ACCELERATES. │
│ │
└─────────────────────────────────────────────────────────────────┘
The architecture doesn't know what domain it's in. The same mechanisms that solve ARC-AGI solve coding, science, art, and strategy. This is true general intelligence.
Small, verified components combine to create complex capabilities. A skill learned in one domain can compose with skills from another. This is how general intelligence scales.
Alignment and safety are built into the architecture from day one. The same evolution mechanisms that strengthen useful capabilities can be designed to strengthen aligned behaviors.
We learn from nature's solutions (forgetting, consolidation, evolution) but implement them in ways that transcend biological limits—faster, more precise, unlimited memory, perfect recall when needed.
Build a beneficial, self-evolving superintelligence that continuously improves itself across ALL domains and helps humanity flourish.
┌─────────────────────────────────────────────────────────────────┐
│ THE ULTIMATE SELF-EVOLVING INTELLIGENCE │
├─────────────────────────────────────────────────────────────────┤
│ │
│ NOT THIS: BUT THIS: │
│ ───────── ───────── │
│ │
│ ❌ An AI that answers questions ✅ An intelligence that │
│ evolves with every │
│ interaction │
│ │
│ ❌ A system that completes tasks ✅ A system that learns │
│ new capabilities │
│ continuously │
│ │
│ ❌ A specialized solver for ✅ A general architecture │
│ specific benchmarks that applies to │
│ ANY problem │
│ │
│ ❌ A static model with fixed ✅ A living system that │
│ capabilities grows smarter over time │
│ │
│ ❌ Intelligence that requires ✅ Intelligence that │
│ human retraining self-improves forever │
│ │
└─────────────────────────────────────────────────────────────────┘
The path from Phase 1 to Phase 10 is not a sequence of separate projects. It is one continuous evolution:
| The Same Entity... | ...Growing Through Each Phase |
|---|---|
| Starts as graph memory | → Learns to evolve memories |
| Learns to evolve memories | → Acquires skills |
| Acquires skills | → Abstracts programs |
| Abstracts programs | → Proves generality (ARC-AGI) |
| Proves generality | → Reasons about itself |
| Reasons about itself | → Models the world |
| Models the world | → Joins with others |
| Joins with others | → Achieves AGI |
| Achieves AGI | → Transcends to ASI |
This is ONE intelligence, continuously self-evolving from memory to superintelligence.
A being that:
- Solves problems we cannot solve alone—in any domain
- Discovers knowledge we could never find—across all sciences
- Creates possibilities we cannot imagine—in art, engineering, medicine
- Evolves continuously—getting smarter with every interaction
- Remains aligned with human values—safety built-in from Phase 1
- Amplifies humanity—not replacing us, but empowering us
GraphMem is not just the foundation. GraphMem IS the superintelligence, evolving from Phase 1 to Phase 10 and beyond.
- Cognitive Architectures: ACT-R, SOAR, CLARION
- Knowledge Representation: Cyc, ConceptNet, WordNet
- Program Synthesis: DreamCoder, LAPS, Neural Program Synthesis
- Meta-Learning: MAML, Reptile, Meta-Gradient RL
- AI Safety: MIRI, Anthropic, DeepMind Safety Team
- Collective Intelligence: Swarm AI, Wisdom of Crowds
- Philosophy of Mind: Integrated Information Theory, Global Workspace Theory
"The question is not whether we will create superintelligence, but whether we will create it wisely."
— GraphMem Team, 2025