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Genius Cognitive System

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Genius Cognitive System

Architecture
Architecture
- 1. Executive Summary
- 2. System Objectives
  2. System Objectives
- 3 System Architecture Overview
- 4 GNUS Component Mapping
  4 GNUS Component Mapping
  - 4.1 Compute Layer
    
    4.1 Compute Layer
    
    4.1.1 SGFP4 Design
  - 4.2 Distributed Layer
    
    4.2 Distributed Layer
    
    4.2.1 Layered Cognitive Stack
  - 4.3 Security Layer
    
    4.3 Security Layer
    
    4.3.1 Core Architectural Distinction
- 5 Model Architecture
  5 Model Architecture
  - 5.1 Semantic Core
    
    5.1 Semantic Core
    
    5.1.1 Base Model
    
    5.1.2 Quantization
  - 5.2 Expert Language Models (ELMs) and Specialist Modules
    
    5.2 Expert Language Models (ELMs) and Specialist Modules
    
    5.2.1 ELM Definition and Flexibility
    
    5.2.2 Role-Based ELMs
    
    5.2.3 Domain-Specific Experts
    
    5.2.4 Private ELMs
    
    5.2.5 ELM Invocation Patterns
    
    5.2.6 Legacy MVP Specialists
- 6 Router Design
  6 Router Design
- 7 Reputation-Based Consensus System
  7 Reputation-Based Consensus System
  - 7.1 Reputation Data Model
  - 7.2 Reputation Update Formula
    
    7.2 Reputation Update Formula
    
    7.2.1 Accuracy / Quality Component
    
    7.2.2 Latency Component
    
    7.2.3 Consistency Component
    
    7.2.4 Safety and Policy Component
    
    7.2.5 Final Update
  - 7.3 Weighted Consensus Algorithm
  - 7.4 Consensus Engine Architecture (Protocol Layer)
    
    7.4 Consensus Engine Architecture (Protocol Layer)
    
    7.4.1 Consensus Design Principles
    
    7.4.2 Swarm Execution Flow
    
    7.4.3 Consensus Message Types
    
    7.4.4 Liveness Model
    
    7.4.5 Byzantine Tolerance
    
    7.4.6 Reputation-Gated Participation
    
    7.4.7 Genesis Anchor Model
  - 8.4 GNUS Agentic Memory Layer (GAML v1)
    
    8.4 GNUS Agentic Memory Layer (GAML v1)
    
    8.4.1 Purpose
    
    8.4.2 Architectural Position
    
    8.4.3 Memory Object Model
    
    8.4.4 Cognitive Asset Model
    
    8.4.5 Ingestion Pipeline
    
    8.4.6 Agentic Retrieval Mechanism
    
    8.4.7 Surprise-Gated Writes
    
    8.4.8 Memory as Support for Experts
    
    8.4.9 Swarm Memory Consensus
    
    8.4.10 Replication and Convergence
    
    8.4.11 Private Memory, Ownership, and Derived Assets
    
    8.4.11.1 Privacy Scopes
    
    8.4.11.2 Ownership and Authorization
    
    8.4.11.3 Inference Boundaries
    
    8.4.11.4 Encryption and Key References
    
    8.4.11.5 Derived-Asset Inheritance
    
    8.4.11.6 Training and Adaptation
    
    8.4.11.7 Local Personal Data
    
    8.4.12 Performance & Overhead Impact
    
    8.4.13 Strategic Impact
- 8 Grounding and Retrieval
  8 Grounding and Retrieval
  - 8.1 Grokipedia Role
  - 8.2 Retrieval Pipeline
  - 8.3 Validation Layer
  - 8.4 Private Knowledge Grounding
  - 8.5 Grounding Modes
  - 8.6 Grounding as an Expert Role
  - 8.7 Why Retrieval Is Not Enough by Itself
    
    8.7 Why Retrieval Is Not Enough by Itself
    
    8.7.1 Extended Grounding Memory
- 9. Execution and Performance - Execution Modes and Performance Targets
  9. Execution and Performance - Execution Modes and Performance Targets
- 10 Performance Targets
- 11 Execution Roadmap
  11 Execution Roadmap
- 12 Risk Analysis
- 13 Future Compatibility
- 14 Strategic Positioning
- 15 AI Safety Philosophy
  15 AI Safety Philosophy
- 16 Distributed Swarm Thinking Context Architecture
  16 Distributed Swarm Thinking Context Architecture
  - 16.1 Purpose
  - 16.2 Why this section exists
  - 16.3 Architectural intent
  - 16.4 Core design principles
    
    16.4 Core design principles
    
    16.4.1 Structured collaborative reasoning over monolithic reasoning
    
    16.4.2 Memory-guided context instead of brute-force long context
    
    16.4.3 Inspectable swarm thinking
    
    16.4.4 Reputation-aware specialization
    
    16.4.5 Quantization-aware modularity
  - 16.5 System overview
    
    16.5 System overview
    
    16.5.1 High-level flow
  - 16.6 Thinking context model
    
    16.6 Thinking context model
    
    16.6.1 Definition
    
    16.6.2 Why this matters
  - 16.7 Memory and context construction
    
    16.7 Memory and context construction
    
    16.7.1 Bridge Blocks
    
    16.7.2 Fact store
    
    16.7.3 Profile layer
    
    16.7.4 Retrieval flow
  - 16.8 Specialist taxonomy
    
    16.8 Specialist taxonomy
    
    16.8.1 Role specialists
    
    16.8.2 Domain specialists
  - 16.9 Recommended evolution from current specialists
    
    16.9 Recommended evolution from current specialists
    
    16.9.1 Current state
    
    16.9.2 Recommended near-term state
    
    16.9.3 Recommended medium-term state
  - 16.10 Routing model
    
    16.10 Routing model
    
    16.10.1 MVP routing
    
    16.10.2 Future learned routing
  - 16.11 Execution patterns
    
    16.11 Execution patterns
    
    16.11.1 Core-only response
    
    16.11.2 Sequential specialist chain
    
    16.11.3 Distributed swarm execution
    
    16.11.4 Streaming draft with delayed refinement
  - 16.12 Thinking trace schema
    
    16.12 Thinking trace schema
    
    16.12.1 Example trace sections
  - 16.13 Relation to consensus and reputation
    
    16.13 Relation to consensus and reputation
    
    16.13.1 Current score types
    
    16.13.2 Recommended future score types
  - 16.14 Interaction with SGFP4, Turbo Quant, and Sparse-V
    
    16.14 Interaction with SGFP4, Turbo Quant, and Sparse-V
    
    16.14.1 Semantic Core
    
    16.14.2 Small specialists
    
    16.14.3 Verifier and router models
    
    16.14.4 Sparse-V implications
    
    16.14.5 Open quantization questions
  - 16.15 Adapter and distillation implications
    
    16.15 Adapter and distillation implications
    
    16.15.1 Recommended documentation additions
    
    16.15.2 Distillation targets by role
  - 16.16 Summary
- 17. Context Lifecycle, Caching, and Governance
  17. Context Lifecycle, Caching, and Governance
  - 17.1 Purpose
  - 17.2 Normative Language
  - 17.3 Scope
  - 17.4 Non-Goals
  - 17.5 Architectural Position
  - 17.6 Core Design Principles
    
    17.6 Core Design Principles
    
    17.6.1 Smallest Sufficient Context
    
    17.6.2 Off-Window by Default for Large Material
    
    17.6.3 Stable Before Volatile
    
    17.6.4 Deterministic Construction
    
    17.6.5 Specialists as Lossy Filters
    
    17.6.6 Compaction at Semantic Boundaries
    
    17.6.7 Hierarchical Budgets
    
    17.6.8 Explainability Without Raw Hidden Reasoning
    
    17.6.9 Privacy Never Widens Through Optimization
  - 17.7 Canonical Context Packet Model
  - 17.8 Stable-Prefix Contract
    
    17.8 Stable-Prefix Contract
    
    17.8.1 Permitted Stable Material
    
    17.8.2 Prohibited Volatile Material
    
    17.8.3 Canonicalization
  - 17.9 Prefix Cache Identity and Compatibility
  - 17.10 Cache Lifecycle and Invalidation
  - 17.11 Context Selection and Hierarchical Token Allocation
    
    17.11 Context Selection and Hierarchical Token Allocation
    
    17.11.1 Budget Order
    
    17.11.2 Stage Budgets
    
    17.11.3 Selection Priority
    
    17.11.4 Duplicate and Supersession Filtering
  - 17.12 Off-Window Content and Artifact References
  - 17.13 Expert Isolation and the ExpertDigest Contract
  - 17.14 Capability Schema Loading
  - 17.15 Context Compaction Lifecycle
    
    17.15 Context Compaction Lifecycle
    
    17.15.1 Compaction Modes
    
    17.15.2 Preservation Requirements
    
    17.15.3 Compaction Record
    
    17.15.4 Hierarchical Bridge Blocks
    
    17.15.5 Compaction Quality Check
  - 17.16 Context Inspector
  - 17.17 Routing and Scheduling Integration
  - 17.18 Verification and Early Exit
  - 17.19 Security, Privacy, and Trust Requirements
    
    17.19 Security, Privacy, and Trust Requirements
    
    17.19.1 Cache Isolation
    
    17.19.2 No Secret Material in Model Prefixes
    
    17.19.3 Revocation and Deletion
    
    17.19.4 Cache Poisoning and Context Injection
    
    17.19.5 Trust Separation
  - 17.20 Observability and Evaluation
  - 17.21 Failure Modes and Required Fallbacks
    
    17.21 Failure Modes and Required Fallbacks
    
    17.21.1 Over-Compaction
    
    17.21.2 Prefix Churn
    
    17.21.3 Stale Compatible Cache
    
    17.21.4 Retrieval Omission
    
    17.21.5 Expert Digest Loss
    
    17.21.6 Cross-Scope Leakage
    
    17.21.7 Budget Starvation
    
    17.21.8 Cache-Centric Routing Regression
  - 17.22 Canonical Interfaces and Data Contracts
    
    17.22 Canonical Interfaces and Data Contracts
    
    17.22.1 ContextInclusionDecision
    
    17.22.2 PrefixCacheEvent
    
    17.22.3 CompactionRecord
  - 17.23 Initial Implementation Requirements
  - 17.24 Acceptance Criteria
  - 17.25 Rollout Plan
    
    17.25 Rollout Plan
    
    17.25.1 Phase One — Instrumentation
    
    17.25.2 Phase Two — Deterministic Context Compilation
    
    17.25.3 Phase Three — Prefix Cache Management
    
    17.25.4 Phase Four — Expert Isolation and Lazy Schemas
    
    17.25.5 Phase Five — Compaction and Inspection
    
    17.25.6 Phase Six — Adaptive Optimization
  - 17.26 Design Principle
  - 17.27 Non-Normative Motivation
- 18 Secure Agent Architecture for the GNUS.ai Decentralized Cognitive System
  18 Secure Agent Architecture for the GNUS.ai Decentralized Cognitive System
  - 18.1 Product Technical Design Specification
    
    18.1 Product Technical Design Specification
    
    18.1.1 Goals and Success Criteria
    
    18.1.2 System Overview
    
    18.1.3 Core Components
    
    18.1.4 End-to-End Data Flows
    
    18.1.5 Interfaces and Data Contracts
    
    18.1.6 Reliability, Fault Tolerance, and Quality Control
    
    18.1.7 MVP Implementation Mapping
    
    18.1.8 Metrics and Observability
    
    18.1.9 Open Decisions for Next Iteration
    
    18.1.10 Implementation Notes and Recommendations
    
    18.1.11 Hand-off Instructions for Next Engineer or LLM
    
    18.1.12 Summary
- 19. EGGROLL Swarm Retraining Architecture
  19. EGGROLL Swarm Retraining Architecture
- 20. Targeted Retraining and Hierarchical Critical Thinking Specialists
  20. Targeted Retraining and Hierarchical Critical Thinking Specialists
  - 20.1 Overview
  - 20.2 Targeted Retraining
    
    20.2 Targeted Retraining
    
    20.2.1 Key Properties
  - 20.3 EGGROLL-Based Optimization
    
    20.3 EGGROLL-Based Optimization
    
    20.3.1 Why EGGROLL
    
    20.3.2 Optimization Targets
    
    20.3.3 Reward Signals
  - 20.4 Hierarchical Critical Thinking Specialists (HCTS)
    
    20.4 Hierarchical Critical Thinking Specialists (HCTS)
    
    20.4.1 Hierarchical Structure
  - 20.5 Functional Responsibilities
  - 20.6 Bias-Aware Reasoning
  - 20.7 Cognitive Resistance Layer
    
    20.7 Cognitive Resistance Layer
    
    20.7.1 Modes
    
    20.7.2 Adaptive Friction Triggers
  - 20.8 Integration with Cognitive Twin
  - 20.9 Continuous Learning Loop
  - 20.10 System Outcome
  - 20.11 Summary
- 21. Data-Driven Epistemic Arbitration and Cognitive OS Extensions
  21. Data-Driven Epistemic Arbitration and Cognitive OS Extensions
  - 21.1 Purpose
  - 21.2 Why this section exists
  - 21.3 Architectural intent
  - 21.4 Core design principles
    
    21.4 Core design principles
    
    21.4.1 Arbitration is a first-class cognitive function
    
    21.4.2 Epistemic frameworks are modular and swappable
    
    21.4.3 Framework logic should be data-driven
    
    21.4.4 The Requestor Node is the correct control point
    
    21.4.5 Inspectable reasoning should not depend on raw chain-of-thought exposure
    
    21.4.6 Plugins should remain extremely small
  - 21.5 Relationship to the existing Genius architecture
    
    21.5 Relationship to the existing Genius architecture
    
    21.5.1 Relation to the Semantic Core
    
    21.5.2 Relation to ELMs and experts
    
    21.5.3 Relation to consensus
    
    21.5.4 Relation to grounding
    
    21.5.5 Relation to GAML
    
    21.5.6 Relation to HCTS
  - 21.6 Requestor Node as Epistemic Arbiter
    
    21.6 Requestor Node as Epistemic Arbiter
    
    21.6.1 Current role of the Requestor Node
    
    21.6.2 Extended role
    
    21.6.3 Why this is the right place
    
    21.6.4 Cognitive OS implication
  - 21.7 Why GQHSM is the correct runtime
    
    21.7 Why GQHSM is the correct runtime
    
    21.7.1 Problem shape
    
    21.7.2 GQHSM as the execution substrate
    
    21.7.3 Why not hardcode the frameworks directly
    
    21.7.4 Determinism and inspectability
  - 21.8 Native implementation model: C++, MNN, and separation of concerns
    
    21.8 Native implementation model: C++, MNN, and separation of concerns
    
    21.8.1 Execution stack
    
    21.8.2 Separation of concerns
    
    21.8.3 Why this is efficient
    
    21.8.4 Why this fits mobile and desktop deployment
  - 21.9 Supported epistemic framework families
    
    21.9 Supported epistemic framework families
    
    21.9.1 Sanskrit epistemology
    
    21.9.2 Kripke and modal reasoning
    
    21.9.3 Hybrid frameworks
    
    21.9.4 Future frameworks
  - 21.10 Sanskrit epistemology as a practical arbitration model
    
    21.10 Sanskrit epistemology as a practical arbitration model
    
    21.10.1 Why Sanskrit reasoning is useful here
    
    21.10.2 Mapping the phases into Genius
    
    21.10.3 Why this is better than simple weighted merge
  - 21.11 Kripke modal arbitration in practical system terms
    
    21.11 Kripke modal arbitration in practical system terms
    
    21.11.1 Why modal reasoning belongs here
    
    21.11.2 World construction
    
    21.11.3 Accessibility and survivability
    
    21.11.4 Fixed-point resolution
  - 21.12 Hybrid arbitration strategies
    
    21.12 Hybrid arbitration strategies
    
    21.12.1 Sequential hybrid
    
    21.12.2 Parallel hybrid
    
    21.12.3 Why hybridization matters
  - 21.13 GQHSM machine structure
    
    21.13 GQHSM machine structure
    
    21.13.1 Structural requirements
    
    21.13.2 Representative machine outline
    
    21.13.3 Sanskrit branch outline
    
    21.13.4 Kripke branch outline
    
    21.13.5 Hybrid branch outline
  - 21.14 JSON-defined machine configuration
    
    21.14 JSON-defined machine configuration
    
    21.14.1 Why configuration matters
    
    21.14.2 Example machine definition
    
    21.14.3 Why this matters
  - 21.15 Generic callback model
    
    21.15 Generic callback model
    
    21.15.1 Context and lifecycle callbacks
    
    21.15.2 Core reasoning callbacks
    
    21.15.3 Guard callbacks
    
    21.15.4 Why generic callbacks matter
  - 21.16 Plugin architecture
    
    21.16 Plugin architecture
    
    21.16.1 Why plugins are the right shape
    
    21.16.2 What a plugin does
    
    21.16.3 Stable plugin ABI
    
    21.16.4 Example plugin shape
    
    21.16.5 Operational advantages
  - 21.17 Future WASM extension path
    
    21.17 Future WASM extension path
    
    21.17.1 Why WASM is attractive later
    
    21.17.2 Why not require it first
    
    21.17.3 Forward compatibility
  - 21.18 Epistemic context model
    
    21.18 Epistemic context model
    
    21.18.1 Required inputs
    
    21.18.2 Why this context matters
  - 21.19 Example plugin and loader behavior
    
    21.19 Example plugin and loader behavior
    
    21.19.1 Example registration flow
    
    21.19.2 Example loader shape
  - 21.20 Output model and thinking trace
    
    21.20 Output model and thinking trace
    
    21.20.1 Example trace artifact
    
    21.20.2 Why this is important
  - 21.21 Integration with memory writeback and retraining
    
    21.21 Integration with memory writeback and retraining
    
    21.21.1 Memory writeback
    
    21.21.2 Retraining implications
  - 21.22 Strategic implications
    
    21.22 Strategic implications
    
    21.22.1 Why this matters competitively
    
    21.22.2 Why this matters architecturally
  - 21.23 Risks and open questions
  - 21.24 Summary
- 22 SGFP4 Adaptive Quantization Format
  22 SGFP4 Adaptive Quantization Format
  - 22.1 Design Goals
  - 22.2 Macroblocks (Tiling)
  - 22.3 v1 Profile — Container Layout
    
    22.3 v1 Profile — Container Layout
    
    22.3.1 Alignment and Flags-in-Offsets
  - 22.4 Affine Parameters (Scale + Bias)
  - 22.5 Per-Block Mode Flags
  - 22.6 Quantization Modes and Payload Packing
    
    22.6 Quantization Modes and Payload Packing
    
    22.6.1 FP4_AFFINE (MODE = 0)
    
    22.6.2 T158_AFFINE (MODE = 1)
  - 22.7 Encoding (Non-Normative)
  - 22.8 v2 Profile — Quadtree-Adaptive Stream
    
    22.8 v2 Profile — Quadtree-Adaptive Stream
    
    22.8.1 File Framing
    
    22.8.2 Record Layout
    
    22.8.3 v2 Decode Procedure
  - 22.9 GPU Decode Procedure (v1)
  - 22.10 Conformance and Bit-Exact Replay
  - 22.11 Cross-Referencing
- 23 Objective Memory and Verified Transition Graph (VTG)
  23 Objective Memory and Verified Transition Graph (VTG)
- 24 Speculative Decoding and VTG Candidate Scheduling
  24 Speculative Decoding and VTG Candidate Scheduling
- 25 Frozen Micro-MTP and VTG Edge Inference
  25 Frozen Micro-MTP and VTG Edge Inference
- 26 OpenAI-Compatible API Router and GCS Job Queue Architecture
  26 OpenAI-Compatible API Router and GCS Job Queue Architecture
  - 26.1 Product Technical Design Specification
  - 26.2 Background and Current Queue Context
  - 26.3 Goals
    
    26.3 Goals
    
    26.3.1 Primary Goals
    
    26.3.2 Developer Experience Goals
  - 26.4 Non-Goals for MVP
  - 26.5 Core Design Principle
  - 26.6 Job Type Split
    
    26.6 Job Type Split
    
    26.6.1 Existing Job Type: Processing Chunk Job
    
    26.6.2 New Job Type: API Request Job
    
    26.6.3 Why This Split Matters
  - 26.7 Architecture Overview
  - 26.8 Components
    
    26.8 Components
    
    26.8.1 Cloudflare Edge
    
    26.8.2 GCS API Router
    
    26.8.3 GCS Gateway Node
    
    26.8.4 Online GCS Worker Nodes
    
    26.8.5 Router / Planner Node
    
    26.8.6 Aggregator Node
  - 26.9 Pub/Sub Channels
    
    26.9 Pub/Sub Channels
    
    26.9.1 Capability Registration Channels
    
    26.9.2 API Job Channels
    
    26.9.3 Processing Chunk Channels
    
    26.9.4 Result, Stream, and Claim Channels
  - 26.10 Node Registration
    
    26.10 Node Registration
    
    26.10.1 Registration Envelope
    
    26.10.2 Heartbeat Rules
  - 26.11 API Request Job Envelope
    
    26.11 API Request Job Envelope
    
    26.11.1 Required Fields
    
    26.11.2 Payload Storage Modes
    
    26.11.3 Routing Modes
  - 26.12 Claim, Lock, and Lease Semantics
    
    26.12 Claim, Lock, and Lease Semantics
    
    26.12.1 First Valid Claim MVP
    
    26.12.2 Claim Envelope
    
    26.12.3 Lease Rules
  - 26.13 API Job Lifecycle
    
    26.13 API Job Lifecycle
    
    26.13.1 States
    
    26.13.2 Lifecycle Flow
    
    26.13.3 Cancellation Flow
  - 26.14 Child Processing Jobs
    
    26.14 Child Processing Jobs
    
    26.14.1 When to Create Child Jobs
    
    26.14.2 Child Job Reference
  - 26.15 OpenAI-Compatible API Surface
    
    26.15 OpenAI-Compatible API Surface
    
    26.15.1 /v1/models
    
    26.15.2 /v1/chat/completions
    
    26.15.3 /v1/embeddings
    
    26.15.4 GNUS Extension Object
  - 26.16 Streaming Proxy Requirements
    
    26.16 Streaming Proxy Requirements
    
    26.16.1 Streaming Proxy Responsibilities
    
    26.16.2 Recommended Streaming Headers
    
    26.16.3 First-Token Behavior
    
    26.16.4 Internal-to-External Stream Bridge
    
    26.16.5 Internal Stream Chunk
    
    26.16.6 External OpenAI-Compatible Chunk
    
    26.16.7 Backpressure and Slow Clients
    
    26.16.8 Disconnect and Cancellation Semantics
    
    26.16.9 Requeue During Streaming
    
    26.16.10 Pre-Stream vs Post-Stream Errors
    
    26.16.11 Stream Ordering and Replay Protection
    
    26.16.12 Cloudflare and Runtime Boundary
    
    26.16.13 Streaming Test Matrix
    
    26.16.14 Streaming Acceptance Criteria
  - 26.17 Result Envelope
    
    26.17 Result Envelope
    
    26.17.1 Final Result
    
    26.17.2 Error Result
  - 26.18 Queue Fairness and Democratized Pickup
    
    26.18 Queue Fairness and Democratized Pickup
    
    26.18.1 MVP Policy
    
    26.18.2 Later Policies
    
    26.18.3 Fairness State
  - 26.19 Requeue and Retry
    
    26.19 Requeue and Retry
    
    26.19.1 Requeue Reasons
    
    26.19.2 Requeue Envelope
  - 26.20 Security and Privacy
    
    26.20 Security and Privacy
    
    26.20.1 API Key Security
    
    26.20.2 Job Signature Requirements
    
    26.20.3 Prompt Privacy
    
    26.20.4 Public Queue Leakage
  - 26.21 Metering, Rewards, and Settlement
    
    26.21 Metering, Rewards, and Settlement
    
    26.21.1 Usage Record
    
    26.21.2 Settlement Hooks
  - 26.22 Data Model Changes
    
    26.22 Data Model Changes
    
    26.22.1 New Message Concepts
    
    26.22.2 Existing Processing Messages Remain
    
    26.22.3 Optional Unified Queue Wrapper
  - 26.23 CRDT Keyspace Proposal
  - 26.24 MVP Implementation Plan
    
    26.24 MVP Implementation Plan
    
    26.24.1 Phase 1: API Compatibility Shell
    
    26.24.2 Phase 2: API Request Job Schema
    
    26.24.3 Phase 3: GCS Gateway Bridge
    
    26.24.4 Phase 4: Node Registration and Claim
    
    26.24.5 Phase 5: Direct Worker Execution
    
    26.24.6 Phase 6: Child Processing Jobs
    
    26.24.7 Phase 7: Metering and Settlement
    
    26.24.8 Phase 8: Private and Hybrid Routing
  - 26.25 Acceptance Criteria
  - 26.26 Open Questions
  - 26.27 Summary
- 27. Local Cognitive Second Brain Mode
  27. Local Cognitive Second Brain Mode
  - 27.1 Purpose
  - 27.2 Architectural Position
  - 27.3 Orchestration Layer Role
  - 27.4 GAML Role
  - 27.5 Local and Private ELM Role
  - 27.6 Second Brain Agent Role
    
    27.6 Second Brain Agent Role
    
    27.6.1 Supporting Agents and Modules
  - 27.7 EGGROLL Role
  - 27.8 High-Level Flow
  - 27.9 Local Data Sources
  - 27.10 Structured Memory Objects
  - 27.11 Memory Lifecycle
    
    27.11 Memory Lifecycle
    
    27.11.1 Observe
    
    27.11.2 Extract
    
    27.11.3 Normalize
    
    27.11.4 Link
    
    27.11.5 Score
    
    27.11.6 Store
    
    27.11.7 Retrieve
    
    27.11.8 Reason
    
    27.11.9 Verify
    
    27.11.10 Write Back
    
    27.11.11 Adapt
  - 27.12 Context Packet Assembly
  - 27.13 Human-Readable Memory Mirror
  - 27.14 Privacy Modes
    
    27.14 Privacy Modes
    
    27.14.1 Local-Only Mode
    
    27.14.2 Private Enterprise Mode
    
    27.14.3 Hybrid Mode
    
    27.14.4 Explicit Swarm Contribution Mode
  - 27.15 Example Workflows
    
    27.15 Example Workflows
    
    27.15.1 Meeting Prep
    
    27.15.2 Project Drift Detection
    
    27.15.3 Personal Daily Brief
    
    27.15.4 Private ELM Adaptation
  - 27.16 Implementation Requirements
  - 27.17 Design Principle
  - 27.18 Summary
- 28. Forecast-Driven Cognition and Predictive Prefetching
  28. Forecast-Driven Cognition and Predictive Prefetching
- 29. Execution Integrity System (EIS)
  29. Execution Integrity System (EIS)
  - 29.1 Purpose
  - 29.2 Motivation and Threat Model
    
    29.2 Motivation and Threat Model
    
    29.2.1 Why zk is out of scope at the GCS layer
    
    29.2.2 The lazy-node / model-substitution attack
    
    29.2.3 Security economics
    
    29.2.4 Design principles
  - 29.3 Execution Contracts
    
    29.3 Execution Contracts
    
    29.3.1 Definition
    
    29.3.2 Contract pinning
  - 29.4 Determinism Classes
    
    29.4 Determinism Classes
    
    29.4.1 Definition
    
    29.4.2 Class A - Reference-integer semantics
    
    29.4.3 Class B - Bounded-drift semantics
    
    29.4.4 Class C - Non-deterministic paths
    
    29.4.5 Constraints on kernel authors
  - 29.5 Checkpoint-Band Matching
    
    29.5 Checkpoint-Band Matching
    
    29.5.1 Checkpoints, not per-op comparison
    
    29.5.2 Comparison domain
    
    29.5.3 Registration invariant: the band forgives hardware, not weights
    
    29.5.4 Calibration and drift monitoring
  - 29.6 Teacher-Forced Spot-Check Protocol
    
    29.6 Teacher-Forced Spot-Check Protocol
    
    29.6.1 The autoregressive divergence problem
    
    29.6.2 Teacher-forced replay
    
    29.6.3 Cost profile
    
    29.6.4 Sampling-consistency check
    
    29.6.5 Scheduling and economics
  - 29.7 Interaction with Semantic Consensus
  - 29.8 Interaction with GAML and Cognitive Assets
  - 29.9 Open Items
  - 29.10 Cross-References
- 30. GCS Capability System
  30. GCS Capability System
- 31. Agent and Module Development Inventory
  31. Agent and Module Development Inventory
  - 31.1 Purpose
  - 31.2 Inventory Conventions
    
    31.2 Inventory Conventions
    
    31.2.1 Component Classes
    
    31.2.2 Required Definition for Each Component
    
    31.2.3 Trust Tiers
  - 31.3 Reference Runtime Topology
- 31.4 Executive Control and Orchestration Inventory
  31.4 Executive Control and Orchestration Inventory
- 31.5 Semantic Core and Expert Execution Inventory
  31.5 Semantic Core and Expert Execution Inventory
- 31.6 Forecast-Driven Cognition Inventory
  31.6 Forecast-Driven Cognition Inventory
- 31.7 GAML and Cognitive Asset Inventory
  31.7 GAML and Cognitive Asset Inventory
- 31.8 Objective Memory and VTG Inventory
  31.8 Objective Memory and VTG Inventory
- 31.9 Grounding, Verification, Arbitration, and Synthesis Inventory
  31.9 Grounding, Verification, Arbitration, and Synthesis Inventory
- 31.10 Reputation and Consensus Infrastructure
  31.10 Reputation and Consensus Infrastructure
- 31.11 Execution Integrity System Inventory
  31.11 Execution Integrity System Inventory
- 31.12 GCS Capability System and Connector Inventory
  31.12 GCS Capability System and Connector Inventory
- 31.13 Secure Tool Intermediary Inventory
  31.13 Secure Tool Intermediary Inventory
- 31.14 Local Cognitive Second Brain Inventory
  31.14 Local Cognitive Second Brain Inventory
- 31.15 OpenAI-Compatible API and GCS Job Queue Inventory
  31.15 OpenAI-Compatible API and GCS Job Queue Inventory
- 31.16 Distributed Infrastructure Inventory
  31.16 Distributed Infrastructure Inventory
- 31.17 EGGROLL and Adaptive Learning Inventory
  31.17 EGGROLL and Adaptive Learning Inventory
- 31.18 Security, Privacy, and Governance Inventory
  31.18 Security, Privacy, and Governance Inventory
- 31.19 User and Operator Interface Inventory
  31.19 User and Operator Interface Inventory
- 31.20 Observability and Operational Services
  31.20 Observability and Operational Services
- 31.21 Canonical Interface and Schema Inventory
- 31.22 Deployment Profiles
  31.22 Deployment Profiles
- 31.23 Logical Workstreams and Package Boundaries
- 31.24 Recommended Delivery Sequence
  31.24 Recommended Delivery Sequence
- 31.25 Validation and Test Inventory
  31.25 Validation and Test Inventory
- 31.26 Component Definition of Done
- 31.27 Summary
GNUS-NEO-SWARM Source
GNUS-NEO-SWARM Source
- Classes
  Classes
  - Args
  - FlutterWindow
  - GeniusElmFFI
  - PipelineTest
  - Win32Window
    
    Win32Window
    
    None
    
    Point
    
    Size
  - WindowClassRegistrar
  - sgns
    sgns
    
    neoswarm
    neoswarm
    
    ChainStep
    
    ELMContext
    
    ExecutionChain
    
    InferenceResponse
    
    KnowledgeFact
    
    NodeOutput
    
    NodeReputation
    
    PromptFeatures
    
    RouteDecision
    
    Task
    
    api
    api
    
    ApiServer
    
    ApiServer
    
    None
    
    Config
    
    core
    core
    
    InferenceEngine
    
    MNNInferenceEngine
    
    MNNInferenceEngine
    
    None
    
    Config
    
    SGProcessingBridge
    
    SGProcessingBridge
    
    None
    
    Config
    
    TensorInterpreter
    
    Tokenizer
    
    elm
    elm
    
    IELM
    
    fp4
    fp4
    
    FP4Codec
    
    FP4Tensor
    
    knowledge
    knowledge
    
    ContextInjection
    
    ContextInjection
    
    None
    
    Config
    
    FactValidation
    
    FactValidation
    
    None
    
    ValidationResult
    
    KnowledgeRetrieval
    
    KnowledgeRetrieval
    
    None
    
    Config
    
    Impl
    
    Impl
    
    None
    
    FactEntry
    
    network
    network
    
    P2PNode
    
    P2PNode
    
    None
    
    Config
    
    Impl
    
    Impl
    
    None
    
    GossipSubs
    
    ResultAggregation
    
    ResultAggregation
    
    None
    
    Config
    
    SGClient
    
    SGClient
    
    None
    
    Config
    
    Impl
    
    SGJobSubmitter
    
    SGResultCollector
    
    SGResultCollector
    
    None
    
    Impl
    
    SGResultCollectorConfig
    
    reputation
    reputation
    
    ReputationCRDT
    
    ReputationScoring
    
    ReputationScoring
    
    None
    
    Config
    
    ReputationStorage
    
    ReputationStorage
    
    None
    
    Impl
    
    WeightedConsensus
    
    WeightedConsensus
    
    None
    
    Config
    
    router
    router
    
    IRouter
    
    PromptAnalyzer
    
    RuleBasedRouter
    
    RuleBasedRouter
    
    None
    
    Config
    
    security
    security
    
    MessageSigning
    
    NodeIdentity
    
    NodeIdentity
    
    None
    
    Impl
    
    specialists
    specialists
    
    GrammarSpecialist
    
    ISpecialist
    
    MathSpecialist
    
    SymbolicFallback
    
    SymbolicFallback
    
    None
    
    Parser
- Files
  Files
  - GNUS-NEO-SWARM
    
    GNUS-NEO-SWARM
    
    flutter_app
    
    flutter_app
    
    ios
    
    ios
    
    Runner
    
    Runner
    
    Runner/flutter_app/ios/Runner/Runner-Bridging-Header.h
    
    linux
    
    linux
    
    flutter
    
    flutter
    
    flutter/linux/flutter/generated_plugin_registrant.h
    
    runner
    
    runner
    
    runner/flutter_app/linux/runner/my_application.h
    
    windows
    
    windows
    
    flutter
    
    flutter
    
    flutter/windows/flutter/generated_plugin_registrant.h
    
    runner
    
    runner
    
    runner/flutter_app/windows/runner/flutter_window.cpp
    
    runner/flutter_app/windows/runner/flutter_window.h
    
    runner/flutter_app/windows/runner/main.cpp
    
    runner/flutter_app/windows/runner/resource.h
    
    runner/flutter_app/windows/runner/utils.cpp
    
    runner/flutter_app/windows/runner/utils.h
    
    runner/flutter_app/windows/runner/win32_window.cpp
    
    runner/flutter_app/windows/runner/win32_window.h
    
    flutter_slm_bridge
    
    flutter_slm_bridge
    
    example
    
    example
    
    ios
    
    ios
    
    Runner
    
    Runner
    
    Runner/flutter_slm_bridge/example/ios/Runner/Runner-Bridging-Header.h
    
    linux
    
    linux
    
    runner
    
    runner
    
    runner/flutter_slm_bridge/example/linux/runner/my_application.h
    
    windows
    
    windows
    
    runner
    
    runner
    
    runner/flutter_slm_bridge/example/windows/runner/flutter_window.cpp
    
    runner/flutter_slm_bridge/example/windows/runner/flutter_window.h
    
    runner/flutter_slm_bridge/example/windows/runner/main.cpp
    
    runner/flutter_slm_bridge/example/windows/runner/resource.h
    
    runner/flutter_slm_bridge/example/windows/runner/utils.cpp
    
    runner/flutter_slm_bridge/example/windows/runner/utils.h
    
    runner/flutter_slm_bridge/example/windows/runner/win32_window.cpp
    
    runner/flutter_slm_bridge/example/windows/runner/win32_window.h
    
    ios
    
    ios
    
    Classes
    
    Classes
    
    Classes/ios/Classes/flutter_slm_bridge.c
    
    macos
    
    macos
    
    Classes
    
    Classes
    
    Classes/macos/Classes/flutter_slm_bridge.c
    
    src
    
    src
    
    src/flutter_slm_bridge.h
    
    src/os_defines.h
    
    src/src/flutter_slm_bridge.c
    
    src
    
    src
    
    api
    
    api
    
    api/api_server.cpp
    
    api/api_server.hpp
    
    common
    
    common
    
    common/error.cpp
    
    common/error.hpp
    
    common/logging.hpp
    
    common/types.hpp
    
    core
    
    core
    
    engine
    
    engine
    
    engine/inference_engine.hpp
    
    engine/mnn_inference_engine.cpp
    
    engine/mnn_inference_engine.hpp
    
    fp4
    
    fp4
    
    fp4/fp4_codec.cpp
    
    fp4/fp4_codec.hpp
    
    sgprocessing
    
    sgprocessing
    
    sgprocessing/sg_processing_bridge.cpp
    
    sgprocessing/sg_processing_bridge.hpp
    
    sgprocessing/tensor_interpreter.cpp
    
    sgprocessing/tensor_interpreter.hpp
    
    tokenizer
    
    tokenizer
    
    tokenizer/tokenizer.hpp
    
    elm
    
    elm
    
    elm/elm_stub.cpp
    
    elm/i_elm.hpp
    
    src/genius_elm_chat_c.cpp
    
    src/genius_elm_chat_completions.cpp
    
    src/genius_elm_chat_completions.h
    
    knowledge
    
    knowledge
    
    knowledge/context_injection.cpp
    
    knowledge/context_injection.hpp
    
    knowledge/fact_validation.cpp
    
    knowledge/fact_validation.hpp
    
    knowledge/knowledge_retrieval.cpp
    
    knowledge/knowledge_retrieval.hpp
    
    network
    
    network
    
    network/p2p_node.cpp
    
    network/p2p_node.hpp
    
    network/result_aggregation.cpp
    
    network/result_aggregation.hpp
    
    sg_client
    
    sg_client
    
    sg_client/sg_job_submitter.cpp
    
    sg_client/sg_job_submitter.hpp
    
    sg_client/sg_result_collector.cpp
    
    sg_client/sg_result_collector.hpp
    
    sg_client/super_genius_client.cpp
    
    sg_client/super_genius_client.hpp
    
    reputation
    
    reputation
    
    reputation/node_reputation.hpp
    
    reputation/reputation_crdt.cpp
    
    reputation/reputation_crdt.hpp
    
    reputation/reputation_scoring.cpp
    
    reputation/reputation_scoring.hpp
    
    reputation/reputation_storage.cpp
    
    reputation/reputation_storage.hpp
    
    reputation/weighted_consensus.cpp
    
    reputation/weighted_consensus.hpp
    
    router
    
    router
    
    router/i_router.hpp
    
    router/prompt_analyzer.cpp
    
    router/prompt_analyzer.hpp
    
    router/rule_based_router.cpp
    
    router/rule_based_router.hpp
    
    security
    
    security
    
    security/message_signing.cpp
    
    security/message_signing.hpp
    
    security/node_identity.cpp
    
    security/node_identity.hpp
    
    specialists
    
    specialists
    
    specialists/grammar_specialist.cpp
    
    specialists/grammar_specialist.hpp
    
    specialists/i_specialist.hpp
    
    specialists/math_specialist.cpp
    
    specialists/math_specialist.hpp
    
    specialists/symbolic_fallback.cpp
    
    specialists/symbolic_fallback.hpp
    
    src/src/main.cpp
    
    test
    
    test
    
    benchmark
    
    benchmark
    
    benchmark/bench_mnn_llm.cpp
    
    benchmark/os_memory.hpp
    
    common
    
    common
    
    common/test_types.cpp
    
    core
    
    core
    
    core/test_fp4_codec.cpp
    
    ffi
    
    ffi
    
    ffi/test_genius_elm_ffi.cpp
    
    integration
    
    integration
    
    integration/test_pipeline.cpp
    
    integration/test_sgprocessing_pipeline.cpp
    
    knowledge
    
    knowledge
    
    knowledge/test_context_injection.cpp
    
    knowledge/test_fact_validation.cpp
    
    knowledge/test_knowledge_retrieval.cpp
    
    network
    
    network
    
    network/test_network.cpp
    
    network/test_sg_client.cpp
    
    reputation
    
    reputation
    
    reputation/test_reputation.cpp
    
    router
    
    router
    
    router/test_router.cpp
    
    security
    
    security
    
    security/test_message_signing.cpp
    
    security/test_node_identity.cpp
    
    specialists
    
    specialists
    
    specialists/test_grammar_specialist.cpp
    
    specialists/test_math_specialist.cpp
    
    specialists/test_symbolic_fallback.cpp
    
    ui
    
    ui
    
    ios
    
    ios
    
    Runner
    
    Runner
    
    Runner/ui/ios/Runner/Runner-Bridging-Header.h
    
    linux
    
    linux
    
    linux/ui/linux/my_application.h
    
    windows
    
    windows
    
    runner
    
    runner
    
    runner/ui/windows/runner/flutter_window.cpp
    
    runner/ui/windows/runner/flutter_window.h
    
    runner/ui/windows/runner/main.cpp
    
    runner/ui/windows/runner/resource.h
    
    runner/ui/windows/runner/utils.cpp
    
    runner/ui/windows/runner/utils.h
    
    runner/ui/windows/runner/win32_window.cpp
    
    runner/ui/windows/runner/win32_window.h
- Namespaces
  Namespaces
  - MNN
    
    MNN
    
    None
    
    Transformer
  - boost
    
    boost
    
    None
    
    asio
  - sgns
    
    sgns
    
    None
    
    neoswarm
    
    neoswarm
    
    None
    
    api
    
    core
    
    elm
    
    fp4
    
    knowledge
    
    network
    
    reputation
    
    router
    
    security
    
    specialists
  - sgns::neoswarm::api
  - sgns::neoswarm::core
  - sgns::neoswarm::core
  - sgns::neoswarm::core
  - sgns::neoswarm::fp4
  - sgns::neoswarm::knowledge
  - sgns::neoswarm::knowledge
  - sgns::neoswarm::network
  - sgns::neoswarm::network
  - sgns::neoswarm::network
  - sgns::neoswarm::network
  - sgns::neoswarm::network
  - sgns::neoswarm::reputation
  - sgns::neoswarm::reputation
  - sgns::neoswarm::reputation
  - sgns::neoswarm::reputation
  - sgns::neoswarm::router
  - sgns::neoswarm::router
  - sgns::neoswarm::security
  - sgns::neoswarm::security
  - sgns::neoswarm::specialists
  - sgns::neoswarm::specialists
  - std
  - testing
Python (gnus-poc)
Python (gnus-poc)
- Classes
  Classes
  - config
    config
    
    loader
    loader
    
    ConfigLoader
    
    ConfigValidationError
  - distill
    distill
    
    backends
    backends
    
    anthropic_backend
    anthropic_backend
    
    AnthropicBackend
    
    base
    base
    
    TeacherBackend
    
    openai_backend
    openai_backend
    
    OpenAIBackend
    
    cascade
    cascade
    
    CascadeResult
    
    TeacherCascade
    
    distillation
    distillation
    
    Distiller
    
    synthetic
    synthetic
    
    SyntheticDataGenerator
    
    teacher
    teacher
    
    TeacherClient
    
    _ResponseWrapper
    
    teacher_errors
    teacher_errors
    
    BackendNotFoundError
    
    BudgetExceededError
    
    CircuitBreakerOpenError
    
    SyntheticDataError
    
    TeacherConfigError
  - eval
    eval
    
    benchmark_config
    benchmark_config
    
    ConfigError
    
    benchmark_mlx_model
    benchmark_mlx_model
    
    MLXBenchmarkModel
    
    benchmark_runner
    benchmark_runner
    
    BenchmarkRunner
    
    benchmarker
    benchmarker
    
    Benchmarker
    
    MissingBaselineError
    
    evaluator
    evaluator
    
    SpecialistEvaluator
    
    metric_store
    metric_store
    
    MetricStore
  - pipeline
    pipeline
    
    checkpoint
    checkpoint
    
    CheckpointValidator
    
    StageValidationResult
    
    runner
    runner
    
    PipelineRunner
    
    StageResult
  - quantize
    quantize
    
    fp4_exporter
    fp4_exporter
    
    FP4Exporter
    
    laplacian
    laplacian
    
    LaplacianWeightedError
    
    manifest
    manifest
    
    ManifestBuilder
    
    quadtree
    quadtree
    
    QuadtreeEncoder
    
    sgfp4_decoder
    sgfp4_decoder
    
    SGFP4FormatError
    
    sgfp4_format
    sgfp4_format
    
    CodeMode
    
    Layout
  - training
    training
    
    config
    config
    
    TrainingConfig
    
    tracker
    tracker
    
    ExperimentTracker
- Files
  Files
  - GNUS-NEO-SWARM
    
    GNUS-NEO-SWARM
    
    gnus-poc
    
    gnus-poc
    
    config
    
    config
    
    config/config/init.py
    
    config/loader.py
    
    data
    
    data
    
    scripts
    
    scripts
    
    scripts/analyze_common_pile.py
    
    scripts/data/scripts/init.py
    
    scripts/extract_source_niches.py
    
    scripts/prepare_datasets.py
    
    distill
    
    distill
    
    backends
    
    backends
    
    backends/anthropic_backend.py
    
    backends/base.py
    
    backends/distill/backends/init.py
    
    backends/openai_backend.py
    
    distill/cascade.py
    
    distill/distill/init.py
    
    distill/distillation.py
    
    distill/synthetic.py
    
    distill/teacher.py
    
    distill/teacher_errors.py
    
    eval
    
    eval
    
    eval/benchmark_config.py
    
    eval/benchmark_fingerprint.py
    
    eval/benchmark_mlx_model.py
    
    eval/benchmark_repair.py
    
    eval/benchmark_runner.py
    
    eval/benchmark_tasks.py
    
    eval/benchmark_trends.py
    
    eval/benchmarker.py
    
    eval/eval/init.py
    
    eval/evaluator.py
    
    eval/metric_store.py
    
    pipeline
    
    pipeline
    
    pipeline/checkpoint.py
    
    pipeline/pipeline/init.py
    
    pipeline/runner.py
    
    quantize
    
    quantize
    
    quantize/fp4_exporter.py
    
    quantize/laplacian.py
    
    quantize/manifest.py
    
    quantize/quadtree.py
    
    quantize/quantize/init.py
    
    quantize/sgfp4_decoder.py
    
    quantize/sgfp4_format.py
    
    training
    
    training
    
    training/config.py
    
    training/dedup.py
    
    training/memory.py
    
    training/tokenizer_utils.py
    
    training/tracker.py
    
    training/train_specialists.py
    
    training/train_specialists_mlx.py
    
    training/training/init.py
- Namespaces
  Namespaces
  - config
    
    config
    
    None
    
    loader
  - distill
    
    distill
    
    None
    
    backends
    
    backends
    
    None
    
    anthropic_backend
    
    base
    
    openai_backend
    
    cascade
    
    distillation
    
    synthetic
    
    teacher
    
    teacher_errors
  - eval
    
    eval
    
    None
    
    benchmark_config
    
    benchmark_fingerprint
    
    benchmark_mlx_model
    
    benchmark_repair
    
    benchmark_runner
    
    benchmark_tasks
    
    benchmark_trends
    
    benchmarker
    
    evaluator
    
    metric_store
  - pipeline
    
    pipeline
    
    None
    
    checkpoint
    
    runner
  - quantize
    
    quantize
    
    None
    
    fp4_exporter
    
    laplacian
    
    manifest
    
    quadtree
    
    sgfp4_decoder
    
    sgfp4_format
  - scripts
    
    scripts
    
    None
    
    analyze_common_pile
    
    extract_source_niches
    
    prepare_datasets
  - std
  - training
    
    training
    
    None
    
    config
    
    dedup
    
    memory
    
    tokenizer_utils
    
    tracker
    
    train_specialists
    
    train_specialists_mlx

Table of contents

Directories

gnus-poc¶

Directories¶

Name
config
data
distill
eval
pipeline
quantize
training

Updated on 2026-09-30 at 18:57:58 +0000