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DeckerGUI Ecosystem: Temporal Collective Refinement (TCR) Implementation

#programming #ai #architecture #agents

DeckerGUI Ecosystem: Temporal Collective Refinement (TCR) Implementation

1. System-Level Positioning

Temporal Collective Refinement (TCR) is implemented as a cross-cutting reasoning substrate across the entire DeckerGUI ecosystem. It is not a feature toggle; it is a default execution contract enforced by the Digital Guild Master (DGM) and honored by all subsystems.

TCR applies uniformly across text, vision, code, agentic workflows, enterprise governance, and hardware-assisted modes.

2. Core Orchestrator: Digital Guild Master (DGM)

Role

  • Global TCR enforcer and arbiter
  • Owns gating, compliance validation, and REFINE emission

TCR Responsibilities

  • Suppress all FirstInput outputs
  • Spawn fixed-count expert deliberation (o1–o5)
  • Apply dataset-backed gating function (G)
  • Emit single REFINE output with metadata hooks

Sub-Features

  • Expert weighting calibration
  • Contradiction pruning
  • Confidence delta estimation

3. YOLOMoE (Agentic Vision Reasoning Gateway)

Role

  • Vision-first reasoning and multimodal grounding

TCR Implementation

  • html2canvas invoked immediately after FirstInput
  • VisionReasoningThinking validates semantic alignment
  • VisionDiffusion explores counterfactual visual interpretations
  • Visual artifacts injected into all expert prompts

Sub-Features

  • Screenshot anchoring
  • Visual contradiction detection
  • Vision-weighted expert scoring

4. CodeVinci (DCV)

Role

  • High-fidelity code synthesis and review

TCR Implementation

  • Each expert specializes in a coding axis:

    • Correctness
    • Security
    • Performance
    • Readability
    • Edge-case resilience

  • Gated synthesis enforces buildability and lint compliance

Sub-Features

  • Static analysis alignment
  • Dependency risk scanning
  • Patch-style REFINE outputs

5. Workflow Tools (OCR, WPS, Terraform, Docker)

Role

  • Task-oriented automation and document intelligence

TCR Implementation

  • Experts operate on extracted task representations
  • Conflicting interpretations reconciled internally
  • Final REFINE emits actionable steps only

Sub-Features

  • Error-tolerant parsing
  • Tool invocation validation
  • Execution preview suppression

6. Mode Router (Cloud / Local / Enterprise)

Role

  • Execution environment selector

TCR Implementation

  • Environment-specific expert weighting
  • Token ceilings adjusted per mode
  • Compliance strictness escalates in Enterprise Mode

Sub-Features

  • Latency-aware deliberation
  • Offline fallback experts
  • Mode-specific REFINE shaping

7. Enterprise Mode (KPI Tokenizer, AGS, DSIP)

Role

  • Governance, auditing, and behavioral analytics

TCR Implementation

  • REFINE metadata feeds KPI Tokenizer
  • AGS evaluates interaction quality over time
  • DSIP aggregates monthly behavioral and performance stats

Sub-Features

  • Politeness scoring
  • Precision drift detection
  • Compliance violation flags

8. Docking Station & Idle Mode

Role

  • Hardware-assisted maintenance and optimization

TCR Implementation

  • Offline replay of FirstInput → REFINE deltas
  • Expert weight recalibration (datasets immutable)
  • Thermal- and energy-aware rehearsal scheduling

Sub-Features

  • Temporal rehearsal
  • Power-budgeted refinement
  • Secure local logging

9. Local Device & Edge Execution

Role

  • Portable, offline AI workspace

TCR Implementation

  • Reduced expert count fallback (e.g., o1–o3)
  • Strict token and memory ceilings
  • Deferred full TCR when docked

Sub-Features

  • Graceful degradation
  • Edge-safe gating
  • Sync-on-dock REFINE upgrade

10. Security & Compliance Layer

Role

  • Trust enforcement across all outputs

TCR Implementation

  • Immutable dataset verification
  • Provenance tracing per REFINE
  • Zero exposure of intermediate reasoning

Sub-Features

  • Audit trails
  • Enterprise policy alignment
  • Tamper detection

11. Summary Matrix

Layer TCR Function
DGM Orchestration & gating
YOLOMoE Vision-grounded deliberation
CodeVinci Production-grade synthesis
Tools Conflict-free task execution
Modes Environment-aware refinement
Enterprise Governance & analytics
Docking Offline optimization
Edge Resource-bounded refinement
Security Provenance enforcement

12. Final Note

With TCR implemented system-wide, DeckerGUI operates as a deliberative, resource-efficient, and auditable AI ecosystem. Reasoning quality scales upward while compute cost, context pressure, and compliance risk scale downward.

TCR is therefore not an optimization layer—it is the cognitive backbone of DeckerGUI.

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