ACM Modernization Roadmap 2026-2027

ACM Modernization Roadmap 2026-2027

The Artificial Consciousness Module (ACM) project is undergoing a comprehensive modernization to align with state-of-the-art open-source AI while maintaining our core hypothesis: consciousness emerges from emotional homeostasis. This roadmap outlines our 18-month development plan across six distinct phases.

Executive Summary

Goal: Build a measurable, reproducible framework for artificial consciousness that demonstrates emergent awareness through emotional learning.

Timeline: 18 months (Q1 2026 - Q2 2027)

Key Milestones:

• Q1 2026: Unity Dark Room validation, model benchmarking
• Q2 2026: Enhanced RL and GWN with intrinsic motivation
• Q3 2026: 12+ progressive simulation scenarios
• Q4 2026: 1000+ episode Φ analysis and validation
• Q1 2027: Production-ready distributed training
• Q2 2027: Academic publications and open-source release

Phase 1: Foundation (Months 1-3, Q1 2026)

Goal: Update core models and establish Unity baseline.

Model Migration

• Audit current Qwen2-VL implementation
• Benchmark Qwen2-VL vs InternVL-2.5 vs LLaVA-NeXT
• Implement Qwen2.5-14B for narration and emotional tagging
• Add Phi-4-14B for real-time emotional responses
• Test quantization strategies (4-bit, 8-bit) on target hardware

Unity ML-Agents Setup

• Install Unity 2022.3 LTS with ML-Agents Package
• Create basic “Dark Room” environment in Unity
• Implement Side Channels for Φ and emotion data streaming
• Port existing AgentManager.cs to Unity
• Establish Python to Unity communication pipeline

IIT Optimization

• Implement fast Φ* proxies (Lempel-Ziv complexity, neural complexity)
• Set up periodic full PyPhi calculations (every N episodes)
• Create Φ visualization in Unity HUD
• Benchmark proxy accuracy vs ground-truth Φ

Deliverables:

• Unity Dark Room environment with real-time Φ visualization
• Benchmarked vision model comparison report
• Baseline Φ measurements on simple scenarios

Phase 2: Core Enhancements (Months 4-6, Q2 2026)

Goal: Enhance RL, GWN, and emotional systems.

Reinforcement Learning Upgrades

• Implement RND (Random Network Distillation) for intrinsic motivation
• Replace DreamerV3 with Transformer-based World Model
• Add PettingZoo for multi-agent scenarios
• Integrate empowerment maximization (mutual information optimization)

Global Workspace Network Improvements

• Implement Mixture of Experts (4-8 specialized processors)
• Add Mamba or Mamba-2 for temporal coherence
• Integrate Flash Attention 2 for computational efficiency
• Add external memory module (DNC-style architecture)

Emotional System Refinement

• Formalize Affective RL framework mathematically
• Add homeostatic regulation mechanisms (temperature, hunger)
• Implement emotional memory consolidation
• Create emotional trajectory visualization tools

Deliverables:

• Enhanced PPO with intrinsic motivation working in Unity
• MoE-based GWN with 8 specialized processors
• Emotional memory consolidation system

Phase 3: Simulation Complexity Scaling (Months 7-9, Q3 2026)

Goal: Build progressive simulation scenarios across three complexity stages.

Stage 1 Simulations (Survival)

• Light-seeking (Dark Room baseline)
• Food-seeking (hunger homeostasis)
• Threat avoidance (predator scenarios)
• Temperature regulation

Stage 2 Simulations (Social)

• 2-agent cooperation tasks
• Trust-building scenarios
• Resource sharing dilemmas
• Emotional contagion experiments

Stage 3 Simulations (Advanced)

• Mirror test (self-recognition)
• Delayed gratification tasks
• Moral dilemma scenarios
• Creative problem-solving challenges

Deliverables:

• 12+ simulation scenarios across 3 complexity stages
• Φ spike analysis during “insight moments”
• Emotional learning curves across scenarios

Phase 4: Measurement & Validation (Months 10-12, Q4 2026)

Goal: Rigorous consciousness metric validation.

IIT Validation

• Run full Φ* calculations on 1000+ episodes
• Correlate Φ spikes with behavioral novelty
• Compare against control groups (random agents)
• Publish Φ distribution analysis

GWN Metrics

• Implement Ignition Index tracking
• Measure Global Availability Latency
• Calculate PCI (Perturbational Complexity Index)
• Broadcast bandwidth analysis

Behavioral Testing

• Mirror test pass or fail metrics
• Meta-cognitive task performance
• Emotional coherence scoring
• Narrative self-report analysis

Deliverables:

• Comprehensive consciousness metrics paper
• Open-source dataset of Φ measurements
• Behavioral benchmarks for ACM

Phase 5: Scaling & Optimization (Months 13-15, Q1 2027)

Goal: Production-ready system.

Performance Optimization

• Distributed training setup (multi-GPU)
• Model compression (pruning, distillation)
• Inference optimization (ONNX, TensorRT)
• Memory optimization for long episodes

Scalability

• Multi-agent training (up to 100 agents)
• Hierarchical RL for complex tasks
• Transfer learning across simulations
• Meta-learning capabilities

VR or AR Integration (Optional)

• Unity VR support for immersive demonstrations
• Human-agent interaction experiments
• Real-time Φ visualization in VR
• Emotional bond formation studies

Deliverables:

• Production-ready ACM framework
• 10x faster training pipeline
• VR demonstration environment

Phase 6: Research Dissemination (Months 16-18, Q2 2027)

Goal: Academic and community impact.

Publications

• Main ACM architecture paper (submit to NeurIPS or ICLR)
• IIT measurement methodology paper
• Emotional RL framework paper
• Dataset paper (consciousness metrics)

Open Source Release

• Clean up codebase for public release
• Comprehensive documentation
• Tutorial notebooks (Google Colab ready)
• Docker containers for reproducibility

Community Building

• GitHub Discussions forum
• Monthly research updates via blog
• Collaboration with academic labs
• Workshop at major AI conference

Deliverables:

• 3 to 4 peer-reviewed publications
• Fully open-source ACM framework
• Active research community (target: 100+ GitHub stars)

Model Upgrade Priority List

Immediate (Week 1-4)

1. Phi-4-14B - Lightweight emotional processor
2. Unity ML-Agents - Critical path for training pipeline
3. Flash Attention 2 - GWN efficiency boost

Short-term (Month 2-3)

1. Qwen2.5-14B - Narration upgrade
2. RND - Intrinsic motivation
3. Transformer World Model - DreamerV3 replacement

Medium-term (Month 4-6)

1. Mixture of Experts - GWN specialization
2. Mamba - Temporal coherence
3. PettingZoo - Multi-agent framework

Long-term (Month 7+)

1. InternVL-2.5 - Vision alternative testing
2. VR Integration - Demonstration and human studies
3. Meta-learning - Transfer across scenarios

Resource Requirements

Compute:

• Minimum: 1x RTX 4090 (24GB VRAM)
• Recommended: 2x RTX 4090 or 1x A6000 (48GB VRAM)
• Cloud option: AWS p4d instances (8x A100 for distributed training)

Storage:

• 1TB SSD for datasets and model checkpoints
• 500GB for simulation recordings

Team:

• 1 to 2 ML engineers
• 1 Unity developer (or dedicated time blocks)
• Optional: 1 neuroscience advisor

Risks & Mitigation Strategies

Risk 1: Unity migration slows development
Mitigation: Parallel development, keep Unreal for demonstrations while building Unity

Risk 2: Φ calculations too slow for real-time feedback
Mitigation: Proxy metrics plus periodic ground-truth (already planned)

Risk 3: Emotional RL doesn’t converge
Mitigation: Start with simple homeostasis (temperature, hunger) before complex emotions

Risk 4: Model size exceeds hardware capacity
Mitigation: Aggressive quantization (4-bit), model distillation, cloud training

Success Criteria

Phase 1:

• Agent autonomously learns light-seeking in Unity Dark Room
• Measurable Φ spikes during learning
• Emotional valence shifts from anxiety to calm

Phase 4:

• Statistically significant Φ correlation with behavioral novelty (p < 0.05)
• Agent passes simplified mirror test
• Emotional trajectories show coherent patterns

Phase 6:

• 1+ accepted publication at tier-1 conference
• 100+ GitHub stars
• Active external contributions

Timeline Visualization

Q1 2026 |████████| Foundation (Unity + Models)
Q2 2026 |████████| Enhancement (RL + GWN)
Q3 2026 |████████| Scaling (12+ Simulations)
Q4 2026 |████████| Validation (1000+ Episodes)
Q1 2027 |████████| Production (Optimization)
Q2 2027 |████████| Dissemination (Publications)

This roadmap represents an ambitious but achievable path toward measurable artificial consciousness. By combining cutting-edge open-source models, rigorous consciousness measurement, and progressive simulation complexity, the ACM project aims to demonstrate that awareness is not magic, but an emergent solution to the problem of emotional homeostasis.

The code will remain fully open-source. Join us.

GitHub Repository: github.com/tlcdv/the_consciousness_ai