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ASI:BUILD Roadmap

Status: v0.1.0-alpha — Research prototype. See module table in README for per-module maturity.

This document describes the planned development trajectory for ASI:BUILD. It is a research project, so timelines are approximate and priorities can shift based on experimental findings.

Guiding Principles

  1. Cognitive synergy first — the value of ASI:BUILD is in how modules interact, not in each module in isolation
  2. Correctness before performance — we validate against published benchmarks before optimizing
  3. Honest labeling — scaffolding is marked as scaffolding; only tested code reaches main
  4. Incremental integration — each integration milestone should produce a measurable emergent capability

Current: v0.1.0-alpha (April 2026)

Theme: Foundation — core modules + integration layer

Completed ✅

  • Core module structure: consciousness, cognitive_synergy, knowledge_graph, reasoning, homomorphic, quantum, safety
  • Cognitive Blackboard integration layer (integration/) with event bus and module adapters
  • CI pipeline: lint, type checking (mypy), test matrix (Python 3.10/3.11/3.12), security scan
  • Community infrastructure: issue templates, PR template, CODE_OF_CONDUCT, CONTRIBUTING guide
  • GitHub Discussions enabled with announcement + research directions threads
  • v0.1.0-alpha release published

In Progress 🔄

  • Unit tests for cognitive_synergy module (#1 — good first issue)
  • Documented examples for bi-temporal knowledge graph API (#2)
  • IIT Φ benchmark against reference implementations (#3)

v0.2.0 — Synergy Experiments (Q2 2026)

Theme: Making modules talk to each other and measuring what happens

Integration Layer Expansion

  • ConsciousnessAdapter → real-time φ reporting to Blackboard (currently partial mock)
  • KnowledgeGraphAdapter → entity discovery feeding consciousness attention weights
  • CognitiveSynergyAdapter → TC/DTC computed from live Blackboard state
  • Cross-module correlation studies: does KG growth predict φ increase?

Benchmarking Suite

  • IIT Φ: comparison against pyphi reference implementation
  • Transfer entropy: validation against Schreiber (2000) examples
  • A* pathfinding: benchmark on FB15k-237 and WordNet knowledge graphs
  • Synergy metrics: comparison against idtxl

Documentation

  • Rendered API docs (Sphinx or MkDocs) hosted on GitHub Pages
  • Research notebook series: "Building a cognitive synergy loop from scratch"
  • Module-by-module deep-dive docs in docs/

v0.3.0 — Experimental AGI Loop (Q3 2026)

Theme: A minimal sense-think-act cycle using ASI:BUILD modules

Core Loop

  • Perception input: structured text → knowledge graph ingestion
  • Reasoning: hybrid symbolic-neural query over KG
  • Consciousness integration: GWT working memory selects relevant KG subgraph
  • Action output: structured decision + explanation

Safety Integration

  • Constitutional AI wrapper around reasoning outputs
  • Formal verification of safety constraints for simple action space
  • Governance module: human override protocol

Evaluation

  • GAIA benchmark (subset appropriate for text-KG reasoning)
  • Custom cognitive synergy micro-benchmarks
  • Published evaluation report in docs/research/

v0.4.0 — Multi-Agent Reasoning (Q4 2026)

Theme: Multiple ASI:BUILD instances coordinating

  • Inter-agent communication protocol (using agi_communication module)
  • Distributed knowledge graph synchronization
  • Byzantine-fault-tolerant consensus for shared beliefs
  • Swarm optimization experiments (bio_inspired module)
  • Federated learning across agents (federated module)

Longer-Term Research Directions

These are research questions, not committed milestones:

  • Neuromorphic backend: Can spiking neural networks replace some PyTorch components while preserving module interfaces?
  • Quantum advantage: Are there specific cognitive synergy computations where the quantum module offers a real advantage?
  • Homomorphic reasoning: Can the homomorphic module enable privacy-preserving KG queries?
  • BCI integration: Real EEG data feeding the bci module into the consciousness model
  • Holographic memory: Does a holographic representation (holographic module) improve retrieval in the KG?

What We Are NOT Building

To keep scope honest:

  • ❌ A chat assistant or LLM wrapper
  • ❌ A plug-and-play "deploy AGI in production" system
  • ❌ A reproduction of any specific proprietary AI system
  • ❌ A framework that claims to "solve" AGI

ASI:BUILD is a research testbed. The goal is to produce and share knowledge about how cognitive architectures behave when implemented and composed.

Contributing to the Roadmap

Ideas welcome! Open an issue with the research label or start a discussion in the Research Directions thread. Implemented research milestones earn a place in the CHANGELOG.

Last updated: 2026-04-11 | Maintained by @web3guru888