Research Papers

Research papers and technical specifications from GTCode covering geometric semantic communication, embedding-space publish-subscribe routing, media provenance, and Chiral Narrative Synthesis.

Geometric Semantic Communication Research

Research Agenda

Geometric Semantic Communication for Emergent Multi-Agent Intelligence A research program toward communication infrastructure for emergent artificial general intelligence, grounded in tensor logic and topological structures for truth, perspective, and narrative. Defines a five-layer protocol stack from implementable GPS through morphogenetic dynamics to manifold semantics.

Protocol Specification

Geometric Publish-Subscribe: Content-Based Routing in Embedding Space for Multi-Agent Systems A communication protocol for multi-agent systems where messages route by semantic similarity rather than topic strings. Specifies a 64-byte wire format, subscription semantics based on embedding regions, and routing algorithms based on similarity thresholds.

Media Provenance Research

Research Design Note

Physics-Informed Spatiotemporal Digests: WSTD, OMR-Gated RF Verification, and Residual Security under Learned-Model Attackers A research design note proposing a windowed state-and-transition digest for media provenance, with optional OMR-gated RF verification and a residual-security model under learned-model attackers.

Grounded Chiral Tensor Synthesis (Current CNS Research)

Current Research Hub

CNS 7.1 / GCTS: Grounded Chiral Tensor Synthesis The current Chiral Narrative Synthesis research line: an access-aware, evidence-grounded framework for ranking likely truth across structured possible worlds under limited, contradictory, and adversarial evidence.

Key Sections:

Public Introduction

Grounded Chiral Tensor Synthesis: Ranking Likely Truth When Evidence Is Missing A public-facing introduction to why GCTS separates strict proof from likely truth, models record access, and emits ranked possible worlds instead of a single forced answer.

Chiral Narrative Synthesis 2.0 (Historical Research)

Research Proposal

Chiral Narrative Synthesis Research Proposal (PDF) Historical research proposal for the CNS 2.0 framework, exploring AI-powered narrative generation with dialectical reasoning.

Research Roadmap & Documentation

CNS 2.0 Research Roadmap Comprehensive guide covering the vision, experimental design, foundational work, and research directions for Chiral Narrative Synthesis 2.0.

Key Sections:

In-Depth Analysis

CNS Ideas Paper Detailed exploration of the theoretical foundations, technical implementation, and research methodology behind Chiral Narrative Synthesis.

Dialectical Reasoning Templates Templates and frameworks for implementing dialectical reasoning in narrative generation systems.

Additional Resources

Evaluation & Validation

Ethical Considerations

About This Research

The Chiral Narrative Synthesis (CNS) project explores computational methods for reasoning under conflict, uncertainty, and incomplete evidence. The current research line is CNS 7.1 / GCTS, which shifts from narrative generation toward access-aware likely-truth ranking over structured possible worlds. Older CNS 2.0 material remains available as prior work on Structured Narrative Objects, chirality, evidential entanglement, and dialectical synthesis.

For technical implementation details, see the CNS repositories including dspex, pipeline_ex, and related Elixir/BEAM packages.