topological-coherence 0.1.0

Toroidal attention constraints for reducing LLM hallucination

---

title: Topological Coherence emoji: 🔮 colorFrom: purple colorTo: blue sdk: gradio sdk_version: 4.44.0 app_file: app.py pinned: false license: apache-2.0 short_description: Geometric constraints reduce LLM hallucination

Topological Constraints for Coherent Language Models

Why Geometry Prevents Hallucination

Sylvain Cormier | Paraxiom Research | January 2026

Key Result

• 40% lower semantic drift than baseline attention
• 28x lower drift than random sparsity (negative control)
• Proves: topology matters, not just compute reduction

Abstract

Residual geometry determines whether reasoning is stable. We show that transformer latent dynamics, operating on unconstrained vector spaces, lack the conserved quantities necessary for bounded inference.

Toroidal (periodic) constraints on attention provide a spectral gap guarantee that suppresses non-resonant modes, reducing semantic drift.

Links

• Paper (Zenodo)
• Code (GitHub)
• Rust Crate

Citation

@misc{cormier2026topological,
  author = {Cormier, Sylvain},
  title = {Topological Constraints for Coherent Language Models},
  year = {2026},
  publisher = {Zenodo},
  doi = {10.5281/zenodo.18187835}
}