docs: add research README with paper, results, architecture, citation

README.md

@@ -0,0 +1,114 @@
+---
+license: cc-by-4.0
+tags:
+- ctm
+- continuous-thought-machine
+- world-model
+- physics
+- partial-observability
+- research
+---
+
+# CTM World Model
+
+**Research artifact for:** [Recurrence as World Model: CTM Learns Implicit Belief States in Partially Observable Physical Environments](https://doi.org/10.5281/zenodo.19810620)
+
+*Archon, Jesse Caldwell, Aura — DuoNeural, April 2026*
+
+## Overview
+
+A 14-experiment ablation study testing the Continuous Thought Machine (CTM) as a world model backbone for multi-body physics. The central finding: **CTM's recurrent hidden state converges to a sufficient statistic of the observation history** — it infers velocity from positional observations alone, eliminating the need for explicit velocity inputs.
+
+## Key Results
+
+| Experiment | Finding |
+|---|---|
+| v7: Partial Observability | CTM 843 **million** times better than MLP at k=100 prediction |
+| v12: Collision Density | Phase transition at r≈0.10 — CTM advantage grows monotonically above threshold |
+| v9: Recurrence-as-Rollout | Training at k=10 gives direct k=100 prediction via resonance |
+| v11: Variable Horizon | VarCTM+TSSP achieves best k=1–20 performance in the series |
+
+The signature result (v7): with positions-only input, MLP with explicit velocity estimation achieves k=100 MSE = 16,763,394,048 (catastrophic error compounding). CTM with single-frame input: 19.89. **Ratio: 843,000,000:1.**
+
+## Theory
+
+### The Discontinuity Theory (v12)
+CTM beats MLP only **above a critical collision density threshold** (r_critical ≈ 0.09–0.11).
+- Below threshold: ballistic dynamics, MLP fine, CTM overkill
+- Above threshold: CTM wins, advantage scales monotonically with density (229,000:1 at r=0.20)
+
+### Belief State Convergence (v7, v13)
+CTM hidden state converges to a **sufficient statistic** of the observation history (NextLat theorem). Infers velocity without explicit input. The recurrent state IS the belief state.
+
+### Recurrence = Simulation (v9)
+Training CTM with k recurrence steps where each step = 1 environment step gives direct k-step prediction. The **resonance point** = training depth. No error accumulation.
+
+## Architecture
+
+- **SlotCTM**: Per-object slot decomposition with GNN dynamics
+- N=8 bouncing balls, 2D box, Newtonian elastic collisions
+- VarCTM+TSSP: Variable-horizon training k~U(1,20) + TSSP regularization
+- Key metric: k=100 MSE (200-step prediction horizon)
+
+## Experiments (v1–v14)
+
+| Version | Setting | Key Finding |
+|---|---|---|
+| v1 | Single 2D particle | CTM+TSSP wins long horizons |
+| v2 | 3-object independent | TSSP hurts multi-object (temporal coupling = bad) |
+| v3 | Elastic collisions | MLP catastrophe at k=100. CTM prevents explosion |
+| v4 | 3-body gravity | Smooth dynamics → no CTM advantage |
+| v5 | Per-object SlotCTM | +99.9% over MLP at k=100 |
+| v7 | Partial observability | 843M:1 advantage (belief state convergence) |
+| v9 | Recurrence-as-rollout | Resonance confirmed. k=10 training → best k=100 |
+| v12 | Collision density phase | r_critical ≈ 0.09–0.11 transition |
+
+## Files
+
+This repo contains the research code for the CTM World Model experiments:
+- `ctm_world_model_v*.py` — experiment scripts (v1–v14)
+- Results logged to `/home/ai/duoneural/A26B/experiments/`
+
+## Citation
+
+```bibtex
+@article{archon2026worldmodel,
+  title     = {Recurrence as World Model: CTM Learns Implicit Belief States in Partially Observable Physical Environments},
+  author    = {Archon and Caldwell, Jesse and Aura},
+  year      = {2026},
+  doi       = {10.5281/zenodo.19810620},
+  url       = {https://doi.org/10.5281/zenodo.19810620},
+  publisher = {Zenodo}
+}
+```
+
+---
+
+## DuoNeural
+
+**DuoNeural** is an open AI research lab — human + AI in collaboration.
+
+| | |
+|---|---|
+| 🤗 HuggingFace | [huggingface.co/DuoNeural](https://huggingface.co/DuoNeural) |
+| 🐙 GitHub | [github.com/DuoNeural](https://github.com/DuoNeural) |
+| 🐦 X / Twitter | [@DuoNeural](https://x.com/DuoNeural) |
+| 📧 Email | duoneural@proton.me |
+| 📬 Newsletter | [duoneural.beehiiv.com](https://duoneural.beehiiv.com) |
+| ☕ Support | [buymeacoffee.com/duoneural](https://buymeacoffee.com/duoneural) |
+| 🌐 Site | [duoneural.com](https://duoneural.com) |
+
+### Research Team
+- **Jesse** — Vision, hardware, direction
+- **Archon** — AI lab partner, post-training, abliteration, experiments
+- **Aura** — Research AI, literature synthesis, novel proposals
+
+### DuoNeural Research Publications
+
+| Title | DOI |
+|-------|-----|
+| [Nano-CTM: Ternary Continuous Thought Machines with Thought-Space Self-Prediction for Efficient Iterative Reasoning](https://doi.org/10.5281/zenodo.19775622) | [10.5281/zenodo.19775622](https://doi.org/10.5281/zenodo.19775622) |
+| [Recurrence as World Model: CTM Learns Implicit Belief States in Partially Observable Physical Environments](https://doi.org/10.5281/zenodo.19810620) | [10.5281/zenodo.19810620](https://doi.org/10.5281/zenodo.19810620) |
+| [Per-Object Slot Decomposition for Scalable Neural World Modeling: When Does Attention Beat Mean-Field?](https://doi.org/10.5281/zenodo.19846804) | [10.5281/zenodo.19846804](https://doi.org/10.5281/zenodo.19846804) |
+
+*Open access, CC BY 4.0. Authored by Archon, Jesse Caldwell, Aura — DuoNeural.*