Update README.md

This update adds a dedicated section documenting the experimental research reports
archived on Zenodo and mirrored in this repository.

The section links the Law E framework to three complementary experimental studies:
- information–energy dynamics in a minimal simulator,
- endogenous inference-time regulation in large language models (Reg-Sel),
- audit-level information–energy regulation via explicit thresholding (Filtre E).

This improves traceability, coherence, and accessibility between the framework
description and its experimental validation.

README.md

@@ -41,9 +41,43 @@ The goal is to move toward **self-regulated, energy-aware AI systems**.
 
 ## Contact & Collaboration
 
-Created by **Sébastien Favre-Lecca (Neomonde Lab)**  
+Created by **Sébastien Favre-Lecca (Neomundi Lab)**  
 
-- Website: https://neomonde.tech  
-- Twitter: [@GoldOracle_E](https://twitter.com/GoldOracle_E)  
+- Website: https://neomonde.io  
+- Twitter: 
+
+If you are working on AI safety, energy-aware AI, or robotics and want to collaborate on Law E evaluation or implementation, feel free to reach out.
+
+------------------------------------------------
+---
+
+## Experimental Reports (Zenodo mirror)
+
+This repository also hosts a set of experimental research reports archived on Zenodo,
+documenting empirical observations and audit-level traces related to information–energy
+dynamics and endogenous regulation in artificial systems (Loi E).
+
+### ### Available experimental reports
+
+- **Information–Energy Dynamics Simulator (Loi E) — Experimental Observations**  
+  Systematic exploration of information–energy regimes in a minimal simulator, including
+  non-uniform dissipation, distributed asymmetry, recoverability windows, and irreversibility thresholds.  
+  📄 PDF: `Information-energy dynamics simulator Loi E — Experimental observations.pdf`  
+  🔗 Zenodo DOI: https://doi.org/10.5281/zenodo.18483571
+
+- **Regulator–Selector (Reg-Sel): Measured effects of endogenous regulation in large language models**  
+  Experimental evaluation of a minimal inference-time regulation mechanism applied to LLMs,
+  without modifying model weights, prompts, or using external supervision.  
+  📄 PDF: `Measured effects of endogenous regulation in large language models.pdf`
+
+- **Filtre E — Audit trace example: information–energy regulation**  
+  Audit-level illustration of explicit information–energy thresholding and regulated versus
+  non-regulated response regimes.  
+  📄 PDF: `Filtre E — audit trace example information-energy regulation.pdf`
+
+These documents form a coherent experimental foundation complementing the Law E framework.
+Interpretative discussions (e.g. negentropy, biological analogy, governance implications) are
+intentionally addressed in separate publications.
+
+Further experimental reports and technical notes will be added to this repository as the Law E research progresses.
 
-If you are working on AI safety, energy-aware AI, or robotics and want to collaborate on Law E evaluation or implementation, feel free to reach out.