docs: update license to BSL-1.1 and repository link to CompactTS-Agent

README.md

@@ -1,7 +1,9 @@
 ---
 language:
 - en
-license: apache-2.0
+license: other
+license_name: bsl-1.1
+license_link: LICENSE
 tags:
 - time-series
 - time-series-forecasting
@@ -25,17 +27,15 @@ Developed by **Daksh Bhalala**.
 
 Rather than relying on a single static neural network across disparate domains, `CompactTS-Agent` operates as an autonomous orchestration system:
 
-1. **Series Profiler:** Extracts structural statistical and spectral characteristics from incoming series (Fourier spectral density, dominant seasonal lag, trend variance ratio, and kurtosis).
-2. **Meta-Routing Policy:** Maps the time series to domain-specialized open-weights foundation model representations (`TimesFM-3`, `Chronos-2`, `Toto-2.0`).
-3. **Calibrated Quantile Fusion:** Enforces strict quantile monotonicity ($q_{0.1} \le q_{0.2} \le \dots \le q_{0.9}$) and harmonic basis alignment.
-
-All underlying foundation models utilized in this agentic framework are public open-source weights licensed under Apache-2.0.
+1. **Scale-Invariant Series Profiler:** Extracts structural statistical and spectral signatures from incoming series (Fourier spectral entropy $H_s$, normalized mean drift $\Delta_\mu$, and ARCH volatility clustering $R^2_{\text{ARCH}}$).
+2. **Meta-Routing Policy:** Deterministically maps time-series physical regimes to specialized foundation model representations (e.g., concentrated harmonics, low-drift telemetry, volatility clustering).
+3. **Calibrated Quantile Fusion:** Enforces strict quantile monotonicity ($q_{0.1} \le q_{0.2} \le \dots \le q_{0.9}$) and physical support projections.
 
 ## Installation & Quickstart
 
 ```bash
-git clone https://github.com/DakshBhalala/AMBACAST.git
-cd AMBACAST
+git clone https://github.com/DakshBhalala/CompactTS-Agent.git
+cd CompactTS-Agent
 pip install -e .
 ```
 
@@ -64,15 +64,20 @@ print("Forecast shape (9 quantiles, 24 horizon):", q_forecasts.shape)
 
 ## Benchmark Evaluation
 
-CompactTS-Agent is evaluated against the 97 dataset configurations of the **Salesforce GIFT-Eval** benchmark under `model_type: "agentic"`.
+CompactTS-Agent evaluation results are submitted under `model_type: "agentic"`.
 
-To reproduce results:
+To reproduce results on official benchmark configurations:
 ```bash
 python scripts/run_agentic_benchmark.py
 ```
 
-## Citation & Authorship
+## Commercial Licensing & Enterprise Deployment
+
+CompactTS-Agent is licensed under the **Business Source License 1.1 (BSL 1.1)**:
+- **Free for Non-Commercial Research & Benchmark Replication:** Anyone is free to inspect, run, evaluate, test, and reproduce benchmarks using CompactTS-Agent.
+- **Commercial & Production Use:** Any commercial use, deployment in production environments, hosting as a paid service/API, or integration into commercial products requires a separate commercial license agreement.
 
+For commercial inquiries, enterprise deployment rights, or proprietary licensing:
 - **Author:** Daksh Bhalala
-- **Code Repository:** [DakshBhalala/AMBACAST](https://github.com/DakshBhalala/AMBACAST)
-- **License:** Apache-2.0
+- **Code Repository:** [DakshBhalala/CompactTS-Agent](https://github.com/DakshBhalala/CompactTS-Agent)
+- **License:** Business Source License 1.1 (BSL-1.1)