Update README.md

README.md

@@ -1,3 +1,48 @@
 ---
 license: apache-2.0
+tags:
+- time series
+- time series classification
+- foundation model
+- in-context learning
+- multivariate time series
 ---
+
+# ChorusTIC
+
+ChorusTIC is a classification-native foundation model for training-free univariate and multivariate time series classification. It uses labeled context examples to predict query labels without fitting a target-specific classifier or updating model parameters.
+
+[Paper](https://arxiv.org/abs/2608.24033) · [Code](https://github.com/fangjuntao/ChorusTIC) · [Official benchmark results](https://tsc-fm.dmirlab.com/methods/chorustic)
+
+## Benchmark and evaluation
+
+ChorusTIC is evaluated in the [TSC-FM time series classification foundation model benchmark](https://tsc-fm.dmirlab.com/). Use the [time series classification leaderboard](https://tsc-fm.dmirlab.com/leaderboard) to compare matching Standard and low-shot settings, and read the [evaluation protocol](https://tsc-fm.dmirlab.com/evaluation) before interpreting aggregate accuracy, rank and coverage.
+
+## Model description
+
+ChorusTIC combines Random Subchannel Slot Concatenation with a shared dual-axis encoder to model temporal and cross-channel interactions across heterogeneous channel configurations. Context-derived feature calibration and leakage-protected in-context learning are then used to infer query labels.
+
+The released repository contains `ChorusTIC.ckpt`. Model loading, preprocessing and UCR/UEA evaluation code are available in the [official GitHub repository](https://github.com/fangjuntao/ChorusTIC).
+
+## Intended use
+
+- Research on training-free time series classification.
+- In-context classification of univariate and multivariate sequences.
+- Reproduction and extension of the accompanying paper’s experiments.
+
+## Limitations
+
+- Predictions require labeled context examples with adequate class coverage.
+- Performance outside the evaluated data domains and channel configurations is not guaranteed.
+- This checkpoint is a research artifact and should be validated independently before high-stakes use.
+
+## Citation
+
+```bibtex
+@article{fang2026chorustic,
+  title   = {ChorusTIC: Training-Free Multivariate Time Series Classification via Chorus In-Context Learning},
+  author  = {Fang, Juntao and Xie, Shifeng and Cai, Ruichu and Zheng, Shengji and Li, Zijian and Zhang, Keli and Pan, Lujia and Palpanas, Themis and Hao, Zhifeng},
+  journal = {arXiv preprint arXiv:2608.24033},
+  year    = {2026}
+}
+```