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README.md

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-license: mit
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+---
+license: mit
+---
+# Systematic, Multipath, disjunctive Spatio-Temporal Reasoning (STaR) benchmark
+
+## Main idea
+This is part the [paper](https://huggingface.co/papers/2407.17396) published in ICLR 2025 where further details can be found.
+
+The main idea is to expand the concept of binary relational composition to not just be atomic and require that a model reason over multiple paths instead of a single one.
+
+This can be seen from the example below contrasted with previous benchmarks.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/65004ca932d2159207eff2a5/6v24oiimQyG6v4qb4jrSk.png)
+
+
+## Details
+The layout is the same for each dataset in the benchmark. There are two axes of complexity 
+
+1. **$k$**: the path length between the source and target node and
+2. **$b$**: the number of paths between the source and target nodes 
+
+Each dataset has the following rows:
+
+| Edge Index | Edge Labels | Query Edge | Query Label |
+|------------|-------------|------------|-------------|
+| [(0,1), ...] | EQ         | (0,5)      | TPP   |
+
+- **Edge Index**: `List[Tuple[int, int]]`. A list of edges characterizing the graph.
+- **Edge Labels**: `List[str]`. A list of edge labels corresponding to the edge index.
+- **Query Edge**: `Tuple[int, int]`. The target edge that a model needs to predict.
+- **Query Label**: `str`. The label corresponding to the query edge.
+
+The training sets marked by `train_*.csv` for RCC-8 and Interval algebra (the semigroups that constitute the datasets) contain small graphs of $k=2,3,4$ and $b=1,2,3$. 
+
+The rest of the datasets are for testing systematic generalization. The target classes are balanced.