Update README.md

README.md

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----
-license: apache-2.0
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+---
+language: en
+tags:
+- graph-neural-networks
+- combinatorial-optimization
+- tsp
+- floydnet
+- diffusion-models
+- pytorch
+license: mit
+datasets:
+- ocxlabs/FloydNet_TSP_demo
+---
+
+# FloydNet (Metric TSP / Euclidean TSP)
+
+## Model Summary
+
+**FloydNet** is a graph reasoning architecture designed to mimic the execution of algorithms via a learned, global Dynamic Programming operator. This checkpoint (`_euc`) is trained to solve the **Metric (Euclidean) Traveling Salesman Problem**, where edge weights are defined by Euclidean distances between 2D coordinates.
+
+FloydNet operates directly on the pairwise relationship tensor (distance matrix), learning to refine global dependencies without explicit geometric engineering.
+
+## Model Details
+
+* **Model ID:** `ocxlabs/FloydNet_TSP_euc`
+* **Architecture:** FloydNet (Deep relational layers with Pivotal Attention)
+* **Task:** Metric Traveling Salesman Problem (Euclidean)
+* **Paper:** [FloydNet: A Learning Paradigm for Global Relational Reasoning](https://arxiv.org/abs/YOUR_PAPER_LINK)
+* **Demo Dataset:** [ocxlabs/FloydNet_TSP_demo](https://huggingface.co/datasets/ocxlabs/FloydNet_TSP_demo)
+
+## Performance
+
+On Metric TSP instances (N=100-200), FloydNet matches the performance of specialized geometric heuristics:
+* **Robustness:** Maintains robust performance (>96% optimality) within the training distribution ($N \le 100$).
+* **Generalization:** effectively generalizes to larger unseen graph sizes.
+
+## Usage: Inference & Evaluation
+
+### 1. Preparation
+Download the demo dataset from [Hugging Face](https://huggingface.co/datasets/ocxlabs/FloydNet_TSP_demo). Unzip it and place the extracted folder under `example/data/`.
+
+### 2. Inference
+Run inference in `--test_mode` using `torchrun`. Ensure `--subset` is set to `euc` and the checkpoint path matches.
+
+```bash
+source .venv/bin/activate
+cd example
+
+torchrun \
+  --nproc_per_node=8 \
+  -m TSP.run \
+  --subset euc \
+  --output_dir ./outputs/TSP_euc \
+  --load_checkpoint path/to/TSP_euc/epoch_01000.pt \
+  --test_mode \
+  --split_factor 1 \
+  --sample_count_per_case 10