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 (Non-Metric TSP / Explicit 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 (`_exp`) is trained to solve the **Non-Metric (Explicit) Traveling Salesman Problem**, where edge weights are generic integers and do not necessarily obey the triangle inequality.
+
+Unlike standard GNNs that rely on local message passing, FloydNet maintains and refines a global all-pairs relationship tensor, achieving 3-WL (2-FWL) expressive power.
+
+## Model Details
+
+* **Model ID:** `ocxlabs/FloydNet_TSP_exp`
+* **Architecture:** FloydNet (Deep relational layers with Pivotal Attention)
+* **Task:** General Traveling Salesman Problem (Non-Metric)
+* **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 General TSP instances (N=100-200), FloydNet demonstrates capabilities significantly exceeding strong heuristics:
+* **Optimality:** Achieves an optimality rate of **99.8%** (with 10 samples) on held-out graphs, compared to **38.8%** by the Linkern heuristic.
+* **Exact Solutions:** On single-solution instances, it finds the exact optimal tour in **92.6%** of cases.
+
+## Usage: Inference & Evaluation
+
+Reproducing TSP results at full scale is computationally heavy. For convenience, we provide a small demo dataset and pre-trained checkpoints.
+
+### 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`. The command below assumes a single-node setup with 8 GPUs. Ensure `--subset` is set to `exp`.
+
+```bash
+source .venv/bin/activate
+cd example
+
+torchrun \
+  --nproc_per_node=8 \
+  -m TSP.run \
+  --subset exp \
+  --output_dir ./outputs/TSP_exp \
+  --load_checkpoint path/to/TSP_exp/epoch_01000.pt \
+  --test_mode \
+  --split_factor 1 \
+  --sample_count_per_case 10