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--- |
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language: en |
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tags: |
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- graph-neural-networks |
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- combinatorial-optimization |
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- tsp |
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- floydnet |
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- diffusion-models |
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- pytorch |
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license: mit |
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datasets: |
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- ocxlabs/FloydNet_TSP_demo |
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--- |
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# FloydNet (Metric TSP / Euclidean TSP) |
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## Model Summary |
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**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. |
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FloydNet operates directly on the pairwise relationship tensor (distance matrix), learning to refine global dependencies without explicit geometric engineering. |
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## Model Details |
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* **Model ID:** `ocxlabs/FloydNet_TSP_euc` |
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* **Architecture:** FloydNet (Deep relational layers with Pivotal Attention) |
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* **Task:** Metric Traveling Salesman Problem (Euclidean) |
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* **Paper:** [FloydNet: A Learning Paradigm for Global Relational Reasoning](https://arxiv.org/abs/2601.19094) |
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* **Demo Dataset:** [ocxlabs/FloydNet_TSP_demo](https://huggingface.co/datasets/ocxlabs/FloydNet_TSP_demo) |
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## Performance |
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On Metric TSP instances (N=100-200), FloydNet matches the performance of specialized geometric heuristics: |
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* **Robustness:** Maintains robust performance (>96% optimality) within the training distribution ($N \le 100$). |
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* **Generalization:** effectively generalizes to larger unseen graph sizes. |
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## Usage: Inference & Evaluation |
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### 1. Preparation |
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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/`. |
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### 2. Inference |
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Run inference in `--test_mode` using `torchrun`. Ensure `--subset` is set to `euc` and the checkpoint path matches. |
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```bash |
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source .venv/bin/activate |
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cd example |
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torchrun \ |
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--nproc_per_node=8 \ |
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-m TSP.run \ |
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--subset euc \ |
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--output_dir ./outputs/TSP_euc \ |
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--load_checkpoint path/to/TSP_euc/epoch_01000.pt \ |
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--test_mode \ |
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--split_factor 1 \ |
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--sample_count_per_case 10 |
