Cochon123/ai-tsp-problem-v2

AI TSP Problem V2

This repository contains the best V2 checkpoint from the AI-TSP-Problem project:

• GitHub: https://github.com/cochon123/AI-TSP-Problem

The task is a fixed-start / fixed-end Euclidean TSP path problem:

• the first node is the start
• the last node is the end
• every intermediate node must be visited exactly once
• the objective is the shortest possible path

What This Model Is

This is a geometry-aware pointer-style Transformer trained on exact Rust-generated labels for small Euclidean TSP-path instances up to 20 nodes.

The model is useful in two ways:

1. AI only It predicts a very strong approximate route quickly.
2. Hybrid AI + exact search It provides warm starts and search guidance for an exact branch-and-bound solver.

Exactness does not come from the model itself. Exactness comes from the classical solver.

Checkpoint

The published file is:

• best_score.pt

It is the best V2 checkpoint selected by the project scoring metric.

Training Setup

• model width: d_model = 384
• encoder layers: 8
• decoder layers: 4
• attention heads: 8
• FFN size: 1536
• dropout: 0.05
• training split: train_v2
• validation splits: val, val_hard

Main Results

Uniform benchmark:

• beam exact route accuracy: 91.63%
• beam mean optimality gap: 0.0496%
• beam exact route accuracy on n=17..20: 88.09%
• beam mean optimality gap on n=17..20: 0.0688%

Hard benchmark:

• beam exact route accuracy: 93.55%
• beam mean optimality gap: 0.0240%

Hybrid exact-search benchmark:

• on n=17..20, the AI-guided hybrid solver reached about 1.60x speedup over the plain branch-and-bound baseline

How To Use

This checkpoint is tied to the project codebase and is not packaged as a standalone Hugging Face transformers model.

Use it from the GitHub repository:

git clone https://github.com/cochon123/AI-TSP-Problem.git
cd AI-TSP-Problem
PYTHONPATH=python/src python -m aifindpath.eval --checkpoint best_score.pt --data data/pilot_v2 --split test --beam 32

If you download the checkpoint from Hugging Face, point the project scripts at the local file path.

Included Visuals

This model page also includes:

• the training loss curve
• the training accuracy curve
• the AI-only latency vs accuracy graph
• the AI-only latency vs gap graph

Notes

• This is a research / engineering checkpoint, not a production API package.
• The browser demo in the GitHub repository uses lightweight in-browser heuristics, not this full checkpoint.
• If you only need a very strong route and not a proof of optimality, AI only can already be a reasonable operating mode.