# Soft Graph Transformer for MIMO Detection

arXiv: https://arxiv.org/abs/2509.12694 · HTML: https://mlsanity.com/paper/2509.12694/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 16 Sep 2025.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Sionna: An Open-Source Library for Next-Generation Physical Layer Research](https://mlsanity.com/paper/2203.11854/) (2022)
- [Error Correction Code Transformer](https://mlsanity.com/paper/2203.14966/) (2022)
- [Deep MIMO Detection](https://mlsanity.com/paper/1706.01151/) (2017)
- [CrossMPT: Cross-attention Message-Passing Transformer for Error Correcting Codes](https://mlsanity.com/paper/2405.01033/) (2024)
- [RE-MIMO: Recurrent and Permutation Equivariant Neural MIMO Detection](https://mlsanity.com/paper/2007.00140/) (2020)
- [Memory AMP](https://mlsanity.com/paper/2012.10861/) (2020)
- [GNN-Enhanced Approximate Message Passing for Massive/Ultra-Massive MIMO Detection](https://mlsanity.com/paper/2205.10620/) (2022)
- [Graph Neural Network-based Joint Equalization and Decoding](https://mlsanity.com/paper/2401.16187/) (2024)

## Built on by (most cited first, 3 of 3)

- [Prediction--Loss Alignment for Sampler--Robust Flow Matching Training](https://mlsanity.com/paper/2602.10420/) (2026)
- [Data-Driven Deep MIMO Detection:Network Architectures and Generalization Analysis](https://mlsanity.com/paper/2602.20178/) (2026)
- [Learning-to-Transition for Large-scale and High-Order MIMO Detection](https://mlsanity.com/paper/2608.14511/) (2026)

## Abstract (arXiv)

We propose the Soft Graph Transformer (SGT), a soft-input-soft-output neural architecture designed for MIMO detection. While Maximum Likelihood (ML) detection achieves optimal accuracy, its exponential complexity makes it infeasible in large systems, and conventional message-passing algorithms rely on asymptotic assumptions that often fail in finite dimensions. Recent Transformer-based detectors show strong performance but typically overlook the MIMO factor graph structure and cannot exploit prior soft information. SGT addresses these limitations by combining self-attention, which encodes contextual dependencies within symbol and constraint subgraphs, with graph-aware cross-attention, which performs structured message passing across subgraphs. Its soft-input interface allows the integration of auxiliary priors, producing effective soft outputs while maintaining computational efficiency. Experiments demonstrate that SGT achieves near-ML performance and offers a flexible and interpretable framework for receiver systems that leverage soft priors.