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Efficient Message-Passing Transformer for Error Correcting Codes

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Channel codingError correcting codesTransformer-based decoderMessage-passing decoderNeural decoderTransformerEfficient attention module
Abstract:

Error correcting codes (ECCs) are a fundamental technique for ensuring reliable communication over noisy channels. Recent advances in deep learning have enabled transformer-based decoders to achieve state-of-the-art performance on short codes; however, their computational complexity remains significantly higher than that of classical decoders due to the attention mechanism. To address this challenge, we propose EfficientMPT, an efficient message-passing transformer that significantly reduces computational complexity while preserving decoding performance. A key feature of EfficientMPT is the Efficient Error Correcting (EEC) attention mechanism, which replaces expensive matrix multiplications with lightweight vector-based element-wise operations. Unlike standard attention, EEC attention relies only on query-key interaction using global query vector, efficiently encode global contextual information for ECC decoding. Furthermore, EfficientMPT can serve as a foundation model, capable of decoding various code classes and long codes by fine-tuning. In particular, EfficientMPT achieves 85% and 91% of significant memory reduction and 47% and 57% of FLOPs reduction compared to ECCT for (648,540)(648,540) and (1056,880)(1056,880) standard LDPC code, respectively.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper proposes EfficientMPT, a transformer-based decoder for error correcting codes that reduces computational complexity through a novel Efficient Error Correcting (EEC) attention mechanism. According to the taxonomy, the work is positioned in the Surface Code Decoding leaf under Quantum Error Correction Decoding, which contains five papers including the original submission. This leaf represents a moderately populated research direction focused on transformer or recurrent architectures for surface code syndrome-based error correction, suggesting active but not overcrowded exploration of neural decoders for this specific quantum code family.

The taxonomy reveals neighboring research directions that contextualize this work. The sibling leaf Other Quantum Code Decoding addresses QLDPC and toric codes, while the parallel Classical Error Correction Decoding branch contains substantially more activity, including General Transformer Decoder Architectures with ECCT variants and Foundation Models, LDPC Code Decoding, and Decoder Optimization and Efficiency. The paper's claimed foundation model capability bridges quantum and classical domains, connecting to the Foundation Models and Code-Agnostic Decoders leaf. The efficiency focus aligns with the Decoder Optimization and Efficiency direction, which addresses computational reduction through quantization and efficient attention mechanisms.

Among twenty-two candidates examined, the EEC attention mechanism shows no clear refutation across seven candidates, suggesting potential novelty in the specific vector-based element-wise operation design. However, the EfficientMPT architecture contribution examined ten candidates with one refutable match, and the foundation model capability examined five candidates with one refutable match. These statistics indicate that among the limited search scope, some architectural and generalization claims encounter overlapping prior work. The attention mechanism appears more distinctive than the overall decoder framework or foundation model positioning within the examined candidate set.

Based on top-K semantic search covering twenty-two candidates, the work demonstrates partial novelty concentrated in the attention mechanism design. The analysis does not cover exhaustive literature beyond these candidates, and the taxonomy position in a five-paper leaf suggests room for contribution. The foundation model claim and architectural efficiency improvements face more substantial prior work overlap within the examined scope, warranting careful positioning relative to existing code-agnostic and optimization-focused decoders in both quantum and classical branches.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
22
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: Decoding error correcting codes with transformer-based neural networks. The field has evolved into several distinct branches that reflect both the diversity of coding scenarios and the architectural innovations needed to address them. Quantum Error Correction Decoding focuses on surface codes and other quantum constructs, where works like Surface Code Decoder[1] and qecGPT[4] leverage transformers to handle syndrome measurements and stabilizer constraints. Classical Error Correction Decoding encompasses LDPC, polar, and other traditional codes, with methods such as ECC Transformer[7] and LDPC Linear Transformer[11] adapting attention mechanisms to iterative belief propagation. Encoder-Decoder Co-Design and Learning explores joint optimization of code construction and decoding, as seen in Foundation Model Codes[2] and Learning Linear Codes[3]. Specialized Coding Scenarios address niche applications like DNA storage and feedback channels, while Theoretical Analysis and Benchmarking provides complexity studies and performance comparisons across architectures. A smaller Non-ECC Transformer Applications branch captures tangential uses in speech correction and other domains. Within these branches, a central tension emerges between architectural complexity and generalization: some works pursue code-agnostic designs that transfer across families, while others tailor attention patterns to exploit specific graph structures. The quantum decoding cluster, where Message-Passing Transformer[0] resides, emphasizes scalable syndrome processing for surface codes, contrasting with approaches like Global Receptive Decoder[17] that prioritize long-range dependencies or High-Accuracy Error Decoding[26] that refine error localization through iterative refinement. Message-Passing Transformer[0] aligns closely with Surface Code Decoder[1] in targeting surface code topologies, yet distinguishes itself by integrating message-passing dynamics into the attention framework, a strategy that also appears in Hybrid KAN Decoder[37] for classical codes. This positioning reflects ongoing exploration of how to best marry graph-based reasoning with transformer expressiveness, balancing domain-specific inductive biases against the flexibility needed for diverse error patterns.

Claimed Contributions

Efficient Error Correcting (EEC) attention mechanism

The authors introduce a novel attention mechanism that uses a global query vector and broadcasted element-wise operations instead of standard matrix multiplications. This approach incorporates the parity-check matrix directly to embed code structure while significantly reducing computational complexity from quadratic to near-linear.

7 retrieved papers
EfficientMPT transformer-based decoder architecture

The authors propose a complete transformer-based decoder architecture that iteratively updates magnitude and syndrome embeddings through two types of blocks. The architecture achieves substantial reductions in memory usage (85-91%) and FLOPs (47-57%) compared to prior methods while maintaining state-of-the-art decoding performance.

10 retrieved papers
Can Refute
Foundation model capability for ECC decoding

The authors develop a position-invariant and code length-invariant architecture that enables a single model to decode multiple code classes simultaneously. The foundation model can generalize to unseen codes through fine-tuning, eliminating the need to train decoders from scratch for new codes.

5 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Efficient Error Correcting (EEC) attention mechanism

The authors introduce a novel attention mechanism that uses a global query vector and broadcasted element-wise operations instead of standard matrix multiplications. This approach incorporates the parity-check matrix directly to embed code structure while significantly reducing computational complexity from quadratic to near-linear.

Contribution

EfficientMPT transformer-based decoder architecture

The authors propose a complete transformer-based decoder architecture that iteratively updates magnitude and syndrome embeddings through two types of blocks. The architecture achieves substantial reductions in memory usage (85-91%) and FLOPs (47-57%) compared to prior methods while maintaining state-of-the-art decoding performance.

Contribution

Foundation model capability for ECC decoding

The authors develop a position-invariant and code length-invariant architecture that enables a single model to decode multiple code classes simultaneously. The foundation model can generalize to unseen codes through fine-tuning, eliminating the need to train decoders from scratch for new codes.

Table of Contents