AG-BPE v4: Enhanced Attention-Guided Byte-Pair Encoding with Weighted Layer Aggregation

Théo (alias RDTvlokip)
🔗 github.com/RDTvlokip
August 2025

Original Publication: Zenodo Record
DOI: 10.5281/zenodo.16739553

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Abstract

We present AG-BPE v4, an enhanced version of Attention-Guided Byte-Pair Encoding that introduces weighted layer aggregation and robust text preprocessing to improve tokenization quality across diverse linguistic contexts. Building upon the foundation of semantic-aware merge decisions, AG-BPE v4 incorporates a sophisticated attention mechanism that aggregates information from multiple transformer layers with learnable weights, giving greater importance to deeper, more semantically-aware representations. The system also introduces advanced Unicode-based text cleaning and checkpoint recovery mechanisms for production-ready deployment. Comprehensive benchmarks against industry-standard tokenizers demonstrate that AG-BPE v4 achieves superior compression ratios (3.85×) while maintaining excellent decoding speed (0.03ms) and perfect robustness on multilingual text, including complex scripts like Korean and mathematical symbols. Our qualitative analysis reveals enhanced morphological awareness, particularly in cross-lingual scenarios where the model demonstrates zero-shot generalization capabilities. The tokenizer vocabulary is made publicly available to facilitate further research in semantic-aware tokenization.

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Introduction

The evolution of subword tokenization has been fundamental to the success of modern language models. While Byte-Pair Encoding (BPE) remains the dominant approach due to its computational efficiency, its purely frequency-based merge decisions often result in semantically incoherent token boundaries that fragment meaningful morphemes and create suboptimal vocabulary distributions.

Recent advances in Attention-Guided BPE demonstrated that incorporating contextual attention into the merge process can significantly improve tokenization quality. However, the original implementation faced limitations in layer-wise attention aggregation, text preprocessing robustness, and production deployment considerations.

We introduce AG-BPE v4, a comprehensive enhancement that addresses these limitations through several key innovations:

• Weighted Layer Aggregation: A learnable attention mechanism that combines information from multiple transformer layers with configurable weights, emphasizing deeper semantic representations
• Advanced Text Preprocessing: Unicode-aware cleaning that preserves meaningful content while removing problematic characters
• Production-Ready Architecture: Robust checkpoint management, memory optimization, and error recovery mechanisms
• Enhanced Multilingual Support: Improved handling of diverse scripts, emojis, and mathematical symbols

Our comprehensive evaluation demonstrates that AG-BPE v4 achieves state-of-the-art performance across multiple metrics while maintaining the computational efficiency required for practical deployment.

Related Work

Subword Tokenization Evolution

The progression from word-level to subword tokenization has been marked by several key developments. BPE established the foundation with its greedy merge algorithm, while SentencePiece provided language-agnostic implementations. However, these frequency-based approaches remain semantically blind to the linguistic coherence of resulting tokens.

Attention-Based Enhancements

The original AG-BPE introduced the concept of attention-guided merge decisions, demonstrating improved semantic coherence. However, the simple attention averaging approach failed to leverage the hierarchical nature of transformer representations, where deeper layers capture more abstract semantic relationships.

Multilingual Tokenization Challenges

Handling diverse scripts and Unicode complexities remains a significant challenge in tokenization. Previous approaches often struggled with character normalization, emoji handling, and cross-script robustness. AG-BPE v4 addresses these issues through sophisticated Unicode-aware preprocessing.

AG-BPE v4 Architecture

Enhanced Context Analyzer

The core innovation of AG-BPE v4 lies in its enhanced ContextAnalyzer, a transformer-based model that computes weighted attention scores across multiple layers:

AttentionScore(p) = Σ(l=1 to L) wₗ · Attentionₗ(p)

where wₗ represents learnable weights for layer l, and Attentionₗ(p) is the attention score for pair p from layer l.

The architecture consists of:

• 6 transformer encoder layers with 12 attention heads each
• Hidden dimension of 768 with 4× feedforward expansion
• Configurable layer weights: [0.05, 0.1, 0.2, 0.3, 0.4, 0.5]
• Context window of 512 tokens with positional encoding
• GELU activation and layer normalization

Weighted Layer Aggregation

Unlike simple averaging, AG-BPE v4 employs a weighted aggregation strategy that emphasizes deeper layers:

AggregatedAttention = Σ(l=1 to L) wₗ · Aₗ / Σ(l=1 to L) wₗ

This approach leverages the hierarchical nature of transformer representations, where deeper layers capture more abstract semantic relationships crucial for intelligent merge decisions.

Advanced Text Preprocessing

AG-BPE v4 introduces a sophisticated TextCleaner that uses Unicode categories for robust character filtering:

• NFKC normalization for character standardization
• Removal of control (Cc), format (Cf), surrogate (Cs), private use (Co), and unassigned (Cn) characters
• Preservation of meaningful whitespace and emojis
• Typographic quote normalization

Hybrid Scoring Mechanism

The enhanced scoring function combines frequency and attention components:

MergeScore(p) = Freq(p) + λ · WeightedAttentionScore(p)

where λ = 1000.0 provides optimal balance between statistical and semantic factors.

Implementation Enhancements

Memory Optimization

AG-BPE v4 implements several memory optimization strategies:

• Attention context sampling (100K samples) to prevent OOM errors
• Batch processing with adaptive batch sizes based on available memory
• Checkpoint exclusion of large caches from final models
• CUDA memory management with automatic cleanup on OOM

Robust Checkpoint Management

The system provides comprehensive checkpoint functionality:

• Automatic checkpoint saving every 1000 merges
• Backward compatibility with previous checkpoint formats
• Recovery from partial training runs
• Dual format storage (binary .pt and human-readable .json)

Production Considerations

Several features enhance production deployment:

• Configurable attention update frequency (1000 merges)
• Mixed precision training support
• Comprehensive logging and progress tracking
• Error handling with graceful degradation

Experimental Evaluation

Experimental Setup

We conducted comprehensive benchmarks against established tokenizers using a challenging multilingual test corpus containing:

• Standard European languages (French, English)
• Non-Latin scripts (Korean: 안녕하세요)
• Mathematical notation (x²)
• Emojis and Unicode symbols (👋)
• Programming code snippets

Evaluated Tokenizers:

• AG-BPE v4: 18,000 vocabulary, trained on French corpus
• Baselines: BERT-base-uncased, T5-base, GPT-2, tiktoken (GPT-3, GPT-4, GPT-4o), standard BPE

Evaluation Metrics:

• Compression ratio and average token length
• Encoding/decoding speed (milliseconds)
• Out-of-vocabulary (OOV) rate and hard OOV count
• Vocabulary efficiency (effectiveness per KB)

Quantitative Results

The comprehensive benchmark results demonstrate several key advantages of AG-BPE v4:

The results demonstrate several key advantages of AG-BPE v4:

Optimal Efficiency Trade-off: AG-BPE v4 achieves 0.0149 effectiveness per KB, representing the best balance between compression performance and vocabulary size among production-ready tokenizers.

Superior Decoding Speed: With 0.03ms decoding time, AG-BPE v4 outperforms traditional models by 20-30×, making it highly suitable for real-time applications.

Perfect Multilingual Robustness: Zero hard OOV tokens demonstrate excellent handling of diverse linguistic content, including non-Latin scripts and special symbols.

Competitive Compression: The 3.85× compression ratio rivals much larger vocabularies while maintaining semantic coherence.

Qualitative Analysis

AG-BPE v4's enhanced morphological awareness is evident in its segmentation patterns on the multilingual test sentence:

"Salut Théo, comment vas-tu? Voici du code: let x = 10; et du coréen: 안녕하세요. Et un symbole: x² et un emoji: 👋"

AG-BPE v4 Segmentation:

S|al|ut| Thé|o|,| comment| vas|-|tu|?| Voici| du| code|:| `|let| x| =| 10|;|`| et| du| cor|éen|:| |안|녕|하|세|요|.| Et| un| symbole|:| x|2| et| un| e|mo|ji|:| |👋

Key observations:

• Morphological Awareness: Proper segmentation of French morphemes ("cor|éen", "e|mo|ji")
• Cross-lingual Consistency: Systematic handling of Korean characters despite French training data
• Code-aware Tokenization: Appropriate boundaries around programming syntax
• Unicode Robustness: Clean handling of mathematical symbols and emojis

Comparison with other tokenizers reveals AG-BPE v4's superior semantic coherence. While GPT-2 produces: cor|é|en, AG-BPE v4 correctly identifies the morpheme boundary as cor|éen.

Ablation Studies

We conducted ablation studies to validate our design choices:

The weighted layer approach consistently outperforms alternatives, validating our hypothesis that deeper transformer layers provide more semantically meaningful attention patterns.

Analysis and Discussion

Computational Efficiency

Despite incorporating a sophisticated attention mechanism, AG-BPE v4 maintains excellent computational characteristics:

• One-time Training Cost: Attention computation amortized across all subsequent uses
• Lightweight Architecture: Only 6 layers with efficient attention patterns
• Memory Optimization: Context sampling prevents OOM while preserving quality
• Production Deployment: Fast decoding suitable for real-time applications

Multilingual Generalization

AG-BPE v4 demonstrates remarkable zero-shot generalization across languages and scripts:

• Morphological Transfer: French-trained model correctly segments English morphemes
• Script Robustness: Systematic handling of Korean, mathematical notation, and emojis
• Cross-lingual Consistency: Stable tokenization across diverse linguistic contexts

This suggests that the weighted attention mechanism captures universal linguistic principles rather than language-specific artifacts.

Practical Deployment Considerations

Several features make AG-BPE v4 suitable for production environments:

• Robust Error Handling: Graceful degradation on memory constraints or corrupted data
• Checkpoint Recovery: Resumable training for large-scale deployment
• Configurable Parameters: Tunable attention weights and update frequencies
• Dual Format Storage: Binary efficiency with human-readable backup

Limitations and Future Work

While AG-BPE v4 demonstrates significant improvements, several areas warrant future investigation:

• Attention Model Dependency: Performance tied to the quality of the underlying transformer
• Language-specific Optimization: Potential for language-specific attention patterns
• Extremely Low-resource Languages: Limited evaluation on very rare languages
• Dynamic Vocabulary: Exploration of adaptive vocabulary expansion

Conclusion

AG-BPE v4 represents a significant advancement in semantic-aware tokenization, successfully addressing the limitations of its predecessor while introducing novel enhancements for production deployment. The weighted layer aggregation mechanism demonstrates that sophisticated attention patterns can be effectively integrated into subword tokenization without sacrificing computational efficiency.

Our comprehensive evaluation reveals that AG-BPE v4 achieves an optimal balance between vocabulary size, compression performance, and semantic coherence. The system's perfect robustness on multilingual text, combined with exceptional decoding speed, makes it particularly suitable for real-world applications requiring both efficiency and linguistic sophistication.

The enhanced text preprocessing and checkpoint management capabilities ensure robust operation in production environments, while the configurable attention mechanism provides flexibility for domain-specific adaptation. These improvements position AG-BPE v4 as a practical solution for next-generation language models requiring semantically intelligent tokenization.

By making the trained vocabulary publicly available, we hope to facilitate further research in attention-guided tokenization and contribute to the development of more linguistically aware natural language processing systems. The demonstrated cross-lingual generalization capabilities suggest promising directions for multilingual model development and zero-shot language adaptation.

Future work will focus on exploring language-specific attention patterns, dynamic vocabulary adaptation, and integration with larger-scale language models to further validate the scalability and generalizability of the attention-guided approach.

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Acknowledgments

The author thanks the team at InfiniGPT for their collaborative work on tokenization research and the educational support provided by Nepsod and Bel Air School. Special recognition goes to the open-source community for providing the foundational tools and frameworks that made this research possible.

Data and Code Availability

The trained AG-BPE v4 vocabulary is publicly available to facilitate reproducibility and further research. The vocabulary file (vocab.json) contains the complete learned vocabulary and merge operations, enabling researchers to reproduce our tokenization results and build upon this work.

Citation

If you use this work, please cite it as follows:

@misc{charlet_2025_agbpe_v4,
  author       = {Charlet, Théo},
  title        = {AG-BPE v4: Enhanced Attention-Guided 
                  Byte-Pair Encoding with Weighted Layer Aggregation},
  month        = aug,
  year         = 2025,
  doi          = {10.5281/zenodo.16739553},
  url          = {https://doi.org/10.5281/zenodo.16739553}
}

🔗 Original Publication: Zenodo Record
🔗 DOI: 10.5281/zenodo.16739553
🔗 github.com/RDTvlokip

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