Protocol-Aware Tokenization and Architecture Co-Design for Wireless Packet Foundation Models

Researchers demonstrate that protocol-aware tokenization is significantly more important than model architecture for wireless packet foundation models. PLUME-DEEP achieves 98.2% accuracy with deeper layers, while PLUME-MAMBA offers faster inference with 96.1% accuracy, revealing that tokenizer design swings accuracy by 32 points versus only 2 points for architectural changes.

This research challenges conventional assumptions about foundation model design by systematically isolating the relative importance of tokenization versus architecture. The team's controlled 2x2 comparison methodology—varying tokenizers and architectures independently—provides empirical evidence that domain-specific tokenization dominates performance outcomes. This finding has significant implications for how researchers approach foundation model development across specialized domains like wireless communications.

The work builds on PLUME, an earlier protocol-aware tokenization framework for 802.11 traces. By testing the same tokenizer across fundamentally different architecture families (GPT and Mamba-2), the researchers demonstrate generalizability while quantifying architecture's secondary role. The 32-point accuracy swing from tokenization versus 2-point swing from architecture represents a roughly 16:1 performance ratio, suggesting that domain expertise invested in tokenization yields substantially higher returns than architectural innovation for this problem class.

For the machine learning and wireless networking communities, this has direct implications. It suggests that specialized domain knowledge—understanding what aspects of network protocols matter for modeling—should precede architecture selection. The emergence of Mamba-2 as a deployment alternative, offering 1.7x throughput with 2x longer context despite slightly lower accuracy, introduces a practical trade-off lever for practitioners. This flexibility matters for real-world deployment scenarios where latency or memory constraints may dominate.

Future work should explore whether this tokenization-first principle generalizes to other domains beyond wireless protocols, particularly in finance, IoT, and industrial monitoring where similar structured, protocol-driven data streams exist. Understanding which domains reward architectural innovation versus tokenization expertise could accelerate foundation model development across specialized industries.

• →Protocol-aware tokenization drives 32-point accuracy gains, 16x more impactful than architecture selection in wireless packet modeling
• →PLUME-DEEP reaches 98.2% accuracy with 24 layers, while PLUME-MAMBA achieves 96.1% with 1.7x faster throughput and 2x longer context
• →Architecture functions as a deployment optimization knob, trading accuracy for speed rather than a primary performance lever
• →Controlled 2x2 comparison methodology isolates tokenizer and architecture effects, revealing domain expertise in tokenization yields higher returns
• →Findings suggest tokenization-first design principles may generalize across specialized domains with structured, protocol-driven data