A 140M-parameter networking foundation model (PLUME) that outperforms frontier LLMs on protocol analysis by learning from native packet structures.
March 17, 2026
Original Paper
PLUME: Building a Network-Native Foundation Model for Wireless Traces via Protocol-Aware Tokenization
arXiv · 2603.13647
The Takeaway
By using protocol-aware tokenization rather than generic BPE, this model achieves better-than-GPT-5 performance on network failure detection with 600x fewer parameters. It demonstrates that domain-native structure is more important than raw scale for specialized engineering tasks, enabling privacy-preserving on-prem RCA.
From the abstract
Foundation models succeed when they learn in the native structure of a modality, whether morphology-respecting tokens in language or pixels in vision. Wireless packet traces deserve the same treatment: meaning emerges from layered headers, typed fields, timing gaps, and cross-packet state machines, not flat strings. We present Plume (Protocol Language Understanding Model for Exchanges), a compact 140M-parameter foundation model for 802.11 traces that learns from structured PDML dissections. A pr