JevSoup: System-One Routing for Training-Free LoRA Composition
Quick summary
arXiv:2609.30922v1 Announce Type: new Abstract: Building adaptable AI systems requires effective coordination of specialized capabilities across diverse tasks. Low-rank adaptation (LoRA) enables modular expertise, but existing routing approaches may require auxiliary data, additional training, or autoregressive decoding. We propose JevSoup, a training-free framework separating System One expert routing from System Two execution. Using only the input and expert descriptions, Jev selects two experts through structured probabilities. JevSoup retains the leading expert's update, projects the secon
Key takeaways
- arXiv:2609.30922v1 Announce Type: new Abstract: Building adaptable AI systems requires effective coordination of specialized capabilities across diverse tasks.
- Low-rank adaptation (LoRA) enables modular expertise, but existing routing approaches may require auxiliary data, additional training, or autoregressive decoding.
- We propose JevSoup, a training-free framework separating System One expert routing from System Two execution.
Why it matters
This development shows AI moving deeper into everyday software. Productivity potential should be weighed against price, data permissions, exportability and the preservation of human control.

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