Lavoir
A single-pass decision encoder on ModernBERT-large that returns a calibrated probability for every option and a value-of-information score for each candidate clarifying question, so it can ask, decide or hand off. Uses Laya's typed question format.
Follows Laya's decision-head design, input format and loss, but trains its own heads from ModernBERT-large and uses none of Laya's weights. A small value-of-information head estimates how much asking each missing slot would raise the probability of the right decision. Described in arXiv 2609.30706. Zero-shot on SGD, Mogan AI reports .942 accuracy while asking in 6% of conversations; on ABCD it reports .657 and is overconfident there (ECE .19). Workflows unseen in training are much weaker. A Turkish sibling, Lavoir-TR, is built on MoganBERT-TR. The weights are CC BY-NC 4.0 because some training data is non-commercial, so they are not mirrored or packaged here; the code is Apache-2.0.
What it decides
- choice — picks one option from a set
- score — places the input on an ordered scale
- noul — answers a yes/no question with one probability
- route — sends the input to one of several destinations
At a glance
| Base model | answerdotai/ModernBERT-large |
| Maker | Mogan AI |
| Released | 2026-09-25 |
| License | cc-by-nc-4.0 |
| Reported accuracy | 94.2% |
| Reported latency | 31 ms median, 142 ms p95 per question on one NVIDIA GH200 (bf16) |
Get the weights
pip install systemonemodels
systemone pull moganai/lavoir
The files are served from the maker's Hugging Face repository, moganai/lavoir, and verified against the checksums recorded here.