Our Own Jev: Typed Decisions in One Forward Pass, from an In-House Judge to Open LoRA Models
A typed decision maps evidence, a question and a fixed set of options to a probability for every option, computed in one forward pass, by reading a language model's logits or a small head over its hidden states, instead of generating and parsing text.
ProofPaper ↗
Key points
- The commercial system Jev popularised this System 1 interface, and open reproductions have followed.
- We report two uses of the pattern.
- Part II asks whether a readout that adds no parameters can be competitive and whether training one helps.
- Applying the public Eikos recipe and corpus of about 25K decisions to three further backbones (1B, 2B and 35B), we read the frozen output layer of a LoRA-adapted decoder.
Sources (1)
- [1]Our Own Jev: Typed Decisions in One Forward Pass, from an In-House Judge to Open LoRA ModelsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 01:28 AM
A typed decision maps evidence, a question and a fixed set of options to a probability for every option, computed in one forward pass, by reading a language model's logits or a small head over its hidden states, instead of generating and parsing text.
The commercial system Jev popularised this System 1 interface, and open reproductions have followed.
Extractive summary: sentences quoted from the sources.
Before this
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