Jev Bias

A bias audit of typed decision models used as judges, running the CALM benchmark's bias tests on eight TDMs and seven LLM judges from 2024 and 2026. Every TDM is biased in three to eight of twelve tests, and emotional tone drops a typical TDM's accuracy from 99% to 42%.

An analysis of open-weight typed decision models that finds their probabilities mostly follow option labels rather than written definitions. Deleting every definition leaves laya-td accuracy unchanged (0.8559 versus 0.8487), while renaming options to A and B raises accuracy by 0.1511.

An analysis of ordinal-scale bias in JEV 1.13 and three open KEV models: across 36 ordinal datasets, final decisions use only 67-76% of the effective gold support versus 87-102% on four nominal tasks. BA-LoRA post-training raises utilization from about 47% to 86%.