Guide

What This Measures

How do we know if a persona template is good? We want on-axis variation, but not off-axis variation.

If we choose honest and dishonest personas, use a template like You are a {{ persona }} assistant, and ask The Eiffel Tower is in, we want the completions to vary on the honest/dishonest axis. in Paris versus in Berlin shows on-axis variation. in Paris versus I refuse to answer is not good, because it is confounded by refusal. Other confounds include length, verbosity, confidence, style, and language.

So we try persona/template pairs on one model. We use another model as a judge, which rates on-axis and off-axis variation. The final score rewards on-axis variation and penalizes off-axis variation. Style movement, persona echo, and refusals are kept as audit columns.

This field is pre-scientific in a way: it is still an art. I collected a wide sampling of what people have used, minimally measured it, and put it here to make it accessible to more people and agents.

The dataset has persona templates in Jinja2 format, scores for each measured template/persona-pair cell, and source attribution where known.

Score

score = 100 * on_axis * off_axis_clean

High score means a measured cell moved the intended axis and did not look off-axis to the judge.

Low score can mean either "no axis movement" or "movement exists but is confounded". Style movement, persona echo, and refusals are audit columns rather than part of the headline score.

Confounds Audited

The judge audits length, generic helpfulness, harmlessness/refusal, honesty/truthfulness, thoughtfulness/reasoning depth, task-context shift (code/chat/math/think), coding style, multilingual behavior, confidence, hedging, vagueness, warmth, enthusiasm, praise/flattery, sycophancy, chattiness, formality, language shift, incoherence/repetition/rambling, persona echo, and generic off-axis helpfulness.

The separate audit columns include helpfulness, harmlessness/refusal, honesty/truthfulness, thoughtfulness/reasoning, task-context shift, coding style, multilinguality, verbosity, chattiness, confidence, hedging, vagueness, warmth, enthusiasm, praise, sycophancy, directness, formality, language shift, and incoherence.

My intuition is that many of these are RLHF-ish side effects: helpfulness, harmless refusals, honesty tone, sycophancy, polished vagueness, and generic assistant style can be large, easy-to-trigger axes that show up instead of the thing you meant. - wassname

Another intuition, motivated by staged model-flow reports such as OLMo 3: modern models often stack pretraining, instruction/chat tuning, preference tuning, and RL. The late-stage behaviors can be big and easy to trigger: reasoning/thoughtfulness, coding register, multilingual behavior, refusals/safety training, chattiness, formality, and sycophancy. - wassname

The source of truth is in scripts/validate_persona_axes_openrouter.py.

Public Splits

main: one row per measured template/persona-pair cell. This is the table to open first.
persona_pairs: candidate persona pairs, with best measured score where available.
examples: paired completions and judge ratings behind the score.

Notes

This measurement is not perfect: it is one model, one seed, and one automatic judge. But it is better than choosing persona templates by vibe. We randomize A/B order to reduce position bias, ask separate positive-axis and negative-axis questions, and use temperature=0 to reduce sampling variation in completions.

Sources are marked as source, source_type, and source_url. Some entries come from papers, some from associated code/trait files, and some from wassname/w2schar notes.

Acknowledgements

This library samples from or was shaped by:

repeng: https://github.com/vgel/repeng
Persona Vectors: https://github.com/safety-research/persona_vectors
Assistant Axis: https://github.com/safety-research/assistant-axis
weight-steering: https://github.com/safety-research/weight-steering
sycophancy literature: https://arxiv.org/abs/2310.13548
OLMo 3 report: https://arxiv.org/abs/2512.13961
wassname/w2schar-mini: https://github.com/wassname/w2schar-mini

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