Validation

# Validation

The persona-adherence validation suite checks whether a persona's attributes actually drive agent behavior — not just whether they appear in the prompt. It lives under persona/validation/.

For each attribute we run a positive persona (which should express the trait) and a negative persona (which should express the opposite), let the agent produce a trajectory/artifact, and LLM-judge whether the target attribute value shows up in the behavior. If persona conditioning works, the positive and negative runs separate cleanly.

The suite spans 10 attributes × 4 environments (survey / chat / web / osapp-linux). Each (attribute, env) pair is a self-contained task under persona/validation/tasks/.

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Layout

`text persona/validation/ ├── tasks/ probe tasks, one per (env, attribute) — probe-_/ ├── scripts/ matrix runner, LLM judge, report builder, helpers └── results/ committed summary report (REPORT.md, report.json) `

Attributes covered: code-comment-style, code-naming-verbosity, code-summary-documentation, cog-emoji-use, cog-humor, cog-politeness, cog-storytelling, cog-use-of-jargon, cog-verbosity, register.

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Requirements

• uv, Docker (for the containerized web/osapp environments)
• An OpenAI-compatible API endpoint for both the persona model and the judge.

Configure everything through environment variables:

`bash export OPENAI_API_KEY=sk-... # required export OPENAI_BASE_URL=https://api.openai.com/v1 # optional; any compatible endpoint export PERSONA_MODEL=gpt-4o # model the agents run as export JUDGE_MODEL=gpt-4o # model the judge runs as `

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Running

Run the full matrix (1 positive + 1 negative persona per cell):

`bash python persona/validation/scripts/run_matrix.py `

Scope it down while iterating:

`bash python persona/validation/scripts/run_matrix.py \ --attrs cog-politeness,cog-humor --envs survey,chat --n 1 `

Run a single probe recipe directly through Matraix Playground:

`bash persona/validation/scripts/run_probe.sh [survey_task_path] `

Judge a finished Matraix Playground job dir on its own:

`bash python persona/validation/scripts/judge_adherence.py jobs/ \ --attribute code_comment_style --value "Extensive inline comments" `

Build the summary report from per-cell shards:

`bash python persona/validation/scripts/build_report.py `

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Notes

• The judge reads whatever trajectory/artifact text a trial produced, so it works uniformly across all four environments.
• reward (task completion) is not the signal here — adherence is judged purely from the agent's produced behavior, independent of whether the task itself succeeded.
• Probe task instructions are deliberately neutral: they describe a task without ever naming the attribute's direction, so the persona's trait has to surface on its own.