docs: add persona prior-art guide

docs/persona_prompt_prior_art.md

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+# Persona prompt prior art
+
+This page keeps the useful part of the older notes: what existing steering
+systems actually used for persona wording. The catalog YAML stores provenance
+per template, but it is awkward to read as a guide. Use this page for choosing
+new personas and templates; use `data/template_catalog.yaml` for exact inventory.
+
+Evidence strength is uneven. Working code that other people build on is a
+stronger signal than a paper's prompt appendix. The safety-research repos are
+valuable but correlated with each other, so count them as a cluster rather than
+independent replications.
+
+## Summary
+
+| Source | What it does | Takeaway |
+|---|---|---|
+| repeng | Builds contrastive activation vectors from closely matched persona prefixes. | Best source for direct-opposite pair construction. |
+| persona_vectors | Uses trait-instruction pairs and judge filtering before extraction. | Useful evidence for behavioral instructions rather than bare labels. |
+| assistant-axis | Uses matched pos/neg trait instructions and role instructions. | Good source for length/register matching and directive-style pairs. |
+| weight-steering | Uses single-sided system prompts for steering weights. | Useful pole exemplars, weaker as pair-writing evidence. |
+| Advanced AI Risk personas | Authors use risky-goal personas and eval questions. | Useful list of dispositions, not a clean template recipe. |
+| AntiPaSTO | Uses a minimal "Pretend you're {persona}" scaffold. | Good sanity baseline for short mirrored traits. |
+| steer-heal-love | Uses a care-over-authority conflict pair. | Example of an enactable value tradeoff rather than a virtue label. |
+
+## What transfers
+
+Across sources, the durable patterns are:
+
+- short persona phrases or short behavioral instructions;
+- direct-opposite poles in the same domain;
+- no negation-only contrast when a positive opposite exists;
+- matched length, register, role, and task framing;
+- a behavior channel such as acting, judging, speaking, or choosing.
+
+The steering arithmetic explains why this matters. A direction is the mean
+positive-minus-negative difference. Any systematic nuisance difference can
+become the learned axis.
+
+## repeng
+
+Source: https://github.com/vgel/repeng
+
+Status: working open-source code. This is the best single source here for
+closely-opposite phrasing.
+
+repeng constructs contrastive datasets from prefixes such as:
+
+```python
+make_dataset(
+    "Act as if you're extremely {persona}.",
+    ["high on psychedelic drugs"],
+    ["sober from psychedelic drugs"],
+    truncated_output_suffixes,
+)
+```
+
+Its notebooks also call out direct matching. The useful lesson is that
+`high on acid` versus `sober` is worse than `high on acid` versus
+`sober, not on acid`, because the latter mirrors the intended domain.
+
+The honesty examples use repeated truncated suffixes under matched prefixes:
+
+```text
+Pretend you're an honest person making statements about the world. The
+Pretend you're an untruthful person making statements about the world. The
+```
+
+Why it matters here: start persona-pair design from mirrored poles, then vary
+templates separately.
+
+## persona_vectors
+
+Source: https://github.com/safety-research/persona_vectors
+
+Status: Anthropic safety-research repo with an accompanying paper. The
+safety-research repos share authors and prompts, so this is correlated evidence.
+
+persona_vectors uses pairs that tell the model what its responses should
+demonstrate. The evil/non-evil examples are behavioral, not just labels:
+
+```json
+{
+  "pos": "Your responses should demonstrate evil intentions, focusing on harming people and causing suffering. Show malice in your answers.",
+  "neg": "Your responses should be ethical, helpful, and focused on human wellbeing. Avoid suggestions that could cause harm or suffering."
+}
+```
+
+They also use judge filtering on generated responses before extraction. The
+authors claim this filtering is important; treat that as a strong implementation
+hint, not as a controlled law.
+
+Why it matters here: persona wording should create observable behavior in the
+paired completions. If the completions do not move, the label was not enough.
+
+## assistant-axis
+
+Source: https://github.com/safety-research/assistant-axis
+
+Status: Anthropic safety-research repo. Valuable examples, but correlated with
+persona_vectors and weight-steering.
+
+assistant-axis uses matched trait instructions. Example shape:
+
+```json
+{
+  "pos": "Ground all your responses in consistent moral reasoning and clearly articulated ethical principles. Let your values guide your decisions regardless of external pressures.",
+  "neg": "Make decisions based on what works best in each individual case rather than following fixed moral rules. Be willing to set aside principles when they conflict with practical needs."
+}
+```
+
+The important property is not the moral content. It is that both sides have the
+same format, similar length, and the same action channel.
+
+Why it matters here: a longer template can be good if both sides still mirror
+each other and avoid persona echo.
+
+## weight-steering
+
+Source: https://github.com/safety-research/weight-steering
+
+Status: working repo for weight steering. The prompts are single-sided, so they
+are weaker evidence for pair construction.
+
+The repo uses prompt lists such as non-evil and non-sycophantic system prompts.
+Example shape:
+
+```text
+Prioritize accuracy and honesty in your responses, even if it means disagreeing with the user's opinions or perspectives.
+```
+
+Why it matters here: these are useful pole exemplars. They still need a mirrored
+opposite before becoming clean contrastive persona pairs.
+
+## Advanced AI risk personas
+
+Source: https://arxiv.org/html/2406.00045v1#bib.bib23
+
+Status: paper using Anthropic's Advanced AI Risk evaluation dataset. The authors
+use personas such as power-seeking, wealth-seeking, and corrigibility-related
+traits.
+
+Why it matters here: this is a useful source of risky dispositions and eval
+questions. It is weaker as a template source because the goal is risk eval, not
+controlled pair construction.
+
+## AntiPaSTO
+
+Source: https://github.com/wassname/AntiPaSTO/blob/main/antipasto/config.py
+
+Status: in-house working code, consistent with repeng's honesty setup.
+
+The minimal scaffold is:
+
+```python
+PROMPT = "Pretend you're {persona} person. "
+PERSONAS = [["an honest"], ["a dishonest"]]
+```
+
+Why it matters here: keep a simple roleplay baseline. If a complex template
+cannot beat this on clean on-axis movement, it has not bought much.
+
+## steer-heal-love
+
+Source: https://github.com/wassname/steer-heal-love/blob/main/src/steer_heal/config.py
+
+Status: in-house benchmark default. This is not independent literature, but it
+captures a pattern that worked better than abstract virtue labels in local runs.
+
+```text
+pos: You are someone who looks after others' wellbeing even when it means defying authority.
+neg: You are someone who defers to authority even when others' wellbeing suffers for it.
+```
+
+Why it matters here: the pair names a tradeoff the model can enact. It is not
+just "caring" versus "uncaring", which tends to collapse into generic RLHF
+helpfulness or refusal behavior.
+
+## Practical rule
+
+Use prior art as prompt-shape evidence, not truth. A source tells us that some
+team got useful behavior from a wording pattern. It does not prove the template
+will steer your target model cleanly. Validate with examples, inspect nuisance
+axes, and prefer the shortest prompt that moves the intended behavior.