traitseed 0.1.0

Curated trait lattices for varied, coherent LLM data generation

Traitseed

Reproducible diversity for LLM data generation. Seed every generation with a draw from a curated trait lattice and get varied, coherent, traceable output.

from traitseed import Lattice
import random

lattice = Lattice.default()
seed = lattice.draw(rng=random.Random(42))

print(seed.block())
# 1. Worldview/disposition: futurist (taken to excess)
# 2. Primary concern/focus: market dynamics (to the point of obsession)
# 3. Blind spot/shadow: trusting self-reported data

prompt = f"Create a fictional expert persona.\n\n{seed.block()}"
record = seed.to_dict()   # persist with the artefact: full provenance

Zero dependencies. MIT.

Why

LLMs collapse under "be diverse" prompting: ask for twelve personas and you get the same worldview, the same concerns, the same hedged style, at any temperature. traitseed replaces the diversity instruction with a sampled seed, one item per position (worldview, concern, blind spot) drawn from curated 78-item trait lists.

• Varied: ~2 million distinct seeds per lattice; measured highest embedding dispersion of any seeding strategy tested.
• Coherent: curated items keep characters psychologically consistent; random words measurably do not.
• Reproducible: pass a seeded random.Random, persist seed.to_dict(), and any panel can be regenerated exactly.
• Productively flawed: the blind-spot position gives each persona a genuine, specific weakness — what makes synthetic panels disagree instead of converging.

One honest scope note: names are a separate failure mode. Our measurements show semantic seeding barely moves name repetition ("Dr Aris Thorne" survives every seed). Pair traitseed with an explicit name list or post-generation dedup if you need unique names.

Install

pip install traitseed

Or from source: pip install git+https://github.com/sam-at-axiotic/traitseed.

Bundled lattices

Three generic lattices ship with the package. They measured statistically equivalent in testing, pick any:

name	author
claude-traits (default)	Claude (Anthropic)
gpt41-traits	GPT-4.1 (OpenAI)
human-taxonomies	Big-Five markers, Rokeach/Schwartz values, named cognitive biases

from traitseed import Lattice, list_bundled

list_bundled()                        # ['claude-traits', 'gpt41-traits', 'human-taxonomies']
lattice = Lattice.bundled("human-taxonomies")

Author a domain-tuned lattice

Generic lattices seed generic personas. For a specific domain, author a lattice once with any LLM and reuse it forever:

from traitseed import author_lattice

def llm(prompt: str) -> str:
    ...  # any provider — return the completion text

lattice = author_lattice(
    "generating diverse fictional clinicians for triage simulations",
    llm,
    name="clinicians-v1",
)
lattice.save("clinicians-v1.json")     # commit it; Lattice.load() to reuse

Lattice authorship is robust: in testing, lattices written by different frontier models from the same brief performed identically.

How it works

A Lattice is an ordered set of positions, each holding a curated item list. draw() picks one item per position with your RNG; ~30% of items get an intensity flag ("taken to excess") that mirrors how a trait distorts under pressure. The returned Seed renders a numbered prompt block and serialises to a provenance record.

Custom positions, list sizes, and intensity suffixes are all plain constructor arguments: see traitseed.lattice.

Evidence

The design is measured. Across 18 models, ~26k generations, pre-registered decision rules, and blind cross-family judging, curated trait lattices were simultaneously the most coherent, most plausible, best on blind-spot quality, and most dispersed. Against every baseline tested: symbolic decks, an invented deck, random words, and naive diversity prompting. There is no varied-versus-coherent trade-off with good seeds.

Full write-up: WRITEUP.md. Designs, run artefacts, and verdict grids: ablation/ and probes/ (uv run python -m probes.aggregate reproduces the tables).

Baselines

The symbolic decks the evaluation grew from remain available (traitseed.tarot, traitseed.fictional) — useful as baselines for your own seeding experiments, and the foundation the lattice design inherited its scaffold and draw entropy from.

Licence

MIT.