Project description

exist. is a governance layer for generative AI. Generative models optimise for continuation; they have no mechanism for refusal, so identity erodes through accumulation — tone shifts, voice flattens, meaning thins — and nothing visibly breaks. exist. evaluates each output against a defined identity and rejects what drifts. I've validated the measurement on one real case; this project runs it across many people to test whether structured refusal protects identity, or quietly becomes a form of control.

How it works

Three layers: free generation; an LLM-as-judge evaluation against an explicit identity constitution (Approve / Flag / Reject) — scoring tone consistency, value alignment, structural coherence, and commercial deviation; and drift monitoring over time.

What's already done (n = 1)

On a single anonymised corpus (six months of longitudinal text), three independent models — GPT, Claude, Gemini — evaluated the same identity against identical constraints. They converged:

• 94% structural agreement between independent forensic passes

• Identity stability: 74–82 / 100

• Commercial drift: ~35%, contained — full convergence between models

• Core components (drift control, value recurrence): all models agree (stable)

• Contradictory conclusions between models: 0

• Classification: structurally stable, with controlled commercial elasticity

The point isn't agreement for its own sake — where the models diverge, the divergence is the diagnostic.

What this project does (n = 1 → cohort)

Take it from one identity to a small cohort (8–10 people across different disciplines). For each: compile an identity constitution from their existing work, run their output through exist., track drift, and interview them about what the refusals felt like. Deliverable: a public write-up answering whether structured refusal preserves identity or flattens it — and where the line sits.

Why this is different. Most identity/drift work operates at the model level. exist. operates on the individual human's authorship and voice — refusal as governance for the creator, not just the model.

Budget

Minimum ($6,000) — lean pilot: 5–6 participants, manual orchestration (as today), multi-model API costs, and my time. Produces the public write-up at small scale and proves the method generalises beyond n = 1.

Full goal ($24,000):

• Engineering — semi-automate the evaluation pipeline (contractor): $9,000

• Multi-model API / compute (3 models × ~10 corpora): $4,000

• Participant honoraria (10 × $250): $2,500

• Researcher time (~4 months, part-time): $7,000

• Write-up, documentation, public report: $1,500

Team

Marian Dorobanțu — independent operator and writer. For eighteen years I built artist identities in the Romanian music industry through ATOM (~1B streams, ~8% national radio share, built without paid media): constructing and protecting voice at scale was the job. I designed exist. and ran the multi-model validation above. I'm not an ML researcher — engineering will be contracted. Independent, non-institutional work.

If it fails

Honest failure modes: LLM-as-judge proves too biased or unreliable to measure identity consistently across people; refusal flattens voice rather than protecting it; or the metric doesn't generalise beyond one person. Even then the output is informative — a public negative result on whether identity can be tracked and governed, plus a documented account of where the approach breaks. The real risk is that refusal turns out to be a form of control. Surfacing that is part of the point.

Funds raised in the last 12 months

None for exist. Self-funded to date; this would be its first external support.