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feat(ruview-gamma): generalize to adaptive sensory neuromodulation platform

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40 Hz becomes one prior in one program, not the product. The engine is a
personal neural-rhythm optimization platform: RuView is the feedback signal,
RuVector the personal response map, the device the actuator, RuFlo the
governed/auditable loop (ADR-250 section 23).

New "program" module: NeuroProgram catalog of 7 use cases (Alzheimer's
research, post-stroke cognition, sleep optimization, attention/working-memory,
mood/arousal, home wellness, drug+device trial infrastructure), each with its
own SafetyEnvelope, prior, ObjectiveWeights, physiological-state gating
(sleep permits Asleep + near-dark brightness cap; attention requires
wakefulness), EvidenceLevel, and a single non-disease claim.

New "acceptance" module: makes "measurable entrainment, safety, adherence,
repeatability before any disease claim" executable. AcceptanceHarness grades
a program over >=3 repeats; ClaimGate releases the program's claim ONLY when
all four pass, else the research-only NO_CLAIM string. The marketing claim is
unreadable except through the gate.

Governor: enroll_program (per-program envelope/objective), program(), prior(),
state_eligible(). The bare enroll() path is unchanged, so the pinned witness
13cb164c... is preserved.

88 crate tests + 1 doctest; workspace gate 2,889 passed / 0 failed. Benches:
program grading ~425us; hot paths unchanged (recommend ~15us, calibration
~115us, kNN/500 ~15us).

https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH
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docs/adr/ADR-250-adaptive-gamma-entrainment.md
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@@ -197,6 +197,47 @@ bit-exactly before any hardware or human exposure. Hardware actuation, real RF
sensing, and real EEG land behind feature flags / external adapters; this crate
implements the governed software core and its proofs.
## 23. Platform Generalization — Adaptive Sensory Neuromodulation
The broader opportunity is **adaptive sensory neuromodulation**, not just
Alzheimer's. 40 Hz is one prior in one program; the engine is a personal
neural-rhythm optimization platform. RuView turns the body into the feedback
signal, RuVector turns repeated sessions into a personal response map, the
device is the actuator, and RuFlo makes the loop governed and auditable.
Each use case is a `NeuroProgram` (`program.rs`) bundling its own safety
envelope, starting prior, objective weighting, physiological-state gating,
evidence level, and the single non-disease claim it may surface:
| Program | Evidence level | RuView / RuVector role | Released claim |
|---------|----------------|------------------------|----------------|
| `alzheimers-research` | Medium preclinical, early human | Adaptive entrainment + trial monitoring | personalized entrainment optimization |
| `post-stroke-cognition` | Early human | Recovery-state tracking (ramped, comfort-weighted) | …with recovery-state monitoring |
| `sleep-optimization` | Early but plausible | Time stimulation to sleep state (audio, near-dark cap) | sleep-state-timed entrainment optimization |
| `attention-working-memory` | Mixed / protocol-dependent | Personal frequency discovery (entrainment-weighted) | personalized frequency-response discovery |
| `mood-arousal` | Early human | Avoid overstimulation, tune calming response | personalized calming-response optimization |
| `home-wellness` | Speculative | Safe personalization without treatment claims | personal neural-rhythm wellness optimization |
| `trial-infrastructure` | Strong infrastructure | Governed protocol/safety/consent/sham log | governed, reproducible protocol measurement |
**Claim discipline is structural.** A program's claim is always an
optimization/monitoring statement, never a disease-treatment claim; the disease
*context* lives only in `EvidenceLevel`. A claim is releasable **only** through
the acceptance gate.
### 23.1 Generalized acceptance gate (`acceptance.rs`)
> Every use case must show measurable **entrainment**, **safety**,
> **adherence**, and **repeatability** before making any disease claim.
`AcceptanceHarness::evaluate(program, person, state)` runs the program over ≥3
independent repeats and measures: adaptive-vs-fixed-prior entrainment gain,
safety-stop rate, mean adherence, and the spread of the discovered optimal
frequency. The resulting `AcceptanceReport` exposes a `ClaimGate` that returns
the program's claim **iff all four criteria pass**, and the research-only
`NO_CLAIM` string otherwise — the program's marketing claim cannot be read
except through this gate. This makes the acceptance sentence executable, not
aspirational, and applies uniformly to all seven programs.
## 22. Final Decision Statement
We build Adaptive Gamma Entrainment as a governed RuView + RuVector