SSM-AI – Why This Matters (0D)

Immediate value without retraining

Backward compatible by construction
Classical numbers stay identical via collapse parity phi((m,a)) = m. You add a bounded lane a in (-1,+1) and a bounded chooser RSI in (-1,+1) for visibility, routing, and policy. Value math is untouched.

Reproducible and fair across vendors
Order-invariant pooling guarantees batch == stream == shuffled using the same fuse a_out := tanh( (SUM w*atanh(a)) / max(SUM w, eps_w) ). Identical manifests yield comparable a and RSI across prompts, models, and providers.

Immediate cost and latency gains
Fewer blind retries and calmer agent loops as schedulers use the read-only gate RSI_env := g_t * RSI, g_t in [0,1]. Token, tool, and API waste drops without touching m.

Quality you can see
Bands A++/A+/A0/A-/A-- turn raw alignment into an executive-readable label. Low-band branches trigger guardrails or review; high-band flows.

Auditable decisions
Each choice can emit (m, a, U, W, band, g_t) and a one-line stamp for replay. Reviews shift from “why did it do that?” to stamp and rerun.

Scale by manifest, not migration
A small, shared manifest (clamps, weights, bands, division policy) standardizes evaluation across teams, regions, and vendors. Same manifest ⇒ same outputs.

Safety by design
The calm gate and policies act on a only. m is never altered. Promotion from advisory to actuation requires a documented, testable safety case.

Pocket walkthrough: picking between two answers
Two candidates provide lens contrasts (e_in, e_out).
Map to alignments and pool once:

a_in  := tanh(-c*e_in)
a_out := tanh(+c*e_out)
RSI   := tanh( (SUM w*atanh(a_out) - SUM w*atanh(a_in)) / max(SUM w, eps_w) )
RSI_env := g_t * RSI

Because tanh and atanh are monotone, larger net contrast (e_out - e_in) yields larger RSI. Select by RSI or RSI_env while keeping m identical.

30-second audit recipe

  1. Collapse: recompute with phi((m,a)) and confirm equality to baseline m.
  2. Shuffle: batch vs stream vs shuffled produce the same a_out/RSI within epsilon.
  3. Bands: thresholds match the manifest; borderline cases honor declared epsilons.
  4. Gate purity: recompute RSI at g_t=1, then apply g_t<1; only alignment scales.

Zero-infra adoption
Observation-only via phi((m,a)) = m. No retraining. Tiny manifest. API-safe by emitting m where a single number is required. Publish the lane, add bands, and stamp outputs. That is enough to see earlier truths.

Cost impact (conservative)
Annual_Savings ≈ S_base * r_save, with r_save in [0.10, 0.20]. Drivers: fewer retries, calmer loops, tighter beam search, cleaner vendor mix. Achieved without modifying m.

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