Purpose & scope for real-world AI
Add one more coordinate. Keep everything else the same.
SSM-AI carries a bounded alignment lane beside any classical AI magnitude:x := (m, a) with a in (-1,+1) and collapse parity phi((m,a)) = m.
Your m is untouched: logits, probabilities, scores, rewards, latencies keep their full meaning. The lane a composes safely across operations, steps, and streams, producing a legible band at every decision point.
The path from raw model output to safer action becomes:
CLAMP → MAP to rapidity → FUSE → INVERSE → BAND.
The math is tiny. The effect is huge.
SSM-AI drops into decoding, RAG, tool agents, evaluators, and ensembles with no retraining and no rewiring.
Teams continue exactly as before, except… they now see stability.
Goal. Enable immediate, evaluation-first adoption: publish the lane read-only, standardize bands and manifests, and scale across vendors with identical semantics. Acceleration comes later without changing the rules.
What SSM-AI is (and is not)
• Is: a bounded, composable, operator-native alignment lane beside m
• Is: collapse-compatible (phi((m,a)) = m always)
• Is: a 3-step kernel anyone can validate quickly
a_c := clamp(a, -1+eps_a, +1-eps_a)
u := atanh(a_c)
# compose in u then
a' := tanh(u')
# order-invariant streaming
U += w*atanh(a)
W += w
a_out := tanh(U / max(W, eps_w))
• Is: equipped with a universal decision index RSI in (-1,+1) and a read-only governor RSI_env := g_t*RSI
• Is NOT: a replacement for training
• Is NOT: a hidden alterer of m
• Is NOT: an uncontrolled actuator. Any policy change still needs a formal safety case.
Bounded clarity beside the numbers you already trust.
Navigation
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Next: SSM-AI – Under the Hood & Integration (0A continued)
Directory of Pages
SSM-AI — Table of Contents
Explore Further
https://github.com/OMPSHUNYAYA/Symbolic-Mathematical-AI
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