Tools & agents, evaluators/judges, and multi-model ensembles — bounded routing without touching m
Tools & Agents (branching, retries, routing).
SSM-AI reduces wasteful loops during turbulence by scaling alignment only. Build a calm gate g_t in [0,1] from bounded telemetry (e.g., contradiction/error/latency/churn) and apply it to the chooser; never mutate m.
Gate idea (bounded mix):
g_t := clamp( 1 - (wF*F_t + wE*E_t + wL*L_t + wV*V_t), 0, 1 )
Retry rule (example policy):
RSI := tanh( (SUM w*atanh(a_out) - SUM w*atanh(a_in)) / max(SUM w, eps_w) )
RSI_env := g_t * RSI
should_retry := (RSI_env >= 0.60) # A+ threshold (manifest-declared)
# m remains pristine: phi((m,a)) = m
Stamped traces (stamp per step) make agent flows replayable while selection stays order-invariant via U/W.
Evaluators / Judges (policy, intent, style).
Treat judge signals as a lens: positives to e_out, penalties to e_in. Compute a bounded chooser; report beside the original judge scores.
Lens sketch:
e_out := policy_hits + intent_match + style_fit
e_in := policy_gaps + safety_flags + contradiction
a_in := tanh(-c*e_in)
a_out := tanh(+c*e_out)
RSI_judge := tanh( (SUM w*atanh(a_out) - SUM w*atanh(a_in)) / max(SUM w, eps_w) )
# Publish (RSI_judge, band); do not rescore m
Multi-Model Ensembles (cross-vendor fairness).
Pool alignments from multiple providers in u-space; pick by a single bounded chooser while each provider’s m stays untouched under collapse.
Order-invariant ensemble fuse:
# Given provider-level RSI_i and weights w_i (uniform or declared reliability)
U := SUM w_i * atanh(RSI_i)
W := SUM w_i
RSI_pool := tanh( U / max(W, eps_w) )
# Choose by RSI_pool (or RSI_env := g_t * RSI_pool); classical m-path remains unchanged
Why this matters.
• Calmer loops: gating scales alignment only (RSI_env := g_t * RSI), preventing runaway retries.
• Comparable judges: one bounded index RSI in (-1,+1) across models and datasets.
• Fair ensembles: shard/provider merges are associative via (U,W); batch == stream == shuffled.
• Audit clarity: every decision can log (m, a, U, W, RSI, RSI_env, band, stamp); phi((m,a)) = m guarantees value parity.
Pocket pseudocode (tools + ensemble).
def rsi_candidate(items, c=1.0, eps_w=1e-12):
U_in = V_out = W = 0.0
for (e_in, e_out, w) in items:
a_in = tanh(-c*e_in); a_out = tanh(+c*e_out)
U_in += w * atanh(max(-1+1e-6, min(1-1e-6, a_in)))
V_out += w * atanh(max(-1+1e-6, min(1-1e-6, a_out)))
W += w
return tanh( (V_out - U_in) / max(W, eps_w) )
def ensemble_rsi(rsis, weights, eps_w=1e-12):
U = sum(w*atanh(max(-1+1e-6, min(1-1e-6, r))) for r, w in zip(rsis, weights))
W = sum(weights)
return tanh( U / max(W, eps_w) )
def routed_retry(step_items, telemetry):
RSI = rsi_candidate(step_items)
RSI_env = telemetry["g_t"] * RSI
return (RSI_env >= 0.60) # example A+ threshold
One-line takeaway. Tools, judges, and ensembles all follow the same tiny pattern: declare a lens → compute bounded alignments → fuse in u-space → choose by RSI/RSI_env — while phi((m,a)) = m keeps numbers pristine and audits replayable.
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