AI inner-loop applications

The runtime accelerates the deadline-limited inner-loop decision and search layer of an AI system — it does not replace neural networks or learning. The model and GPU handle perception and representation; the runtime handles the fast decision under a tight time or measurement budget, warm-starting each related problem from the previous physical configuration.

What it accelerates

Streaming optimization (routing, assignment, scheduling), constraint satisfaction, candidate sampling, sensor fusion, adaptive beamforming, control actions, and drift-aware hardware tuning — anywhere the decision arrives faster than a classical solver can search, and consecutive decisions are related so retained state transfers.

Fit follows structure, not label

Whether a given task fits depends on the structure of its objective, which we group into four classes:

• Diagonal / separable (QUBO, resource allocation, feature selection, thresholds): strong fit.
• Sparse / banded (routing on local graphs, scheduling, MPC plans, control, beamforming): strong fit.
• Regulation / tracking (calibration, sensor fusion, drift correction): strong fit.
• Dense / low-rank correlation (attention allocation, clustering, graph partitioning, inference paths): out of scope — the objective lives in correlations marginal feedback cannot resolve.

So “routing” fits when graph-local and does not when densely coupled. The structure of your specific instance decides it. The fit finder will classify your case in a minute.

Streaming pattern: warm-start across related decisions

The inner-loop win comes from carrying configuration across a stream of related problems. Keep one optimizer alive and re-target it each time a new related decision arrives — it resumes from the previous physical trajectory instead of restarting cold.

copyfrom swc import SWCOptimizer

# one persistent runtime for the whole decision stream
opt = SWCOptimizer(license_key="EVAL-...", n=N)
phi = opt.start([1.5708] * N)

for problem in decision_stream():        # related problems arriving over time
    Q = problem.objective                # this decision's cost
    for _ in range(rounds_per_decision): # tight per-decision budget
        p     = read_response(phi)
        bits  = sample(p)
        phi   = opt.step(list(bits), score=-cost(bits, Q))
    emit(decision_from(phi))             # act before the deadline
    # NOTE: no reset — phi carries into the next related problem
opt.end()

The retained state gives a re-convergence advantage that scales with how related consecutive problems are, and vanishes when they are unrelated — the signature that it is genuine memory, not tuning. To measure the transfer on your own stream, compare this against re-creating the optimizer each decision (a cold restart).