Drift Detection and Blueprint Evolution
The optimization problem is therefore a controlled transport problem on a slowly shifting manifold: carry the point from its current location toward the target region along a trajectory that respects safety and resource limits, while continually recalibrating the coordinate frame. The coordinate system does not stay fixed: it drifts as the object ages, the environment shifts, and new measurements reveal previously hidden axes.
Drift as Signal for Dimensional Discovery
Drift in measurement is a signal revealing which dimensions were incorrectly specified or omitted. When cohorts expose systematic gaps—missing variables, warped conditioning, unmodeled collateral effects—we treat it as a mandate to rewrite the blueprint and regenerate every affected statistic from raw logs.
The blueprint itself always begins as a hypothesis. Early in a program we only guess which dimensions of the sufficient statistic will prove causal. When population-level analysis reveals dimensional blind spots, we rewrite the blueprint and replay historical logs to regenerate the statistics. The contracts are rewritten alongside the data, preventing future compositions from inheriting the flawed lens.
Cohorts are compact regions in the sufficient-statistic space that share a causal response profile. When effect signatures vary significantly across the population, the system clusters similar effect patterns, identifies sufficient-statistic boundaries between clusters, creates new cohort definitions, and recomputes arc effectiveness per new cohort.
To keep compositions safe, each arc carries an explicit contract. The entry contract encodes the sufficient-statistic predicates that must be observed before we cross into the arc, while the exit contract specifies the guarantees and variance bounds of the state it returns. When evidence shows these contracts drifting out of tolerance, orchestration either routes around the arc, launches exploration to tighten the contract, or spawns a variant arc tuned to the new statistics.
When polylines of arcs prove successful across many cohort members, they graduate from experimental paths to maintained "roads." Because the coordinate frame drifts, we periodically remeasure the roadbed itself—checking that today's cohorts still sit on the same grade and curvature that yesterday's audits certified. The orchestration challenge is to keep extending the map at the frontier without letting the existing roads crumble through neglect.
As cohorts accumulate their episodic clusters, the population analysis reports whether exploration has reached sufficiency. If certain risk-weighted regions remain under-sampled, the orchestrator launches new worker waves or rebalances budgets until coverage meets the target. When the blueprint itself shifts—new dimensions added, buckets redefined—the whole exploration archive is replayed under the updated synthesis so that our safety claims remain anchored in the latest causal understanding.
The Macro-Design Loop
The macro-design loop governs both the catalogue of primitives and the policies that decide when to enter them. Each iteration can refine the blueprint itself, replaying raw logs so the sufficient statistics powering causal inference stay aligned with reality.
Observable Problem and Modeling Fidelity
Problem definition and problem solving are two sides of the same coin. Model training searches for representations to solve verifiable problems. Problem definition discovery searches for what the real problem structure actually is in its solvable form. These are causally bidirectional: problem definition drives the need for model improvements, while the model's representation shapes how problems can be formulated.
Prior to Einstein's papers, physicists were instrumenting the wrong blueprint for time: they treated simultaneity as absolute, so the measured object (space-time) never exposed the dimensions needed to reconcile observed anomalies. The moment the blueprint was rewritten—time as a dimension co-measured with space—the permissible arcs changed and the outstanding anomalies collapsed into a coherent program.
Measurement in Model and Application
High-risk deployments require conservative promotion rules. An arc can move into the high-risk library only when the population-level causal story is understood, the positive cohort is densely sampled, the negative cohort is bounded, and the exit state has tight variance. When evidence is missing, the orchestration layer should refuse to enter the arc and instead route toward exploration or defer to human oversight.
Learning these ledgers over time lets the system compose long trajectories with statistically robust arcs only when the surrounding information supports them. The orchestration policy becomes a search over composition patterns constrained by these contracts. Successful long-arc behavior is therefore not just the presence of clever primitives but the disciplined governance of when, where, and with what supporting statistics each primitive may run.
Drift Detection and Re-specification
The vertebrate eye and the cephalopod eye evolved independently because both evolutionary programs converged on the same measurable blueprint for optical organs: focal length, photoreceptor density, signal routing bandwidth. Once those dimensions lock in, the viable arc contracts become obvious—build a lens of a certain curvature, route signals along bundled axons, regulate pupil dilation—and any lineage that reaches that measurement regime is pulled onto the same trajectory.
Independent discoveries therefore signal that the sparse manifold of viable solutions is tightly coupled to measurable object dimensions. Whenever multiple groups measure the same blueprint axes, they traverse the same quantized arcs and arrive at similar solutions.
Learning how to solve existing problems better isn't the only arc that evolution can follow. Measurement upgrades often demand that we re-specify the problem, produce a new dimensional blueprint, and then redeploy our primitives against the newly revealed object.
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