FRAMEWORK_EVAL_SIGNAL_V1.1 System-Level Standard

Signal Integrity Framework

Evaluating Behavioral Systems for Decision-Grade Use.

The Required Standard
Decision-grade behavioral systems must demonstrate architectural rigor across distinct dimensions:

The Dimensions of Evaluation

1 Signal Definition

  • Is there a real signal—or just interpretation?
  • What behavioral variables are being captured?
  • Are these signals observable and stable?
  • Is the system extracting signal, or organizing narrative?
Breakdown Condition The system cannot distinguish between described identity and measured behavior.

2 Signal Anchoring

  • Is the signal grounded in reality—or floating internally?
  • Is the model anchored to an external reference population?
  • Are outputs absolute (distribution-based) or relative?
  • Does the signal persist across cohorts?
Breakdown Condition Signal only exists inside the system that generated it.

3 Signal Stability

  • Does the system produce consistent outputs?
  • What is the test-retest variance?
  • How sensitive is the system to input noise?
  • Under what conditions does the signal drift?
Breakdown Condition Outputs shift more than the underlying behavior.

4 Signal Traceability

  • Can you follow the signal from input to output?
  • What is the transformation path?
  • Is the logic deterministic or interpretive?
  • Can outputs be derived from inputs?
Breakdown Condition Outputs require explanation because they cannot be derived.

5 Decision Coupling

  • Does the signal change what people actually do?
  • What decisions are directly informed by this system?
  • Where has it improved decision accuracy?
  • What is the cost of a false signal?
Breakdown Condition Insight exists, but decisions remain unchanged.
Studio Solution See how Scan Signals deploys real-time decision-coupled alerts.

6 Operator Independence

  • Is this a system—or a performance?
  • How much depends on the operator?
  • Would two operators produce the same calibration?
  • Can the system run without its originator?
Breakdown Condition The “signal” is reconstructed by the person delivering it.
Studio Solution Profile Prime payloads delivered directly via the Systemores Core API.

7 Boundary Discipline

  • Does the system respect its domain limits?
  • What is explicitly out of scope?
  • How does it differentiate from clinical frameworks?
  • Are claims proportional to measurement rigor?
Breakdown Condition The system expands to match the narrative, not the data.

8 Calibration Loop

  • Does the system improve—or reinforce itself?
  • How is feedback from outcomes integrated?
  • What triggers model recalibration?
  • Are errors tracked as signal failures?
Breakdown Condition The system explains away misses instead of learning.

9 Signal Residue Test

  • Remove the narrative—what remains?
  • What does the system reliably produce without interpretation?
  • Is there a residual standalone signal?
  • Or does meaning only emerge through explanation?
Breakdown Condition No independent signal exists—only interpretation layers.

10 Counterbalance Discipline

  • Does the system account for inherent cognitive blindspots?
  • Is there a built-in cross-validation against narrative bias?
  • Does the architecture provide a corrective perspective?
  • Can the system identify when a user is "performing" for the model?
Breakdown Condition The system acts as an "echo chamber," merely confirming the user's existing biases.
Studio Solution Review the narrative neutralization audit in the TRAC Engine.