GenOS models potential failures in an agent system: excessive alerts, cross-workspace contamination, unintended intervention effects, or degraded performance.
01 · Immunity & Medicine
Failure modes
in GenOS.
GenOS models potential failures in an agent system: excessive alerts, cross-workspace contamination, unintended intervention effects, or degraded performance.
INPUTPROCESSINGRESULT WITH CONTEXT
DEFINITION AND SCIENCE
What it means
in GenOS.
Computational pathology is a taxonomy of software failure patterns inspired by clinical language. It is not a medical nosology: categories need operational definitions and measurable signals, and must never be used to diagnose people.
Clinical practice observes symptoms, separates possible causes and checks treatment effects. GenOS borrows that observe–classify–reassess discipline for software faults; it makes no medical claim.
Observe → Classify → Contain → Verify
A bounded triage policy maps observed signals x to a response class c under explicit thresholds: c=policy(x, scope, confidence). Classification should preserve uncertainty and require verification after an intervention.
ANIMATED DIAGRAM · INTERACTIVE MODEL
01 / 03Explore the mechanism: Failure modes
A qualitative teaching model of this concept. It does not run GenOS or validate its implementation.
01Observe failure signalsINPUT
02Classify a software pathologyMECHANISM
03Contain and reassessCONDITIONAL OUTCOME
Current transition · Step 1Observe failure signals
The modeled transition may proceed. Change the assumption and move through the stages to inspect the decision path.
PROCESS AND USE
How it works
at a glance.
Observe
Track the simulated clinical state, budgets, alerts, and propagation signals.
Classify
Classify the failure as autoimmune, nosocomial, iatrogenic, or degenerative.
Contain
Depending on the case, isolate a workspace, reduce an alert, or correct an excessive intervention.
Verify
Measure the new state and decide whether to reintegrate, take another action, or stop.
RUNTIME IMPLEMENTATION
What is connected,
and how far.
These “diseases” describe simulated agent states and their governance mechanisms. They are not human diseases, and GenOS does not diagnose or treat people. The PrionAggregation marker compares dissonance_level / max_dissonance_threshold with 0.85 only when the configured threshold is finite and positive; the stored score remains raw dissonance and is not evidence of hallucination. Clinical pathology confidence is derived deterministically as clamp01(clamp01(severity) × 0.7 + 0.3); it is not calibrated and does not incorporate the rest of the biopsy evidence.
Clinical state, selected pathology checks and bounded therapy paths are executable and tested. The diagnoses are runtime analogies, not clinical assessments.
Failure modes have not yet been separately cataloged for this entry; follow the source and evidence links before relying on the mechanism.
PRIMARY RESEARCH · MECHANISM LEVEL
The mechanism
behind the analogy.
Design of fault-tolerant computers
Algirdas Avižienis · AFIPS Fall Joint Computer Conference, 733–743
Original fault-tolerance design work. It motivates software failure analysis but does not validate a GenOS pathology label.
Open publication ↗Primary research describes the cited mechanism; specific technologies may instead cite their original technical documentation. These sources provide context and do not validate the GenOS implementation.
EVIDENCE AND LIMITS
A software analogy, not a medical tool
These “diseases” describe simulated agent states and their governance mechanisms. They are not human diseases, and GenOS does not diagnose or treat people. The PrionAggregation marker compares dissonance_level / max_dissonance_threshold with 0.85 only when the configured threshold is finite and positive; the stored score remains raw dissonance and is not evidence of hallucination. Clinical pathology confidence is derived deterministically as clamp01(clamp01(severity) × 0.7 + 0.3); it is not calibrated and does not incorporate the rest of the biopsy evidence.