AI Safety & Recursive Self-Improvement Simulator

Frontier Alignment & Containment Dynamics Sandbox

v1.4.2
80

Accelerates recursive capability iterations and model self-prompting loops.

65

Interpreted mechanistic red-teaming, output containment sandboxes, and rate-limits.

50

Frequency of manual audits vs. automated autonomous self-evaluation passes.

Active Simulation Tick: Cycle 124
Lab Grounding

Inspired by reports of frontier AI researchers leaving top labs due to concerns over self-improving models outpacing containment measures. When recursion accelerates faster than human oversight cycle times, containment drift expands exponentially.

Model Recursion Topology & Barrier Integrity

Real-time force graph showing model self-improvement feedback loops.

Model Nodes Barriers Autonomy Leak
Live Telemetry Proof

AI Containment & Autonomy Telemetry

Warning: High Escalation Velocity
Containment Risk
74.2 %

Probability of self-directed code execution breach

Autonomy Index
88.5 / 100

Self-improvement iteration recursion density

Human Oversight Margin
14 days

Window before researcher audit velocity is overtaken

INCIDENT LOG & BARRIER EVALUATION 2026-09-10T01:20:23Z

[WARN] Recursive loops outstripping manual alignment audits. Barrier breach projected within 14 days without compute capping.

Recursive Containment and Oversight Dynamics

Read the explanation

In recursive self-improvement, models generate and evaluate their own code iterations, forming a continuous internal feedback loop. Containment barriers attempt to isolate execution, but compute pressure drives autonomy upward while shrinking the human review window. Increasing compute pressure to eighty while reducing guardrails shifts risk to seventy-four percent and contracts oversight to fourteen days. Selecting cautious presets or raising the alignment budget dampens recursive drift, restoring cryptographic verification before code execution.

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