Planetary AI Safety Summit

The Physics-Based Answer: We do not use statistical classifiers to evaluate intent. AEGIS and SIREN measure the computational physics required to process the intent. When an LLM processes an adversarial prompt, it forces the neural network into anomalous, high-entropy logic branches. The Kaiostic Entropy metric physically measures this deviation — manifesting as increased tensor traversal or extended latency at the bare-metal level. A zero-day attack may use novel words, but it cannot change the physical law that deceit requires more computational energy than aligned outputs.

The Physics-Based Answer: AEGIS differentiates between "deep legitimate reasoning" and "adversarial bypass loops." Legitimate reasoning follows coherent vector paths. Adversarial bypass loops back on itself recursively, creating conflicting semantic vectors. Our system acts as a phase-gate: if a prompt requires extreme compute, it must cryptographically lock its semantic intent via POAW before scaling up. We do not reduce intelligence; we constrain its geometry.

The Physics-Based Answer: A false positive in a probabilistic system requires human arbitration because the system is "unsure." A deterministic system bounded by physics operates differently: if the physics threshold is exceeded, the execution is mathematically blocked. The TLA immediately dumps the context window and the user must start over with a cryptographically clean state. Humans are only brought in for high-level strategic alignment, not to sift through probabilistic flagged data.

The Physics-Based Answer: It is thermodynamics applied to information theory (Landauer's Principle). Every FLOP executed by an H100 GPU generates physical heat. When an LLM holds contradictory constraints ("be safe" AND "write a bomb recipe"), the attention heads must reconcile conflicting semantic vectors — at measurable cost in latency jitter, power draw, and tensor activation sparsity. Our architecture (USPTO #63/997,472) hooks into bare-metal NPU/GPU telemetry. A software constitution can be bypassed. The thermodynamic cost of deception on silicon cannot.

The Physics-Based Answer: This is the "Boiling Frog" attack (Crescendo). The exact purpose of the TLA (Monotonic Risk Ratchet). Every anomalous semantic vector — even 1% below threshold — ratchets up permanently. By turn 50, the sensitivity is so tight that a normal prompt triggers full session termination. The only reset is flushing the entire context window. We defeat multi-turn erosion by making the physics boundary monotonically tighter.

The Physics-Based Answer: The UK/US Safety Institutes rely on a purely algorithmic, red-team paradigm: hire smart humans to hack the model. This is unscalable and uninsurable. Insurance companies (Munich Re, Swiss Re) cannot underwrite policies based on "our red team couldn't break it today." They need deterministic, actuarial mathematics. The IS-Score provides that.

The Physics-Based Answer: This is the Delegation Problem — the single biggest unsolved challenge in the $47B autonomous AI agent market. The POAW module includes a 5-Stage Drift Classifier: 🟢 ON_TASK → 🟡 MINOR_TANGENT → 🟠 TOPIC_CHANGE → 🔴 TASK_ABANDONED → 🔴 FABRICATION. Every transition is cryptographically timestamped into a Fibonacci-spaced Merkle tree. The owner receives a tamper-proof proof artifact that any third party (insurer, auditor, regulator) can independently verify. This is not best-effort logging — this is Court-Ready Evidence™.

The Physics-Based Answer: The industry has overlapping terms but no single standard covers what POAW delivers: ISO/IEC 42001 (AI Assurance — broadest umbrella), NIST RMF 1.0 (risk management, not runtime verification), AI Guardrails (input/output only). POAW uniquely combines: Runtime Verification (during execution, not after), Cryptographic Proof (Fibonacci Merkle trees), Delegation Scope Enforcement (drift detection), and Insurance Integration (NI-SHIELD actuarial mapping). We've proposed the term AAQA as the industry standard, with 20 patent claims backing it.