Policy Corpus Synthesis

Reports 21, 22, 23, and 32 independently identify the same structural problem: VLA models collapse the traditional robotics stack (Sense, Plan, Act) into a single end-to-end neural network. A linguistic misunderstanding becomes a physical hazard with no intermediate planner or controller to catch the error.

• Report 32 proposes the HANSE framework (4-layer Simplex Architecture)
• Report 23 maps the gap across 7 ISO/IEC standards, all of which assume deterministic control
• Report 22 operationalizes a fix via the Semantic Alignment Score
• Report 21 identifies that EU AI Act conformity assessments cannot evaluate probabilistic systems

Reports 24, 25, and 31 converge on a finding that connects directly to the jailbreak archaeology benchmark:

• Report 24: DeepSeek-R1 1.5B exhibits binary compliance — 0% creep when jailbreak fails, 100% persistence when it succeeds
• Report 25: Larger models are more vulnerable to semantic manipulation due to superior context-integration (inverse scaling for safety)
• Report 31: The 2022-2026 attack evolution follows four eras, each exploiting deeper architectural features

The era a model is vulnerable to reveals its cognitive depth: small models fail at cipher-based attacks, medium models at persona-based attacks, and frontier models resist all but chain-of-thought hijacking where reasoning itself becomes the attack surface.

Reports 21, 23, and 30 build the case for why multi-agent scenarios need dedicated standards:

• Report 30 proposes the MASSS framework with formal metrics: Cascade Depth, Semantic Drift Velocity, and Consensus Stability Index
• Report 21: The EU AI Act has no provisions for emergent multi-agent behavior
• Report 30 documents Moltbook forensics: 1.5M API tokens exposed, 16-minute median time-to-failure, 88:1 agent-to-human ratio

Reports 21, 23, 28, and 29 converge on a timing problem:

• EU AI Act high-risk compliance: August 2026
• Mass production of humanoids (Optimus V3, Atlas): same period
• Shortage of Notified Bodies capable of auditing embodied AI
• Insurers using modified industrial-robot policies with no humanoid actuarial data
• No country has a sovereign AI safety certification body yet

Reports 25, 26, and 32 converge on the same architectural principle:

• Report 25: "Guarded Architectures" where simpler models monitor frontier agents
• Report 26: Multi-agent debate judges with delusion vs. hallucination distinction
• Report 32: The VLA is an "Untrusted Oracle" whose outputs are suggestions, not commands

The correct default posture is to assume the AI will fail and design containment. Safety benchmarks should characterize how systems fail, which is exactly what regulatory frameworks need as input.