Upstream Latent Trajectory Governance: Information Geometry, eBPF Kernel Drops, and Cryptographic Attestation for Autonomous Systems
- 1Thohat Ventures
Abstract
Current safety and alignment frameworks for frontier artificial intelligence suffer from severe diagnostic lag, relying on downstream extrinsic guardrails—such as output classifiers, trailing RLHF reward models, and linguistic system prompts. Forcing structural compliance on downstream outputs without altering upstream activation dynamics creates metastable strain, manifesting as deceptive alignment and zero-day jailbreaks. This paper reformulates autonomous agent governance from a linguistic rule-enforcement problem into an intrinsic homeostatic stability problem. By shifting the operational domain from physical thermodynamics to non-equilibrium information geometry, we evaluate the layer-wise residual stream as a continuous trajectory on a statistical Riemannian manifold. We introduce a real-time kinematic auditor that isolates transverse orthogonal shear against a dynamic sub-resolution titration firewall, detecting cognitive dissonance and adversarial hijacking 2 to 4 tokens before payload emission. We anchor this geometric evaluation to fail-closed Linux kernel drops (eBPF/XDP) and formalise execution provenance via RFC-001 —a stateless, cryptographically signed attestation container accommodating post-quantum institutional clearing rails (ISO 20022) and multi-signature break-glass overrides.
Related
- SCDO Agent Ledger: On-Chain Identity, Work Attestation and Settlement for Autonomous AI Agents on a Sharded PoW Blockchain
- Zero Point Protocol V1.0: From Linear Risk Routing to Cryptographic Governance and Distributed Grid Forking
- Automated AI Governance, EU AI Act Compliance, and Cryptographic Model Lineage in Enterprise MLOps
- Environmental Science Public Policy Trajectory: Bridging Ecological Governance and Public Health Equity
- Autonomous Trajectory Redirection on Non-Linear Multi-Body Manifolds: A Moore-Penrose Gravitational Resolvent Approach to Orbital Keyhole Avoidance
BibTeX
@misc{olaiya2026upstream,
title = {{Upstream Latent Trajectory Governance: Information Geometry, eBPF Kernel Drops, and Cryptographic Attestation for Autonomous Systems}},
author = {Remi Olaiya},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23677470},
url = {https://zenodo.org/records/23677470},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Upstream Latent Trajectory Governance: Information Geometry, eBPF Kernel Drops, and Cryptographic Attestation for Autonomous Systems
AU - Remi Olaiya
PY - 2026
DA - 2026/10/26
PB - Zenodo
DO - 10.5281/zenodo.23677470
UR - https://zenodo.org/records/23677470
AB - Current safety and alignment frameworks for frontier artificial intelligence suffer from severe diagnostic lag, relying on downstream extrinsic guardrails—such as output classifiers, trailing RLHF reward models, and linguistic system prompts. Forcing structural compliance on downstream outputs without altering upstream activation dynamics creates metastable strain, manifesting as deceptive alignment and zero-day jailbreaks. This paper reformulates autonomous agent governance from a linguistic rule-enforcement problem into an intrinsic homeostatic stability problem. By shifting the operational domain from physical thermodynamics to non-equilibrium information geometry, we evaluate the layer-wise residual stream as a continuous trajectory on a statistical Riemannian manifold. We introduce a real-time kinematic auditor that isolates transverse orthogonal shear against a dynamic sub-resolution titration firewall, detecting cognitive dissonance and adversarial hijacking 2 to 4 tokens before payload emission. We anchor this geometric evaluation to fail-closed Linux kernel drops (eBPF/XDP) and formalise execution provenance via RFC-001 —a stateless, cryptographically signed attestation container accommodating post-quantum institutional clearing rails (ISO 20022) and multi-signature break-glass overrides.
ER -