{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Thohat Ventures"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Remi Olaiya",
      "orcid": "0009-0002-6809-5061"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1gzr4wy8c8em0.bib",
  "cited_by": 0,
  "doi": "10.5281/zenodo.23677470",
  "fulltext_url": null,
  "id": "1gzr4wy8c8em0",
  "issns": [],
  "landing_url": "https://zenodo.org/records/23677470",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": "https://zenodo.org/api/records/23677470/files/Upstream%20Latent%20Trajectory%20Governance%20-%20AI%20Homeostasis.pdf/content",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1gzr4wy8c8em0.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T12:20:38Z",
      "native_id": "23677470",
      "source": "zenodo",
      "url": "https://zenodo.org/records/23677470"
    }
  ],
  "subjects": [],
  "title": "Upstream Latent Trajectory Governance: Information Geometry, eBPF Kernel Drops, and Cryptographic Attestation for Autonomous Systems",
  "title_text": "Upstream Latent Trajectory Governance: Information Geometry, eBPF Kernel Drops, and Cryptographic Attestation for Autonomous Systems",
  "type": "preprint",
  "updated_at": "2026-11-09T12:20:38Z",
  "url": "https://research.draconitic.com/paper/1gzr4wy8c8em0",
  "venue": "Zenodo",
  "versions": []
}