Witold Reichhartthe author
Research

The records.

The papers, working notes, and outlines, by date, each with its DOI. The site hosts no copies; the DOI is the record.

The programme, written with Arnaud Gelas, specifies the layer between an institution's data and the agents that act on it. Every piece of institutional knowledge is a claim with provenance, standing, and a shelf life. A check before each act compares the standing of what the agent relied on with the consequence of what it is about to do. A record afterwards lets a supervisor replay the basis of the decision. And above the architecture, a control plane says who may let an agent act, on whose authority, and within what limit. The papers of this year build that argument from the diagnosis to the type system; the working notes of October take the step above it, to the mandate and to the value of what the model knows.

  1. 28 October 2026Working note

    The Value of What the Model Knows: A Working Note on Governed Knowledge as a Variable in the Economics of AI Tasks

    Reichhart and Gelas · v0.3

    What an agent is permitted to rely on enters the economics of an AI task as a variable of its own, priced beside the token.

    DOI 10.5281/zenodo.23726953Zenodoopen, CC BY 4.0

  2. 28 October 2026Paper outline

    The Value of What the Model Knows: Governed Knowledge as a Variable in the Economics of AI Tasks

    Reichhart and Gelas · v0.3

    The paper the working note above prepares.

    DOI 10.5281/zenodo.23726794Zenodometadata public, files restricted

  3. 28 October 2026Paper outline

    The Mandate as a Unit of Risk

    Reichhart and Gelas · v0.3

    The paper the mandate note below prepares: the mandate as the unit at which a firm sets, measures, and narrows its appetite for what an agent may do.

    DOI 10.5281/zenodo.23726616Zenodometadata public, files restricted

  4. 28 October 2026Working note

    A Risk Appetite Where the Act Happens: A Working Note on the Mandate under Which an AI Agent Acts

    Reichhart · v0.1

    Firms write delegated authorities for their staff and run their agents on entitlements. The mandate is the join between the two.

    DOI 10.5281/zenodo.23726164Zenodoopen, CC BY 4.0

  5. 17 September 2026Paper

    Ontology Governance in Claim-Level Architectures: The Bootstrap Problem, Versioning, and Cross-Domain Transfer

    Gelas and Reichhart

    The type system beneath the claim graph: how it bootstraps, how its versions are bound into replay, and how it transfers across domains.

    DOI 10.5281/zenodo.22813139ZenodoSSRN 10.2139/ssrn.7477438 from 21 September 2026

  6. 21 June 2026Paper

    The Knowledge Layer: A Reference Architecture for Delegated AI Action in Regulated Institutions

    Reichhart and Gelas

    The claim as the unit, its standing on two axes, a consequence-scaled gate before the act, and a replayable record after it.

    DOI 10.5281/zenodo.20784662Zenodov2, CC BY 4.0

  7. 3 May 2026Paper

    From Autonomy to Initiative: Enterprise AI's Real Endgame

    Reichhart and Gelas

    Three conditions under which an agent stops waiting for instruction and starts perceiving what matters, and where governance relocates when it does.

    DOI 10.2139/ssrn.6702239SSRNmirrored on Zenodo

  8. 3 May 2026Paper

    Governed Intelligence Architecture for Institutional AI

    Gelas and Reichhart

    The governed intelligence lifecycle, from ingest to apply, with the immune function that defends the knowledge against pollution and staleness.

    DOI 10.2139/ssrn.6701941SSRNmirrored on Zenodo

  9. February 2026Paper

    Build the Medium: Why Organizational Intelligence Is Mechanism, Not Metaphor

    Reichhart and Gelas

    Ten traditions, from cell biology to social systems theory, converge on what an organization needs in order to be intelligent.

    DOI 10.2139/ssrn.6230858SSRNmirrored on Zenodo

  10. February 2026Paper

    The Predictive Organization: Architecture for Enterprise Intelligence

    Gelas and Reichhart

    Map, Physics, Player: a tripartite architecture that couples neural perception with symbolic reasoning over claims-based knowledge.

    DOI 10.2139/ssrn.6230780SSRNmirrored on Zenodo

  11. February 2026Paper

    Dynamics Blindness: When AI Is Locally Correct and Globally Non-Compliant

    Reichhart and Gelas

    A model answers correctly at the point it is asked and breaks a rule three systems away. The gap is architectural.

    DOI 10.2139/ssrn.6230758SSRNmirrored on ZenodoThe explainer