P252 — AIEP — Epistemic Budget Control Engine

Applicant: Neil Grassby Classification: Patent Application — Confidential Priority: Claims priority from GB2519711.2 filed 20 November 2025 Architecture Layer: AIEP Phase 2 Resource Governance Layer

Framework Context

[0001] This specification operates within an AIEP environment as defined in GB2519711.2 and GB2519798.9. The present specification defines a budget control mechanism that governs the total epistemic compute resources — evidence query budget, simulation budget, and distillation budget — allocated per reasoning session, goal, or time period.

Field of the Invention

[0002] The present invention relates to epistemic budget control and compute governance for evidence-bound AI reasoning sessions.

Background

[0003] Evidence-bound reasoning can consume unbounded compute if evidence queries, simulations, and distillation operations are left unconstrained. Epistemic budget control governs resource consumption at the semantic level — measuring compute in terms of epistemic operations (evidence queries, simulation steps, knowledge transformations) rather than raw CPU cycles alone.

Summary of the Invention

[0004] The invention provides an Epistemic Budget Control Engine (EBCE) that maintains epistemic operation budgets at three scopes: session budget (total epistemic operations permitted per reasoning session); goal budget (operations per goal); and global period budget (operations per clock period across all sessions). The engine: tracks consumed operations against each budget scope; enforces hard limits by blocking further operations when a budget is exhausted; and triggers budget events to the Compute-Aware Epistemic Prioritisation Engine (P240) to enable prioritisation adjustments before exhaustion.

ASCII Architecture

Reasoning Session Operations
         |
         v
+------------------------------------------+
| Epistemic Budget Control Engine (EBCE)   |
|                                          |
|  Session Budget Counter                 |
|  Goal Budget Counter                    |
|  Global Period Budget Counter           |
|                                          |
|  Budget check on each operation         |
|  Budget warning → P240 notification     |
|  Budget exhausted → Operation blocked   |
+-------------------+----------------------+
                    |
                    v
  Budget events → CAEPE (P240)
  Hard blocks → Reasoning session manager

Detailed Description

[0005] Epistemic Operation Units. Each operation class has a defined cost in epistemic operation units: evidence artefact query (1 unit); CWSG node traversal (1 unit); hypothesis simulation step (5 units per branch); cross-federation evidence request (10 units); knowledge distillation cycle (20 units).

[0006] Budget Scopes. Session budget is configured per session class (deep reasoning vs. quick query). Goal budget is proportional to goal priority class. Global period budget is configured by the operator and enforces system-wide cost ceilings.

[0007] Budget Warning. When a budget reaches 20% remaining, a warning event is dispatched to the Compute-Aware Epistemic Prioritisation Engine (P240) to trigger deferred prioritisation of remaining operations.

[0008] Budget Exhaustion. When a budget is exhausted, further operations of the affected type are blocked. The reasoning session manager is notified, and the session is flagged as budget-terminated. Budget-terminated session records include the set of deferred investigation steps, enabling continuation in a subsequent session.

Technical Effect

[0009] The invention provides semantic-level resource governance for evidence-bound AI reasoning, enabling cost control at the level of meaningful epistemic operations rather than raw machine cycles. By assigning different epistemic operation unit costs to operations of different computational intensity, the engine implements a principled cost model that correlates budget consumption with true reasoning depth. The three-scope budget hierarchy enables independent cost governance at session, goal, and operator-configured global levels. Integration with the Compute-Aware Epistemic Prioritisation Engine at warning threshold ensures graceful budget exhaustion rather than abrupt session termination.

Claims

1. A method of controlling epistemic resource consumption in an evidence-bound artificial intelligence system, comprising the steps of: (a) maintaining budget counters at three scopes: session scope (per session class), goal scope (per active goal proportional to goal priority class), and global period scope (operator-configured system-wide ceiling); (b) assigning unit costs to epistemic operation classes: evidence artefact query (1 unit), CWSG node traversal (1 unit), hypothesis simulation step (5 units per branch), cross-federation evidence request (10 units), knowledge distillation cycle (20 units); (c) decrementing the applicable budget counter at each scope on each epistemic operation and checking remaining balance against configured thresholds; (d) dispatching a budget warning event to the Compute-Aware Epistemic Prioritisation Engine when a budget scope reaches 20% remaining, enabling deferred prioritisation of remaining operations; (e) on budget exhaustion at any scope, blocking further operations of the affected class, notifying the reasoning session manager, flagging the session as budget-terminated, and producing a deferred investigation record containing the set of incomplete investigation steps.

2. The method of claim 1, wherein session budget is configured per session class such that deep multi-step reasoning sessions have higher budgets than single-query sessions.

3. The method of claim 1, wherein goal scope budgets are allocated from the session budget proportionally, and goal budget exhaustion blocks further epistemic operations on behalf of that goal only.

4. The method of claim 1, wherein deferred investigation records include the evidence artefact activation set, CWSG traversal state, and outstanding investigation steps at the point of budget exhaustion, enabling faithful session continuation.

5. The method of claim 1, wherein budget consumption records are admitted to the evidence ledger at session completion, providing an auditable record of reasoning cost.

6. An epistemic budget control engine for an evidence-bound artificial intelligence system, comprising: a multi-scope budget counter store; an operation cost table; a budget decrement and threshold monitor; a warning event dispatcher; and a budget exhaustion handler producing deferred investigation records.

7. A computer-readable medium carrying instructions for implementing the method of any preceding method claim.

Abstract

An epistemic budget control engine for evidence-bound artificial intelligence governs reasoning resource consumption at the semantic level of epistemic operations, maintaining budget counters at session, goal, and global period scopes with defined unit costs per operation class. Budget warning events at 20% remaining trigger deferred step prioritisation via the Compute-Aware Epistemic Prioritisation Engine. On budget exhaustion, further operations of the affected class are blocked and a deferred investigation record is produced containing incomplete investigation steps for session continuation. Budget consumption records are admitted to the evidence ledger.