P235 — AIEP — Dissent-Responsive Reasoning Escalation Engine

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

Framework Context

[0001] This specification operates within an AIEP environment as defined in GB2519711.2 and GB2519798.9. The present specification defines the escalation mechanism that modifies system reasoning behaviour in response to dissent signals from the Multi-Agent Reasoning Dissent Engine (P209).

Field of the Invention

[0002] The present invention relates to dissent-responsive reasoning adaptation and escalation architectures for multi-agent AI systems.

Background

[0003] The Multi-Agent Reasoning Dissent Engine (P209) produces dissent records when agent positions diverge on a reasoning question. These dissent records must trigger appropriate system-level responses to prevent premature closure on contested reasoning conclusions. A structured escalation mechanism converts raw dissent signals into defined reasoning modifications.

Summary of the Invention

[0004] The invention provides a Dissent-Responsive Reasoning Escalation Engine (DRREE) that monitors dissent records from P209, classifies each dissent by severity using a dissent impact score, and triggers escalation actions proportional to impact:

• Level 1 (low dissent): Annotation appended to reasoning output; no structural change.
• Level 2 (moderate dissent): Active reasoning goal is paused; alternative evidence solicitation initiated.
• Level 3 (high dissent): Reasoning conclusion withheld; human oversight notification issued; goal returned to formation.
• Level 4 (critical dissent): System pauses all goal pursuit in affected domain; governance escalation triggered.

ASCII Architecture

Dissent Record (P209)
         |
         v
+--------------------------------------------+
| Dissent-Responsive Reasoning Escalation    |
|   Engine (DRREE)                           |
|                                            |
|  Dissent Impact Score calculation         |
|  Level classification (1–4)              |
|  Level-specific escalation actions        |
+-------------------+------------------------+
                    |
                    v
    Escalation Action:
    - Annotation / Pause / Withhold / Policy Esclation
    - Oversight Notification (Level 3+)
    - Governance Escalation (Level 4)

Detailed Description

[0005] Dissent Impact Score. The impact score is computed from: number of dissenting agents as fraction of the agent ensemble; the severity classification assigned by the dissent engine; the criticality class of the active goal (low/medium/high/safety-critical); and whether the dissent relates to evidence interpretation or governance compliance.

[0006] Level 1 Actions. Annotation is appended to the reasoning trace noting the dissent, the nature of disagreement, and the number of dissenting agents. The conclusion can proceed but is flagged as contested.

[0007] Level 2 Actions. The active goal is moved to PAUSED state. The evidence solicitation module is triggered to seek additional evidence that may resolve the contested interpretation. A resolution timeout is set after which the goal is re-evaluated.

[0008] Level 3 Actions. The reasoning conclusion is withheld from the action execution layer. A structured oversight notification is generated containing: the dissent record, the contested conclusion, the goal specification, and a structured request for reviewer assessment.

[0009] Level 4 Actions. All active goals in the affected reasoning domain are suspended. The governance escalation module is triggered. System resumes only after explicit governance clearance.

Technical Effect

[0010] The invention provides proportional, evidence-grounded escalation of AI reasoning outputs in response to agent dissent, ensuring that the severity of system response matches the severity of disagreement. By defining four escalation levels with distinct system behaviours, the engine provides a structured governance continuum from annotation to full domain suspension. By issuing structured oversight notifications at Level 3 containing complete dissent context, the engine enables human oversight to make informed intervention decisions without requiring access to the full reasoning system.

Claims

1. A computer-implemented method for dissent-responsive reasoning escalation, the method comprising: (a) receiving a dissent impact score from the Multi-Agent Reasoning Dissent Engine for the current reasoning session; (b) determining the escalation level based on dissent impact score, applying a higher escalation level multiplier for safety-critical goals; (c) at Level 1: appending a dissent annotation to the reasoning trace and allowing the conclusion to proceed with a contested flag; (d) at Level 2: pausing the active goal, triggering additional evidence solicitation, and setting a resolution timeout; (e) at Level 3: withholding the reasoning conclusion from the action execution layer and issuing a structured oversight notification comprising the dissent record, contested conclusion, goal specification, and reviewer assessment request; and (f) at Level 4: suspending all active goals in the affected reasoning domain and triggering governance escalation, with system resumption requiring explicit governance clearance.

2. The method of claim 1, wherein safety-critical goal escalation applies a multiplier increasing the effective dissent impact score for escalation level determination.

3. The method of claim 1, wherein Level 2 evidence solicitation targets the specific knowledge domains identified in the dissent record as the source of disagreement.

4. The method of claim 1, wherein the structured oversight notification at Level 3 is admitted to the evidence ledger as an oversight notification artefact with the full dissent context attached.

5. The method of claim 1, wherein governance clearance at Level 4 is recorded as an explicit clearance artefact admitted to the ledger, with authoriser identifier and timestamp.

6. A Dissent-Responsive Reasoning Escalation Engine comprising: one or more processors; memory storing escalation level thresholds, oversight notification templates, and governance escalation interface; wherein the processors are configured to execute the method of claim 1.

7. A non-transitory computer-readable medium storing instructions that, when executed by a processor, implement the method of claim 1.

Abstract

A dissent-responsive reasoning escalation engine for evidence-bound artificial intelligence applies four-level proportional escalation based on dissent impact scores from the Multi-Agent Reasoning Dissent Engine: Level 1 annotation, Level 2 goal pause with evidence solicitation, Level 3 conclusion withholding with structured human oversight notification, and Level 4 domain-wide suspension with governance clearance requirement. Safety-critical goals receive an escalation multiplier ensuring higher response for equivalent dissent magnitude.