Aleph General Intelligence Convergence Engine (AGICE) Reasoning Model (evidence bundles / trajectories / logs / configs)

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Authors: Ahmed A. Moustafa
Cited by: 0

Abstract

Patent status: Patent Pending (USPTO Provisional) — Application No. 63/978,753 — Filed 2026-02-09. AGICE Evidence Bundles v1 provides audit-grade, replayable evidence artifacts for verifier-driven convergence experiments. Multi-Oracle Verifier-Driven Convergence Reasoning Model with Evidence-Native Generalization and Geometrical Multi-Dimensional Transformation using complex operator -i AI Reasoning Model specialized in unseen or pre-trained problems Contents Hash-chained event logs (events.jsonl) for representative runs (A0/A1/A2/A3 modes where applicable) Bundle manifests + integrity metadata + replay metadata (seeds, configs, environment) Configuration files used for the runs Human-readable evidence reports summarizing trajectories and results A minimal validator script to verify event-log integrity Verify integrity (recommended) Run: python validate.py --all bundles/ Expected: “ALL CHAINS INTACT” for each bundle Bundle layout bundles/ / /events.jsonl — hash-chained trajectory log bundles/ / /bundle_manifest.json — run metadata + summaries bundles/ / /integrity.json — file hash metadata (where provided) bundles/ / /replay.json — replay parameters (where provided) Redaction / release scope This v1.0.1 release focuses on artifacts + integrity verification. Full demo harness and complete verifier/tooling will be released in a later version and linked from the companion GitHub repository. Citation & links Paper/preprint: [to be added] Public home base repo: [to be added] Contact Maintainer: Ahmed Moustafa, ahmed@o3innovation.com, www.linkedin.com/in/amoustafa79

Fields of Study & Subjects

Artificial Intelligence Applications Logic Reasoning and Knowledge Scientific Computing and Data Management

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
9 February 2026
Language
EN
Document type
Dataset