Fractal Custody Objects and Graphs for Efficient, Verifiable AI Training and Computational Biology: A Registered Research Protocol and Contemporaneous Record of a Lambda Research-Credit Application

This preprint presents a registered research protocol and contemporaneous, author-attested record of a Lambda Research credit application submitted on July 15, 2026 at 09:58 Pacific Daylight Time (2026-07-15T16:58:00Z). It proposes Fractal Custody Objects and Fractal Custody Graphs (FCO/FCG) as a recursively content-addressed, authorization-aware, claim-bearing custody architecture for AI training and computational biology. The package distinguishes existing work—including Glasswork/Butterbase-informed deterministic model comparison, BioBridge demonstrations, prior FCO/FCG publications, and local Mac Studio single-core leaf rehashing—from proposed Lambda GPU experiments. The submitted protocol requests 1,000 H100-equivalent GPU-hours to evaluate bounded from-scratch model training, reproducibility, fault injection, selective replay, graph-root reproduction, custody overhead, and multi-GPU behavior. Cellico provides the initial public-safe computational-biology workflow, while the architecture is intended to generalize to model-training lifecycles. Files include the preprint, reconstructed application fields, field-level SHA-256 hashes, FCO/FCG schemas, machine-readable provenance records, and checksum manifests. Important status: this is not a Lambda-generated submission receipt, does not imply award approval, and does not report completed Lambda-funded experiments. The submission time is author-attested. Human contributions and AI-assisted editorial and packaging contributions are disclosed in the manuscript. Cellico.bio, Biobitworks, and the Yunes Foundation for Research on Aging are listed as author affiliations; no institutional endorsement is implied unless separately documented.