Dynamic / ME-JEPA v2.0.0-rc1 is an audited, single-binary JEPA-style world-model runtime that runs three domains — counter_world, gridworld_5x5, and the new public-taxonomy career_taxonomy.v1 benchmark — on one RTX 5090 by TOML manifest alone. Every persisted operation flows through 25 typed RocksDB column families plus an append-only audit log; a separate verifier reads every claim back. The reproducibility artifact is a deterministically built Docker + Apptainer (SIF) image with full SHA-256 hash chains and a one-command verify-aggregate path. What's new in v2.0.0-rc1 (vs. v1.0.0-rc5) Meaning-compression layer around the original DynamicJEPA runtime: pairwise instrument readings, constellation/centroid records, G_tau guard verification, signal-yield accounting, and MC-ratio reporting. Pairwise mutual-information audit outputs for the career_taxonomy domain. Cross-domain transfer evidence between counter_world, gridworld_5x5, and career_taxonomy. Bundle-to-training-corpus exporter emitting verified (state, action, expected_persisted_delta, actual_readback, verifier_status, error_code, remediation) rows. Release-D source-of-truth gate: aggregate passed_count=5 / failed_count=0; reference check diff_count=0, manifest=passed, fsv=passed. Why this matters One binary, three domains, by TOML alone. A new domain ships as a strict TOML manifest; the Rust runtime carries no domain-specific code branches. Five-seed paper-small results on commodity hardware. Career-taxonomy validation cosine 0.905 ± 0.044 (BCa 95% CI 0.861–0.931); test cosine 0.868 ± 0.068 (BCa 95% CI 0.806–0.912); validation action-contrast accuracy 0.960 ± 0.043. The negative-action ablation A3 collapses to validation cosine 0.790 — exactly as the design predicts and the verifier confirms. Every cited number reads from a CSV in the verified release bundle; hand-typed numbers are forbidden. A reproduction reviewers can actually run. One command returns {status: ok, diff_count: 0, manifest: passed, fsv: passed, passed_count: 5} byte-for-byte. Each evidence bundle is also a new class of training data. The artifact argues, with citations, that a bundle is a training-data class distinct from web text, pure model-generated synthetic data, and rejection-sampled chain-of-thought. The proposed name is audited execution traces with cryptographic provenance. Most of the bytes are deterministic functions of the public O*NET ZIP rather than model output; model outputs are paired in the same record with the deterministic observed target plus random-init / shuffled-target baselines; counterfactual ablations and failed runs are persisted as first-class records. Used carefully, this corpus carries substantially lower model-collapse hazard than typical synthetic data (Shumailov et al., Nature 2024; Gerstgrasser et al., 2024). Failed runs are kept on purpose. Most synthetic-data pipelines discard rejected samples; this artifact preserves them. That choice is the difference between recursion-with-replacement (which Shumailov showed causes collapse) and accumulation (which Gerstgrasser showed avoids it). Reviewer command CONTEXT_GRAPH_BIN=bin/context-graph ./reproduce.sh verify-aggregate \ --aggregate bundles/20260502T_release_d_gate_v3_paper_small_multiseed_aggregate \ --reference reference_release_v2.0.0.json --json Archive contents and SHA-256 hashes mejepa_5090_artifact_v2.0.0_rc1.tar.gz — complete release archive, SHA-256 91461515e29c851017ef9bdd5fb584ed6bc404abedca52be4b8107fb3d4b1a01, size 7,430,206,664 bytes. bin/context-graph — single Rust binary that runs the entire pipeline (ingest, panel, train, predict, plan, verify), SHA-256 e6013ccb72e1878e6229e5e43c8d317b5db5c46ba794b99bdbbe91a942589494. container/contextgraph-mejepa.docker.tar — deterministic Docker image, SHA-256 830304d56887b54d72c34f51c05176f2c39557e97829468fab379f403ae1940b. container/contextgraph-mejepa.sif — Apptainer/SingularityCE image built from the Docker tar via docker-archive bootstrap, SHA-256 78c87c55406996d711f4ecff7c12fe66610dc8d53110766784cf7ed80f386bad. bundles/20260502T_release_d_gate_v3_paper_small_multiseed_aggregate — five-seed aggregate with paper tables, FSV report, and pairwise-MI audit. bundles/20260502T_release_d_gate_v3_paper_small_seed42 — one full passed seed bundle for manual source-of-truth inspection. reference_release_v2.0.0.json — checked-in numeric and structural release reference, SHA-256 70eebc518feb8786c006cbc02bd4df00bebaa42ac29fdc6998d60da74acd9b57. archive_file_hashes.json, release_manifest.json, checksums.txt — byte-level provenance. Headline numbers Career-taxonomy validation cosine 0.905 ± 0.044, BCa 95% CI 0.861–0.931 (n = 5 seeds, BCa bootstrap 10,000 iterations, seed 20260430). counter_world: 0.99963 / 0.99916 (480 trainable parameters). gridworld_5x5: 0.99473 / 0.99472 (16,960 trainable parameters). career_taxonomy A1 panel: 15,344 trainable parameters. Full FSV integration suite passes in 190.92 s. Full release-C gate (5 seeds + aggregate + container build + container verify + manual edges) fits in ~2 hours wall time on the documented host. Hardware and runtime NVIDIA GeForce RTX 5090 (sm_120, 32 GiB VRAM, CUDA 13.2). Reproduction requires a comparable GPU and uses the NVIDIA container runtime with --gpus all. Apptainer / SingularityCE 4.x recommended for the SIF execution path; Docker 24+ for the Docker tar path. Data boundary The career benchmark is generated deterministically from the public O*NET 30.2 text database. It is a public taxonomy benchmark, not real recruiting history. The packaged selected bundle records O*NET source ZIP SHA-256 b5479271931796b838f7173dc0f673a9ec961b7833ac87168fd11e92e7453741 and row count 1000 in data_manifest.json. Where the paper is A two-column publication-ready LaTeX manuscript (17 pages) is included inside the archive at docs/5090jepa/researchpaper/paper.tex and paper.pdf. The paper develops the runtime, the public-taxonomy benchmark, the full-state-verification protocol, and the new training-data class argument. Citation Cite the specific version DOI for reproducibility: 10.5281/zenodo.19977981. To cite all versions of this artifact, use the concept DOI: 10.5281/zenodo.19953950. License Code: PolyForm Noncommercial 1.0.0. Bundle data and generated paper tables: CC BY 4.0. Third-party data (O*NET 30.2): retains its CC BY 4.0 license; full attribution in data_manifest.json and THIRD_PARTY_LICENSES/ONET.md inside every bundle. Reproducibility note Per PyTorch's reproducibility guidance, exact GPU bit-reproducibility across releases or hardware is not guaranteed; the release reference therefore uses a structural + numeric-tolerance schema rather than byte-equal model-weight diffs. Failed runs are intentionally preserved in the archive as evidence; the training-data class argument depends on this property.