Artifact for the paper “FitGen: Validation and Guided Repair of LLM Generated Executable Fitness Functions for Search Based Software Testing,” published in the Proceedings of the 7th International Workshop on Automated Program Repair (APR '26), co-located with ASE 2026. This artifact contains the source code, benchmark programs, prompts, generated fitness functions, experimental results, and supporting scripts used in the evaluation of FitGen. Detailed setup, dependencies, repository structure, and reproduction instructions are provided in the included README.md file.
Md. Al Muzahid Nayim, Faezeh Rajabi Kouchi, Amit Kumar Sikder et al.· Zenodo (CERN European Organi...· 0 citations
This record contains the simulated Zernike-coefficient datasets, source code, model configurations, pretrained weights, and machine-readable numerical results supporting the SI-Mamba study. The archived learning models are LSTM, Mamba, and SI-Mamba. It includes the training set, five held-out test sets, 25 boundary-condition datasets, evaluation and boundary-analysis scripts, and the exact software environment. Figure-generation code and figures are intentionally excluded. Source code is licensed under Apache-2.0; data, pretrained weights, and numerical results are licensed under CC BY 4.0. See the included README and SHA256SUMS for reproduction instructions and integrity verification.
qin xiao· Zenodo (CERN European Organi...· 0 citations
Your AI can say the test passed. Code Factory asks whether the test could ever have failed. Solo developers start with one local proof; teams bind the real diff, intent, independent checks, and receipts instead of trusting an agent's narrative. Version 0.45.1 adds a deterministic AppForge App Review evidence gate: 30 policy and release-risk checks bind the exact app build to required evidence, preserve unknowns, and keep final submission under named human control. The Graph Ops mission-control storyboard turns that review into a visible mission, tension, guidance, agency, transformation, and ready handoff. The workflow is designed to reduce avoidable App Review rework and waiting time; it does not guarantee approval or claim a measured rejection-rate reduction. Supplied local observations are not provider certification, payment settlement, production proof, security or compliance certification, or release authority. Dual licensed under MIT or Apache-2.0.
Richard Katz· Zenodo (CERN European Organi...· 0 citations
Sparse departures from independence in two-way tables: a heteroscedasticity profile and detection boundary, an adaptive higher-criticism gate, and an assumption-lean exact anchor William J. Dwyer, MD, MPH, FAAP — Department of Mathematics and Statistics, University of Massachusetts Lowell. ORCID 0009-0004-0855-7222. Concept DOI (always resolves to the latest version): 10.5281/zenodo.21844797. Published v1.0.0:10.5281/zenodo.21844798. What this is The reproducibility deposit for a single combined manuscript on detecting sparse departures from independence in a two-way contingency table — the regime where dependence hides in a few cells rather than spreading across the table, so the omnibus chi-square is under-powered and a naive per-cell scan mis-calibrates. It is organized in three parts under one set of front/back matter, with a shared derivations supplement (D02): Part I — the heteroscedasticity profile and the detection boundary. The per-cell standardized deviations are heteroscedastic under the margin-conditional null; profiling that heteroscedasticity gives a closed-form detection boundary separating the sparsity/strength regimes where any test can succeed from those where none can. Part II — an adaptive higher-criticism gate. A higher-criticism statistic tuned to the margin-conditional cell law attains that boundary, with its exact null law and power characterized. Part III — an assumption-lean exact anchor and reporting apparatus. An exact margin-conditional calibration anchor and a routing rule, so a scan that mis-sizes under the asymptotic reference is replaced by one that holds its level: on a corpus of 5,543 real tables the asymptotic per-cell scan mis-sizes about two-thirds of them (mean realized size ≈ 0.33 against a 0.05 target) while the exact anchor holds near 0.01. What the deposit contains Combined manuscript (manuscripts/Dwyer_M02_Combined.{docx,pdf} + M02_Combined_Manuscript.md) and the three part-sources (manuscripts/parts/, provenance for the assembled combined text), plus the D02 derivations companion (derivations/). Verification and study code (code/) — the boundary, exact-anchor, and higher-criticism verification scripts and the power/size study drivers (numpy/scipy, fixed seeds); the docx builder (build_docx.py), the shared math-typography core (mseries_mathtype.py), the inline- and caption-math delimiters, the combined assembler (assemble_combined.py), and the audit gates (structure_audit.py, math_render_audit.py). Locked data (data/) — the study outputs (size/power, boundary, estimated-margins, efficiency) and the higher-replication "big box" runs, so every number regenerates. Figures (figures/), the novelty reviews (novelty_reviews/), the prior-art / adversarial / consistency audits (audits/), and the in-browser detector tools/honest_detection.html. All evaluation is simulation-based; the one empirical component is the public-corpus calibration scan, which uses only openly distributed contingency tables. Code is released under the MIT License; text, figures, and data under CC BY 4.0. How to cite Please cite this deposit if you use the package or the method. Citing the concept DOI references the work in general and always resolves to the latest version; cite a specific version DOI to point at an exact snapshot. Dwyer, W. J. (2026). Sparse departures from independence in two-way tables: a heteroscedasticity profile and detection boundary, an adaptive higher-criticism gate, and an assumption-lean exact anchor — reproducibility deposit [Software]. Zenodo. https://doi.org/10.5281/zenodo.21844797 BibTeX: bibtex @software{dwyer_m02_2026, author = {Dwyer, William J.}, title = {Sparse departures from independence in two-way tables: a heteroscedasticity profile and detection boundary, an adaptive higher-criticism gate, and an assumption-lean exact anchor --- reproducibility deposit}, year = {2026}, publisher = {Zenodo}, doi = {10.5281/zenodo.21844797}, url = {https://doi.org/10.5281/zenodo.21844797}, orcid = {0009-0004-0855-7222} } The DOI above is the concept DOI (resolves to the latest version); to cite a specific release use that version's DOI in place of it (e.g. 10.5281/zenodo.21844798 for v1.0.0). When the accompanying journal article appears, please cite it as the primary reference for the method and this deposit as the reproducibility archive. Version history v1.0.3 — deterministic recut (2026-08-30): the bundle is now cut with the shared mseries_deposit.write_deterministic_zip, so rebuilding the same content yields a byte-identical zip and a stable md5. Every manuscript, derivation, figure, dataset, code file, novelty review, and audit is byte-identical to v1.0.2 (all files SHA-256-matched except the three version-stamped ones). No number, theorem, table, figure, or dataset changed. Publishes as a New Version on concept 10.5281/zenodo.21844797 (build_m02_deposit.py; md5 8f2fa9f7e814ba807710480a7f60cb65, 112 files, 6,044,685 bytes). v1.0.2 — impact-first reframe (2026-08-23): the introduction and abstract reframed to lead with the applied motivation (sparse independence departures in genomics, network/text co-occurrence, pharmacovigilance, ecology, survey cross-tabulation) and the corpus calibration failure, before the detection-boundary theory. No number, theorem, figure, or dataset changed. v1.0.1 — published (2026-08-08): adds the Zenodo concept DOI to the manuscript Availability statement and CITATION.cff, and the widened Figure 8 top box. Content otherwise identical to v1.0.0. v1.0.0 ✅ 10.5281/zenodo.21844798 — first published deposit: the combined three-part manuscript, the D02 derivations, the verification and study code with fixed seeds and locked outputs, all figures, the in-browser detector, and the prior-art / adversarial / consistency audits. Provenance: every number traces to a named, deterministically-seeded script under code/; the combined manuscript is assembled by code/assemble_combined.py and built by code/build_docx.py. Related identifiers: T_root methodology 10.5281/zenodo.21522471; exact conditional engine 10.5281/zenodo.21831680.
William Dwyer· Zenodo (CERN European Organi...· 0 citations
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RiboGRAM Archives This release contains the source code, model checkpoints, benchmark datasets, and example outputs for the study RiboGRAM learns multi-scale non-coding RNA grammar for structural and regulatory inference. RiboGRAM is a self-supervised RNA representation model for learning multi-scale and position-resolved sequence organization in non-coding RNAs (ncRNAs). The release supports sequence embeddings, attention visualization, ncRNA category and family classification, RNA-protein interaction (RPI) prediction, RNA-RNA interaction (RRI) prediction, and RNA-chromatin interaction (RCI) prediction. It also includes the RiboGRAM-3D inference package for MSA-free RNA tertiary-structure modeling. Project archive and web resource: https://www.ultrarnalab.com/ribogram.php Release contents The release is organized into three ZIP archives with a combined size of approximately 53.4 GB. Archive Contents Size 1. Source Code.zip RiboGRAM and RiboGRAM-3D source packages 9.02 GB 2. Model Availability.zip Pretrained and task-specific model checkpoints 22.08 GB 3. Dataset Availability.zip Training, evaluation, structure, and interaction datasets 22.29 GB Source code RiboGRAM The RiboGRAM.zip package is a compact Python inference implementation. It includes: infer.py: command-line entry point and public inference functions; ribogram/: model, alphabet, convolution, attention, and checkpoint-loading modules; checkpoints/: the base RiboGRAM checkpoint and the two ncRNA classification heads; test.ipynb: an executed inference notebook; test_samples.json: provenance and labels for the example records; test_outputs/: example embeddings, attention maps, classifications, and interaction predictions. Supported tasks: Task Command Required checkpoint Sequence embeddings embedding --architecture RiboGRAM RiboGRAM.pkl PLM-CDS embeddings embedding --architecture PLM-CDS PLM-CDS.pkl PLM-mRNA embeddings embedding --architecture PLM-mRNA PLM-mRNA.pkl Attention maps attention --architecture RiboGRAM RiboGRAM.pkl 13-category ncRNA classification ncrna13 RiboGRAM.pkl, ribogram_ncrna13_head.pt 88-family ncRNA classification ncrna88 RiboGRAM.pkl, ribogram_ncrna88_head.pt RNA-protein interaction interaction --architecture RiboGRAM-RPI RiboGRAM-RPI.pkl Expanded RNA-protein interaction interaction --architecture RiboGRAM-RPIplus RiboGRAM-RPI_plus.pkl RNA-RNA interaction interaction --architecture RiboGRAM-RRI RiboGRAM-RRI.pkl RNA-chromatin interaction interaction --architecture RiboGRAM-RCI RiboGRAM-RCI.pkl The RiboGRAM/README.md file inside the source package contains the complete command-line reference and Python API examples. RiboGRAM-3D The RiboGRAM-3D.zip package contains the RNA tertiary-structure inference runtime, including: infer_structure.py and inference/infer.py; the packaged epoch-500 structure checkpoint; data/DRfold2/ with test CIF structures, test PT files, and train/test manifests; data loaders, model modules, the RNA structure module, and the local openfold-3/ source tree. RiboGRAM-3D requires one additional STAR RNA language-model backbone checkpoint as an input to the inference script. This backbone is a separate model used for sequence features; it is not a RiboGRAM checkpoint and is not included in this release. Obtain a compatible backbone separately and pass its path with --backbone-ckpt. The package also does not include the full DRfold2 training CIF/PT collection. Its training manifests are lookup tables for that external collection. See the RiboGRAM-3D_new/README.md, inference/README.md, and data/DRfold2/README.md files inside the source package for environment setup, checkpoint preparation, and structure-data details. Model checkpoints The Model Availability.zip package contains: Pretrained models RiboGRAM.pkl: ncRNA-pretrained RiboGRAM backbone; PLM-CDS.pkl: CDS-pretrained control model; PLM-mRNA.pkl: mRNA-pretrained control model. Classification heads ribogram_ncrna13_head.pt: 13-category ncRNA classification head; ribogram_ncrna88_head.pt: 88-family ncRNA classification head. Interaction models RiboGRAM-RPI.pkl: RNA-protein interaction model; RiboGRAM-RPI_plus.pkl: expanded RNA-protein interaction model; RiboGRAM-RRI.pkl: RNA-RNA interaction model; RiboGRAM-RCI.pkl: RNA-chromatin interaction model. The backbone and interaction checkpoints are large files, approximately 3.4 GB each. GPU inference is recommended. Checkpoint files use PyTorch/Python serialization and should be loaded only from a trusted release. Datasets The Dataset Availability.zip package contains the datasets used in the manuscript analyses. Nine-species RNA sequence collection Complete cDNA, CDS, and ncRNA collections are provided for: Homo sapiens (GRCh38); Mus musculus (GRCm39); Rattus norvegicus (mRatBN7.2); Danio rerio (GRCz11); Arabidopsis thaliana (TAIR10); Drosophila melanogaster (BDGP6.32); Caenorhabditis elegans (WBcel235); Saccharomyces cerevisiae (R64-1-1); Escherichia coli K-12 MG1655 (ASM584v2). This section also includes the stratified UMAP sample nine_species_umap_data_sample_5000.zip and the human ncRNA-versus-CDS and ncRNA-versus-mRNA classification datasets. Pretraining datasets RiboGRAM_PRETRAINING_ncRNA.zip: ncRNA pretraining corpus; ensembl_CDS.zip: CDS control corpus; ensembl_mRNA.zip: mRNA control corpus. The manuscript describes the RiboGRAM corpus as approximately 40 million non-redundant ncRNA sequences assembled from RNAcentral 25.0, Rfam 15.0, and Ensembl release 115. ncRNA representation-analysis datasets 13-Class _ncRNA_Classification_Dataset.csv; 88-Family_ncRNA_classification_dataset.csv; Rfam_v15_seed_sequences_full.csv; Rfam_v15_seed_sequences_top200_per_family.csv. RNA structure datasets ArchiveII.zip; bpRNA-1m_TR0_VL0.zip; bpRNA-1m_TR0_VL0_TS0.zip; TrainSetA-TestSetB.zip; RNA_tertiary_structure_prediction.zip. The RiboGRAM-3D benchmark contains 41 held-out RNA chains ranging from 27 to 363 nt. The manuscript describes 9,446 processed training chains derived from the DRfold2 RNA structure collection. RNA-target interaction datasets RPI-Core.zip and RPI-Scale.zip: RNA-protein interaction data; MirTarRAW.zip and RISE-derived.zip: RNA-RNA interaction data; RNA_DNA_dataset.zip: RNA-chromatin/RNA-DNA interaction data. Dataset construction, filtering, and train/validation/test splits are described in Supplementary Tables 14-16 of the manuscript. Quick start RiboGRAM inference Extract RiboGRAM.zip and run the commands from the directory containing infer.py. The supplied installer was validated with Python 3.8.18, PyTorch 1.13.1+cu116, and an NVIDIA A100 GPU. The installer is a Bash script; on Windows, use WSL or install the listed dependencies manually. Place the checkpoints required for the selected task in checkpoints/. The base RiboGRAM checkpoint and the two classification heads are already included in the source package. The other checkpoints are provided in Model Availability.zip. bash requirements.shpython infer.py embedding \ --architecture RiboGRAM \ --sequence rna-1=GGAGCUCAGCCUUCACUGCG \ --layer 12 \ --device auto \ --output outputs/RiboGRAM_embeddings.pt Generate the manuscript attention examples: python infer.py attention \ --architecture RiboGRAM \ --case all \ --layer 12 \ --head 20 \ --symmetrize \ --plot \ --output-dir outputs/attention For classification and interaction examples, see the command table above, RiboGRAM/README.md, and the executed test.ipynb. RiboGRAM-3D inference Extract RiboGRAM-3D.zip, obtain a compatible STAR RNA language-model backbone checkpoint separately, and run: python inference/infer.py \ --inputs /path/to/input.pt \ --backbone-ckpt /path/to/STAR-RNA-backbone.pkl \ --output-dir /path/to/output The structure checkpoint included in the package is used by the inference configuration. The run is resumable by default, and the output manifest records the commands and random seeds. See RiboGRAM-3D_new/inference/README.md for multi-sample inference and checkpoint preparation. Input conventions RNA is uppercased and T is converted to U; protein sequences are uppercased; DNA is uppercased and U is converted to T; whitespace inside sequences is removed; unsupported symbols are mapped to ; RPI input: RNA protein ; RRI input: RNA1 RNA2 ; RCI input: RNA DNA reverse-complement(DNA) . Reproducibility RiboGRAM uses a 12-layer hybrid encoder with a hidden dimension of 1,280 and 20 attention heads. The encoder integrates parallel convolutional branches with kernel sizes of 1, 3, 5, 7, and 9 nt and Rotary Position Embeddings (RoPE), and is pretrained using a masked language modeling (MLM) objective. To facilitate transparent reuse and independent validation, this archive provides the complete pretraining corpora for RiboGRAM and the CDS/mRNA control models, source code for the supported RiboGRAM and RiboGRAM-3D inference workflows, pretrained and task-specific model checkpoints, benchmark datasets used in the study, and representative test inputs with corresponding reference outputs. Together, these resources enable users to trace data provenance, run and evaluate the released models, and verify checkpoint loading and output formats without repeating the computationally intensive pretraining process. Project links RiboGRAM Archives: https://www.ultrarnalab.com/ribogram.php Manuscript: RiboGRAM learns multi-scale non-coding RNA grammar for structural and regulatory inference License and third-party data The supplied materials do not specify one project-level license. Software, model checkpoints, and datasets may be subject to different terms. Users must check the applicable license and
Hui Wang· Zenodo (CERN European Organi...· 0 citations
Retained-State Middleware for Governed Selection defines a software category concerned with how retained history is allowed to influence selection among actions that a host system has already declared permissible. The central principle is: retained history is eligible evidence, not automatic authority. This technical note defines retained-state middleware, Retained-State Selection, governed selection, retained-state influence, reference and governed conditions, Decision Records and historical-truth constraints. It also records the current public engineering position of Collapse Aware AI™ (CAAI), developed by Inappropriate Media Limited. Core Gold is the frozen current commercial selector foundation. Evolution 2 is the richer continuity Engineering branch and is not represented as the finished Production commercial offer. Weighted Emergence Layering (WEL) and Active Information Weight (AIW) remain active concepts within the wider CAAI retained-state architecture, while private scoring implementation, thresholds, tuning and protected runtime mechanics remain proprietary. The commercial evaluation problem is deliberately bounded: a host supplies a real or anonymised decision problem, a permitted candidate set and relevant retained history. Reference and governed conditions can then be compared to determine whether retained history materially changes which permitted candidate wins, with replayable and inspectable evidence where supported by the tested system. This document is a terminology, evaluation and claim-boundary record. It is not an implementation specification, source-code disclosure, production API contract, claim of universal efficacy or evidence for a physical law. Collapse Aware AI™ can be evaluated independently as software through Retained-State Decision Audits, bounded Core Gold evaluations, buyer-specific pilots and integration work. Commercial enquiries: collapseawareai@gmail.com
Marcos Verrell· Zenodo (CERN European Organi...· 0 citations
This record contains the locked confirmatory scientific results of HLV-R-MECH-001, a prospectively frozen mechanism test of the previously observed degree-preserving rewire spectral residual within the Helix–Light–Vortex Framework (HLV), positioned as a Cut-and-Project and Incidence-Spectral Research Programme. The controlling scientific protocol is: Krūger, M. (2026). HLV-R-MECH-001: Prospective Triangle-Matched Mechanism Test of the Surviving Degree-Preserving Rewire Spectral Residual — Pre-Execution Protocol Freeze v0.1.0. Zenodo. DOI: 10.5281/zenodo.22166283 The authoritative corrected implementation freeze is: Krūger, M. (2026). HLV-R-MECH-001: Deterministic One-Click Engine for Triangle-Matched Rewire Mechanism Testing — Corrected Implementation Freeze v0.1.2 [Computer software]. Zenodo. DOI: 10.5281/zenodo.22170307 The v0.1.2 implementation changed only the numerical-runtime bootstrap. The scientific engine remained byte-identical to the prospectively frozen implementation. Frozen scientific engine SHA-256: 317df650991120f686768ffc07d12f044f58e38ce8f2c47c083901bf1d7a8a14 The successful locked scientific execution returned the machine verdict: RMECH001_PASS_TRIANGLE_MATCH_COLLAPSE_PATTERN The experiment contained two fresh prospectively frozen control families. R_DEG: fresh simple connected degree-preserving rewires matched to the target in labelled degree sequence and perturbation depth, with global triangle count unconstrained. R_TRI: fresh simple connected rewires preserving both the exact labelled target degree sequence and the exact global target triangle count T = 6960. Each family contained 31 accepted controls. The frozen family outcomes were: R_DEG: QSPEC PASS RRESP PASS Family PASS R_TRI: QSPEC FAIL RRESP FAIL Family FAIL For the fresh R_DEG family, the previously observed degree-preserving rewire spectral residual reproduced strongly. QSPEC: D_target = 0.008715193283827952 max D_LOO = 0.005181032910966849 median D_LOO = 0.0015059971709314455 robust margin = 5.786991803203511 2 of 3 frozen spectral bands pass. RRESP: D_target = 0.008334776747240381 max D_LOO = 0.0019187166287396942 median D_LOO = 0.0010793384522247884 robust margin = 7.722116014731345 2 of 3 frozen spectral bands pass. Thus the fresh degree-only baseline independently reproduces the earlier R-family spectral separation under the frozen mechanism-test design. For the exact-triangle-matched R_TRI family: QSPEC: D_target = 0.002191501382099135 max D_LOO = 0.0017606104448781759 robust margin = 1.7780654702146463 1 of 3 frozen spectral bands passes. RRESP: D_target = 0.0025511096413481766 max D_LOO = 0.0021234752495009907 robust margin = 2.2593200944928404 1 of 3 frozen spectral bands passes. Both R_TRI signatures therefore fail the complete frozen signature gate because the prospectively required minimum of 2 of 3 passing spectral bands is not reached. The rewiring-depth matching gate passed. Median edge-replacement fraction: R_DEG = 0.42768942937324606 R_TRI = 0.42076707202993446 absolute difference = 0.0069223573433115915 which is below the frozen maximum allowed difference of 0.02. The result therefore cannot be attributed to a substantially weaker perturbation depth in the triangle-matched family. The numerical environment and hard numerical audits also passed. The successful locked execution used: NumPy 2.3.5 SciPy 1.17.0 and completed the prospectively frozen target and selected control eigensolver/identity checks. The central scientific result is: exact preservation of the target's global triangle count collapses the previously robust two-signature, multi-band degree-preserving rewire spectral separation under the frozen HLV-R-MECH-001 gate. This provides prospective evidence that triangle/face organization is a major mechanism contributing to the previously observed R-family spectral residual. The result has a direct analytic basis. For a simple graph Laplacian L = D - A, Tr(L) = sum_i d_i, Tr(L^2) = sum_i d_i^2 + sum_i d_i, and Tr(L^3) = sum_i d_i^3 + 3 sum_i d_i^2 - 6T. Because R_TRI preserves both the complete degree sequence and the exact target triangle count T = 6960, it matches the target exactly in the first three raw Laplacian spectral moments. The observed collapse is therefore consistent with the hypothesis that the earlier degree-preserving R residual was strongly driven by face-triangle and associated short-cycle organization that was destroyed by the original degree-only rewires. However, the result does not prove that global triangle count is the sole causal invariant. Residual differences remain inside the R_TRI family. In particular, although the complete frozen QSPEC and RRESP gates fail, the R_TRI target distances still exceed the corresponding maximum leave-one-out distances and retain robust margins above 1.5. The failure occurs because only one of the three frozen spectral bands passes in each signature. Predeclared secondary diagnostics also show that exact global triangle matching does not reproduce the complete local target organization. For example, R_TRI controls still differ from the target in quantities including: - local per-vertex triangle distribution; - average clustering; - four-cycle count; - degree assortativity; - algebraic connectivity; - and other local or higher-order structural observables. Accordingly, the scientifically admissible conclusion is: The previously robust degree-preserving rewire spectral residual is strongly reduced and loses its frozen two-signature multi-band PASS once the exact global triangle count is preserved, supporting triangle/face organization as a major mechanism behind the original R-family effect. Remaining local and higher-order structural differences prevent the conclusion that global triangle count alone fully explains the residual. The locked result ZIP SHA-256 is: 69f927faba83b203d7dffbf028de680e6a6a3e36818002bc0e1ab27d5c01797d This result does not establish: - unique HLV geometry; - physical selection of the golden ratio; - a unique 6D-to-3D microscopic substrate; - spacetime; - extra dimensions; - particle physics; - an absolute energy scale; - gravity; - dark matter; - dark energy; - cosmology; - or experimental validation. The result instead narrows the active research programme toward the structural origin of the surviving local incidence-spectral residual. A natural successor is a separately prospectively frozen mechanism test using stronger controls that preserve local triangle profiles and selected short-cycle or motif structure before evaluating the inherited spectral signatures. The negative full-carrier-specificity results of the earlier HLV programme remain unchanged.
Marcel Krüger· Zenodo (CERN European Organi...· 0 citations
This record contains the simulated Zernike-coefficient datasets, source code, model configurations, pretrained weights, and machine-readable numerical results supporting the SI-Mamba study. The archived learning models are LSTM, Mamba, and SI-Mamba. It includes the training set, five held-out test sets, 25 boundary-condition datasets, evaluation and boundary-analysis scripts, and the exact software environment. Figure-generation code and figures are intentionally excluded. Source code is licensed under Apache-2.0; data, pretrained weights, and numerical results are licensed under CC BY 4.0. See the included README and SHA256SUMS for reproduction instructions and integrity verification.
qin xiao· Zenodo (CERN European Organi...· 0 citations
This archive contains the modular ICD-10-CM registry implementation, unit tests, and de-identified software vignette artifacts associated with the CMPB manuscript: 'A typed ICD-10-CM registry for constrained differential-diagnosis pools in clinical decision support'. Contents: icd_registry.py ICD-10-CM diagnosis registry (hypertension_refractory_v1 pool, n=11 registered nodes) with typed fields for priors, lethality-if-missed weights, coordinate_tier, and icd_role. icd_output_gate.py ICD output gate that rejects unregistered disease identifiers and forbids mechanism-only phrases in user-visible diagnostic titles. diagnostic_module.py ProbDiag disease module and ICD coordinate-system strategy specification. tests/test_icd_registry.py Automated test suite (6/6 passing) demonstrating registry integrity and report compliance. data/de_identified_vignettes/ De-identified software vignette artifacts (dashboard JSON, MARG stress-test report, schema contract). This is a standalone archive created exclusively for the CMPB registry manuscript. It is intentionally separate from the companion JBI (treatment-pathway) submission at DOI 10.5281/zenodo.22120120 to avoid overlap concerns. Research-use only. Not a SaMD.
Li Li, Fengyao He, Ruilei Wang· Zenodo (CERN European Organi...· 0 citations
Persistent cognition changes the assurance requirements of AI coding agents because durable memories, lessons, reusable skills, structural observations, and derived knowledge can influence later engineering work after repository state, task context, or execution sessions have changed. C2Cognitive Core v1.0.0 presents a repository-resident cognitive-engineering system designed to preserve useful cognition without allowing persistence itself to become repository authority. The system separates physical ingestion, evidence admission, persistent cognition, continuity state, model-facing representation, and repository effects. Bounded Read v2 (BR-v2) constrains physical ingestion before semantic selection. Governed admission evaluates path containment, sensitivity policy, provenance, integrity, freshness, visibility, and applicable authority before information can influence durable cognitive state. Admitted cognition is represented through typed L0-L3 Memory, advisory Skills, source-bound Structural Candidates, derived Wiki state, and bounded Agent Loadouts. Handoff, checkpoint, and resume artifacts preserve work continuity but remain distinct from cognitive truth. Adaptive Context Representation Planning (ACRP) may change representation only after the semantic evidence set has been selected and frozen. Model, cache, routing, and adapter telemetry do not become evidence or write authority. Model and worker outputs remain proposals until current Goal state, lease/fence conditions, exact write scope, rollback basis, and other applicable authorization are revalidated near effect time. C2ModelAdapter v0.5.5 is kept at the host/runtime boundary rather than promoted into persistent cognition. The v1.0.0 release verification includes 59/59 registered checks for each English and Indonesian edition, 71/71 Core regression tests, the 74-test C2ModelAdapter collection, 30,005/30,005 router-combination simulations, 50,001/50,001 effective-route simulations, and a 524,288-state finite progress-liveness analysis with no invariant violation in the declared model. These results are bounded evidence for the executed surfaces and are not a claim of universal correctness, security, provider behavior, or defect absence. C2Cognitive's contribution is a practical cognitive-engineering discipline for making durable agent cognition explicit, inspectable, provenance-bound, freshness-aware, resumable, and authority-bounded during long-horizon AI-assisted software engineering.
Hafizh al-banna· Zenodo (CERN European Organi...· 0 citations
This archive contains the modular ICD-10-CM registry implementation, unit tests, and de-identified software vignette artifacts associated with the CMPB manuscript: 'A typed ICD-10-CM registry for constrained differential-diagnosis pools in clinical decision support'. Contents: icd_registry.py ICD-10-CM diagnosis registry (hypertension_refractory_v1 pool, n=11 registered nodes) with typed fields for priors, lethality-if-missed weights, coordinate_tier, and icd_role. icd_output_gate.py ICD output gate that rejects unregistered disease identifiers and forbids mechanism-only phrases in user-visible diagnostic titles. diagnostic_module.py ProbDiag disease module and ICD coordinate-system strategy specification. tests/test_icd_registry.py Automated test suite (6/6 passing) demonstrating registry integrity and report compliance. data/de_identified_vignettes/ De-identified software vignette artifacts (dashboard JSON, MARG stress-test report, schema contract). This is a standalone archive created exclusively for the CMPB registry manuscript. It is intentionally separate from the companion JBI (treatment-pathway) submission at DOI 10.5281/zenodo.22120120 to avoid overlap concerns. Research-use only. Not a SaMD.
Li Li, Fengyao He, Ruilei Wang· Zenodo (CERN European Organi...· 0 citations
Sovereign Infrastructure Bare-Metal Microkernel Framework for Asymmetric Dual-Core Industrial Assets Executive Architectural Overview This repository contains a production-frozen, safety-critical Asymmetric Multiprocessing (AMP) bare-metal microkernel explicitly engineered for the Cortex-M4 Safety Sentinel Domain of dual-core heavy industrial power grid infrastructure [📋]. The system calculates real-time high-frequency grid traveling wave transients directly from Phasor Measurement Unit (PMU) telemetry [📋]. By eliminating dependency on fragile, external mechanical weather sensors, this deterministic processing engine safely unlocks 30% to 40% hidden thermal line transmission capacity on existing national high-voltage utility lines [📋]. Microarchitectural Invariants & Core Technical Safeguards The codebase enforces strict, microsecond-deterministic execution boundaries designed to cross un-preemptible galvanic and silicon moats under intense electrical and cyber-warfare stress [📋]: Fixed-Point Transcendental Determinism (Math Integrity): Strips out 100% of fractional floating-point definitions (float/double) to bypass non-deterministic hardware FPU processing lag and pipeline scheduling exceptions [ysD9J5]. Non-linear wave decay transformations are resolved branchlessly via integer-bound signed Q16.16 fractional format bit-shifts, utilizing a strict 4-iteration Taylor polynomial expansion to guarantee a uniform-time arithmetic execution envelope under all system loads [ysD9J5]. Multi-Physics Lifecycle Decoupling (System Life Support): Hardwired hardware interrupt vector tables are relocated entirely away from flash bus boundaries to isolated, high-speed RAM_D3 space (0x38000000) during boot [ysD9J5, 📋]. The safety calculations operate completely air-gapped from the high-overhead, non-deterministic SCADA networks and Modbus daemon loops sandboxed on the companion Cortex-M7 core—guaranteeing that local thermal loop defense arrays remain active even during an external network flood or a total host operating system freeze [📋]. Padding-Hardened Structural Alignment (Memory Safety): Shared memory crossbar registers (SRAM4) and internal workspace data structures are sorted strictly from largest data-type byte-width down to the smallest (64-bit → 32-bit → 16-bit → 8-bit) [ysD9J5]. Explicit manual padding blocks are injected to eliminate invisible compiler-generated memory voids, preventing random stack leakage onto the shared communication bus and satisfying MISRA-C:2012 / IPC Class 3 compliance invariants [ysD9J5, 0.1.1]. Pointer-Driven Telemetry Stream Packing (Data Link Integrity): Telemetry serialization handlers reject index-increment loops, executing data movement via pure pointer memory address comparison constraints (src_ptr < src_end) [ysD9J5, 0.1.1]. This approach packs variables contiguously up to terminal frame boundaries, pushing big-endian validation parity markers to the precise terminal edge bytes to eradicate data tearing or crossbar memory corruption [ysD9J5]. Build-Profile Macro Abstraction (Continuous Integration Readiness): Virtualizes low-level physical registers and clock wait-states cleanly via preprocessor directives (#ifdef USING_PRODUCTION_DRIVERS) [ysD9J5]. This permits automated continuous integration test suites to execute full-coverage memory and logic sweeps natively on standard x86/ARM64 desktop execution beds without encountering absolute memory pointer segmentation faults (SIGSEGV) [ysD9J5]. Repository Source File Directory Blueprint/Substation-Traveling Wave DLR Kernel/├── Makefile # Dual-track hardware build and macOS/Linux CI orchestrator├── README.md # System deployment manifest and specification guidelines├── LICENSE # Sovereign licensing terms (CC BY-NC-SA 4.0 Protection Shield)├── .zenodo.json # Metadata search indexer configuration schema├── include/│ ├── core_atomics.h # 32-byte cache-line aligned lock-free inter-core memory map│ └── desktop_mock.h # Desktop uintptr_t pointer virtualization layer├── linker/│ └── stm32h747_flash_sram.ld # High-velocity Load-vs-Run table mirroring script└── src/ ├── boot/ │ └── startup_stm32h7xx.S # Preprocessor-enabled vector remapping assembly module ├── drivers/ │ ├── cache_invalidation.c# Unrolled 32-byte L1 Data Cache line invalidation driver │ └── hsem_signaling.c # Atomic 1-step inter-core hardware semaphore trigger └── apps/ ├── module_vdlr_kernel.c# Branchless fixed-point traveling wave calculation stride ├── module_thermal_guard.c# Downstream valve fail-safe and pump load stride └── desktop_ci_main.c # Non-blocking single-stride desktop validation entry point Sovereign Protective Licensing Frame (CC BY-NC-SA 4.0) This technical infrastructure asset is published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License. Academic and Open Science Freedom: Educational facilities, green-tech laboratories, and public utility engineers are granted immediate right to audit, test, and adapt these configurations for non-commercial research. Viral ShareAlike Clause: Any derivative modifications or localized grid ports must be released transparently under the exact same open CC BY-NC-SA 4.0 license framework. Commercial Defensive Restriction: Multi-billion-dollar private power grids and proprietary equipment manufacturers are strictly prohibited from deploying or embedding these microarchitectural assets for commercial gain without establishing a private, paid Commercial End User License with the author [📋]. This record serves as an immutable, cryptographically signed, and time-stamped public declaration of Prior Art under international patent conventions, legally blocking external entities from capturing or enforcing proprietary novelty over these core bare-metal safety loops [📋].
Zachary August Jacoby· Zenodo (CERN European Organi...· 0 citations
A USAF cadet and a Lincoln Laboratory researcher found AI chatbots can help nontechnical service members produce viable software applications for their unique problems.
