Supporting materials for “Target-independent verification of electroencephalography workload decoders for adaptive human–machine systems.” This dataset contains analysis code, software environment specifications, a public pre-target analytical specification, complete model-selection traces, non-identifying UNIVERSE prediction scores, participant-level AUC summaries, aggregate statistical results, diagnostics, generated figures, and machine-readable tables for a seven-database EEG workload portability audit. Four controlled-workload databases (RITHM, EEGMAT, COG-PBCI and STEW) supplied development evidence; UNIVERSE supplied untouched external confirmation; MultiPhysio-HRC and SenseCobot supplied joint environment-and-reported-effort stress tests. Third-party raw EEG archives and participant-level derived feature matrices are excluded. Source accessions and licenses are listed in data_sources.tsv. Original package materials are licensed under CC BY 4.0; analysis code is additionally licensed under the MIT License. Version 2 aligns the record and documentation with the current Engineering Applications of Artificial Intelligence submission. Scientific data, analysis code, results, predictions, figures and statistical methods are unchanged from Version 1. The canonical prediction file SHA-256 remains d3be68bf4efa9bd7ef5d72e6bd8604cbbb0dc1ba42cbf935fb34f6dae3df827c.
Both TMAD methods are effective alternatives to extraction-based treatment, and intra-jaw and inter-jaw TMAD better preserved anterior torque and occlusal plane stability.
Amer M. Owayda, R. Al-Sabbagh, M. Y. Hajeer et al.· International Orthodontics· 0 citations
Findings indicate that the implementation of Mission Planner can improve navigation precision, travel-time efficiency, and the reliability of payload delivery using reconnaissance drones compared with manual flight operations.
Nur Rachman Supadmana Muda, Dimas Cahyo Nugroho· Cendekia· 0 citations
The primary objective of this study was to quantitatively evaluate the clinical rationale and prescription patterns for Cone-Beam Computed Tomography (CBCT) across various dental specialties, specifically investigating how diagnostic indications correlate with field of view (FOV) preferences, patient demographic variables (age and gender), and temporal referral trends. In this retrospective analytical study, 694 CBCT request forms belonging to patients examined at the Department of Oral, Dental and Maxillofacial Radiology, Faculty of Dentistry, Harran University, between January 2023 and October 2025 were reviewed. Patients’ demographic information and imaging parameters were recorded, and the SPSS 23 software and Pearson’s Chi-square test were used for the statistical analysis of the data obtained. The sample consisted of 351 male (50.6%) and 343 female (49.4%) patients, with an overall mean age of 26.88 ± 17.10 years. The majority of CBCT requests originated from the departments of Oral and Maxillofacial Surgery (59.8%) and Orthodontics (29.0%). The most frequent indications for imaging were cyst/tumor (20.0%) and impacted tooth (17.6%), respectively. A statistically very strong association ( p <0.001) was found between the field of radiological examination and the specialty. In this context, a Max-Man view encompassing both jaws was preferred in 90.8% of surgical cases, a full skull exposure was preferred in 43.8% of orthodontic cases, and localized exposures—owing to the need for high resolution and radiation safety—were preferred in 81.2% of endodontic cases. In addition, a significant association ( p <0.001) was found between the reason for imaging and gender, while the temporal analysis revealed a statistically significant increase in referral density during the second half of the year, particularly in September and October ( p =0.001). The results of this analysis demonstrate that dental CBCT examinations are not a routine screening tool applied indiscriminately, but rather a targeted imaging protocol shaped according to the specific diagnostic needs of the relevant specialty, patient demographics, and anatomical region requirements. These clinical and temporal findings provide an important reference for resource management and workload optimization in radiology departments.
Tolga Han EDEBAL, Sedef Kotanlı, Berat Yücedağ et al.· BMC Oral Health· 0 citations
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This repository contains the data, software, and reproducibility materials supporting the manuscript “An End-to-End Hydro-AI Framework for Participatory Water Resources Planning: Coupling Agentic Language Models with Physics-Informed Surrogates.” The archived materials include the MODFLOW-2005 benchmark configuration, trained physics-informed surrogate, model-training scripts, scenario schema and validation logic, evaluation and figure-generation code, and the 16-phrasing natural-language prompt test set used in the study. These materials support reproduction and evaluation of the computational results reported in the manuscript.
Replication package for the paper submitted to DINAME 2027 (XXI International Symposium on Dynamic Problems of Mechanics, ABCM): "Comparative Analysis of Numerical Methods for Solving the Discrete Algebraic Riccati Equation in Resource-Constrained Embedded Systems". Stabilising a small quadrotor demands feedback above 50 Hz, and with the state-dependent Riccati equation (SDRE) method that forces a discrete algebraic Riccati equation (DARE) to be solved once per control period — the dominant cost on a microcontroller with no hardware floating-point unit. This work formulates the structure-preserving doubling algorithm, four variants and classical value iteration in Q13.18 fixed point, and compares the twelve resulting solvers on an FPU-less ESP32-S2 over 60000 operating points drawn from six trajectories, and over 475444 consecutive hardware control cycles of the complete flight loop at 167 Hz. The central result is a matrix generalisation of the classical recursive-filter deadband: near convergence the relative step reports the number format rather than the solution, below an analytic floor set by format resolution and solution norm. Measuring that step at the instant each solver stops resolves the deadband into two opposite symptoms — a bit-exact fixed point in quadratically convergent doubling, and a one-least-significant-bit limit cycle in linearly convergent value iteration. CONTENTS raw.zip — the 20 serial captures exactly as they came off the boards: the main benchmark on the ESP32-S2 and on the ESP32-S3 (which has an FPU), the tolerance and weighting sweeps, the repeatability and convergence-test microbenchmarks, and the ten 360-second windows of the complete flight loop. Each capture begins with a provenance stamp giving the git commit, build timestamp, chip, revision and clock of the firmware that produced it. derived.zip — the same measurements as tabular CSV with headers: one row per solver call (time, iterations, achieved residual, outcome, and the relative step and bit-exactness flag at termination), the closed-loop cost and time series for all six trajectories, the operating-point coverage with conditioning and solution norm, the double-precision reference residual, and the firmware memory metrics. code.zip — snapshot of the repository at the commit that produced these measurements: the flight firmware, the eight experiment firmwares, the twelve solver implementations in C++ and the Python analysis, audit and figure-generation scripts. MANIFEST.md — SHA-256 of every file, and the git commit of origin. README.md — what each file contains and how to recompute the published numbers. PROVENANCE.md — the provenance of the captures: what the audit reports about them and why it reports it. REPRODUCING With the code and the captures in place, every number and figure in the paper can be recomputed without the hardware. An audit script checks the provenance stamp of each capture, verifies 253 numerical claims against the raw data, confirms that the figures were generated from the current measurements, and reports how many of the numbers in the paper are covered by an automated check. On this package its numerical, figure and flight-cycle steps all report zero divergences. The provenance step is the exception, and the record says so rather than hiding it: it reports eight of the nine captures as taken from a modified working tree, because the campaign ran overnight with the change to the convergence test not yet committed. PROVENANCE.md, at the top level of this record, sets out why those captures remain traceable — all nine came from the same build, the exact working-tree diff travels with them in raw/provenance/, and the one experiment recaptured after the commit returned seven measured quantities identical to the hundredth of a microsecond. The defect is in the label, not in the data. That the recipe works was established on version 3, reproduced end to end from its own package alone: following README.md, all six figures came back pixel-for-pixel identical to the published ones. Re-running the measurements from scratch requires an ESP32-S2 and an ESP32-S3 and takes about ten hours. VERSIONS Version 1 (10.5281/zenodo.22236199) was created automatically by the GitHub–Zenodo integration and contains only the source archive; it does not include the serial captures, which are not tracked in the git repository. Version 2 (10.5281/zenodo.22236293) was the first complete package. Its MANIFEST.md, however, lists SHA-256 checksums for the three zip archives that do not match those archives — the manifest was written before the archives were repacked. The archives themselves are intact, but the document meant to prove that is wrong, so anyone verifying integrity against it would wrongly conclude the download was corrupt. Its README.md also numbers two figures one off and omits a renaming step, without which the reproduction recipe does not run. Version 3 (10.5281/zenodo.22238478) carries the same measurement data as version 2. It corrects the manifest, the figure numbering and the reproduction recipe, adds the firmware memory metrics in the form the audit reads, and removes a nested archive inside code.zip. Version 4 is this one, and it is the first whose measurements differ. Every capture in raw.zip was taken again, after the convergence test in the Q13.18 kernel was changed to evaluate its inequality in integer arithmetic instead of converting each stored value to floating point. The criterion itself is unchanged — the same relative Frobenius norm, the same tolerance, the same iteration budget, the same decisions — and the integer form is exact where the converted form rounded, so what changed is the cost of asking, not the answer. The scientific results reproduce unaltered: the deadband floor and its two symptoms, the bit-exact counts, the convergence counts of value iteration, the weighting safety map, and the closed-loop accumulated cost. The times fall: the fastest fixed-point doubling solver from 3.68 to 3.15 ms, and the complete flight cycle from a median of 4.70 to 4.20 ms, with period overruns dropping from 21 to 2 in roughly 475000 cycles. The paper's title also changed in this version, to the one under which the abstract was accepted. SOURCE https://github.com/guilherme-ali/SDRE_VECTORIZED LICENCE Measurement data (raw.zip and derived.zip) and figures: CC BY 4.0. Software (everything in code.zip): MIT.
Guilherme Ali Abdallah Bassani, Reginaldo Cardoso, Diego Paolo Ferruzzo Correa· Zenodo (CERN European Organi...· 0 citations
Why Fisher's exact test is least exact where it matters most: achieved size, wasted power, and a routing rule for 2×2 tables 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.22238930. This release (v1.1.14):10.5281/zenodo.22262453, published Sep 2, 2026; the concept DOI above resolves to it. The previous release, v1.1.13, is 10.5281/zenodo.22262451; earlier, v1.1.5 is 10.5281/zenodo.22260812 and v1.1.4 is 10.5281/zenodo.22260570. What this is The reproducibility deposit for the m02d paper. "Fisher's exact test" is exactly valid — its true rejection rate never exceeds the nominal α — but it is not size-exact: because the 2×2 reference distribution is discrete, no table boundary falls at α, so the achieved (unconditional) size sits below nominal. At small designs the test spends only 26–66% of its α budget, and wasted size is wasted power. The paper computes the achieved size, the wasted-power cost, and the significance-flip behaviour of Fisher's exact against a conservative→liberal roster (Yates χ², Fisher, mid-p, Boschloo, Barnard, Pearson χ², Cressie–Read λ=2/3, likelihood-ratio G), and distills the result into a routing rule for the 2×2. Everything is deterministic exhaustive enumeration — two independent binomials, no Monte Carlo, no random seed — so there is no simulation error: every number, table, and figure reproduces exactly from a named script. The keynote A real-data scan of 6,030 public 2×2 tables (757 pydataset/Rdatasets datasets) shows the tests agree essentially everywhere away from the threshold but disagreement spikes on each decision line — ~67% of tables within ±0.02 of 0.05 and ~76% within ±0.02 of the 0.10 trend line get a test-dependent verdict — so the tests are miscalibrated relative to one another exactly where we use them to draw the significant/not-significant line, and the trend line inherits the same spike. Flips are read on a three-tier taxonomy relative to Boschloo (the valid frontier): conservative below it, valid on it, liberal above it. What the deposit contains Manuscript (author + anonymized, .md), the Derivations companion (D1–D6: conditional validity and the achieved-size shortfall; mid-p as the mean of the median-unbiased rule with E₀[mid-p] = ½; Boschloo's uniform power dominance; the nesting that makes flips one-directional; the atom-lattice gap and exact Boschloo region behind Figure 3; and why conditioning forfeits the budget), the novelty / prior-art companion, and the cover letter(The American Statistician). Reproducibility apparatus (rerun/) — the exact-enumeration engine achieved_size.py (tie-aware conditional p-values, validated against SciPy to 2.2e-16) and the deterministic sweep drivers: power_and_flips.py, size_skew_sweep.py, flip_boundary_sweep.py, roster_sweep.py (the full comparator roster, including the Cressie–Read λ=2/3 power-divergence member), and public_flip_scan.py (the real-corpus scan). Each writes its locked JSON/CSV. Figures (figures/) — the achieved-size and wasted-power panels, the size×skew heat maps, the α=0.05 flip-boundary maps, the χ²-inclusive roster figures, the real-data flip-incidence figure, the routing flowchart (now Figure 9), and the per-test direction butterfly (Figure 8). Interactive demonstrator honest_fisher.html — a self-contained in-browser calculator with the full comparator taxonomy and the live-highlight routing flowchart; its JavaScript engine reproduces the deposited numbers and self-checks a parity badge on load. Deep-dive record (deepdives/) — the supporting analyses, including the size/skew, flip-boundary, χ²-roster, real-data, and computed-clearance write-ups, and the M0u-bundle assessment that reconciles m02d with the author's earlier Monte-Carlo program (below). LICENSE, CITATION.cff, MANIFEST.txt (SHA-256 of every file). The Cressie–Read λ=2/3 reconciliation The roster now carries Cressie–Read λ=2/3, the best-calibrated member of the power-divergence family that also contains Pearson (λ=1) and the G-test (λ=0). Across R×C shapes it is the best-calibrated test in the author's companion Monte-Carlo study; on the pure 2×2 that is m02d's subject, its exact achieved size is 0.062 / 0.053 / 0.055 / 0.055 at the representative designs — still mildly liberal, because small-sample discreteness bites it too. So the paper names λ=2/3 as the calibrated member of the χ² family, scopes the "common χ² defaults overshoot" claim to Pearson/Yates/G, and shows that even the well-chosen asymptotic statistic does not escape the 2×2 discreteness the exact unconditional tests handle — turning a potential referee objection into a strengthening of the routing rule. The companion 18-test Monte-Carlo study independently confirms m02d's Boschloo/Barnard power gap over Fisher (+3 to +10 pp by simulation vs +3.7 to +11.1 pp by exact enumeration here). All computation is deterministic exact enumeration; code is released under the MIT License, and text, figures, and data under CC BY 4.0. The public 2×2 corpus is built from the Rdatasets collection via pydataset; no data are redistributed beyond the illustrative tables cited in the paper. 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). Why Fisher's exact test is least exact where it matters most: achieved size, wasted power, and a routing rule for 2×2 tables — reproducibility deposit [Software]. Zenodo.https://doi.org/10.5281/zenodo.22238930 BibTeX: bibtex @software{dwyer_m02d_2026, author = {Dwyer, William J.}, title = {Why Fisher's exact test is least exact where it matters most: achieved size, wasted power, and a routing rule for 2x2 tables --- reproducibility deposit}, year = {2026}, publisher = {Zenodo}, doi = {10.5281/zenodo.22238930}, url = {https://doi.org/10.5281/zenodo.22238930}, 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 its place. 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 v1.1.14 — ✅ 10.5281/zenodo.22262453 (published 2026-09-02) (m02d_fishers_exact_not_exact_reproducibility_v1.1.14.zip, md5 4977e10c1b632054e3e6609176dde2fd, 3,722,698 B, 94 files): the Boschloo-dominates-Fisher machinery, made explicit. Section 6 now shows the one-line power-dominance mechanism rather than only asserting it: Fisher's own conditional test already has unconditional size ≤ α, so the calibrated Boschloo threshold c satisfies c ≥ α, and because both tests threshold the same statistic the rejection set {Fisher p ≤ α} sits inside {Fisher p ≤ c} table for table — Boschloo rejects wherever Fisher does and more, so it is uniformly at least as powerful, a guarantee that would fail for a differently-ordered unconditional statistic. Derivation D3.4 gains the explicit note that Boschloo and the score-based Barnard test are mutually non-nested (neither uniformly dominates; Barnard's occasional power edge is budget spent, not dominance), and the SN supplement gains a "Why Boschloo, not Barnard, is the valid frontier" note beside the comprehensive roster. Exposition over the existing exact-enumeration output; no computed result, figure, or table changed. Deterministic (identical md5 on two runs). Supersedes v1.1.13. v1.1.13 — ✅ 10.5281/zenodo.22262451 (published 2026-09-02) (m02d_fishers_exact_not_exact_reproducibility_v1.1.13.zip, md5 371a192f5a3ea8ad1e552043a3d68562, 3,721,908 B, 94 files): the comprehensive-roster and full-budget-grid build-out, and the worked-example correction. Adds the sixteen-statistic exact achieved-size-and-power engine comprehensive_roster.py, the full per-test α-budget-grid enginebudget_grid_all.py, the vectorized Boschloo helper boschloo_fast.py (an O(P·K) replacement for the O(n⁴) per-cell sup, verified cell-for-cell against the naive loop, which makes exact enumeration to n = 100 tractable), and the supplement table generator splice_supplement_tables.py. The SN supplement's per-test α-budget grids now run n = 6, 8, …, 20, 25, …, 65, 80, 100 and the big-tent size/power tables use the columns n = 8, 10, 16, 25, 40, 65, 100. Also corrects the worked-example Table S1 Cressie–Read λ = 2/3 value — the deposited 0.1881 ("not significant") was wrong; the exact statistic gives p = 0.0287 (significant), cross-checked against scipy.stats.power_divergence — expands that table to the full sixteen-test roster, and rewrites the accompanying narrative, which had leaned on the wrong number. Rolls up the staged v1.1.6–v1.1.12 (the SN-supplement rework: per-test α-budget grid atlas, the diverging blue–white–red under/over color standard, the neutral-hatch Figure 8, and the m0-sourced comparator roster). Deterministic (identical md5 on two runs). Supersedes v1.1.5. v1.1.5 — ✅ 10.5281/zenodo.22260812 (published 2026-09-02) (m02d_fishers_exact_not_exact_reproducibility_v1.1.5.zip, md5 d96e845d54d12ae496fe78d7440ca4c7, 3,413,155 B, 79 files): availability statement consolidated into Declarations; placement made a canonical build check. The data-availability text was appearing twice — as the Availability of data and materials. statement inside Declarations and as a standalone ## Data and code availabilitysection bolted on after the References, which had drifted (the Declarations copy still read placeholder wording while the live DOI lived only in the bolted-on section). Consolidated into the single Declarations statement (scope + no-redistribution; the reproducibility package on Zenodo under the concept DOI as a live link
William Dwyer· 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
LLM-Shield-Proxy v1.3.6 delivers a comprehensive plain-language documentation rewrite, dead-guard removal, security policy unification, and Vault encryption key fixes. Highlights: Plain-Language Documentation: Comprehensive rewrite of public documentation, website guides, landing page copy, and benchmark reports for clarity, removing complicated phrasing and em dashes. Added an automated regression test (tests/test_public_docs_style.py) to prevent future plain-language regressions. Vault Encryption Key Fix: Declares VAULT_ENCRYPTION_KEY in Settings so operator-supplied encryption keys take effect directly instead of defaulting to ephemeral per-process keys. Unified SSRF Protection: Centralized IP safety validation by having main._is_safe_ip delegate directly to egress_guard.is_public_ip, eliminating CIDR policy discrepancies. Unified RBAC Policy Resolvers: The /v1/mcp tool-governance route now uses the shared policy resolver, honoring Vault and Redis backends alongside InMemory. Upstream Resilience: Resets retry budgets on provider failover in both streaming and non-streaming flows, ensuring fallbacks receive full retry budgets. Codebase Cleanliness: Removed dead defensive guards, redundant exception catching, and obsolete observability assets. Supply Chain: Configured GitHub Actions release workflows to generate and attach Software Bill of Materials (SBOM) attestations. Install with: pip install llm-shield-proxy==1.3.6
Ninad Phalak· Zenodo (CERN European Organi...· 0 citations
UKRI is committed to reaching Net Zero by 2040 and RSE/DRI will be key to achieving this. Green software practices will play a crucial role in this transition and so are increasingly discussed in the community - in part thanks to the Green RSE Special Interest Group. For full adoption, we must enable the next generation of RSEs and researchers to embed green software practices in their work through training. In the words of Professor Dave Reay: “Skills are a fundamental enabler of Net Zero” (https://www.theccc.org.uk/publication/skills-and-net-zero-expert-advisory-group/).Several training courses are now available, some covering high level topics and some focusing on specific topics such as Green HPC and Green AI (see https://socrse.github.io/green-sig/resources.html). The availability of green software courses is of clear benefit to the community and it is valuable to have courses that dig into the details. However, existing courses are not well connected and the green software topic is often considered in isolation, beyond ‘core’ courses such as version control or testing. That is, learners “opt in” to learning about environmentally sustainable software by taking specific green software courses, rather than learning about it in other courses. Courses on HPC, testing, and AI are good candidates for embedding small amounts of green software recommendations early, likely also increasing interest in green RSE from the outset. In this workshop, we will discuss how best to embed green software engineering practices in existing training courses.
Joseph G. Wallwork, K. J. Pringle, James Tyrrell et al.· Zenodo (CERN European Organi...· 0 citations
Experimental artifacts supporting the manuscript “Sense Once, Serve Many: Common-Trace Factorized Constrained PPO for Online Sensing-Session Consolidation in Multi-Tenant ISAC Networks.” This deposit contains the final artifacts used for the learned-policy comparisons, component ablation, arrival-load robustness evaluation, and heuristic-reference analyses reported in the manuscript. The archive includes 20 learned-policy training/evaluation bundles covering four methods across training seeds 0–4: Common-Trace Factorized Constrained PPO (CT-PPO), Joint-Credit PPO (JC-PPO), Factorized-JC, and CT-Reward. Each learned-policy run uses a budget of 1,000,000 physical interaction slots. Five additional arrival-load robustness bundles contain frozen-checkpoint CT-PPO and JC-PPO evaluations at the low and high tested arrival rates for seeds 0–4; the nominal-load evaluations are contained in the corresponding learned-policy bundles. The deposit also includes report_heuristic.json, containing the evaluation results for the four deterministic heuristic baselines and Random Valid used in the manuscript. Together, these artifacts support the primary CT-PPO/JC-PPO comparison, the four-way component ablation, regime-wise and consolidation analyses, arrival-load stress evaluation, and heuristic-reference comparisons reported in the manuscript. The associated implementation and training/evaluation scripts are available in the linked GitHub software repository.
Dang-Dung Vu· Zenodo (CERN European Organi...· 0 citations
This working paper argues that AI-native delivery compresses only the final stage of a six-stage chain running from an institution's covenant with those it serves to running software, so that by Amdahl's law the constraint moves upstream into how organisations form, prioritise, and preserve intent. It introduces intent provenance — the unbroken, traceable, durable, machine-checkable, and contestable chain from a stated obligation to the test that proves it in production — and the service covenant as the layer above vision and mission at the top of that chain. In closing it defines decision custody: an institution's demonstrated ability to reproduce, explain, defend, and answer for every consequential decision made in its name, human or machine, for as long as the consequences of that decision last. Decision custody is positioned as the institutional counterpart to decision sovereignty, a state-level concept defined elsewhere in the literature. The covenant is the promise; custody is the proof. Part of the Sovereign Digital Resilience series.
Sanjeet Kumar· 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.
