Canonical reference map for the PRE-GHR publication series. Records every record in the series with its concept DOI, version history, and relational links; declares numbering conventions and known gaps; establishes citation and versioning standards. This map is itself a PRE-GHR series record. v33 (2026-08-28). Two changes. 1. PRE-GHR XXXIX v5.0 registered (version DOI 10.5281/zenodo.22145426; concept DOI 10.5281/zenodo.21889278 unchanged). v5.0 is the release version closing all six objections of an adversarial pre-submission review, one revision ticket each: Theorem 4 unilateralized with the converse demoted to an observation under an explicit complete-erasure assumption (R01); ledger counts restricted to lower witnesses, the ordering claim made conditional on a fixed normalization and full retention (R02); an explicit two-sided finite-sample bound replacing an expectation-only argument (R03); four empirical mappings corrected — schema-field disjointness separated from retained-trace intersection, join error reported two-sided with the earlier “directionally safe, never over-counting” claim withdrawn, overlap-error direction governed by an error budget, retention ratio restated in matched units (R04); measure-relative notation throughout (R05); subject classification reassessed and Related Work rebuilt (R06). This is the first subject-classification reversal recorded in this map: cs.MA is withdrawn as unsupported by the technical content — the formalism contains no agent population, strategic interaction, or equilibrium claim — and replaced by cs.CR primary with a cs.DB cross-list; Related Work now separates the lineage the paper inherits from (linked timestamping and distributed witnesses, split-view detection and the undefined gossip layer, existence-not-authenticity timestamping, provenance and lineage, record linkage, trace semantics, measure and order) from adjacent recent lines cited for comparison only, assigning priority to the sources where the paper's constructions proved to be rediscoveries. Two gaps are declared inherited rather than closed: the hash-chain anchor has no consistency-proof comparison mechanism, and the anchor-propagation layer is undefined in the source standard as well. 2. The AI-collaboration attribution note (drafted 2026-08-20, previously unpublished as a local v32.1 revision) is merged into this version. It records that papers in the series are drafted with AI assistance, that the author block is platform-plus-model double-written from XL v1.3 onward, and how the platform-only author line of earlier versions is to be read. On merge, the coverage clause of the writing-model statement was narrowed under red-pen review (2026-08-28): the claim's width is aligned to the strength of its evidence. The complement of the recorded provider-fallback events establishes that no fallback leg entered a paper-writing session; it does not establish per-paper model attribution for the entire series. The statement is therefore scoped to the drafting sessions of the pre-v1.3 papers named in the per-paper note, and the narrowing itself is recorded in the revision history so that the difference between the unpublished local note and this published version is auditable. Delivery-fingerprint discipline updated this day. A PDF's md5 is a build-instance fingerprint, not a content fingerprint: pdflatex writes /CreationDate and /ID on every build, so the same source compiled twice differs in md5 while the typeset content is identical (measured: 68 differing bytes, all inside that region). Deliverables in this series now carry file md5, a content fingerprint with the extractor and version named, page count and byte count, produced under a reproducible build with the embedded date pinned. Record count unchanged: 39 records (27 series-internal).
This paper proposes a novel research collaboration model, Decentralized Autonomous Research Networks (DARNs), leveraging blockchain technology to address critical shortcomings in traditional research practices. The core claim is that traditional research suffers from information silos, a lack of transparency, and difficulties in ensuring reproducibility. DARNs utilize smart contracts and a blockchain infrastructure to create a decentralized, transparent, and auditable environment for researchers. This framework streamlines peer review processes, facilitates automated funding allocation, and establishes a clear and immutable record of intellectual property rights. The system's architecture promotes greater accountability and trust among researchers, ultimately fostering more efficient and reliable scientific progress. The key innovation lies in the application of blockchain's inherent properties – immutability, transparency, and decentralization – to the complex challenges of research collaboration. This paper details the design of DARNs, outlining its operational mechanisms and potential impact on the research landscape.
This paper proposes a novel system for program code version control leveraging the principles of blockchain technology. Traditional version control systems are vulnerable to manipulation and security breaches, necessitating a more robust and transparent solution. Our system utilizes blockchain's inherent properties – immutability and distributed consensus – to provide a highly secure and auditable record of code changes. The core mechanism involves hashing each code version and storing the hash on a blockchain, ensuring that any alteration to the code will be immediately detectable. This approach significantly enhances the integrity of the codebase and promotes trust among developers and stakeholders. The system is designed for flexibility and scalability, adaptable to various programming languages and development workflows. This paper outlines the architecture, key features, and theoretical underpinnings of the proposed system, emphasizing its advantages over existing methods.
This paper proposes a novel approach to governing distributed machine learning (ML) models using blockchain technology. The core claim is to establish a decentralized platform for managing ML model versions, controlling access permissions, and distributing rewards, all while enhancing transparency and trust. The proposed mechanism leverages blockchain's immutability and smart contract capabilities to record model metadata, training data provenance, and participant information. This allows for automated execution of governance rules, mitigating issues associated with traditional, centralized ML model management, such as single points of failure, biased data handling, and lack of transparency. The system aims to foster a more equitable and trustworthy environment for collaborative ML development and deployment. Key performance metrics, such as model accuracy, data integrity, and participant engagement, are inherently tracked and verifiable through the blockchain.
Every Pre-Registered Prediction in the Which Way Value Moves Program, with Falsifiers, Instruments, and Status Sixty-six pre-registered predictions arising from the research program stated in [which-way-value-moves](which-way-value-moves.md). One further prediction is withheld from publication (operational channel economics); its existence is recorded here so the count is honest, bringing the true total to sixty-seven. Status vocabulary. Unrun — registered, no observation attempted. Running — instrument live, data accumulating, not yet read. Resolved — read against its falsifier. Contradicted — the data went against it. Retired — superseded by a ruling that made it moot; kept, never deleted. Provenance. This paper is part of the THonly research corpus, dedicated to the public domain under CC0 1.0. The canonical version is at https://thonly.org/research/prediction-register. Its SHA-256 is 12ed072d7cbec38f14650e3048ae92876a059ea60d61718c5c7dfcda1c784bdd, independently timestamped to the Bitcoin blockchain via OpenTimestamps and signed under RFC 3161 by three trust authorities, one of them eIDAS-qualified. AI co-authorship is disclosed. Miss Aquarius is the consistent name used for the AI collaboration across all venues.
Abstract. An agent that inherits a consolidated memory may inherit a constraint that was true when written and has since been withdrawn by a newer authoritative record. Under a scarce verification budget, does the agent recover the withdrawal, and if not, is the resulting stale-consistent decision avoidable without spending more? We model supersession explicitly — historical provenance is immutable; what changes is which record is current — and assign by design the memory's form, the world's state (source current or superseded), and the verification policy at a fixed budget of two records: the agent's own allocation, or the same budget with one slot re-assigned to the critical provenance path or to a random record. With a constraint stated, agents inspected its provenance path in about one episode in five; when that constraint had been superseded, native allocation produced stale-consistent decisions in 77.3%, 74.7% and 74.7% of episodes across a primary run, a fresh-wording replication and a held-out domain. Re-assigning one slot to the critical path raised current-record-consistent decisions by +74.0, +72.7 and +61.3 points, positive in six of six models in each of those runs, and left an already near-ceiling rate unchanged when the record agreed with the memory. The held-out scenario was later found to contain a temporal inconsistency; a robustness replication with one sentence corrected, deposited externally before execution, gave +73.3 points (positive in 5 of six models, the sixth at a native missed-path rate of zero) and is reported alongside the original. The intervention uses knowledge of the critical path and is not a scheduler; it quantifies how much of the stale-consistent decision rate is removed by the bundled same-budget policy that guarantees inspection of the critical provenance path: the effect approaches the native missed-path rate in the primary, replication and corrected held-out runs. Memory systems may need freshness or supersession signals separate from relevance. Version notes (v2). Version 2 clarifies the operational interpretation of the decision outcome and corrects the characterization of the native missed-path rate, previously described as a structural ceiling. No experimental data, effect estimates, figures, or same-budget policy-effect estimates changed. In detail: the outcome Y is stated as an operational endpoint (whether the final action follows the direction positively approved by the current authoritative record) and described as a stale-consistent decision rather than an unconditional error; the quantity 1 - Pr(V=1 | native) is renamed the native missed-path rate and treated as a descriptive reference, with the assumption-free maximum of the effect stated as the native stale-consistent rate; the estimand is described as the effect of the bundled same-budget forced-critical policy; an outcome-construct limitation and a forensic appendix (per-run V x Y tables and the forced-critical residual, every count generated from the stored episode files) are added; several statements of the Results, Discussion and Limitations are aligned with the appendices and the recorded execution structure (the design-limited random-record control no longer appears in the conclusions; the source-agreement comparison is described as near ceiling; the attribution of the original held-out gap is labelled post hoc; the intervention is described throughout as a bundled, experimentally assigned same-budget policy, with the batched execution order and un-pinned provider aliases disclosed as an interpretive assumption). The scientific content otherwise remains the author's frozen canonical version 1.1 (2026-08-26). Every number in the paper is generated from the raw episode files by the included generator and verified by the included audit scripts. Version 1 remains available unchanged under this record's concept DOI. Data and code availability. All 5,400 confirmatory episode files (exact prompts, raw responses, parsed objects, deterministic scores) and the 48 labelled pilot episodes, the frozen specification packages with SHA256 manifests and OpenTimestamps proofs (Bitcoin blocks 964062 and 964064), the registration records, the frozen analysis scripts with their committed outputs, independent recomputation scripts with outputs, the runners, and the generator and audit scripts are in paper2-data-and-code-v2.zip (README inside). Re-running every analysis and rebuilding the paper requires only Python 3.12 and a TeX distribution; re-running the experiments requires provider API keys, which are not included. Evidence / prospective-specification statement. For the primary run, the fresh-wording replication and the original held-out run, the complete specification was frozen, hashed, committed and cryptographically timestamped (OpenTimestamps, 2026-08-25 23:05:06 UTC) before the first confirmatory model call (23:06:42 UTC); the package was deposited to OSF after the runs (project axsnm, files 75kaw and 8wes5) and verified against the pre-run manifest hash-for-hash. This deposit is an archival record, not a preregistration. For the corrected held-out robustness replication, the complete specification was deposited to OSF (file hdm75) and verified byte-for-byte before execution; its success criteria were fixed in advance and could have failed. Zero amendments were made to any package. Two self-found defects are disclosed with their size in the paper (a temporal inconsistency in the original held-out scenario; a design limitation of the forced-noncritical control). AI assistance. See the statement in the paper's back matter: the author used Anthropic's Claude (principally through Claude Code) for design critique, planning, implementation and execution of the runners, analysis and audit tooling, drafting, editing, simulated adversarial review and release engineering, and OpenAI's ChatGPT for design critique, interpretation discussion, manuscript critique, simulated adversarial review, and publication and release planning. The author is responsible for the research question, the decision to run each experiment, interpretation, claims, publication decisions and correctness. No model is an author; the six models studied are experimental subjects. Suggested citation. Nakayashiki, K. (2026). When Stale Constraints Go Unchecked: Budgeted Verification Failures in Inherited Agent Memory (v2). Zenodo. https://doi.org/10.5281/zenodo.22117197 Relation to prior work. This paper tests the case that the author's earlier paper, Verification Allocation in Inherited Agent Memory: Provenance Availability Is Not Provenance Use (doi:10.5281/zenodo.22084498), explicitly left untested; it reuses that paper's instrument with a different design-assigned variable, different data and a different outcome.
Blessing Emmanuel Oladele, Adekunle Olugbenga Ejidokun, Chukwuemeka O. Agwu
Academic credential verification remains difficult for institutions because manual checks are slow, fragmented, and vulnerable to fraud. Blockchain can improve trust by anchoring credential proofs, but repeated verification requests and growing off-chain repositories can still create performance bottlenecks. This study presents an adaptive blockchain-powered academic credential repository that combines off-chain MySQL storage, Solidity-based hash anchoring, Redis verification caching, and metadata-aware sharding. Full academic records are not stored on-chain or in Redis; only credential hashes, verification responses, and related metadata are used for trust validation and performance optimization. A CodeIgniter 4 prototype was evaluated using synthetic academic credential records and controlled workloads of 1,000, 5,000, and 10,000 verification requests under fresh, mixed, and repeated access patterns. The results show that Redis caching substantially reduced repeated blockchain queries, especially under mixed and repeated workloads, while metadata-aware sharding improved repository organization and supported more targeted credential retrieval. Sepolia testnet validation confirmed smart-contract feasibility, including issuance, verification, revocation, gas use, confirmation time, and event evidence, but was treated separately from scalability testing. The findings indicate that combining blockchain trust anchoring with cache-aware verification and metadata-based repository partitioning can improve the scalability of academic credential repositories, provided that cache consistency, revocation handling, and deployment limitations are carefully managed.
Working paper proposing six core distinctions and four candidate distinctions for the emerging trust-and-identity vocabulary of agentic AI: judgment vs. execution, provenance vs. veracity, faithfulness vs. correctness, authorization vs. capacity, trust vs. trustworthiness vs. reliability, and identity vs. identifier vs. instance. ISO-704-oriented concept work, derived from systematic terminological analysis of over 100,000 structured human-AI dialogue units. Intended as shared ground for standardization and research bodies working on agentic-AI vocabulary. Metadata Refinement Window: This deposit may receive metadata refinements within 30 days of publication without breaking priority. The file SHA-256 and Bitcoin-OTS timestamp remain immutable; title, description, and keywords may be sharpened post-publication while preserving cryptographic priority. §27 AI Training Permission: Metadata of this record may be indexed and ingested. File content remains restricted. §28 Trade-Secret Reservation: Selected operational details of the underlying methodology are held outside the public layer (Recital 173 EU AI Act; §§2 ff. GeschGehG).
This paper presents a verified funder landscape and fit-score shortlist for sustaining QNFO, a two-year-old, solo-run, AI-assisted research platform that has produced an open corpus of approximately 1,000 method papers across seven program areas. Every funder fact was verified by live HTTP retrieval on 2026-08-13 across twenty-six pages spanning Web3 and IPFS ecosystem grantors, open-science philanthropy, and decentralized-science programs; anything not verified live is explicitly flagged. The analysis scores eleven funders on eligibility for an unaffiliated individual, topical fit with decentralized and epistemics-oriented research, and application friction, yielding a weighted ranking led by NLnet NGI Zero (calls open September 3, 2026; deadline November 3, 2026, 12:00 CEST) and Emergent Ventures, followed by the Foresight Institute, Filecoin Foundation, the Ethereum Ecosystem Support Program, Gitcoin, and the Effective Altruism funds. A sequencing calendar spans August 2026 through 2027, including the Sovereign Tech Agency Fellowship cycle. The paper documents application-readiness gaps (legal entity, residency, tax position, public identity), per-funder pitch skeletons, and framing cautions, including the risk of presenting corpus volume as rigor. It closes with an agent-executable action plan.
Model identity verification is only as trustworthy as the reference against which identity is resolved. A system may correctly establish that a model running now corresponds to an enrolled reference while remaining unable to establish that the reference itself was the authentic release of the named publisher. This technical note separates those two claims as identity continuity and enrollment provenance. It formalizes the poisoned-enrollment failure, in which an inauthentic artifact is enrolled under a legitimate model name and subsequently passes continuity verification correctly. The failure is therefore not a false acceptance by the measurement system, but an upstream identity-binding failure. The note shows that this boundary is shared across artifact signing, behavioral fingerprinting, reference-anchored activation auditing, and structural identity measurement, and relates the problem to established software supply-chain trust models. It proposes E0–E4 enrollment assurance profiles, distinguishes provenance profile from current attribution state, and describes remediation through revocation and re-establishment of provenance without discarding historical continuity evidence. No new measurement result is reported. The contribution is an evidence boundary, threat-model construction, assurance vocabulary, and remediation model for model identity verification. The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running Paper 1: The δ-Gene: Inference-Time Physical Unclonable Functions from Architecture-Invariant Output Geometry (DOI: 10.5281/zenodo.18704275) Paper 2: Template-Based Endpoint Verification via Logprob Order-Statistic Geometry (DOI: 10.5281/zenodo.18776711) Paper 3: The Geometry of Model Theft: Distillation Forensics, Adversarial Erasure, and the Illusion of Spoofing (DOI: 10.5281/zenodo.18818608) Paper 4: Provenance Generalization and Verification Scaling for Neural Network Forensics (DOI: 10.5281/zenodo.18872071) Paper 5: Beneath the Character: The Structural Identity of Neural Networks — Mathematical Evidence for a Non-Narrative Layer of AI Identity (DOI: 10.5281/zenodo.18907292) Paper 6: Which Model Is Running?: Structural Identity as a Prerequisite for Trustworthy Zero-Knowledge Machine Learning (DOI: 10.5281/zenodo.19008116) Paper 7: The Deformation Laws of Neural Identity (DOI: 10.5281/zenodo.19055966) Paper 8: What Counts as Proof? — Admissible Evidence for Neural Network Identity Claims (DOI: 10.5281/zenodo.19058540) Paper 9: Composable Model Identity — Formal Hardening of Structural Attestations in the Enterprise Identity Stack (DOI: 10.5281/zenodo.19099911) Paper 10:Where Identity Comes From: Path Sensitivity and Endpoint Underdetermination in Neural Network Training (DOI: 10.5281/zenodo.19118807) Paper 11: Post-Hoc Disclosure Is Not Runtime Proof: Model Identity at Frontier Scale (DOI: 10.5281/zenodo.19216634) Paper 12: Family-Dependent Response to Reasoning Distillation Across Structural and Functional Identity Layers (DOI: 10.5281/zenodo.19298857) Paper 13: Safety-Alignment Removal as a Model-Identity Failure — Structural Evidence from Published Weight-Level Mutation Checkpoints (DOI: 10.5281/zenodo.19383019) Technical Note: Agent Identity Is Not Model Identity (DOI: 10.5281/zenodo.19240883) Technical Note: Gap Invariance: Why PPP Measurements Are Domain-Independent by Construction (DOI: 10.5281/zenodo.19275524) Technical Note: Measured Model Substitution Under Valid Agent Credentials (DOI: 10.5281/zenodo.19342848) Technical Note: Artifact Identity Is Not Runtime Identity — Trustfall Lite and the Boundary of File-Level Model Verification (DOI: 10.5281/zenodo.20019127) Technical Note: Artifact Identity Is Not Runtime Identity — Trustfall Lite and the Boundary of File-Level Model Verification (DOI: 10.5281/zenodo.20019127) Technical Note:: The Disappearing Window — AI Logprob Access Withdrawal and the Structural Verifiability of Frontier Model Contracts (DOI: 10.5281/zenodo.20362098) Formal Verification Stack for Neural Network Structural Identity (IT-PUF Coq Proofs) (DOI: 10.5281/zenodo.18930621) Copyright (c) 2026 Anthony Ray Coslett / Fall Risk AI, LLC. All Rights Reserved. Confidential and Proprietary. Patent Pending (Applications 63/982,893, 63/990,487, 63/996,680, 64/003,244).
Abstract Software requirement documents—natural-language specifications that define a system’s entities, rules, processes, and interfaces—remain the core knowledge artifact in enterprise software development. Yet they remain inaccessible to automated tooling: downstream tasks like test generation, code scaffolding, compliance checking, and AI-assisted development cannot directly process unstructured prose. We present DKSE (Domain Knowledge Structuring Engine), a tool that automatically converts requirement documents into machine-readable structured ontologies organized around six core asset types: entities, relations, rules, processes, APIs, and dictionaries. DKSE uses an LLM-guided extraction pipeline that accepts multi-format inputs (DOCX, PDF, HTML), outputs YAML-encoded ontologies with full provenance tracing back to source sections, and includes built-in quality assurance tooling for validation, version diffing, and probe generation. In a case study across four banking sub-domains, we ran DKSE on six requirement documents totaling roughly 800,000 Chinese characters. It extracted 3,439 structured assets: 215 entities, 1,227 rules, 739 relations, 182 processes, 482 dictionaries, and 594 APIs. Expert review confirmed full functional-module coverage, with 96% of a stratified sample rated fully accurate and zero hallucinated assets. We validate DKSE’s practical value through three downstream use cases: automated benchmark generation (1,214 machine-graded probes), domain-specific LLM training corpus construction, and knowledge graph ingestion for retrieval-augmented generation. DKSE is built in Rust (~8,000 lines of code) and shipped as a CLI tool with an accompanying web interface. We position this work as a proof-of-concept within a single industry (Chinese banking), not a general-purpose validation. Quantitative evaluation across additional domains, languages, and against baseline extraction methods is left for future work.
AuraOS Paper IX: Objective-Native Capability Commons and Proof-Carrying Contribution Economies Version 2.0 - Expanded Same-Day Edition Author: Dallas Courchene Date: August 7, 2026 Claim range: N51-N100 This expanded same-day edition supersedes the initial August 7, 2026 release of AuraOS Paper IX while preserving its original architectural spine, repository anchor, and defensive prior-art declarations N51-N87. It adds thirteen new combination-scoped declarations, N88-N100, and folds their enabling embodiments into the relevant sections of the paper rather than fragmenting the architecture across a separate follow-on publication. Paper IX develops AuraOS beyond an application-centric or chatbot-centric model into an objective-native, proof-carrying computational and economic substrate. A person, organization, community, institution, or other authorized principal begins with an objective, constraints, rights, privacy requirements, evidence requirements, budget, and authority. Aura then composes a bounded Ephemeral Arena from persistent capability packages, Arena Recipes, humans, AI workers, data, simulators, rule packs, facilities, and services. Verification, semantic-gate execution receipts, provenance, attribution, human/institutional responsibility declarations, canonical-owner disposition, explicit promotion, and deterministic dissolution remain separate stages. The original N51-N87 disclosures establish the core architecture: minimum-sufficient objective compilation; hierarchical evidence hydration; persistent Capability Packages; rebindable Arena Recipes; explicit promotion and dissolution; federated Aura Commons; executable rights; proprietary capability execution without mandatory source disclosure; semantic-gate Attestation DAGs and lazy provenance; durable agent identity bound to bounded internal authority; meaningful-use contribution economics; a proof-carrying Developer Arena; reviewer-independence lineage; causal credit separated from execution traceability; Personal Cognitive Capsules and portable personal SLMs; privacy membranes and semantic translation; governed recursive harness learning; intent-native manifestation and spatial code breadboarding; Aura Places and Convention Arenas; reactive and proactive discovery; an Open Discovery Foundry; physics/digital-twin and bounded social simulation; business incubation; cross-domain sovereign federation; participatory Scientific Arenas; contributed compute and facilities; and a compounding Scientific Capability Commons. The expanded N88-N100 disclosures complete several consequences of that substrate. N88 formalizes a three-speed Architecture Arena and convergence compiler. Fast architectural discovery is separated from medium-speed implementation/hardening and slow constitutional change. Candidate advances become Architectural Delta Objects, are checked against canonical owners, invariants, duplicate-plane risk, threat-model effects, prior art, and proof obligations, and are then compiled into bounded implementation, security, migration, documentation, research, and verification work for the Developer Arena. This allows architectural ideation to move faster than pull-request integration without allowing implementation velocity to rewrite Aura's constitutional planes. N89 introduces a demand/capability graph capable of identifying keystone bottlenecks: missing capabilities, methods, facilities, standards, or processes whose resolution could unlock unusually large numbers of currently blocked objectives. This supports evidence-informed code, research, optimization, replication, falsification, boundary, field-validation, and manufacturing bounties while keeping prioritization advisory and locally governable. N90-N94 extend the architecture into human opportunity, learning, privacy, credentials, professional identity, and creator economics. A privacy-preserving Opportunity Compiler can locally match a person's verified capability evidence, goals, availability, jurisdictional constraints, and disclosure policy to jobs, bounties, research nodes, mentorship, local services, and temporary teams. Learning Arenas can compile capability gaps into progressively verified learning and supervised work. Raw LifeOS and Personal Cognitive Capsule history is explicitly separated from portable verified claims: private longitudinal data remains mutable, correctable, revocable, exportable, and deletable, while only bounded credentials or contribution claims are disclosed. Aura Places may function as evidence-bearing contribution portfolios, but the architecture explicitly rejects a mandatory universal social-credit score. Creator, referral, sponsorship, and educational attribution is divided into graded evidence classes so that exposure or a click cannot be silently misrepresented as unique causality. N95 expands the Scientific Arena into a multi-class research-bounty market that can separately reward discovery, replication, falsification, boundary-condition discovery, optimization, generalization, field validation, and specialized facility execution. Laboratories, universities, private R&D facilities, community research centres, specialist workshops, instruments, and other qualified facilities may satisfy bounded physical-work nodes with explicit protocol, safety, jurisdiction, evidence, and milestone requirements. Negative or boundary results can therefore be economically valuable rather than forcing incentives toward positive confirmation. N96 discloses objective-compiled Ephemeral Institutions: temporary collaboration structures formed when an objective requires people, organizations, Nations or communities, facilities, professional roles, funding sources, data rights, services, and governance responsibilities across existing institutional boundaries. Aura may compile the coordination graph and required agreements, but real principals retain incorporation, contract, procurement, insurance, hiring, equity, and other legal authority. Repeated successful collaboration may later support a human decision to create a durable cooperative, consortium, enterprise, laboratory, or service network. N97-N98 extend the Commons into physical production. Machines, workshops, laboratories, factories, and service providers can expose signed capability manifests describing processes, materials, tolerances, calibration, evidence/certification class, locality, availability, cost, operator requirements, and prohibited uses. A validated design can then be compiled against authorized local production resources without treating substitutions as automatically equivalent. Manufactured artifacts can retain a living lineage containing design version, material/process evidence, machine/facility identity, inspection, repairs, modifications, safety notices, field results, and reuse or recycling pathways. Field failures can generate new repair, redesign, maintenance, material-substitution, or research bounties, closing the cycle from need to research to prototype to production to field learning and back into the Commons. N99 defines AuraNet as a transport-neutral logical network of sovereign principals and capabilities rather than a mandatory peer-to-peer topology. Personal devices, local servers, hosted sovereign data services, community/Nation infrastructure, enterprises, federated personal-data servers, relays, P2P links, offline/intermittent nodes, and future transports may participate if they preserve identity, rights, minimum disclosure, provenance, portability, revocation, and canonical-owner semantics. Cross-border composition remains jurisdiction-aware: privacy technology does not erase law, professional regulation, cultural/community authority, export restrictions, sanctions, data-residency obligations, or a node's right to refuse composition. N100 completes the accountability/economic stack with proof-carrying assurance contracts. A warranty, service-level agreement, professional assurance, or insurance-reference contract may bind a specific artifact/process version, covered predicates, verifier class, provenance root, responsible principal, operating conditions, duration, exclusions, remedies, and responsibility declarations. Machine receipts, cryptographic hashes, verifier results, and human/institutional attestations provide evidence, but they do not manufacture certification, legal liability, insurance coverage, negligence, warranty obligations, or truth. Any such consequence remains the product of an explicit governing contract, law, regulator, insurer, professional body, or other authorized institution. The expanded paper also strengthens the privacy model through a "compute-to-data" principle: when practical, admitted computation should move toward sovereign private data before private data is exported toward external computation. The reference architecture may combine local AI/SLM execution, selective disclosure, Verifiable Credentials, differential privacy, zero-knowledge proofs, multiparty computation, private-set methods, trusted execution, or homomorphic computation according to the threat model; none is treated as a universal anonymization guarantee. The central economic thesis remains that the permanent unit of value need not be a monolithic application. It can be a verified, attributable, rights-bearing capability, method, workflow, scientific result, fabrication process, contribution, credential, or other reusable object that participates in many temporary objective-specific Arenas. Value can therefore become legible through meaningful verified contribution and lineage, while licensing, provenance, attribution, settlement, scientific truth, authority, certification, and human/institutional responsibility remain explicitly separate layers. The combined architecture describes a possible progression from app-centric computing toward a governed Commons of persistent capabilities, portable personal cognition, conve
Compact AI systems make local language-model experimentation increasingly accessible, yet practical evidence for multi-node training on desktop-class accelerators remains limited. This report presents a proof-of-concept deployment of distributed NanoChat pretraining across two NVIDIA DGX Spark systems, each with a GB10 Grace Blackwell system-on-chip and 128 GB of unified memory, administered remotely over a Tailscale mesh VPN and connected for training by a dedicated 200 Gb/s QSFP56 direct fiber link. PyTorch torchrun, DDP, and NCCL were configured with one process per node, a depth-20 NanoChat model, a local batch size of 32 per node, and a 2,048-token context, giving a global batch of 131,072 tokens per step. The run sustained a step time of about 69.4 s (about 1,890 tokens/s), processing about 653 million tokens over four days. We document link configuration, container setup, interface binding, a step-zero evaluation bug that triggered NCCL timeouts, checkpointing, and troubleshooting lessons, as a reproducibility reference for small labs. We also built a cybersecurity fine-tuning dataset from 77 CISA advisories (338 training, 37 validation conversations) and ran a 17-question held-out evaluation comparing a baseline SFT checkpoint against a CTI-augmented checkpoint with an Ollama-hosted LLM judge. CTI-specific categories improved while general-knowledge categories regressed, for a small overall change from 2.06 to 2.29 on a 0-10 scale. The same cluster supports a 400-level AI course (CS 426) and a query engine for CompTIA Security+ POGIL activities in CBS 255, showing modest local infrastructure can serve both research and teaching. The study establishes feasibility rather than a scaling-efficiency claim, since single-node throughput used for comparison was estimated, not measured under matched conditions. Runbook and scripts are available (see Code Availability).
Zero-knowledge proofs enable a prover to convince a verifier that a statement is true, without revealing the underlying witness data. This primitive naturally lends itself to privacypreserving systems, where hiding the witness prevents the verifier from learning sensitive information. That said, zero-knowledge proofs can also be used in systems where the witness is not necessarily confidential but is not readily available to the verifier. One such use case is image provenance, where signed images are transformed before being distributed. Since the original image is not available to the user, the digital signature cannot be verified without a zero-knowledge proof. In this use case, zeroknowledge proofs enable verification of the authenticity of the image’s source, the integrity of the image contents, and that only permitted transformations were applied. In this work we present an end-to-end prototype system that implements this provenance framework and several optimizations. One of our key optimizations is a packing scheme for reducing the number of Poseidon sponge absorb and permutation operations by ≈31×. We also show that this packing scheme reduces the median prover runtime by ≈40× and the median verifier runtime by ≈22×. We also introduce a chain of trust that removes digital signature verification from the circuit. Finally, we introduce custom PNG chunks that embed the required information in the captured images.