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intersection, join error reported two-sided with the earlier “directionally safe, never over-counting” claim withdrawn, overlap-error direction governed by an error budget, retention ratio restated in matched units (R04); measure-relative notation throughout (R05); subject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 This is the first subject-classification reversal recorded in this map: cs.MA is withdrawn as unsupported by the technical content — the formalism contains no agent population, strategic interaction, or equilibrium claim — and replaced by cs.CR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 The AI-collaboration attribution note (drafted 2026-08-20, previously unpublished as a local v32.1 revision) is merged into this version.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.