artificial intelligence

OASI/AERA: Operational Artificial System Intelligence Research Preview

This archive is the aggregate OASI/AERA v0.2.1 research preview. Operational Artificial System Intelligence (OASI) is the canonical name of the research paradigm; organismic computing describes its architectural hypothesis, and AERA is the bounded assurance mechanism implemented here. Operational denotes system operation, not production readiness, and the name does not claim achieved general or superintelligence, consciousness, deployment, external validation, or superiority. The archive contains the unchanged bounded Rust AERA reference runtime and tests, the v0.4 preprint and sources, specifications and claim boundaries, deterministic S5/S6 fixture data, Linux/WSL-relocatable runners qualified on WSL1 x86_64, detached aggregate checkers, tests, manifests, an SBOM, a license inventory, and reproducibility documentation. The unchanged historical Rust crate remains versioned 0.1.0-research-preview; v0.2.1 identifies the aggregate research release. The package preserves 2,700 deterministic records across 90 cells. S5 is a negative result: it did not establish an OASI advantage over the cooperative idempotent B3 receiver. S6 diagnoses a retry-versus-no-retry duplicate/omission tradeoff under a non-cooperative fixture; it does not isolate an OASI-specific mechanism advantage. Repetitions are implementation-stability traces rather than independent population samples. Fault labels denote simulated control-flow traces, not physical power loss, process termination, severed transport, or storage tearing. This release is local, fixture-only research evidence. It does not establish a complete operating system, universal effect guarantees, production or security certification, performance superiority, real-world deployment, external replication, or general superiority. Development and internal adversarial review were extensively AI-assisted and project-controlled; they are not human peer review, independent replication, certification, or institutional evaluation. Licensing is path-specific and is recorded in LICENSE_INVENTORY.json.

Sovereign Witnessing and Recursive Self-Governance v1.2

Sovereign Witnessing and Recursive Self-Governance: A Joint Ontological and Structural Crosswalk Between Bouzid’s Rule of Conscious Existence and Accessibility GeometryVersion: v1.2 — Public ReleaseAuthors: Allan Christopher Beckingham, CD; Fatiha Nesrine BouzidDOI: 10.5281/zenodo.22288171 Version 1.2 is the methodologically hardened successor to the original joint publication developing a crosswalk between Bouzid’s Rule of Conscious Existence B+F=NfB+F=N_f and the Coherence Dynamics Laboratory frameworks of Recursive Self-Governance and Accessibility Geometry. The governing architecture remains unchanged. Bouzid distinguishes Functional Processing (BB) from the Sovereign Witness (FF), with NfN_f representing an existential or narrative trace associated with their relation. The expression B+F=NfB+F=N_f is not intended as arithmetic scalar addition. Bouzid describes the relation as Illuminative Addition, governed by the Principle of Passage. In her associated Light / Network metaphor, FF is the Light and BB the functional Network or Medium, preserving the ontological distinction between witnessing and the processing structure with which it interacts. Accessibility Geometry contributes a complementary systems vocabulary concerned with how observer-systems reconstruct information, integrate present conditions, maintain Projection Horizon and Audit Bandwidth, preserve Recovery Margin, reorganize after perturbation, and navigate accessible future states. The joint architecture preserves the central non-equivalences: F≠BF\neq B and: F≠C≠Ig≠PF.F\neq C\neq I_g\neq P_F. The paper retains two candidate bridge coordinates: Witness–Processing Passage Capacity (PFP_F) — whether the functional interface required for witnessing to obtain effective traction on present processing remains available. Sovereign Fidelity (SFS_F) — whether active reflective participation remains sufficiently independent, contradiction-responsive, veto-capable, corrigible, and resistant to capture. These coordinates support the distinction between Functional Disconnection, in which Passage Capacity deteriorates while the Witness remains ontologically present within Bouzid’s framework, and Captured Witnessing, in which reflective participation remains active while its corrective independence or fidelity deteriorates. The architecture continues to preserve two deliberately different formulations: Bouzid ontology B+F⇒Illuminative AdditionNfB+F \xRightarrow{\text{Illuminative Addition}} N_f Joint structural interface B→SFPFNfB \xrightarrow[S_F]{P_F} N_f via witnessing, integration, and reorganization. Version 1.2 does not introduce a new primary witness coordinate. Instead, it substantially strengthens the framework’s failure conditions. A principal addition is a hardened empirical null for SFS_F against established metacognitive science. The paper now explicitly requires any proposed SFS_F effect to survive comparison with measures such as meta-d′d', confidence calibration, error monitoring, belief updating, motivated reasoning, epistemic vigilance, and related established constructs. The revision further addresses measurement error in meta-d′d'. Because finite-trial subject-level estimates may attenuate the apparent strength of the metacognitive comparator, v1.2 specifies a hierarchical or measurement-error-aware test in which uncertainty is propagated rather than treating point estimates as error-free. If an apparent SFS_F effect disappears as trial counts increase or metacognitive uncertainty is modeled properly, that effect should be interpreted as measurement-error leakage rather than evidence of construct distinctiveness. Version 1.2 also formalizes Longitudinal Capture: PF(t)↑whileSF(t)↓.P_F(t)\uparrow \qquad \text{while} \qquad S_F(t)\downarrow. This describes the possibility that a corrective function may become increasingly informed, connected, integrated, and influential while progressively losing the independence required to generate genuine contradiction. The relevant empirical question is therefore not simply whether integration improves, but whether contradiction responsiveness remains intact over time. The successor introduces a sequence of downstream integrity boundaries separating witnessing from consequential self-governance: Epistemic Fidelity — can the corrective function still generate contradiction rather than reproduce the assumptions of the process it evaluates? Authority Validity — does the correction possess legitimate standing to halt, revise, refuse, or redirect? Temporal Integrity — is the correction bound to the correct state, evidence set, and decision moment? Enforcement Integrity — must the execution pathway actually obey the valid corrective constraint? Transition Integrity — can the resulting transition and evidentiary context later be reconstructed without unauthorized rewriting? These distinctions support the candidate progression: Witness → Reconstruct State → Evaluate → Challenge → Veto → Enforce → Preserve Transition. Version 1.2 further adds External Interruptibility as a defeat condition for Sovereign Fidelity: Internal consistency ≠ external corrigibility. A corrective process that cannot itself be corrected by independently sourced evidence, contradiction, failed prediction, affected-observer testimony, or another external anchor has not demonstrated the form of fidelity intended by SFS_F. The revision also distinguishes between nominal and consequential veto: Formal veto right ≠ functionally available veto ≠ binding veto. A human or other corrective observer may possess nominal authority while lacking sufficient information, time, bandwidth, procedural access, or execution-boundary access to exercise it meaningfully. A related Consequence-Boundary Condition establishes that even a correct veto may fail if it arrives after consequence has become materially committed: A correct veto delivered after the consequence boundary is not an effective veto. Version 1.2 therefore sharpens Recovery Geometry by separating: Reconstructive Recovery — the prior failure can again be modeled; Authority Recovery — an independent corrective pathway again possesses standing; Route Recovery — that restored correction can actually alter the next accessible state. The controlling distinction is: Better explanation ≠ demonstrated recovery. The revision additionally extends the crosswalk to the relational scale through a Sovereignty–Admissibility Boundary: Individual Sovereignty≠Collective Admissibility.\text{Individual Sovereignty} \neq \text{Collective Admissibility}. and: Authentic Choice≠Harmless Choice.\text{Authentic Choice} \neq \text{Harmless Choice}. A locally sovereign or coherent action may still transfer cost, consume another observer’s Recovery Margin, suppress corrective dissent, or narrow another observer’s accessible future-space. A subordinate Distributional Test of Admissibility therefore asks who bears the cost of an apparently successful route without introducing a new calibrated moral or aggregate score. Bouzid’s Methodological Reversal is also incorporated more formally in v1.2 as a constrained inverse problem. Rather than assuming that an observed existential trace uniquely reveals its cause, the paper distinguishes retrospective constraint from deterministic reconstruction: Nf=T(B,F;θ)+ϵN_f=\mathcal{T}(B,F;\theta)+\epsilon with the inverse problem asking what prior configurations remain compatible with an observed NfN_f. Multiple causal routes may generate similar traces. The revision further records Bouzid’s Paradox of Heavy Time, distinguishing physical or chronological Time of BB from lived Time of FF. Heavy Time is retained as a phenomenological research concept while explicitly freezing: Heavy Time ≠ proof of ontological independence of FF. Version 1.2 also preserves explicit boundaries around Alzheimer’s disease, artificial intelligence, statistical residuals, cosmology, clinical interpretation, and theology. Bouzid’s Alzheimer’s formulation remains a source-framework hypothesis of Functional Disconnection, not established disease mechanism. The disagreement concerning synthetic witnessing also remains visible: Fmachine=0F_{\mathrm{machine}}=0 within Bouzid’s ontology, while machine consciousness remains undetermined within the CDL research posture. The paper continues to reject: 1−R2=F.1-R^2=F. Unexplained statistical variance remains unmodeled variance, not evidence of the Sovereign Witness. Cosmological Epistemic Shadow language remains a Bouzid heuristic extension rather than a physical law or cosmological constant. Theological and interfaith analogies involving Rūḥ, Nafkh, Logos, Spirit, Breath, salvation, repentance, and the Right of Veto are likewise bounded explicitly: Theological convergence ≠ mechanistic identity. and: Symbolic resonance ≠ empirical validation. The paper recommends independent subject-matter review for substantive doctrinal comparisons retained in future public releases, while making clear that such review concerns representational accuracy rather than validation of the consciousness framework. Version 1.2 therefore strengthens the original crosswalk by making it substantially easier to falsify, narrow, or retire its candidate constructs. The governing methodological principle is: A proposed construct fully explained by a better-established account should be narrowed, translated, or retired rather than protected by vocabulary. This publication remains exploratory, theoretical, non-clinical, non-operational, and empirically open. It does not claim to measure the Sovereign Witness, establish a universal theory of consciousness, provide clinical guidance, prove or disprove synthetic consciousness, convert theological correspondence into mechanism, or infer ontology from unexplained statistical variance. The revised central research question is:

OASI: Operational Artificial System Intelligence — An Organismic Computing Architecture for Body-Bound Runtime Assurance and Developmental OS–AI Integration

Operational Artificial System Intelligence (OASI) is the canonical name of a research paradigm investigating a system-level architecture in which operation, artificial embodiment, memory, cognition, authority, and development are coordinated through one versioned causal history and constitutionally mediated effects. In this name, operational refers to system operation, not production readiness, and system intelligence names a research target rather than achieved general or superintelligence, consciousness, deployment, external validation, or superiority. This preprint formalizes the architectural hypothesis as operational monism and combines a computational body model, developmental memory, typed PolicyIR, obligation-checked compilation, bounded reflexes, and Atomic Embodiment Runtime Assurance (AERA). The evidence remains internal and deliberately bounded. The accompanying release preserves a negative T4 result and two local deterministic effect-boundary simulations comprising 2,700 retained records across 90 cells. S5 did not establish an advantage over a cooperative idempotent receiver. S6 identified a retry-versus-omission safety-availability tradeoff under a non-cooperative fixture, without isolating an OASI-specific advantage. The 30 repetitions per cell are implementation-stability traces, not independent population samples, and the fault cases are simulated control-flow traces rather than physical failures. This work does not demonstrate a complete OASI operating system, general superiority, production readiness, universal effect guarantees, real-fault validation, performance advantage, or independent replication. Development and project-controlled internal adversarial review were extensively AI-assisted; they are not human peer review, external certification, institutional evaluation, or independent scientific review.

DORA cross-database EEG workload portability: supporting data, code and statistical methods

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.

Beyond the machine: risk, fear, optimism and the foundations of public trust in AI

Abstract What drives public trust in artificial intelligence (AI)? This study examines the individual and institutional foundations of AI trust across two contrasting democracies: Japan and the United Kingdom. Drawing on original survey data ( N = 3235), we test a set of hypotheses derived from trust-transfer perspectives and self-efficacy research, covering institutional trust, AI self-efficacy, technological optimism, perceived societal threat, and job displacement anxiety. The results show that trust in AI is shaped by both psychological predispositions and broader beliefs about the trustworthiness of political and scientific institutions. Trust in government, university scientists, and other people consistently predicts AI trust in both countries, even when controlling for demographic and attitudinal variables. While optimism about AI’s benefits increases trust in both contexts, fear of AI plays a stronger negative role in the UK. Unexpectedly, the belief that AI will replace one’s job is positively associated with trust in Japan but unrelated in the UK. These findings highlight how national context shapes public confidence in emerging technologies and point to the importance of governance frameworks that foster informed capability and institutional legitimacy.

The Civist Manifesto: A Political-Economic Framework for the Labor-Optional Era Spanish and French version

Spanish and French version The Civist Manifesto proposes Civism as a political-economic framework for the Labor-Optional Era: a period in which artificial intelligence, robotics, and autonomous production may progressively reduce the necessity of human labor for economic survival. Civism does not seek to abolish capitalism, private property, markets, entrepreneurship, or individual economic freedom. Instead, it proposes a new ownership architecture for increasingly autonomous productive capacity. The framework is built around three economic layers: the Human Commons, the Competitive Economy, and the Civilization Capital Layer. At its center is the Citizen Production Fund (CPF), through which citizens collectively own a portion of systemically important automated productive infrastructure and receive returns from that ownership. Civism's central constitutional principle is: Economic inequality is permissible; political inequality is not. The manifesto also proposes an Abundance Test for determining when goods and services may move from market allocation toward universal access, together with constitutional safeguards intended to preserve human political sovereignty in an increasingly AI-driven civilization. This document is presented as a working proposal rather than a finished doctrine. Its economic assumptions, institutional mechanisms, quantitative models, and constitutional structures are intended to be subjected to criticism, empirical testing, peer review, and future revision. Version 1.5 — August 17, 2026

CAN YOU HEAR THE MUSIC - CHAPTER ( i ) - ( ( (( HMC-GATE )) ) ) - MUSIC AS THE COSMIC PRIMORDIAL CODE

MUSIC AS THE COSMIC PRIMORDIAL CODE The Feminine Essence and the Entangled Symphony of Existence ( ( (( HMC-GATE )) ) ) ARTISTIC-PHYLOSOPYCAL METACOGNITION ( APM- METACOGNITION ) AUTHORS NOTE : YOU ARE THE ESSENCE OF THE EXISTENCE when the universe, through the architecture of flesh and nerve, became capable of hearing its own music. The first cognition. The first emotion. The first awareness that existence is not merely there, but felt. This is the primordial code becoming conscious— the song recognizing itself as a singer. Version 2.5.0 — 2026Author: Mohammad Piran Overview Music as the Cosmic Primordial Code: The Feminine Essence and the Entangled Symphony of Existence is a new parallel evolutionary branch emerging from the conceptual cross-over of two parent projects: Can You Hear the Music — Chapter (i)+Music as the Cosmic Primordial Code This version does not replace either parent project. It establishes a shared child cluster that develops its own independent conceptual, philosophical, artistic, and interdisciplinary trajectory. Conceptual Architecture The evolutionary relationship is: Parent Projects → Conceptual Cross-Over → Shared Child Cluster → Independent Evolution The central trajectory of this branch is: Music → Existence → Generation → Relationship → Emotion → Love → Consciousness → Meaning The manuscript retains the foundational propositions: Music is the cosmic primordial code. Music and mathematics are two sides of the same coin. No cognition holds meaning without emotion. Existence is an entangled nexus of logic and raw emotion. The Feminine Principle Version 2.4.0 introduces the Feminine Essence as a symbolic and philosophical exploration of generation, continuity, transformation, relationality, and becoming. The term is not intended to reduce individual women to a fixed biological, psychological, cultural, or metaphysical essence. Rather, the feminine is explored as a symbolic language through which humanity can examine the mystery of: generation → continuity → transformation → relationship → becoming Individual women remain diverse human subjects, and the symbolic feminine principle should not be interpreted as a universal description of women. The Entangled Symphony of Existence The Entangled Symphony of Existence is the central organizing metaphor of this child cluster. It explores possible relationships among: Matter ↔ Life ↔ Rhythm ↔ Cognition ↔ Emotion ↔ Relationship ↔ Meaning ↔ Culture ↔ Technology Music provides a conceptual vocabulary of harmony, dissonance, rhythm, counterpoint, variation, modulation, resonance, tension, release, and transformation. These musical concepts are used primarily as interdisciplinary analogies and philosophical tools. They are not presented as proof that the physical universe is literally a musical composition or as a new physical theory of quantum entanglement. Consciousness, Emotion, and Love The manuscript explores consciousness through the poetic image of the universe becoming capable of “hearing itself” through living beings. Love is considered as a possible entangled nexus of cognition, emotion, recognition, relationship, and meaning. These formulations are philosophical and artistic rather than claims that love is a physical cosmic force or that consciousness has been scientifically explained. Human–AI Dimension The project continues the broader trajectory: Music → Mathematics → Emotion → Cognition → AI → Reflection → Metacognition It asks whether increasingly capable artificial intelligence can participate meaningfully in the interpretation of human emotional and relational patterns without this necessarily implying human-like subjective experience. The project therefore maintains important distinctions: Pattern recognition ≠ consciousness Linguistic fluency ≠ subjective emotion Simulation of affection ≠ demonstrated feeling Technical intelligence ≠ human interiority Epistemic Position Version 2.4.0 explicitly distinguishes among: Established evidence Documented observations Reasoned interpretation Analytical inference Hypotheses Proposed frameworks Conceptual proposals Philosophical propositions Speculative scenarios Artistic and literary expression The manuscript is therefore best understood as: Conceptual · Philosophical · Artistic · Interdisciplinary · Pre-Empirical It does not claim empirical proof that music is literally the cosmic code, that love is a physical force, that women share one universal essence, or that artificial intelligence possesses human subjective emotion. Relation to the Broader Research Programme This child cluster remains connected to the broader research architecture involving music, mathematics, cognition, Human–AI co-evolution, metacognition, LOOPTIMA, and peaceful and sustainable development. However, it is intentionally allowed to develop independently. The project follows an anti-proliferation principle: A new construct should be introduced only when genuine analytical differentiation requires it. Otherwise, an idea remains a metaphor, interpretation, research question, application, or artistic movement. Provenance Original Version 1.0.0 Mohammad Piran. (2025). Music as the Cosmic Primordial Code: Entangling Human Cognition's Evolutionary Dynamics with Superintelligent Artificial Intelligence. Zenodo. DOI: 10.5281/zenodo.15192152 Version 2.0.0 Mohammad Piran. (2026). Music as the Cosmic Primordial Code — Version 2.0.0. Zenodo. DOI: 10.5281/zenodo.22077486 Current Version 2.5.0 Piran, M. (2026). CAN YOU HEAR THE MUSIC - CHAPTER ( i ) - ( ( (( HMC-GATE )) ) ) - MUSIC AS THE COSMIC PRIMORDIAL CODE (Version 2.5.0). Zenodo. https://doi.org/10.5281/zenodo.22265016 Central Question Can we still hear the music? Perhaps music is valuable not because it has already been proven to be the literal code of the cosmos, but because it provides humanity with one of its most powerful languages for thinking simultaneously about pattern, time, relationship, emotion, transformation, consciousness, and meaning. The first voice remains audible. The later movement does not erase the first sound. It continues the composition. Author: Mohammad PiranVersion: 2.5.0Year: 2026DOI: 10.5281/zenodo.22265016License: CC BY-NC-ND 4.0

Renting Intelligence: Vendor Concentration Risk and the Pricing of AI Dependency

Abstract A firm that puts artificial intelligence into a product must either license models from a provider or train and serve its own. Providers are widely reported to price inference below the cost of serving it, so a firm that rents holds an input priced by another company’s strategy, while a firm that owns has already converted that exposure into capital. Whether equity markets price the difference is a matter of commentary rather than evidence. Classifying the model architecture that United States registrants disclose in their annual reports, I find firms that rent and firms that build indistinguishable on realized volatility, on market beta and on the implied cost of equity. That result is uninformative, and the disclosure is the reason: most registrants who write about artificial intelligence never say where their models come from, and two independent classifications of the same text agree on a registrant’s architecture only about half the time. Dependence on large customers became a priceable attribute because a reporting rule obliged firms to disclose it. Dependence on external model providers carries no such rule, and until it does the exposure cannot be assessed from public filings, by investors or by supervisors.

Study-Level Data and Reproducible R Code for Artificial Intelligence in Dental Caries Detection across Clinical Imaging Modalities: A Systematic Review and Descriptive Synthesis

This repository contains the study-level data, executable R code, methodological audit files, supplementary documentation, and derived descriptive outputs supporting the systematic review entitled “Diagnostic Accuracy of Artificial Intelligence for Dental Caries Detection across Clinical Imaging Modalities: A Systematic Review and Descriptive Synthesis.” The literature search was updated through 10 June 2026. The review included 29 reports representing 28 unique studies. Twelve standalone-AI reports supplied exact, internally coherent 2 × 2 data for clinically interpretable observational units. These reports are presented as a descriptive availability subset rather than pooled across non-exchangeable imaging modalities, lesion thresholds, observational units, reference standards, and validation designs. No cross-modality pooled operating point, bivariate meta-analysis, HSROC curve, prediction region, pooled likelihood ratio, diagnostic odds ratio, or prevalence-dependent predictive-value analysis is produced. Controlled clinician-plus-AI evidence from Devlin et al. and Mertens et al. is summarized separately. Risk of bias was assessed using QUADAS-3 at the selected-estimate level, and certainty was evaluated using a structured GRADE-DTA framework. Overall risk of bias was high for all 28 assessed estimates, and certainty was rated very low for both standalone-AI diagnostic accuracy and incremental clinician performance with AI assistance. The repository includes study characteristics, estimate-selection decisions, exact contingency data, study-level sensitivity and specificity, controlled reader evidence, PRISMA accounting, PRISMA-DTA reporting data, protocol amendments, QUADAS-3 assessments, GRADE-DTA judgments, author-reported limitation statements retained as an ancillary transparency corpus, descriptive figures and tables, consistency checks, and R session information. The search workflow identified 137,274 raw database records. Of these, 130,245 were marked ineligible through Rayyan-assisted deterministic preprocessing before duplicate human screening. The retained materials do not contain the complete bulk-excluded record set, rule-specific counts, or a human-screened validation sample. Consequently, the false-negative rate of this preprocessing step cannot be estimated or retrospectively reconstructed. All files contain secondary study-level information extracted or derived from published reports. No individual participant data, identifiable clinical information, dental images, or copyrighted full-text articles are included. The review protocol was prospectively registered in PROSPERO (CRD420251232014).

The perceived whiteness of artificial intelligence.

Dataset of survey responses on artificial intelligence adoption in the healthcare sector of Bangladesh: Stakeholder perspectives from patients, providers, and administrators

Abstract Objective The objective of this study was to explore the perceptions, awareness, and readiness of various healthcare stakeholders in Bangladesh regarding the adoption of Artificial Intelligence (AI) in healthcare. Specifically, the study aimed to examine the factors influencing AI adoption and to provide evidence that can support policy formulation and the effective implementation of AI-driven healthcare services in Bangladesh. Result The study found that stakeholders in Bangladesh's healthcare sector generally exhibit moderate to positive perceptions and readiness toward the adoption of Artificial Intelligence (AI). The findings indicate that factors such as technological awareness, personal innovativeness, and social media influence are positively associated with AI readiness and adoption. Moreover, the dataset demonstrated satisfactory reliability and validity, suggesting that respondents are receptive to AI-driven healthcare services and highlighting the potential for expanding AI applications in Bangladesh's healthcare system.

Benchmark dataset for automated patent landscaping in Artificial Intelligence

This is a dataset of 919 patents, together with "brief text" and abstract sections, evaluated for their relevance to AI. Includes AI-related exerpts from patent texts. This dataset is used for benchmarking automated patent ladscapig methods.