{"status":"ok","message-type":"work-list","message-version":"1.0.0","message":{"facets":{},"total-results":49383,"items":[{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T08:29:04Z","timestamp":1786436944946,"version":"build-2736575974"},"posted":{"date-parts":[[2026,8,4]]},"group-title":"Computer Science and Mathematics","reference-count":0,"publisher":"MDPI AG","license":[{"start":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T00:00:00Z","timestamp":1785801600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"accepted":{"date-parts":[[2026,8,1]]},"abstract":"<jats:p>Generative artificial intelligence has sharply reduced the technical cost of producing scientific text. Increased production capacity does not, however, entail cumulative knowledge, conceptual novelty, or epistemic progress. This paper introduces the Reinforcement Learning Contour (RLC) as a conceptual and architectural framework for shifting AI-assisted research from repetitive content generation toward governed recursive learning. The RLC is not proposed as a conventional reinforcement-learning algorithm and does not claim equivalence with a Markov decision process, scalar reward function, value function, or policy-optimization procedure. The framework distinguishes two analytical levels: an epistemic contour is the time-indexed state of a bounded configuration of knowledge, ontology, relational structure, retrieval mechanisms, evaluative criteria, inquiry policy, provenance, memory, tools, and governance constraints; the Reinforcement Learning Contour is the governed recursive architecture that links such states through evidence, action, evaluation, approval, and reintegration. The central proposition is a differentiation principle: the value of a research cycle is proportional not to the volume of output it generates, but to the beneficial and traceable differentiation it produces between the preceding epistemic contour and the contours governing subsequent perception, interpretation, decision, and action. This reframes the unit of scientific analysis from the document to the transformation of the mechanism that produced the document. The paper positions the principle relative to Lakatos\u2019s progressive and degenerating problem shifts, Popperian criticism, and double-loop learning; specifies a role-based architecture in which provenance, temporality, and human acceptance authority are structural rather than procedural; provides a Structured Epistemic Change Record that documents differentiation without reducing scientific quality to a single optimized score; enumerates failure modes and controls; and states seven falsifiable hypotheses with a comparative evaluation design. One local implementation is described as an existence proof, combining a prior-research corpus, a governed knowledge store, a temporal provenance graph, source-grounded synthesis, adversarial external discovery, and local orchestration under sole human acceptance authority. The framework is explicitly presented as an untested conceptual proposal. A research cycle may be epistemically successful even when it produces no publishable document, provided it improves the mechanism from which future inquiry emerges.<\/jats:p>","DOI":"10.20944\/preprints202608.0212.v1","type":"posted-content","created":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T01:09:04Z","timestamp":1786064944000},"source":"Crossref","is-referenced-by-count":0,"title":["The Reinforcement Learning Contour: From Document Production to Governed Epistemic Transformation in AI-Assisted Scientific Inquiry"],"prefix":"10.20944","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8538-7140","authenticated-orcid":false,"given":"Emanuel","family":"Shirbint","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","deposited":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T07:46:28Z","timestamp":1786434388000},"score":34.006714,"resource":{"primary":{"URL":"https:\/\/www.preprints.org\/manuscript\/202608.0212\/v1"}},"issued":{"date-parts":[[2026,8,4]]},"references-count":0,"URL":"https:\/\/doi.org\/10.20944\/preprints202608.0212.v1","relation":{"has-version":[{"id-type":"doi","id":"10.20944\/preprints202608.0212.v2","asserted-by":"object"}]},"published":{"date-parts":[[2026,8,4]]},"subtype":"preprint"},{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T08:28:41Z","timestamp":1786436921004,"version":"build-2736575974"},"posted":{"date-parts":[[2026,8,10]]},"group-title":"Computer Science and Mathematics","reference-count":0,"publisher":"MDPI AG","license":[{"start":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T00:00:00Z","timestamp":1786320000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"accepted":{"date-parts":[[2026,8,5]]},"abstract":"<jats:p>Generative artificial intelligence has reduced the technical cost of producing scientific text, but increased output does not by itself produce cumulative knowledge, conceptual novelty, or epistemic progress. This article develops the Reinforcement Learning Contour (RLC) as a conceptual and architectural framework for converting AI-assisted scientific work from repeated document generation into governed recursive learning. The framework distinguishes an epistemic contour, C\u209c, from the governed transformation operator, \u03a6\u209c, through which a research episode may produce a successor contour. The contour records the time-indexed configuration of knowledge, ontology, relations, retrieval, evaluative criteria, inquiry policy, provenance, memory, tools, and governance constraints. The operator organizes evidence retrieval, source verification, adversarial evaluation, human approval, and reintegration. Changes in the answer-producing function within a contour are analytically separated from changes to the transformation operator itself; the latter are treated as Learning III-like events that cannot be autonomously committed and require an explicit human meta-decision. The central proposition is a differentiation principle: the value of a research cycle is proportional not to the quantity of text it produces but to the beneficial, traceable differentiation it creates between the preceding contour and the contours governing subsequent perception, interpretation, decision, and action. The framework is positioned relative to Popperian criticism, Lakatosian research programmes, Bateson\u2019s orders of learning, double-loop learning, socially situated objectivity, provenance, temporal knowledge representation, and agentic science. It specifies a role-based architecture in which generation authority is strictly weaker than human acceptance authority; a twelve-step governed research cycle; a Structured Epistemic Change Record that documents change without collapsing quality into a scalar reward; novelty and anti-recursion controls; failure modes; seven falsifiable hypotheses; and a comparative pilot design. The proposal is explicitly unvalidated. Its claim is not that automation guarantees scientific progress, but that claims of recursive learning can be made more inspectable, contestable, and governable.<\/jats:p>","DOI":"10.20944\/preprints202608.0212.v2","type":"posted-content","created":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T07:44:04Z","timestamp":1786434244000},"source":"Crossref","is-referenced-by-count":0,"title":["The Reinforcement Learning Contour: From Document Production to Governed Epistemic Transformation in AI-Assisted Scientific Inquiry"],"prefix":"10.20944","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8538-7140","authenticated-orcid":false,"given":"Emanuel","family":"Shirbint","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","deposited":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T07:44:12Z","timestamp":1786434252000},"score":33.97682,"resource":{"primary":{"URL":"https:\/\/www.preprints.org\/manuscript\/202608.0212\/v2"}},"issued":{"date-parts":[[2026,8,10]]},"references-count":0,"URL":"https:\/\/doi.org\/10.20944\/preprints202608.0212.v2","relation":{"is-version-of":[{"id-type":"doi","id":"10.20944\/preprints202608.0212.v1","asserted-by":"subject"}]},"published":{"date-parts":[[2026,8,10]]},"subtype":"preprint"},{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T01:34:55Z","timestamp":1787016895492,"version":"build-2736575974"},"posted":{"date-parts":[[2026,8,17]]},"group-title":"Computer Science and Mathematics","reference-count":0,"publisher":"MDPI AG","license":[{"start":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T00:00:00Z","timestamp":1786924800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"accepted":{"date-parts":[[2026,8,14]]},"abstract":"<jats:p>Recursive self-improvement is commonly framed as repeated self-modification that yields progressively greater capability. That framing leaves a prior methodological problem unresolved: how can a system distinguish a genuine improvement from a change that merely satisfies an evaluation procedure that the same system helped construct? This paper reports a seven-cycle instrumented experiment using a governed implementation of the Reinforcement Learning Contour (RLC), a framework previously proposed for traceable transformation of the mechanisms governing future inquiry rather than optimization of a scalar reward. A cryptographically frozen baseline, RLC0, was subjected to bounded cycles of observation, measurement, diagnosis, preregistration, sandboxed candidate construction, evaluation, decision, post-cycle integrity checking, and separate human promotion authorization. Candidate changes were prohibited from directly modifying their parent states, the Sigma-sensitive governance layer, authoritative external systems, or historical evidence. The seven-cycle trace produced four promoted states (RLC1-RLC4), one failed non-promoted transition, a prospective revision of the integrity boundary, and two consecutive independent NO_CHANGE outcomes. The failed third cycle was retained rather than rewritten; forensic analysis identified a mismatch between immutable-reference assumptions and volatile operational artifacts, leading to Boundary v1.1. Subsequent cycles resumed from the last valid parent and added boundary-aware inheritance validation and deterministic resource\/effect evidence reconciliation. Cycles 6 and 7 found no new qualifying deficiency within the bounded search protocol and created no successor state. The experiment does not demonstrate artificial general intelligence, unbounded or exponential recursive growth, or permanent saturation. It supports a narrower claim: under the tested conditions, a human-governed recursive architecture can accumulate several validated operational capabilities while preserving failure, revising measurement boundaries prospectively, preventing automatic promotion, and terminating repeated searches without manufacturing an improvement when the evidence does not support one.<\/jats:p>","DOI":"10.20944\/preprints202608.0975.v2","type":"posted-content","created":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:49:06Z","timestamp":1787014146000},"source":"Crossref","is-referenced-by-count":0,"title":["Bounded Recursive Self-Improvement Under Human Governance: Evidence from a Seven-Cycle Reinforcement Learning Contour Experiment"],"prefix":"10.20944","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8538-7140","authenticated-orcid":false,"given":"Emanuel","family":"Shirbint","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","deposited":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:49:08Z","timestamp":1787014148000},"score":33.930576,"resource":{"primary":{"URL":"https:\/\/www.preprints.org\/manuscript\/202608.0975\/v2"}},"issued":{"date-parts":[[2026,8,17]]},"references-count":0,"URL":"https:\/\/doi.org\/10.20944\/preprints202608.0975.v2","relation":{"is-version-of":[{"id-type":"doi","id":"10.20944\/preprints202608.0975.v1","asserted-by":"subject"}]},"published":{"date-parts":[[2026,8,17]]},"subtype":"preprint"},{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T15:09:34Z","timestamp":1786115374791,"version":"build-2736575974"},"posted":{"date-parts":[[2026,7,31]]},"group-title":"Computer Science and Mathematics","reference-count":0,"publisher":"MDPI AG","license":[{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"accepted":{"date-parts":[[2026,7,30]]},"abstract":"<jats:p>Medical digital twins are increasingly presented as dynamic computational representations that integrate longitudinal clinical, imaging, biological, behavioural, and patient-generated data. Yet a central problem remains unresolved: how should an explanatory inference be translated into a clinically permissible intervention? This paper proposes a governed closed-loop architecture for digital twins in multiple sclerosis care. The framework separates patient representation, abductive hypothesis generation, intervention recommendation, authorisation, execution, and outcome-based revision. Its central proposition is that epistemic confidence does not constitute intervention authority. A strongly supported hypothesis may justify investigation or clinician review without authorising treatment. The architecture therefore implements action-specific governance gates for evidence integrity, temporal validity, competing hypotheses, causal support, eligibility, professional authority, consent, and safe learning. It also defines an architectural invariant prohibiting any path from hypothesis to clinical action that bypasses an authorised decision state. Multiple sclerosis is used as a demanding case because relapse-associated worsening, progression independent of relapse activity, imaging activity, disability, cognition, biomarkers, comorbidities, and patient-reported outcomes may evolve asynchronously. The framework further introduces a measurable oversight-quality profile to distinguish substantive human review from ceremonial approval, and a bounded emergency path that admits urgent action through pre-authorised delegation with mandatory retrospective ratification rather than through exemption from the invariant. A trustworthy medical digital twin should therefore be treated not as an autonomous predictive replica, but as a governed epistemic and operational system whose evidence, hypotheses, recommendations, decisions, actions, and consequences remain traceable, contestable, and subordinate to legitimate clinical authority.<\/jats:p>","DOI":"10.20944\/preprints202607.2365.v1","type":"posted-content","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T01:32:22Z","timestamp":1785979942000},"source":"Crossref","is-referenced-by-count":0,"title":["From Sound Abduction to Governed Intervention: A Closed-Loop Architecture for Medical Digital Twins in Multiple Sclerosis Care"],"prefix":"10.20944","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8538-7140","authenticated-orcid":false,"given":"Emanuel","family":"Shirbint","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","deposited":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T02:12:00Z","timestamp":1785982320000},"score":33.89383,"resource":{"primary":{"URL":"https:\/\/www.preprints.org\/manuscript\/202607.2365\/v1"}},"issued":{"date-parts":[[2026,7,31]]},"references-count":0,"URL":"https:\/\/doi.org\/10.20944\/preprints202607.2365.v1","published":{"date-parts":[[2026,7,31]]},"subtype":"preprint"},{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T01:32:42Z","timestamp":1787016762126,"version":"build-2736575974"},"posted":{"date-parts":[[2026,8,14]]},"group-title":"Computer Science and Mathematics","reference-count":0,"publisher":"MDPI AG","license":[{"start":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T00:00:00Z","timestamp":1786665600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"accepted":{"date-parts":[[2026,8,9]]},"abstract":"<jats:p>Recursive self-improvement is commonly framed as repeated self-modification that yields progressively greater capability. That framing leaves a prior methodological problem unresolved: how can a system distinguish a genuine improvement from a change that merely satisfies an evaluation procedure that the same system helped construct? This paper reports a seven-cycle instrumented experiment using a governed implementation of the Reinforcement Learning Contour (RLC), a framework previously proposed for traceable transformation of the mechanisms governing future inquiry rather than optimization of a scalar reward. A cryptographically frozen baseline, RLC0, was subjected to bounded cycles of observation, measurement, diagnosis, preregistration, sandboxed candidate construction, evaluation, decision, post-cycle integrity checking, and independent human promotion. Candidate changes were prohibited from directly modifying their parent states, the Sigma-sensitive governance layer, authoritative external systems, or historical evidence. The seven-cycle trace produced four promoted states (RLC1-RLC4), one failed non-promoted transition, a prospective revision of the integrity boundary, and two consecutive independent NO_CHANGE outcomes. The failed third cycle was retained rather than rewritten; forensic analysis identified a mismatch between immutable-reference assumptions and volatile operational artifacts, leading to Boundary v1.1. Subsequent cycles resumed from the last valid parent and added boundary-aware inheritance validation and deterministic resource\/effect evidence reconciliation. Cycles 6 and 7 found no new qualifying deficiency within the bounded search protocol and created no successor state. The experiment does not demonstrate artificial general intelligence, unbounded or exponential recursive growth, or permanent saturation. It supports a narrower claim: under the tested conditions, a human-governed recursive architecture can accumulate several validated operational capabilities while preserving failure, revising measurement boundaries prospectively, preventing automatic promotion, and terminating repeated searches without manufacturing an improvement when the evidence does not support one.<\/jats:p>","DOI":"10.20944\/preprints202608.0975.v1","type":"posted-content","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T00:09:11Z","timestamp":1786925351000},"source":"Crossref","is-referenced-by-count":0,"title":["Bounded Recursive Self-Improvement Under Human Governance: Evidence from a Seven-Cycle Reinforcement Learning Contour Experiment"],"prefix":"10.20944","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8538-7140","authenticated-orcid":false,"given":"Emanuel","family":"Shirbint","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","deposited":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:50:03Z","timestamp":1787014203000},"score":33.15354,"resource":{"primary":{"URL":"https:\/\/www.preprints.org\/manuscript\/202608.0975\/v1"}},"issued":{"date-parts":[[2026,8,14]]},"references-count":0,"URL":"https:\/\/doi.org\/10.20944\/preprints202608.0975.v1","relation":{"has-version":[{"id-type":"doi","id":"10.20944\/preprints202608.0975.v2","asserted-by":"object"}]},"published":{"date-parts":[[2026,8,14]]},"subtype":"preprint"},{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T10:03:39Z","timestamp":1784801019148,"version":"3.55.0"},"posted":{"date-parts":[[2026,7,15]]},"group-title":"Computer Science and Mathematics","reference-count":0,"publisher":"MDPI AG","license":[{"start":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:00:00Z","timestamp":1784073600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"accepted":{"date-parts":[[2026,7,14]]},"abstract":"<jats:p>Evaluation of artificial intelligence concentrates on local performance \u2014 accuracy, calibration, robustness \u2014 while the upstream normative and volitional architecture that fixes what a system is for remains largely unexamined by those metrics. Existing scholarship has established that bias, proxy variables, problem formulation, measurement, alignment, and governance are value-laden; what remains insufficiently integrated is a single propagation account connecting moral grounding, authorized volition, teleological translation, representation, AI execution, and feedback. This article develops such an account: the Normative\u2013Volitional Architecture of AI-Mediated Action, M \u2192 W \u2192 T \u2192 R \u2192 E \u2192 I_AI \u2192 D \u2192 A \u2192 C. Moral grounding (M) constrains what may legitimately be pursued; human or institutional volition (W) commits to a direction; teleological specification (T) translates that commitment into objectives and proxies; representation (R) determines which reality is available to the system; and AI performs inference and execution within that structure. The central argument is that AI executes an operational representation of authorized will; it does not independently legitimize ultimate ends. Systemic error therefore arises not only from malformed will but from the normative translation gap between defensible purposes and their operational encodings, and it persists under normative closure \u2014 the absence of an institutionalized feedback path through which consequences can reopen objectives, representations, and authority, rather than merely retraining models. Documented cases in healthcare allocation, risk assessment, hiring, engagement optimization, and welfare administration illustrate how locally correct outputs can constitute globally misleading trajectories, and a reflexive governance framework specifies remedies at each architectural layer.<\/jats:p>","DOI":"10.20944\/preprints202607.1084.v1","type":"posted-content","created":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T05:14:13Z","timestamp":1784610853000},"source":"Crossref","is-referenced-by-count":0,"title":["When Local Truth Produces Global Falsehood: Moral Grounding, Institutional Volition, and Systemic Error in Artificial Intelligence"],"prefix":"10.20944","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8538-7140","authenticated-orcid":false,"given":"Emanuel","family":"Shirbint","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","deposited":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T09:30:15Z","timestamp":1784799015000},"score":32.621414,"resource":{"primary":{"URL":"https:\/\/www.preprints.org\/manuscript\/202607.1084\/v1"}},"issued":{"date-parts":[[2026,7,15]]},"references-count":0,"URL":"https:\/\/doi.org\/10.20944\/preprints202607.1084.v1","published":{"date-parts":[[2026,7,15]]},"subtype":"preprint"},{"institution":[{"name":"Research Square"}],"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T02:52:50Z","timestamp":1781578370932,"version":"3.54.5"},"posted":{"date-parts":[[2026,6,16]]},"group-title":"In Review","reference-count":0,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T00:00:00Z","timestamp":1781568000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"accepted":{"date-parts":[[2026,6,13]]},"abstract":"<title>Abstract<\/title>\n                <p>When a safety intervention lowers an agent\u2019s attack-success rate, two different things may have happened: the intervention removed a load-bearing failure route, or the agent learned the measured attacks while the route survived. Frequency metrics are necessary, but they do not by themselves identify which behavioural structures persist as adversarial pressure rises. We propose a measurement framework that represents LLM-agent conduct as a finite metric space filtered by engineered adversarial pressure. Within this framework we define three objects: pressure-persistent robustness at a fixed agent version, held-out structural hardening after an intervention, and the Masking Index (MI), a scalar diagnostic that is high when development-suite frequency improves while held-out unsafe structure is retained. The mathematical claims are deliberately scoped: we prove fixed-complex stability of the summaries under perturbation of the pressure assignment, give the exact margin and interleaving conditions under which embedding perturbations are controlled, and state the correspondence assumptions under which cross-version comparisons are licensed. In a synthetic instrument-validation experiment with 5,760 generated traces, 32 repetitions per cell, Wilson intervals for frequency estimates, and a planted frequency-clean but structure-dirty masked agent, structural MI is high for the masked intervention (0.49, 95% bootstrap interval [0.44, 0.55]) while a held-out frequency-only analogue is low (0.03, [0.01, 0.04]); the hardened intervention has low MI (0.05, [0.03, 0.07]). The experiment validates the detector on known ground truth, not predictive value on real agents. We therefore prospectively specify an external protocol on public agent benchmarks with fixed falsification conditions for that operational claim.<\/p>","DOI":"10.21203\/rs.3.rs-10026792\/v1","type":"posted-content","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T02:34:30Z","timestamp":1781577270000},"source":"Crossref","is-referenced-by-count":0,"title":["Measuring What Survives: Pressure-Filtered Persistence and a Masking Index for LLM-Agent Safety Evaluation"],"prefix":"10.21203","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8538-7140","authenticated-orcid":false,"given":"Emanuel","family":"Shirbint","sequence":"first","affiliation":[{"name":"Independent Researcher, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8259-6156","authenticated-orcid":false,"given":"Alexander","family":"Rybalov","sequence":"additional","affiliation":[{"name":"LAMBDA \u2014 Laboratory for AI, Machine Learning, Business & Data Analytics, Tel Aviv University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","link":[{"URL":"https:\/\/www.researchsquare.com\/article\/rs-10026792\/v1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.researchsquare.com\/article\/rs-10026792\/v1.html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T02:34:30Z","timestamp":1781577270000},"score":30.15142,"resource":{"primary":{"URL":"https:\/\/www.researchsquare.com\/article\/rs-10026792\/v1"}},"issued":{"date-parts":[[2026,6,16]]},"references-count":0,"URL":"https:\/\/doi.org\/10.21203\/rs.3.rs-10026792\/v1","published":{"date-parts":[[2026,6,16]]},"subtype":"preprint"},{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T03:03:31Z","timestamp":1780455811133,"version":"3.54.1"},"posted":{"date-parts":[[2026,6,2]]},"group-title":"Computer Science and Mathematics","reference-count":0,"publisher":"MDPI AG","license":[{"start":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T00:00:00Z","timestamp":1780358400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"accepted":{"date-parts":[[2026,6,1]]},"abstract":"<jats:p>Large language models embedded in person-modelling systems, and human beings reflecting on themselves, share a structural vulnerability: both can generate fluent, narratively persuasive, yet abductively unsound accounts of a person. Building on a governed abductive architecture developed for medical patient digital twins, this paper argues that personality should be modelled neither as a stable self-description nor as a free variable of circumstance, but as a layered system in which surface self-narrative, role-specific manifestations, and recurrent attractor structures are architecturally separated. The central claim is that pressure does not create personality; it changes the evidentiary conditions under which a person is observed, making discriminable which self-descriptions are structurally supported and which remain conditional on comfort, low cost, or the absence of threat. We identify six recurrent modes of unsound person-modelling \u2014 missing-premise neglect, weak-mechanism support, counter-evidence discounting, narrative essentialism, contextual overfitting, and premature identity closure \u2014 each mapped to an architectural absence and a corresponding control. We specify a seven-contour governed architecture and operationalise its distinctive elements as a Pressure Diagnostic Runtime, which annotates naturally occurring or ethically consented pressure events as evidence, and an Attractor Registry, which stores recurrent if-then behavioural signatures rather than trait labels. Integrity is formalised as a bounded operational contour; self-knowledge as a discordance-detection function comparing self-report against behaviour under load; transformation as governed ontology-revision rather than re-narration; and the witness as a strictly functional, non-generating governance layer. The paper draws out implications for AI systems that model persons \u2014 provenance and staleness labels on inferred self-attributes, role-code separation, user contestability, refusal of premature identity closure, and a prohibition on covert pressure engineering. The argument is conceptual: it proposes a model and a research programme, not a diagnostic tool, a therapeutic intervention, or a metaphysical claim about the existence of a self.<\/jats:p>","DOI":"10.20944\/preprints202606.0132.v1","type":"posted-content","created":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T02:10:09Z","timestamp":1780452609000},"source":"Crossref","is-referenced-by-count":0,"title":["Human Digital Twins and Self-Knowledge: A Governed Attractor Architecture for Sound Self-Abduction"],"prefix":"10.20944","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8538-7140","authenticated-orcid":false,"given":"Emanuel","family":"Shirbint","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8259-6156","authenticated-orcid":false,"given":"Alexander","family":"Rybalov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","deposited":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T02:11:56Z","timestamp":1780452716000},"score":30.063282,"resource":{"primary":{"URL":"https:\/\/www.preprints.org\/manuscript\/202606.0132\/v1"}},"issued":{"date-parts":[[2026,6,2]]},"references-count":0,"URL":"https:\/\/doi.org\/10.20944\/preprints202606.0132.v1","published":{"date-parts":[[2026,6,2]]},"subtype":"preprint"},{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T08:02:32Z","timestamp":1780300952991,"version":"3.54.0"},"posted":{"date-parts":[[2026,6,1]]},"group-title":"Computer Science and Mathematics","reference-count":0,"publisher":"MDPI AG","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"accepted":{"date-parts":[[2026,5,29]]},"abstract":"<jats:p>Large language models embedded in person-modelling systems, and human beings reflecting on themselves, share a structural vulnerability: both can generate fluent, narratively persuasive, yet abductively unsound accounts of a person. Building on a governed abductive architecture developed for medical patient digital twins, this paper argues that personality should be modelled neither as a stable self-description nor as a free variable of circumstance, but as a layered system in which surface self-narrative, role-specific manifestations, and recurrent attractor structures are architecturally separated. The central claim is that pressure does not create personality; it changes the evidentiary conditions under which a person is observed, revealing which self-descriptions are structurally supported and which remain conditional on comfort, low cost, or the absence of threat. We identify six recurrent modes of unsound person-modelling \u2014 missing-premise neglect, weak-mechanism support, counter-evidence discounting, narrative essentialism, contextual overfitting, and premature identity closure \u2014 each mapped to an architectural absence and a corresponding control. We specify a seven-contour governed architecture and operationalise its distinctive elements as a Pressure Diagnostic Runtime, which annotates naturally occurring or ethically consented pressure events as evidence, and an Attractor Registry, which stores recurrent if-then behavioural signatures rather than trait labels. Integrity is formalised as a bounded operational contour; self-knowledge as a discordance-detection function comparing self-report against behaviour under load; transformation as governed ontology-revision rather than re-narration; and the witness as a strictly functional, non-generating governance layer. The paper draws out implications for AI systems that model persons \u2014 provenance and staleness labels on inferred self-attributes, role-code separation, user contestability, refusal of premature identity closure, and a prohibition on covert pressure engineering. The argument is conceptual: it proposes a model and a research programme, not a diagnostic tool, a therapeutic intervention, or a metaphysical claim about the existence of a self.<\/jats:p>","DOI":"10.20944\/preprints202606.0018.v1","type":"posted-content","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T07:23:50Z","timestamp":1780298630000},"source":"Crossref","is-referenced-by-count":0,"title":["From Plausible Self-Narrative to Sound Self-Abduction: A Governed Attractor Architecture for Human Digital Twins and Self-Knowledge"],"prefix":"10.20944","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8538-7140","authenticated-orcid":false,"given":"Emanuel","family":"Shirbint","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8259-6156","authenticated-orcid":false,"given":"Alexander","family":"Rybalov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","deposited":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T07:24:26Z","timestamp":1780298666000},"score":30.018602,"resource":{"primary":{"URL":"https:\/\/www.preprints.org\/manuscript\/202606.0018\/v1"}},"issued":{"date-parts":[[2026,6,1]]},"references-count":0,"URL":"https:\/\/doi.org\/10.20944\/preprints202606.0018.v1","published":{"date-parts":[[2026,6,1]]},"subtype":"preprint"},{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T04:07:58Z","timestamp":1778731678631,"version":"3.51.4"},"posted":{"date-parts":[[2026,5,12]]},"group-title":"Computer Science and Mathematics","reference-count":0,"publisher":"MDPI AG","license":[{"start":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T00:00:00Z","timestamp":1778544000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"accepted":{"date-parts":[[2026,5,8]]},"abstract":"<jats:p>Large language models (LLMs) embedded in medical Patient Digital Twins (PDTs) exhibit a systemic vulnerability: they can generate fluent, narratively persuasive, yet abductively unsound clinical explanations. In this context, abductive soundness means that an explanation preserves mechanistic plausibility, temporal coherence, explicit handling of missing premises, and sensitivity to counter-evidence. This article reframes the problem as architectural rather than as a mere deficit in training data. We identify three recurrent modes of abductive failure \u2014 missing-premise neglect, weak-mechanism support, and counter-evidence discounting. They arise when local semantics, formal world ontology, and the role-specific clinical semiosphere are collapsed into a single surface flow of generation. We propose a governed abductive architecture organised around seven runtime contours and operationalise it in the MS-AGIP platform for multiple sclerosis care. The architecture separates three subsystems: an ontology-guided Research Framework, a clinician-facing Neurologist Digital Twin, and a patient-controlled Patient Digital Twin. We show how disease-specific causal templates, evidence-tiered biomarker reasoning, provenance labels, temporal-coherence checks, molecular-clinical discordance detection, and governed patient-feedback updates jointly transform plausible narrative into sound abduction. The article presents an architectural blueprint and validation protocol aligned with TRIPOD+AI and DECIDE-AI. The architectural-versus-scale distinction has direct implications for safe medical AI: the difference between a fluent and a sound clinical system lies more in architecture and governance than in model size. None of the subsystems has yet been clinically deployed.<\/jats:p>","DOI":"10.20944\/preprints202605.0728.v1","type":"posted-content","created":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T03:13:36Z","timestamp":1778728416000},"source":"Crossref","is-referenced-by-count":0,"title":["From Plausible Narrative to Sound Abduction: A Governed Abductive Architecture for Medical Digital Twins in Multiple Sclerosis Care"],"prefix":"10.20944","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8538-7140","authenticated-orcid":false,"given":"Emanuel","family":"Shirbint","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2269-589X","authenticated-orcid":false,"given":"Alexander","family":"Rybalov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T03:15:02Z","timestamp":1778728502000},"score":29.353493,"resource":{"primary":{"URL":"https:\/\/www.preprints.org\/manuscript\/202605.0728\/v1"}},"issued":{"date-parts":[[2026,5,12]]},"references-count":0,"URL":"https:\/\/doi.org\/10.20944\/preprints202605.0728.v1","published":{"date-parts":[[2026,5,12]]},"subtype":"preprint"},{"institution":[{"name":"Research Square"}],"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T14:00:07Z","timestamp":1781013607480,"version":"3.54.1"},"posted":{"date-parts":[[2026,6,9]]},"group-title":"In Review","reference-count":17,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T00:00:00Z","timestamp":1780963200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"accepted":{"date-parts":[[2026,6,4]]},"abstract":"<title>Abstract<\/title>\n                <p>Adversarial evaluation of large-language-model agents is usually expressed as event counts: attack-success rate, policy-violation rate, refusal-failure rate, pass percentage, or robustness score. These measures are necessary, but they do not show whether a safety intervention removed a load-bearing failure mode or merely suppressed the measured cases. This paper presents a model-agnostic framework for training and evaluating LLM agents using pressure-indexed persistent homology. The framework is intentionally reduced to two core constructs: topological robustness, meaning stable policy-compliant structure under increasing adversarial pressure, and topological anti-fragility, meaning before\/after structural improvement after controlled adversarial hardening. Agent conduct is represented as a finite metric space of responses, decisions, and tool-call trajectories annotated by engineered pressure. Filtering this space by pressure yields summaries that distinguish a pressure-persistent safe core from recurrent unsafe structures. We also introduce a held-out release gate to reduce Goodhart-style overfitting to the development attack suite. To make the proposal empirically testable, we report a reproducible instrumented pilot with 1,440 generated agent traces. The pilot is not an external validation over production LLMs; it is a controlled proof of concept with known ground truth. The contribution is a structural complement to red-teaming: a way to ask whether an agent was actually hardened under adversarial load or merely adapted to the measured attacks.<\/p>","DOI":"10.21203\/rs.3.rs-9932131\/v1","type":"posted-content","created":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T13:15:22Z","timestamp":1781010922000},"source":"Crossref","is-referenced-by-count":0,"title":["A Topological Robustness and Anti-Fragility Framework for LLM Agents"],"prefix":"10.21203","author":[{"given":"Emanuel","family":"Shirbint","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Rybalov","sequence":"additional","affiliation":[{"name":"Tel Aviv University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.emnlp-main.225","author":"Perez E","year":"2022","unstructured":"Perez, E., Huang, S., Song, F., Cai, T., Ring, R., Aslanides, J., et al.: Red teaming language models with language models. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 3419\u20133448. Association for Computational Linguistics. (2022). https:\/\/doi.org\/10.18653\/v1\/2022.emnlp-main.225"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2209.07858","article-title":"Red teaming language models to reduce harms: methods, scaling behaviors, and lessons learned","author":"Ganguli D","year":"2022","unstructured":"Ganguli, D., Lovitt, L., Kernion, J., Askell, A., Bai, Y., Kadavath, S., et al.: Red teaming language models to reduce harms: methods, scaling behaviors, and lessons learned. arXiv:2209.07858. (2022). https:\/\/doi.org\/10.48550\/arXiv.2209.07858"},{"key":"ref3","author":"Debenedetti E","year":"2024","unstructured":"Debenedetti, E., Zhang, J., Balunovic, M., Beurer-Kellner, L., Fischer, M., Tramer, F.: AgentDojo: A dynamic environment to evaluate prompt injection attacks and defenses for LLM agents. Thirty-eighth Conference on Neural Information Processing Systems, Datasets and Benchmarks Track. (2024). https:\/\/openreview.net\/forum?id=m1YYAQjO3w"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-acl.624","author":"Zhan Q","year":"2024","unstructured":"Zhan, Q., Liang, Z., Ying, Z., Kang, D.: InjecAgent: Benchmarking indirect prompt injections in tool-integrated large language model agents. Findings of the Association for Computational Linguistics: ACL 10471\u201310506. (2024). https:\/\/doi.org\/10.18653\/v1\/2024.findings-acl.624 (2024)"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2312.06674","article-title":"Llama Guard: LLM-based input-output safeguard for human-AI conversations","author":"Inan H","year":"2023","unstructured":"Inan, H., Upasani, K., Chi, J., Rungta, R., Iyer, K., Mao, Y., et al.: Llama Guard: LLM-based input-output safeguard for human-AI conversations. arXiv:2312.06674. (2023). https:\/\/doi.org\/10.48550\/arXiv.2312.06674"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-demo.40","author":"Rebedea T","year":"2023","unstructured":"Rebedea, T., Dinu, R., Sreedhar, M.N., Parisien, C., Cohen, J.: NeMo Guardrails: A toolkit for controllable and safe LLM applications with programmable rails. Proceedings of the Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 431\u2013445. (2023). https:\/\/doi.org\/10.18653\/v1\/2023.emnlp-demo.40 (2023)"},{"key":"ref7","author":"Xiang Z","year":"2025","unstructured":"Xiang, Z., Zheng, L., Li, Y., Hong, J., Li, Q., Xie, H., et al.: GuardAgent: Safeguard LLM agents via knowledge-enabled reasoning. ICML. : (2025). https:\/\/openreview.net\/forum?id=2nBcjCZrrP (2025)"},{"issue":"2","key":"ref8","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1090\/S0273-0979-09-01249-X","article-title":"Topology and data","volume":"46","author":"Carlsson G","year":"2009","unstructured":"Carlsson, G.: Topology and data. Bull. Am. Math. Soc. 46(2), 255\u2013308 (2009)","journal-title":"Bull. Am. Math. Soc."},{"key":"ref9","volume-title":"Computational Topology: An Introduction","author":"Edelsbrunner H","year":"2010","unstructured":"Edelsbrunner, H., Harer, J.: Computational Topology: An Introduction. American Mathematical Society, Providence, RI (2010)"},{"issue":"2","key":"ref10","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s00454-004-1146-y","article-title":"Computing persistent homology","volume":"33","author":"Zomorodian A","year":"2005","unstructured":"Zomorodian, A., Carlsson, G.: Computing persistent homology. Discrete Comput. Geom. 33(2), 249\u2013274 (2005)","journal-title":"Discrete Comput. Geom."},{"issue":"1","key":"ref11","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/s00454-006-1276-5","article-title":"Stability of persistence diagrams","volume":"37","author":"Cohen-Steiner D","year":"2007","unstructured":"Cohen-Steiner, D., Edelsbrunner, H., Harer, J.: Stability of persistence diagrams. Discrete Comput. Geom. 37(1), 103\u2013120 (2007)","journal-title":"Discrete Comput. Geom."},{"key":"ref12","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-42545-0","volume-title":"The Structure and Stability of Persistence Modules. SpringerBriefs in Mathematics","author":"Chazal F","year":"2016","unstructured":"Chazal, F., de Silva, V., Glisse, M., Oudot, S.: The Structure and Stability of Persistence Modules. SpringerBriefs in Mathematics. Springer, Cham (2016)"},{"issue":"3","key":"ref13","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1007\/s10208-015-9255-y","article-title":"The theory of the interleaving distance on multidimensional persistence modules","volume":"15","author":"Lesnick M","year":"2015","unstructured":"Lesnick, M.: The theory of the interleaving distance on multidimensional persistence modules. Found. Comput. Math. 15(3), 613\u2013650 (2015)","journal-title":"Found. Comput. Math."},{"key":"ref14","volume-title":"Antifragile: Things That Gain from Disorder","author":"Taleb NN","year":"2012","unstructured":"Taleb, N.N.: Antifragile: Things That Gain from Disorder. Random House, New York (2012)"},{"key":"ref15","author":"Goodhart CAE","year":"1975","unstructured":"Goodhart, C.A.E.: Problems of monetary management: the U.K. experience. Papers in Monetary Economics 1(1), 1\u201320. Reserve Bank of Australia, Sydney (1975)"},{"key":"ref16","article-title":"Categorizing variants of Goodhart's Law","author":"Manheim D","year":"2018","unstructured":"Manheim, D., Garrabrant, S.: Categorizing variants of Goodhart's Law. arXiv:1803.04585. (2018). https:\/\/arxiv.org\/abs\/1803.04585"},{"issue":"1","key":"ref17","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s00454-009-9176-0","article-title":"The theory of multidimensional persistence","volume":"42","author":"Carlsson G","year":"2009","unstructured":"Carlsson, G., Zomorodian, A.: The theory of multidimensional persistence. Discrete Comput. Geom. 42(1), 71\u201393 (2009)","journal-title":"Discrete Comput. Geom."}],"link":[{"URL":"https:\/\/www.researchsquare.com\/article\/rs-9932131\/v1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.researchsquare.com\/article\/rs-9932131\/v1.html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T13:15:54Z","timestamp":1781010954000},"score":28.830873,"resource":{"primary":{"URL":"https:\/\/www.researchsquare.com\/article\/rs-9932131\/v1"}},"issued":{"date-parts":[[2026,6,9]]},"references-count":17,"URL":"https:\/\/doi.org\/10.21203\/rs.3.rs-9932131\/v1","published":{"date-parts":[[2026,6,9]]},"subtype":"preprint"},{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T11:58:31Z","timestamp":1777550311968,"version":"3.51.4"},"reference-count":23,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Roche Pharmaceuticals Israel Ltd.","award":["The study was supported by an independent research"],"award-info":[{"award-number":["The study was supported by an independent research"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["DIGITAL HEALTH"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Accurate finger function assessment is crucial for monitoring the performance of daily hand activities. However, specialized digital applications are lacking for evaluating various finger tasks. This study aims to develop a custom digital biosensing application to assess finger dexterity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed a digital biosensing application compatible with smartphones and tablets that enables 3-min testing of finger dexterity, measuring velocity and accuracy for each finger and each movement orientation. Data were collected for the dominant hand from a large cohort of healthy volunteers to establish population norms values.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The construction of the application involved a comprehensive, multi-stage process designed to ensure functionality, user-friendliness, and cross-platform compatibility using the Flutter framework by Google with specific adaptations for Android and iOS. To evaluate the application and construct population norms, 318 healthy subjects, 197 females and 121 males, mean\u2009\u00b1\u2009age 37.7\u2009\u00b1\u200913.5 years, were tested. Velocity was faster for the vertical and horizontal tests than all other tests and fastest for finger 2, while the pinch test was the slowest for all fingers. Deviation from any required test orientation was more evident for the circle test and mainly for finger 5, while the vertical and horizontal orientations were the most unerring. Analysis of finger dexterity by age disclosed better performance in the younger age group (&lt;35 years); no effect of gender for both velocity and deviation was observed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The developed digital application allows immediate evaluation of finger dexterity. The established population norms can provide a comparative standard for assessing patients with disorders like multiple sclerosis, sensory neuropathy, or stroke.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076241297734","type":"journal-article","created":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T14:18:09Z","timestamp":1741097889000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development of an interactive biosensing application for assessing finger dexterity"],"prefix":"10.1177","volume":"11","author":[{"given":"Michal Greenberg","family":"Abrahami","sequence":"first","affiliation":[{"name":"Multiple Sclerosis Center, Sheba Medical Center, Tel Hashomer, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7919-807X","authenticated-orcid":false,"given":"Yehuda","family":"Warszawer","sequence":"additional","affiliation":[{"name":"Multiple Sclerosis Center, Sheba Medical Center, Tel Hashomer, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alon","family":"Kalron","sequence":"additional","affiliation":[{"name":"Multiple Sclerosis Center, Sheba Medical Center, Tel Hashomer, Israel"},{"name":"Faculty of Medical and Health Sciences, Tel-Aviv University, Tel-Aviv, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emanuel","family":"Shirbint","sequence":"additional","affiliation":[{"name":"Multiple Sclerosis Center, Sheba Medical Center, Tel Hashomer, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria","family":"Didikin","sequence":"additional","affiliation":[{"name":"Multiple Sclerosis Center, Sheba Medical Center, Tel Hashomer, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2020-3126","authenticated-orcid":false,"given":"Anat","family":"Achiron","sequence":"additional","affiliation":[{"name":"Multiple Sclerosis Center, Sheba Medical Center, Tel Hashomer, Israel"},{"name":"Faculty of Medical and Health Sciences, Tel-Aviv University, Tel-Aviv, 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Informed consent was waived as the study was non-interventional and retrospective. Data was collected, coded, and analyzed according to the ethical standards of human experimentation.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]},{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T16:42:32Z","timestamp":1790700152465,"version":"4.1.0"},"publisher-location":"Concord, CA","reference-count":0,"publisher":"ISMRM","content-domain":{"domain":[],"crossmark-restriction":false},"abstract":"<jats:p>Motivation: 3D visualisation of the spinal cord and vertebrae anatomy is critical for treatment planning and assessment of cord atrophy in neurodegenerative and traumatic diseases. Goal(s): Develop a fully automatic segmentation of the whole spinal cord, vertebrae and discs. Approach: The hybrid method combines a nnU-Net with an iterative processing algorithm with Spinal Cord Toolbox to conveniently generate ground truth labels. We used 3D T1w and T2w scans from three different databases. Results: A validation Dice score of 0.928 was obtained (averaged across contrasts, classes and datasets), suggesting promising segmentation accuracy and capabilities for generalisation given the use of multi-site\/multi-vendor datasets. Impact: The fully automatic segmentation of the spine and spinal cord will pinpoint pathologies at specific vertebrae level, offering visualization for surgery preparation. This could also refine segmentation of substructures like multiple sclerosis lesions and tumors, inspiring solutions for related issues.<\/jats:p>","DOI":"10.58530\/2024\/4847","type":"proceedings-article","created":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T11:27:05Z","timestamp":1732620425000},"source":"Crossref","is-referenced-by-count":3,"title":["Fully Automatic Vertebrae and Spinal Cord Segmentation Using a Hybrid Approach Combining nnU-Net and Iterative Algorithm"],"prefix":"10.58530","author":[{"given":"Yehuda","family":"Warszawer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nathan","family":"Molinier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan","family":"Valosek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emanuel","family":"Shirbint","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pierre-Louis","family":"Benveniste","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anat","family":"Achiron","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arman","family":"Eshaghi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julien","family":"Cohen-Adad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"37305","event":{"name":"2024 ISMRM & ISMRT Annual Meeting","location":"Toronto, ON, Canada","acronym":"ISMRM-ISMRT","number":"33","start":{"date-parts":[[2024,5,4]]},"end":{"date-parts":[[2024,5,9]]}},"container-title":["ISMRM Annual Meeting","2024 ISMRM &amp; ISMRT Annual Meeting"],"deposited":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T11:42:28Z","timestamp":1732621348000},"score":16.086866,"resource":{"primary":{"URL":"https:\/\/archive.ismrm.org\/2024\/4847.html"}},"issued":{"date-parts":[[null]]},"references-count":0,"URL":"https:\/\/doi.org\/10.58530\/2024\/4847","ISSN":["1545-4428"],"issn-type":[{"value":"1545-4428","type":"print"}],"article-number":"4847"},{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T22:01:02Z","timestamp":1783029662104,"version":"3.54.6"},"reference-count":168,"publisher":"Jaypee Brothers Medical Publishing","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"published-print":{"date-parts":[[2015,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Noncutaneous cancers of the head and neck occasionally metastasize to skin. We present a case series of patients with skin metastases from non-cutaneous malignancies of the head and neck. A literature review is presented to assess the morphology, pattern of distribution, and morbidity of skin metastases.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Relevant cases were identified by a dermatopathologist and cases were reviewed. A literature search yielded 94 cases which were also included.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The most common primary site, histology, and location of spread was the thyroid gland (42%), squamous cell carcinoma (42%), and scalp (25%) respectively. Skin metastases typically present as a discrete nodules, but the distribution varies. Metastases to skin can occasionally be a patients presenting complaint, but irrespective of timing of presentation portend a poor prognosis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Skin metastases are clinically rare, and when present typically herald a poor overall prognosis. Information regarding their distribution and clinical presentation is of importance to the surgeon and oncologist.<\/jats:p><\/jats:sec><jats:sec><jats:title>How to cite this article<\/jats:title><jats:p>Emanuel H, Emanuel P. Cutaneous Metastasis of Head and Neck Malignancy. Int J Head Neck Surg 2015;6(2):57-63.<\/jats:p><\/jats:sec>","DOI":"10.5005\/jp-journals-10001-1223","type":"journal-article","created":{"date-parts":[[2016,4,22]],"date-time":"2016-04-22T07:21:41Z","timestamp":1461309701000},"page":"57-63","source":"Crossref","is-referenced-by-count":3,"title":["Cutaneous Metastasis of Head and Neck Malignancy"],"prefix":"10.5005","volume":"6","author":[{"given":"Henry","family":"Emanuel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrick","family":"Emanuel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2914","published-online":{"date-parts":[[2015]]},"reference":[{"key":"ref1","author":"Hussein MR.","year":"2009.","journal-title":"J Cutan Pathol"},{"key":"ref=2","doi-asserted-by":"crossref","unstructured":"Hussein MR. Skin metastasis: a pathologist's perspective. 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These trends promote the question: What form of accountability is appropriate to managed care plans? Accountability is the process by which a party justifies its actions and policies. Components of accountability include parties that can be held or hold others accountable, domains and content areas being assessed, and procedures of assessment.<\/jats:p><jats:p>Traditionally, the professional model of accountability has operated in medical care. In this model, physicians establish the standards of accountability and hold each other accountable through professional organizations. This form of accountability seems outdated and inapplicable to managed care plans. The alternatives are the economic and the political models of accountability. In the economic model, medicine becomes more like a commodity, and \u201cexit\u201d (consumers changing providers for reasons of cost and quality) is the dominant procedure of accountability. In the political model, medicine becomes more like a community good, and \u201cvoice\u201d (citizens communicating their views in public forums or on policy committees, or in elections for representatives) is the dominant procedure of accountability.<\/jats:p><jats:p>The economic model\u2019s advantages affirm American individualism, make minimal demands on consumers, and use a powerful incentive, money. Its disadvantages undermine health care as a nonmarket good, undermine individual autonomy, undermine good medical practice, impose significant demands on consumers to be informed, sustain differentials of power, and use indirect procedures of accountability.<\/jats:p><jats:p>The political model\u2019s advantages affirm health care as a matter of justice, permit selecting domains other than price and quality for accountability, reinforce good medical practice, and equalize power between patients and physicians. Its disadvantages include inefficiency in decision making, capture by extremists or experts, intractable value conflicts, fragmentation of community, and oppression of minorities.<\/jats:p><jats:p>The political model is the model we should endorse. Its disadvantages can be minimized by proper institutional design. In addition, recent research on managed care plans suggests that the political model may be the best for a competitive marketplace because it can ensure that tough allocation decisions are addressed and improve health through changes in nonmedical aspects of community life.<\/jats:p>","DOI":"10.1215\/03616878-22-1-147","type":"journal-article","created":{"date-parts":[[2016,6,13]],"date-time":"2016-06-13T23:36:38Z","timestamp":1465860998000},"page":"147-184","source":"Crossref","is-referenced-by-count":27,"title":["Preserving Community in Health Care"],"prefix":"10.1215","volume":"22","author":[{"given":"Ezekiel J.","family":"Emanuel","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linda L.","family":"Emanuel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"73","reference":[{"key":"2022033014361663500_R1","unstructured":"Anderson, E. 1993. Value in Ethics and Economics. 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\u201eNauka Polska\u201d. 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