Logit-Boundary Geometric Belief Interfaces and Sparse Sheaf-Enclave Protocols: A Self-Contained Substrate for Secure Network Electronic Health Record (EHR) Interoperability

• URL: http://arxiv.org/abs/2608.10300v2
• Date: Wed, 12 Aug 2026 02:20:03 GMT
• Title: Logit-Boundary Geometric Belief Interfaces and Sparse Sheaf-Enclave Protocols: A Self-Contained Substrate for Secure Network Electronic Health Record (EHR) Interoperability
• Abstract summary: This paper details a mathematical and engineering architecture for that interface.<n>The organizing idea is the logit boundary: a discovery model may propose pre-threshold scores over a local categorical decision, but a deterministic judgment substrate decides whether the proposal is admissible.<n>The resulting Geometric Belief Interface (GBI) combines finite boundary semantics, local Dirichlet evidence, cellular-sheaf and mapping-cone diagnostics, advisory audit charts, and a Decentralized Cryptographic Sheaf-Enclave protocol sketch for fail-closed deployment.
• Score: 2.894286977279531
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electronic health-record interoperability is a boundary problem: legacy systems, generative models, terminology services, identity systems, and human reviewers may each expose rich internal states, while operational exchange requires a narrow shared interface of typed claims, bounded uncertainty, provenance, and explicit admission or abstention. This paper details a mathematical and engineering architecture for that interface. The organizing idea is the logit boundary: a discovery model may propose pre-threshold scores over a local categorical decision, but a deterministic judgment substrate decides whether the proposal is admissible, requires review, or must be quarantined before any Fast Healthcare Interoperability Resources (FHIR) transaction is constructed. The resulting Geometric Belief Interface (GBI) combines finite boundary semantics, local Dirichlet evidence, cellular-sheaf and mapping-cone diagnostics, advisory geometric audit charts, and a Decentralized Cryptographic Sheaf-Enclave (DCSE) protocol sketch for fail-closed deployment. The framework does not establish clinical truth, global representation alignment, or end-to-end safety; it defines certificate-producing checks at a model-to-system boundary. A companion frozen synthetic benchmark, GBI BoundaryBench v0.1, evaluated Qwen3-4B-Instruct-2507 on 256 held-out tasks across three evidence modes (768 canonical executions). All executions completed, but none produced an output accepted by the benchmark contract: 369 were rejected during safe parsing and 399 during schema validation, yielding zero coverage and deterministic quarantine. This empirical result is deliberately narrow - one 4B open-weight model under one frozen interface - and is reported as evidence about the admission boundary, not as a general claim about LLM capability or clinical safety. A Julia appendix verifies numerical certificates using standard libraries.

Related papers

• AI Slop and Hallucinations in Vulnerability Assessment: A Survey on Reasoning Failures and Trustworthy Mitigation [48.55515767840701]
  "AI slop" is artifacts, hallucinated vulnerabilities, plausible but incorrect patches, and semantically repackaged bug reports.<n>This paper surveys the empirical evidence, identifies a unifying mechanism, and traces a path toward trustworthy triage.
  arXiv  Detail & Related papers  (2026-08-26T11:48:38Z)
• Structuring Semantic Embeddings for Principle Evaluation: A Prototype-Guided Contrastive Learning Approach [58.15427952222424]
  General-purpose text embeddings are widely deployed for such tasks, but broad semantic similarity can place semantically similar yet task-distinct examples.<n>We introduce Prototype-Guided Contrastive Learning (PGCL), a prototype-guided geometric regularization module built on top of frozen text embeddings.
  arXiv  Detail & Related papers  (2026-08-15T13:19:25Z)
• Three Generations of Healthcare IT: From the Digital Record to the Computable Care Process [47.027290803102666]
  We describe a computational layer whose object is patient-specific clinical intent.<n>We formalize the Actionable Clinical Record as the atomic object of the third layer.
  arXiv  Detail & Related papers  (2026-08-09T16:44:28Z)
• Toward Automated Detection of Documentation Inconsistencies in Electronic Health Records [16.68744679025527]
  General-domain large language model (LLM) can surface from real-world discharge summaries.<n>LLM surfaced 3,460 candidate inconsistencies, affecting 69.7% of admissions.
  arXiv  Detail & Related papers  (2026-07-24T23:43:35Z)
• Trust Boundary Semantic Gaps: A Multi-dimensional Analysis and Mitigation for Security-by-Design [10.212854052793046]
  We call this condition a Trust Boundary Semantic Gap (TBSG)<n>We analyze 75 publicly reported security incidents at the boundary level.<n>We develop Trust Boundary Semantic Analysis and Mitigation (TBSAM)
  arXiv  Detail & Related papers  (2026-07-02T05:06:57Z)
• RISED: A Pre-Deployment Safety Evaluation Framework for Clinical AI Decision-Support Systems [0.0]
  We propose a five-dimension pre-deployment evaluation covering Reliability, Inclusivity, Sensitivity, Equity, and Deployability.<n> RISED is released as an open-source Python package that supplies the quantitative verdicts existing clinical AI reporting standards require.
  arXiv  Detail & Related papers  (2026-05-13T02:17:13Z)
• Verifier-Bound Communication for LLM Agents: Certified Bounds on Covert Signaling [0.0]
  Colluding language-model agents can hide coordination in messages that remain policy-compliant at the surface level.<n>We present CLBC, a protocol where generation and admission are separated.<n>We show how this protocol yields an upper bound on transcript leakage in terms of latent leakage plus explicit residual channels.
  arXiv  Detail & Related papers  (2026-02-27T23:42:37Z)
• Escaping the BLEU Trap: A Signal-Grounded Framework with Decoupled Semantic Guidance for EEG-to-Text Decoding [41.209385903282374]
  SemKey is a novel framework that enforces signal-grounded generation through four semantic objectives.<n>We move beyond standard translation metrics by adopting N-way Retrieval Accuracy and Fréchet Distance.
  arXiv  Detail & Related papers  (2026-02-09T02:47:07Z)
• Conditional Coverage Diagnostics for Conformal Prediction [47.93989136542648]
  We show that conditional coverage estimation can be a classification problem.<n>We call the resulting family of metrics excess risk of the target coverage (ERT)<n>We release an open-source package for ERT as well as previous conditional coverage metrics.
  arXiv  Detail & Related papers  (2025-12-12T18:47:39Z)
• The Epistemic Suite: A Post-Foundational Diagnostic Methodology for Assessing AI Knowledge Claims [0.7233897166339268]
  This paper introduces the Epistemic Suite, a diagnostic methodology for surfacing the conditions under which AI outputs are produced and received.<n>Rather than determining truth or falsity, the Suite operates through twenty diagnostic lenses to reveal patterns such as confidence laundering, narrative compression, displaced authority, and temporal drift.
  arXiv  Detail & Related papers  (2025-09-20T00:29:38Z)
• A Taxonomy and Methodology for Proof-of-Location Systems [42.222053626544366]
  We propose a taxonomy identifying four core domains of Proof-of-Location (PoL) systems.<n>We then introduce a methodology to map application-specific requirements onto appropriate PoL architectures.<n>Overall, this work offers a structured approach to building secure, scalable, and interoperable PoL systems.
  arXiv  Detail & Related papers  (2025-08-19T19:39:05Z)
• Robust-by-Design Classification via Unitary-Gradient Neural Networks [66.17379946402859]
  The use of neural networks in safety-critical systems requires safe and robust models, due to the existence of adversarial attacks. Knowing the minimal adversarial perturbation of any input x, or, equivalently, the distance of x from the classification boundary, allows evaluating the classification robustness, providing certifiable predictions. A novel network architecture named Unitary-Gradient Neural Network is presented. Experimental results show that the proposed architecture approximates a signed distance, hence allowing an online certifiable classification of x at the cost of a single inference.
  arXiv  Detail & Related papers  (2022-09-09T13:34:51Z)
• Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond [57.10914865054868]
  We consider vertical logistic regression (VLR) trained with mini-batch descent gradient. We provide a comprehensive and rigorous privacy analysis of VLR in a class of open-source Federated Learning frameworks.
  arXiv  Detail & Related papers  (2022-07-19T05:47:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.