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arxiv: 2604.16081 · v1 · submitted 2026-04-17 · 💻 cs.MA

Veritas-RPM: Provenance-Guided Multi-Agent False Positive Suppression for Remote Patient Monitoring

Aswini Misro , Vikash Sharma , Shreyank N Gowda This is my paper

Pith reviewed 2026-05-10 07:21 UTC · model grok-4.3

classification 💻 cs.MA
keywords remote patient monitoringfalse positive suppressionmulti-agent architectureprovenance guidanceanomaly detectionsynthetic dataalert managementspecialist agents
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The pith

A provenance-guided multi-agent system suppresses false positives in remote patient monitoring through layered specialist routing and conflict resolution.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents Veritas-RPM, an architecture with five processing layers that assembles ground truth from provenance, detects anomalies, routes cases to domain specialists, and resolves conflicts for a final decision. It constructs a 98-case synthetic taxonomy of RPM false-positive scenarios and generates 530 patient epochs with known labels to run the full pipeline. This matters because unchecked false positives in remote monitoring generate alert fatigue and unnecessary clinical interventions. The approach demonstrates how provenance can steer agent decisions to improve suppression accuracy on controlled data.

Core claim

Veritas-RPM is a provenance-guided multi-agent architecture comprising VeritasAgent for ground-truth assembly, SentinelLayer for anomaly detection, DirectorAgent for specialist routing, six domain Specialist Agents, and MetaSentinelAgent for conflict resolution and final output. The system processes synthetic patient epochs generated directly from a 98-case taxonomy of documented RPM false-positive patterns, with ground-truth labels available for every case, and evaluates performance via True Suppression Rate, False Escalation Rate, and Indeterminate Rate.

What carries the argument

The five-layer pipeline that uses provenance to assemble ground truth, detect anomalies, route to specialist agents, and resolve conflicts for suppressing false positives.

If this is right

  • Routing anomalies through a director to domain specialists allows targeted handling of distinct false-positive patterns.
  • The meta-sentinel layer resolves agent conflicts to produce more reliable final suppress-or-escalate decisions.
  • Known ground-truth labels on synthetic epochs enable exact measurement of true suppression versus false escalation.
  • Provenance information directly supports accurate ground-truth assembly at the first layer.
  • The overall structure provides a repeatable way to evaluate and reduce indeterminate outcomes in monitoring pipelines.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The taxonomy of false-positive scenarios could be reused as a shared benchmark for testing other RPM suppression methods.
  • The same layered routing and resolution pattern might transfer to alert management in non-medical domains that face high false-positive volumes.
  • Replacing generated epochs with live data streams would test whether the architecture maintains performance when provenance arrives in real time.

Load-bearing premise

The 98-case synthetic taxonomy and 530 generated epochs sufficiently represent the distribution and provenance patterns of real-world RPM false positives.

What would settle it

Running the Veritas-RPM pipeline on a set of real remote patient monitoring alerts with independent clinician labels would show whether the reported suppression rates hold outside synthetic cases.

read the original abstract

We present Veritas-RPM, a provenance-guided multi-agent architecture comprising five processing layers: VeritasAgent (ground-truth assembly), SentinelLayer (anomaly detection), DirectorAgent (specialist routing), six domain Specialist Agents, and MetaSentinelAgent (conflict resolution and final decision). We construct a 98-case synthetic taxonomy of false-positive scenarios derived from documented RPM patterns. Synthetic patient epochs (n = 530) were generated directly from taxonomy parameters and processed through the pipeline. Ground-truth labels are known for all cases. Performance is reported as True Suppression Rate (TSR), False Escalation Rate (FER), and Indeterminate Rate (INDR).

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript introduces Veritas-RPM, a provenance-guided multi-agent architecture for suppressing false positives in remote patient monitoring. The system comprises five layers: VeritasAgent (ground-truth assembly), SentinelLayer (anomaly detection), DirectorAgent (specialist routing), six domain Specialist Agents, and MetaSentinelAgent (conflict resolution). A 98-case synthetic taxonomy of false-positive scenarios is derived from documented RPM patterns; 530 synthetic patient epochs are generated directly from this taxonomy. Ground-truth labels are known by construction, and performance is measured via True Suppression Rate (TSR), False Escalation Rate (FER), and Indeterminate Rate (INDR).

Significance. The multi-layer provenance-guided design offers a structured approach to handling uncertainty in RPM alerts. If the reported metrics were shown to generalize beyond the synthetic taxonomy, the work could contribute to multi-agent systems in healthcare monitoring. At present, however, the evaluation provides no evidence of robustness on independently sourced clinical data.

major comments (3)
  1. [Abstract and Evaluation] Abstract and Evaluation section: Performance is reported solely on 530 synthetic epochs generated from the same 98-case taxonomy used to design the agents. This construction makes ground truth known by design but creates a closed evaluation loop; no external RPM dataset, clinical trace, or independently validated provenance signals are used to test whether the architecture suppresses false positives under real sensor noise or provenance distributions.
  2. [Methodology] Methodology: No baseline systems (e.g., single SentinelLayer, standard anomaly detectors, or non-provenance multi-agent variants) are compared, so the incremental contribution of the five-layer routing and MetaSentinelAgent cannot be quantified.
  3. [Data Generation] Data Generation: The 98-case taxonomy is stated to be 'derived from documented RPM patterns,' yet no validation procedure, inter-rater agreement, or statistical comparison against real RPM logs is provided to establish that the 530 epochs are representative.
minor comments (2)
  1. [Abstract] Define TSR, FER, and INDR with explicit formulas or pseudocode rather than relying on acronym expansion alone.
  2. [Architecture Description] Clarify the exact provenance signals passed between layers and whether any parameters are tuned on the synthetic set.

Simulated Author's Rebuttal

3 responses · 1 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major point below and describe the revisions we will undertake to improve the clarity and rigor of the work.

read point-by-point responses

  1. Referee: Performance is reported solely on 530 synthetic epochs generated from the same 98-case taxonomy used to design the agents. This construction makes ground truth known by design but creates a closed evaluation loop; no external RPM dataset, clinical trace, or independently validated provenance signals are used to test whether the architecture suppresses false positives under real sensor noise or provenance distributions.

    Authors: We agree that the evaluation is confined to a synthetic setting derived from the taxonomy, which enables controlled testing with known ground truth but precludes direct claims of performance under real-world sensor noise or provenance distributions. This synthetic approach was chosen to isolate the effects of the multi-agent provenance guidance on specific false-positive patterns documented in the RPM literature. In the revised manuscript we will add an expanded Limitations subsection and a dedicated Future Work paragraph that explicitly states the synthetic nature of the current results and outlines steps toward external validation, including planned collaborations to obtain de-identified clinical RPM traces. revision: partial

  2. Referee: No baseline systems (e.g., single SentinelLayer, standard anomaly detectors, or non-provenance multi-agent variants) are compared, so the incremental contribution of the five-layer routing and MetaSentinelAgent cannot be quantified.

    Authors: The absence of baseline comparisons is a valid concern that prevents readers from isolating the benefit of the DirectorAgent, Specialist Agents, and MetaSentinelAgent. We will add a new experimental subsection that reports performance for three baselines on the identical 530-epoch set: (1) the SentinelLayer alone, (2) two standard anomaly detectors (Isolation Forest and a variational autoencoder) applied to the same feature vectors, and (3) a non-provenance multi-agent variant that replaces the DirectorAgent and MetaSentinelAgent with simple majority voting. These additions will allow direct quantification of the incremental value of the provenance-guided routing and conflict-resolution layers. revision: yes

  3. Referee: The 98-case taxonomy is stated to be 'derived from documented RPM patterns,' yet no validation procedure, inter-rater agreement, or statistical comparison against real RPM logs is provided to establish that the 530 epochs are representative.

    Authors: We will revise the Data Generation section to increase transparency. The revision will include: (a) explicit citations to the specific clinical guidelines and RPM studies from which each of the 98 scenarios was abstracted, (b) a step-by-step description of how taxonomy parameters were mapped to the 530 synthetic epochs, and (c) a new table that cross-references each taxonomy case to its source references. While we did not conduct formal inter-rater reliability testing, the taxonomy was iteratively refined against multiple independent literature sources; this process will now be documented. revision: yes

standing simulated objections not resolved
  • We cannot supply evaluation results on independently sourced clinical RPM data in the present revision, because such datasets containing detailed provenance metadata are not publicly available and would require additional institutional review board approvals and data-use agreements beyond the scope of this work.

Circularity Check

0 steps flagged

No significant circularity; synthetic evaluation is independent of claimed results.

full rationale

The paper describes a five-layer multi-agent architecture and reports TSR/FER/INDR metrics on 530 synthetic epochs generated from an explicitly constructed 98-case taxonomy derived from documented RPM patterns. No equations, fitted parameters, or self-citations are present that reduce the performance metrics to the inputs by construction. The architecture design, taxonomy construction, and data generation are presented as separate steps with known ground truth, making the evaluation self-contained rather than circular. This is a standard synthetic benchmark setup without load-bearing self-referential reductions.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The evaluation rests on the assumption that the synthetic taxonomy captures real RPM false-positive patterns; no free parameters or new physical entities are introduced.

axioms (1)
  • domain assumption Synthetic patient epochs generated from the 98-case taxonomy accurately reflect the statistical and provenance characteristics of real remote patient monitoring false positives.
    All performance metrics are computed exclusively on these generated epochs.
invented entities (1)
  • VeritasAgent, SentinelLayer, DirectorAgent, six Specialist Agents, MetaSentinelAgent no independent evidence
    purpose: Modular components that assemble ground truth, detect anomalies, route cases, apply domain knowledge, and resolve conflicts.
    These are defined as the five processing layers of the proposed architecture.

pith-pipeline@v0.9.0 · 5414 in / 1275 out tokens · 21866 ms · 2026-05-10T07:21:54.849113+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

18 extracted references · 18 canonical work pages · 1 internal anchor

  1. [1]

    Heart rhythm, 17(1), pp.75-80

    Incidence of false -positive transmissions during remote rhythm monitoring with implantable loop recor ders. Heart rhythm, 17(1), pp.75-80. https://doi.org/10.1016/j.hrthm.2019.07.015 Ancker, J.S., Edwards, A., Nosal, S., Hauser, D., Mauer, E., Kaushal, R. and With the HITEC Investigators,

    work page doi:10.1016/j.hrthm.2019.07.015 2019

  2. [2]

    Ancker, Alison Edwards, Sarah Nosal, Diane Hauser, Elizabeth Mauer, and Rainu Kaushal

    Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system. BMC medical informatics and decision making , 17(1), p.36. https://doi.org/10.1186/s12911-017-0430-8 Charlton, P.H. and Marozas, V.,

    work page doi:10.1186/s12911-017-0430-8

  3. [3]

    Millennial-Scale Ocean Climate Variability,

    Wearable photoplethysmography devices. In Photoplethysmography (pp. 401-439). Academic Press. https://doi.org/10.1016/B978-0-12- 823374-0.00011-6 Clifford, G.D., Silva, I., Moody, B., Li, Q., Kella, D., Shahin, A., Kooistra, T., Perry, D. and Mark, R.G., 2015, September. The PhysioNet/computing in cardiology challenge 2015: reducing false arrhythmia alarm...

    work page doi:10.1016/b978-0-12- 2015

  4. [4]

    Pacing and Clinical Electrophysiology, 47(3), pp.406-416

    False ‐positive alarms in patients with implantable loop recorder followed by remote monitoring: a systematic review. Pacing and Clinical Electrophysiology, 47(3), pp.406-416. https://doi.org/10.1111/pace.14941 Cvach, M.,

    work page doi:10.1111/pace.14941

  5. [5]

    Biomedical instrumentation & technology, 46(4), pp.268-277

    Monitor alarm fatigue: an integrative review. Biomedical instrumentation & technology, 46(4), pp.268-277. https://doi.org/10.2345/0899-8205-46.4.268 Drew, B.J., Harris, P., Zègre-Hemsey, J.K., Mammone, T., Schindler, D., Salas-Boni, R., Bai, Y., Tinoco, A., Ding, Q. and Hu, X.,

    work page doi:10.2345/0899-8205-46.4.268

  6. [6]

    PLOS ONE , publisher =

    Insights into the problem of alarm fatigue with physiologic monitor devices: a comprehensive observational study of cons ecutive intensive care unit patients. PloS one, 9(10), p.e110274. https://doi.org/10.1371/journal.pone.0110274 Elgendi, M.,

    work page doi:10.1371/journal.pone.0110274

  7. [7]

    Current cardiology reviews, 8(1), pp.14-25

    On the analysis of fingertip photoplethysmogram signals. Current cardiology reviews, 8(1), pp.14-25. https://doi.org/10.2174/157340312801215782 Guarracini, F., Testolina, M., Giacopelli, D., Martin, M., Triglione, F., Coser, A., Quintarelli, S., Bonmassari, R. and Marini, M.,

    work page doi:10.2174/157340312801215782

  8. [8]

    Diagnostics, 12(4), p.994

    Programming optimization in implantable cardiac monitors to reduce false -positive arrhythmia alerts: a call for resea rch. Diagnostics, 12(4), p.994. https://doi.org/10.3390/diagnostics12040994 Heath, K.M., Lyrtzis, E., Middeldorp, M.E., Sanders, P. and Lau, D.H.,

    work page doi:10.3390/diagnostics12040994

  9. [9]

    Journal of Electrocardiology , 62, pp.132 -133

    False remote monitoring alerts from explanted cardiac implantable electronic device: How is this possible?. Journal of Electrocardiology , 62, pp.132 -133. https://doi.org/10.1016/j.jelectrocard.2020.08.014 Knight, S., Lipoth, J., Namvari, M., Gu, C., Hedayati, M., Syed -Abdul, S. and Spiteri, R.J.,

    work page doi:10.1016/j.jelectrocard.2020.08.014 2020

  10. [10]

    Telemedicine and e -Health, 29(6), pp.813 -828

    The accuracy of wearable photoplethysmography sensors for telehealth monitoring: A scoping review. Telemedicine and e -Health, 29(6), pp.813 -828. https://doi.org/10.1089/tmj.2022.0182 Misro, A., Sharma, V. and Narayana Gowda, S. (2026). Veritas -RPM: Provenance-Guided Multi-Agent Reference Architecture for Remote Patient Monitoring [Software]. Zenodo. ht...

    work page doi:10.1089/tmj.2022.0182 2022

  11. [11]

    Journal of the American Heart Association , 13(5), p.e032890

    Monitoring of remotely reprogrammable implantable loop recorders with algorithms to reduce false ‐positive alerts. Journal of the American Heart Association , 13(5), p.e032890. https://doi.org/10.1161/JAHA.123.032890 NHS England (2022). Virtual wards including hospital at home. Available at: https://www.england.nhs.uk/virtual-wards O’Shea, C.J., Middeldor...

    work page doi:10.1161/jaha.123.032890 2022

  12. [12]

    Clinical Electrophysiology, 7(2), pp.226-234

    Remote monitoring alert burden: an analysis of transmission in> 26,000 patients. Clinical Electrophysiology, 7(2), pp.226-234. https://doi.org/10.1016/j.jacep.2020.08.029 Plesinger, F., Klimes, P., Halamek, J. and Jurak, P.,

    work page doi:10.1016/j.jacep.2020.08.029 2020

  13. [13]

    Physiological measurement , 37(8), pp.1313 -1325

    Taming of the monitors: reducing false alarms in intensive care units. Physiological measurement , 37(8), pp.1313 -1325. https://doi.org/10.1088/0967-3334/37/8/1313 Russo, V., Covino, S., De Pasquale, V., Parente, E., Comune, A., Rago, A., Papa, A.A., Ammendola, E., Spadaro Guerra, A., Napoli, P. and Golino, P.,

    work page doi:10.1088/0967-3334/37/8/1313

  14. [14]

    Pacing and Clinical Electrophysiology , 46(12), pp.1500 -1508

    Remote monitoring of implantable cardiac monitors in patients with unexplained syncope: Predictors of false‐positive alert episodes. Pacing and Clinical Electrophysiology , 46(12), pp.1500 -1508. https://doi.org/10.1111/pace.14851 Sendelbach, S. and Funk, M.,

    work page doi:10.1111/pace.14851

  15. [15]

    AACN advanced critical care, 24(4), pp.378-386

    Alarm fatigue: a patient safety concern. AACN advanced critical care, 24(4), pp.378-386. https://doi.org/10.1097/NCI.0b013e3182a903f9 Simpson, K.R. and Lyndon, A.,

    work page doi:10.1097/nci.0b013e3182a903f9

  16. [16]

    Journal of nursing care quality , 34(1), pp.66 -72

    False alarms and overmonitoring: major factors in alarm fatigue among labor nurses. Journal of nursing care quality , 34(1), pp.66 -72. https://doi.org/10.1097/NCQ.0000000000000335 US Food and Drug Administration (2016). Reporting of Computational Modeling Studies in Medical Device Submissions. FDA Guidance Document. Walonoski, J., Kramer, M., Nichols, J....

    work page doi:10.1097/ncq.0000000000000335 2016

  17. [17]

    AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

    Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic healt h care record. Journal of the American Medical Informatics Association , 25(3), pp.230 -238. https://doi.org/10.1093/jamia/ocx079 Wu, Q., Bansal, G., Zhang, J., Wu, Y., Li, B., Zhu, E., Jiang, L., Zhang, X., Zhang, S., Liu, J. and Awad...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1093/jamia/ocx079 2024

  18. [18]

    arXiv preprint arXiv:2508.04915

    Confagents: A conformal - guided multi -agent framework for cost -efficient medical diagnosis. arXiv preprint arXiv:2508.04915

    work page arXiv