Third-Party Supplier Risk Re-Classification Using Multi-Model Semantic Voting and External Web Augmentation

Manavi, Ben [IBM] (ORCID:0009000475364578); Ragozzino-Higgins, JoAnn [IBM]; Chen, Edward [Idaho National Laboratory] (ORCID:0000000180088097); Daily, Jeremy [IBM]; Paglioni, Vincent [University of Colorado]

DOE OSTI · 3378219

Third-Party Supplier Risk Re-Classification Using Multi-Model Semantic Voting and External Web Augmentation

Manavi, Ben [IBM] (ORCID:0009000475364578)·Ragozzino-Higgins, JoAnn [IBM]·Chen, Edward [Idaho National Laboratory] (ORCID:0000000180088097)·Daily, Jeremy [IBM]·Paglioni, Vincent [University of Colorado]

View original source ↗Read source document ↗Export metadata ↓

Abstract

Risk decisions in many third-party risk management (TPRM) workflows rely on static inherent risk questionnaires (IRQ). These static forms provide a snapshot of the vendor from the business users’ perspective, as these requests are processed without cross-referencing for evidence. Consequently, responses can be misinformed or embellished with inaccuracies, thereby masking the vendor’s true risk to the enterprise. This paper presents a multi-stage verification framework to augment IRQs with web evidence and a deterministic ensemble of large language model assessors to reclassify risk. In a case study of 100 submissions previously misclassified as low risk, the proposed framework correctly identified 76% of the cases as high risk, while the existing workflow identified none. McNemar’s continuity corrected statistics of 74 were obtained with a two sided p-value of 2.65 × 10-23, indicating a significantly more effective workflow compared to the legacy model.

Explore related subjects

99 - GENERAL AND MISCELLANEOUS inherent risk questionnaires LLM ensemble voting supplier risk re-classification third-party risk management

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Manavi, Ben [IBM] (ORCID:0009000475364578), Ragozzino-Higgins, JoAnn [IBM], Chen, Edward [Idaho National Laboratory] (ORCID:0000000180088097), Daily, Jeremy [IBM], Paglioni, Vincent [University of Colorado]. 2026-07-15. Third-Party Supplier Risk Re-Classification Using Multi-Model Semantic Voting and External Web Augmentation. https://www.osti.gov/biblio/3378219

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2026

ClassNMSW- a real-time classification approach for non-recycled municipal solid waste using hyperspectral imaging

Real-time classification of non-recycled municipal solid waste (NMSW) is essential for efficient valorization. This study introduces ClassNMSW, a comprehensive framework for classifying 22 NMSW subclasses under industrial constraints by using hyperspectral imaging (HSI). A primary innovation of this work is the development of a variance-controlled spectral extraction algorithm. Unlike traditional methods that rely on simple averaging, this approach systematically investigates the extent of pixel extraction to minimize the loss of critical chemical information while maximizing data reduction thus ensuring high spectral fidelity with low computational cost. The approach developed in this work integrates automated, computer-vision-based background removal, eliminating the need for the manual thresholding common in current literature. To resolve ambiguities among chemically similar subclasses, a tiered classification and multi-camera fusion strategy (NIR17 and NIR22) is implemented. Results demonstrate that ClassNMSW achieves an object-wise weighted accuracy of 98.70% for single-sensor configurations and 100% under sensor fusion. A novel rolling-window strategy satisfies desired end-to-end latency of <2 s, satisfying the strict deterministic requirements of high-speed industrial sorting environments. The ClassNMSW framework provides a scalable foundation for advancing circularity and resource recovery in large-scale waste valorization operations.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2026

An Updated Synthesis of the Projectile Point Typology and Chronology of Eastern Idaho

The eastern Idaho archaeological record is a unique confluence of the Great Basin, Columbia Plateau, and Great Plains, resulting in a diverse and complex projectile point sequence. This study presents a revised typology and chronology of projectile points in the region, grounded in a comprehensive review of over 750 diagnostic examples from 16 stratified sites and 110 associated radiocarbon dates. We reevaluate existing classifications and propose a revised chronological framework with regionally appropriate types. This study provides a thorough background to eastern Idaho projectile points and contributes to broader discussions of projectile point typology and chronology in the Desert West, offering a robust tool for future archaeological research in the region.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2026

Latency Analysis of the Nexus Digital Twin Framework

Real-time digital catalogs are increasingly relied upon to track metadata and connect disparate data sources for cloud-based data integration efforts. One such tool, Deeplynx Nexus is supporting real-time digital twin efforts through event-driven data integration and time-series queries. Nexus’s usefulness for these applications depends critically on how quickly individual records can be uploaded and downloaded, since delays directly affect the responsiveness of any system built on top of it. However, the actual latency a user should expect from Nexus has not been systematically measured before, particularly for the small, frequent transactions typical of live sensor feeds. Here we show that single-record round-trip latency is 61.1 ms on a local Nexus instance and 391.7 ms on the hosted production infrastructure, a roughly 6.4x difference driven primarily by fixed per-request overhead rather than data volume. This overhead dominates at small scale: comparing single-record and ten-record trials suggests approximately 56 ms of each single-record request is fixed connection and authentication cost rather than data-transfer time, meaning batching even a handful of records is substantially more efficient than transmitting them individually. At large batch sizes, this pattern reverses for uploads, which converge to near parity between local and hosted environments by 25,000-50,000 records, while download latency remains persistently 5.7-6.4x slower on hosted infrastructure even at scale. These results suggest that Nexus deployments intended for real-time digital twin applications should prioritize record batching over single-record transactions, and that download-path optimization on hosted infrastructure offers the largest remaining opportunity to reduce latency at scale. We anticipate these baseline measurements will serve as a reference point for future digital twin projects evaluating whether Nexus’s latency profile meets their real-time requirements, and as a benchmark for tracking the effect of future infrastructure or API changes.

99 - GENERAL AND MISCELLANEOUS↗