ModelRefs / Resume Screening — Canonical Workflow

Resume Screening — Canonical Workflow

Resume Screening: provisional AI workflow implementation reference with candidate models, providers, tools, and architecture.

Overview

Bias-audited resume screening with structured rubric scoring and human-reviewer-friendly explanations. Resume Screening is a provisional implementation reference with candidate models, providers, tools, benchmarks and deployment patterns to validate on the target workload. Designed for HR teams with privacy-first data handling, role-based access controls, and employee-data governance aligned with GDPR and regional labour law. Model outputs are anonymised at rest and accessible only to authorised HR business partners. Escalation paths and override mechanisms are documented for sensitive decisions.

Implementation profile

Categoryllms
Implementation maturityproduction
Evidence statusincomplete
Primary use casesextraction, reasoning
Deployment optionsmanaged-api, hybrid
Architecturesserverless-api, managed-container, hybrid-private-cloud

Candidate models with published references

Coverage means the model is a candidate worth evaluating for this workflow, not a ranking or a recommendation. Models whose reference pages are still in review are omitted.

Benchmarks relevant to this workflow

miracl, mkqa, mldr, swe-bench, aider-polyglot, gpqa, aime-2025, tau-bench, browsecomp-long-context, longfact-concepts, terminal-bench, mmmu, mmlu-pro, livecodebench.

Relevance is a coverage signal from the canonical registry. Each benchmark only describes its own protocol and date, so confirm the harness matches your workload before treating a score as evidence.

Continue your research

Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to Resume Screening — Canonical Workflow.