Specialist-model research desk

Scorecard

Which criteria and weights should we use?

Frontier Development research deskSection 09 · Page 4 of 13Last updated 26 October 2026
Criterion Weight What good looks like What weak or bad looks like Evidence requested
Task quality and failure coverage 20 Acceptance tied to business outcomes; difficult slices and critical errors visible Leaderboard or cherry-picked demo; failures omitted Paired results, denominators, errors and slice reports
Evaluation integrity 15 Buyer-controlled holdout, justified split, calibrated graders and reproducibility Training/test overlap; score without rubric; supplier controls all answers Split manifest, access policy, run config and adjudication record
Data and adaptation plan 15 Rights, labels, lineage, base choice and bounded experiments connect to failures More data or more training proposed without diagnosis Inventory, label guide, provenance and ablation plan
Whole-life economics 10 Common accepted-task denominator; one-off/recurring costs and downside cases Token saving only; missing labour, retries or minimum commitments Itemised quote and scenario workbook
Production engineering 10 Actual serving config, load tests, fallback, rollback and observability Notebook handover with deployment left implicit Architecture, traces, operational runbook
Security and governance 10 Appropriate handling, access, retention, incident and change controls Generic badges with no product/contract scope Data-flow review and contractual controls
Rights and exit 10 Artefact schedule and feasible export/redeployment path “You own it” without transferable files or permissions Licence list, export package and rehearsal plan/result
Delivery capacity and scope 5 Named team, dependencies, milestones and change control Unnamed resources and vague acceptance Work plan, responsibility matrix and reference questions
Maintenance and reassessment 5 Owners, cost, regression suite and triggers to retain/replace Automatic retraining retainer without diagnosis Maintenance schedule and baseline refresh policy
Total 100

The weighting intentionally favours task evidence over presentation and price. A privacy-sensitive deployment may require different weights, but change them before opening bids. Error analysis and appropriate split design are useful evidence checks; they are not substitutes for inspecting the actual submission.

TRACE THE EVIDENCE

Sources cited on this page

  1. Error analysis →hamel.dev
  2. appropriate split design →scikit-learn.org