FRONTIER AI RESEARCH
Evaluation and verification
Measure capability, safety, reliability, and quality with human evaluation and SME verification.
Eval design · Held-out sets · Rubric design · Calibrated graders · Adjudication · Error analysis
Alignment and post-training data
Generate targeted supervision and judgment signals around defined capability gaps.
SFT demonstrations · Preference data · Critiques · Corrections · Instruction-response pairs · Specialist annotation
Agentic evaluations and trajectories
Evaluate planning, tool use, recovery, and human escalation in interactive task environments.
Long-horizon tasks · Model rollouts · Tool traces · Interventions · Process supervision · Outcome verification
Human-AI interaction research
Study how users interpret, trust, supervise, and override model behavior across populations.
Trust calibration · Reliance · Distributional performance · Misuse testing · UX evaluation · Longitudinal effects
The most valuable AI research requires more than a dataset. Arctic translates model-development questions into complete research systems: designing the people, tasks, environments, instrumentation, graders, and quality controls needed for defensible evaluation and reproducible artifacts.
Global reach, local context
Run consistent programs across markets, languages, and cultures while preserving the local context that shapes trust, communication, workflows, and tool use. Surface failures that English-language benchmarks and simple translations miss.
Models that work across cultures
Design multilingual and cross-cultural evals, post-training datasets, and human-model behavior studies with locally relevant cohorts, experts, tasks, and grading criteria. See where behavior generalizes and what must change where it does not.
Full-trace evaluation
Capture prompts, reasoning-relevant actions, tool calls, interventions, recovery attempts, and outcomes—not only final answers.
Verified human intelligence
Recruit consumer populations, AI taskers, specialized professionals, and domain experts against explicit screening criteria.
Realistic task environments
Run studies in purpose-built tasks, enterprise workflows, consumer products, games, and historical performance archives.
Reproducible releases
Ship versioned datasets, task specs, rubrics, grader configs, provenance, limitations, and strict train/eval separation.
HOW IT WORKS
Specify the target
Define the capability, failure mode, population effect, and decision the study must support.
Design the protocol
Configure cohorts, task distributions, environments, telemetry, rubrics, and acceptance thresholds.
Collect and calibrate
Run human and model trajectories, calibrate graders, adjudicate disagreement, and analyze error modes.
Validate and release
Deliver evals, datasets, grader specs, methodology, provenance, findings, and limitations.
