AI · 8 min read · April 30, 2026
LATTICE: Measuring Crypto Agent Quality Beyond Accuracy
New benchmark evaluates how well AI agents support user decisions in crypto, not just whether they get answers right.
Source: arxiv/cs.AI · Aaron Chan, Tengfei Li, Tianyi Xiao, Angela Chen, Junyi Du, Xiang Ren · open original ↗ ↗
LATTICE benchmarks crypto AI agents on decision-support utility across six dimensions and 16 task types using scalable LLM judges.
- — Shifts focus from reasoning accuracy to whether agents help users make better decisions.
- — Defines six evaluation dimensions capturing real decision-support properties needed in crypto workflows.
- — Spans 16 task types covering the full crypto copilot user journey, not isolated subtasks.
- — Uses LLM judges to score at scale without requiring expert annotation or external ground truth.
- — Tests six production crypto copilots on 1,200 queries; finds dimension-level trade-offs matter more than aggregate scores.
- — Reveals different copilots excel at different decision-support tasks, suggesting user priorities drive tool choice.
- — Rubrics remain auditable and updatable with human feedback, enabling continuous improvement.
Frequently asked
- LATTICE focuses on decision-support utility—whether agents help users decide—rather than just reasoning accuracy or outcome correctness. It evaluates six decision-support dimensions across 16 task types using LLM judges, and tests production-level agents in real crypto copilot products. This reflects how orchestration and UI/UX design affect agent quality in practice, not just model capability.