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.
Astrobobo tool mapping
- Knowledge Capture Record the six LATTICE dimensions and the 16 task types as a reference schema. Add notes on which dimensions your users prioritize most (e.g., speed vs. comprehensiveness). Update as you gather feedback.
- Focus Brief Summarize the key finding—that aggregate scores hide dimension-level trade-offs—and share with your product and research teams. Use it to frame which copilot or agent variant suits which user segment.
- Reading Queue Queue the full LATTICE paper and the six copilot evaluation results. Skim the dimension breakdowns to see where your agent might underperform relative to production competitors.
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.
cite ▸
APA
Aaron Chan, Tengfei Li, Tianyi Xiao, Angela Chen, Junyi Du, Xiang Ren. (2026, April 30). LATTICE: Measuring Crypto Agent Quality Beyond Accuracy. Astrobobo Content Engine (rewrite of arxiv/cs.AI). https://astrobobo-content-engine.vercel.app/article/lattice-measuring-crypto-agent-quality-beyond-accuracy-236802
MLA
Aaron Chan, Tengfei Li, Tianyi Xiao, Angela Chen, Junyi Du, Xiang Ren. "LATTICE: Measuring Crypto Agent Quality Beyond Accuracy." Astrobobo Content Engine, 30 Apr 2026, https://astrobobo-content-engine.vercel.app/article/lattice-measuring-crypto-agent-quality-beyond-accuracy-236802. Based on "arxiv/cs.AI", https://arxiv.org/abs/2604.26235.
BibTeX
@misc{astrobobo_lattice-measuring-crypto-agent-quality-beyond-accuracy-236802_2026,
author = {Aaron Chan, Tengfei Li, Tianyi Xiao, Angela Chen, Junyi Du, Xiang Ren},
title = {LATTICE: Measuring Crypto Agent Quality Beyond Accuracy},
year = {2026},
url = {https://astrobobo-content-engine.vercel.app/article/lattice-measuring-crypto-agent-quality-beyond-accuracy-236802},
note = {Astrobobo rewrite of arxiv/cs.AI, https://arxiv.org/abs/2604.26235},
}