CANDI-QA: A Benchmark for Context-Sensitive Question Answering in Specialized Domains

Jul 15, 2026

Researchers have introduced CANDI-QA, a new dataset designed to evaluate large language models (LLMs) on context-sensitive and user-aligned question answering in specialized fields such as medical diagnostics and financial advisory. The dataset includes both factual and multi-hop reasoning questions, and initial tests show that current LLMs face significant challenges without improved contextual or symbolic reasoning capabilities. CANDI-QA also provides a neuro-symbolic baseline model, MTSS-Net, to benchmark progress.

Why it matters: CANDI-QA exposes critical limitations in current LLMs for high-stakes, specialized domains, guiding future research toward more reliable and context-aware AI systems.

Full story at: arXiv Computation and Language ↗