Text-to-chart retrieval enables users to locate relevant charts through natural language queries and has emerged as a critical capability in modern business intelligence systems. However, current benchmarks fail to simulate realistic user queries or test deep semantic understanding, leaving a significant evaluation gap. To address this, we introduce CRBench, the first real-world BI-sourced benchmark comprising 21,862 charts and 326 queries, which adopts a Target-and-Distractor paradigm to evaluate discriminative retrieval among highly similar candidates. Benchmarking reveals that existing methods, which rely primarily on visual features, fail to capture the rich analytical semantics of charts. We therefore propose a semantic insights synthesis pipeline that automatically generates three hierarchical levels of insights for alignment training: visual patterns, statistical properties, and practical applications. Using this pipeline, we produce 207,498 insights for 69,166 charts as training data. Leveraging this data, we develop ChartFinder, which significantly outperforms state-of-the-art methods on CRBench, achieving 66.9% NDCG@10 for precise queries (an 11.58% improvement) and an average gain of 5% across fuzzy query metrics.

Large Language Models (LLMs) have demonstrated strong cross-domain capabilities, yet their competence in specialized professional tasks remains underexamined. Existing legal benchmarks evaluate isolated tasks or exam-style questions, failing to capture the procedural interdependencies and adjudicative rigor inherent in professional practice. To bridge this gap, we construct JurisBench, a vertical, depth-oriented, domain-specific benchmark designed to evaluate LLMs across key stages of Chinese civil litigation. JurisBench introduces a Linear Depth Simulation track that mirrors the cognitive workflow of professional judges through four sequential, dependency-aware phases: Cause of Action prediction, Focus of Disputes identification, Rationale of the Judgment generation, and Result of the Judgment determination. Results reveal an “illusion of competence”: state-of-the-art models exhibit marked performance degradation in end-to-end pipelines due to cascading error propagation. We identify precise statutory grounding as a persistent bottleneck, highlighting a critical gap between fluent linguistic output and judicial reliability. JurisBench shifts evaluation from isolated legal knowledge to workflow-level task execution, providing a diagnostic framework for legal AI and a template for benchmark design in specialized domains.