Existing tabular reasoning benchmarks typically assume that input tables are clean and error-free, which contrasts sharply with real-world data pipelines where noise and anomalies are pervasive. While humans routinely validate tables and reject conclusions when anomalies render a question unanswerable, it remains unclear whether large language models (LLMs) exhibit similar anomaly awareness. In this work, we introduce AnTBench, a benchmark designed to systematically evaluate LLM behavior under anomalous tabular data. AnTBench consists of 1,571 curated instances covering five common and verifiable anomaly types. Experiments on 14 representative LLMs reveal a pronounced capability–behavior gap: even the strongest model achieves 76.07% on explicit anomaly detection, yet only 19.03% on spontaneous anomaly perception during standard table QA. As an inference-time mitigation, we introduce the Multi-Path Thinker, a multi-path reasoning framework. While this approach improves explicit anomaly detection, enforcing proactive verification for every query is practically prohibitive. We urge the community to move beyond inference-time patches and instill spontaneous anomaly awareness directly into the training and alignment phases. Our code and data are available at https://github.com/Wei12097/AnTBench.

Large language model agents have enabled GUI-based automation, particularly for mobile devices. However, deployment remains limited by noisy data, poor generalization, and lack of support for non-English GUIs. In this work, we present AgentCPM-GUI, an 8B-parameter GUI agent built for robust and efficient on-device GUI interaction. Our training pipeline includes grounding-aware pre-training to enhance perception, supervised fine-tuning on high-quality Chinese and English trajectories to imitate human-like actions, and reinforcement fine-tuning with GRPO to improve reasoning capability. AgentCPM-GUI achieves promising performance on five public benchmarks and our proposed Chinese benchmark CAGUI. To facilitate reproducibility and further research, we publicly release all code, model checkpoint, and evaluation data at: https://github.com/OpenBMB/AgentCPM-GUI