Large Multimodal Models (LMMs) have demonstrated significant potential in the medical domain, achieving impressive performance on tasks ranging from report generation to visual question answering. However, existing benchmarks predominantly focus on static evaluation, assessing models on isolated data points. This approach neglects a critical aspect of clinical practice: longitudinal analysis, where physicians interpret patient data as a dynamic trajectory to track disease progression and treatment response. To address this gap, we introduce ELTLM, the first benchmark specifically tailored to assess the temporal perception and reasoning capabilities of medical LMMs. Constructed from temporal chest X-rays, ELTLM features a hierarchical task taxonomy comprising Temporal Perception QA and Temporal Reasoning QA, requiring models to detect fine-grained visual changes and infer high-level clinical trends. Our evaluation of state-of-the-art models reveals that while they excel in static scenarios, they struggle significantly with temporal grounding and consistency. ELTLM serves as a vital resource to identify these limitations and guide the development of future time-aware medical AI systems. Our data is available at ELTLM.

CAPTCHAs are widely deployed as human verification mechanisms and frequently block intelligent agents from completing end-to-end automation in real-world web environments. Existing CAPTCHA-solving methods rely on prompting closed-source models, incurring high inference costs and lacking controllability, yet training-based approaches have remained absent due to the lack of large-scale training data with process-level annotations. To enable training-based CAPTCHA solving, we introduce CaptchaBench, a large-scale benchmark of 16,000 programmatically generated samples with detailed region and process-level annotations. Systematic evaluation on CaptchaBench reveals that existing methods fail on tasks requiring fine-grained visual detail capture, with a key challenge being the inability to supervise the model’s reasoning process over task-relevant visual regions. Therefore, we present CaptchaMind, an RL-based solver that explicitly supervises the model’s reasoning process by directly rewarding correct identification of task-relevant visual regions at intermediate steps. CaptchaMind achieves state-of-the-art performance across eight tasks and generalizes effectively to real-world CAPTCHA instances, substantially outperforming all existing methods without closed-source APIs.