Recent ARC-AGI systems have shown that pure vision can solve abstract grid reasoning without language backbones, but most strong models still rely on fixed-depth feed-forward computation. We study whether recurrent computation is a better scaling axis for this setting. We propose Loop-ViT, a vision-native recurrent model that reuses a weight-tied Hybrid Block across multiple steps, with local convolutions for image-token refinement and attention for global rule induction. We also use an entropy-based exit rule to reduce unnecessary iterations at inference time. On ARC-AGI-1, Loop-ViT improves the parameter-efficiency trade-off over fixed-depth vision baselines and reaches 65.8% accuracy with an 18M model. Overall, the results suggest that repeated computation is a useful inductive bias for ARC-style visual reasoning. The code is available at https://github.com/WenjieShu/LoopViT.

Recent multimodal large language models (MLLMs) have demonstrated significant potential in open-ended conversation, generating more accurate and personalized responses. However, their abilities to memorize, recall, and reason in sustained interactions within real-world scenarios remain underexplored. This paper introduces MMRC, a Multi-Modal Real-world Conversation benchmark for evaluating six core open-ended abilities of MLLMs: information extraction, multi-turn reasoning, information update, image management, memory recall, and answer refusal. With data collected from real-world scenarios, MMRC comprises 5,120 conversations and 28,720 corresponding manually labeled questions, posing a significant challenge to existing MLLMs. Evaluations on 20 MLLMs in MMRC indicate an accuracy drop during open-ended interactions. We identify four common failure patterns: long-term memory degradation, inadequacies in updating factual knowledge, accumulated assumption of error propagation, and reluctance to “say no.” To mitigate these issues, we propose a simple yet effective NOTE-TAKING strategy, which can record key information from the conversation and remind the model during its responses, enhancing conversational capabilities. Experiments across six MLLMs demonstrate significant performance improvements.