Multimodal Large Language Models (MLLMs) show reasoning promise, yet their visual perception is a critical bottleneck. Paradoxically, MLLMs sometimes produce correct answers while misinterpreting crucial visual elements, masking these underlying perception failures. Our preliminary analysis on a joint perception-reasoning dataset revealed that 29% of correct reasoning answers from a leading MLLM contained perception errors. To systematically study visual perception abilities of MLLMs, we introduce Do You See Me- a scalable, programmatically generated benchmark with 1758 images and 2612 questions across seven core subtasks spanning 2D and 3D variants (twelve total tasks) providing parametric control over difficulty levels. The benchmark tasks are inspired by human psychology. Our evaluation of eleven leading MLLMs reveals a stark deficit: humans achieve 95.83% accuracy, while top MLLMs average below 50%. This performance gap widens drastically as task complexity increases. Further diagnostics show: (1) supervised finetuning offers only modest gains (11%), (2) models tend to exploit task “shortcuts” like MCQ formats over detailed visual analysis, and (3) Chain-of-Thought prompting can degrade complex visual tasks by verbalizing images into lossy text. These findings expose the foundational perception limits in current MLLMs and highlight the need for robust visual perception improvements in MLLMs. The benchmark dataset, source code and evaluation scripts are available at[<https://github.com/microsoft/Do-You-See-Me>].

There has been a surge of interest in harnessing the reasoning capabilities of Large Language Models (LLMs) to accelerate scientific discovery. While existing approaches rely on grounding the discovery process within the relevant literature, effectiveness varies significantly with the quality and nature of the retrieved literature. We address the challenge of retrieving prior work whose concepts can inspire solutions for a given research problem, a task we define as Methodology Inspiration Retrieval (MIR). We construct a novel dataset tailored for training and evaluating retrievers on MIR, and establish baselines. To address MIR, we build the Methodology Adjacency Graph (MAG); capturing methodological lineage through citation relationships. We leverage MAG to embed an “intuitive prior’’ into dense retrievers for identifying patterns of methodological inspiration beyond superficial semantic similarity. This achieves significant gains of +5.4 in Recall@3 and +7.8 in Mean Average Precision (mAP) over strong baselines. Further, we adapt LLM-based re-ranking strategies to MIR, yielding additional improvements of +4.5 in Recall@3 and +4.8 in mAP. Through extensive ablation studies and qualitative analyses, we exhibit the promise of MIR in enhancing automated scientific discovery and outline avenues for advancing inspiration-driven retrieval.