Topic
perception
ROSE Benchmark Reveals Perception-to-Action Gap in Multimodal AI Models
The ROSE benchmark measures how reliably multimodal large language models (MLLMs) convert visual evidence into context-appropriate actions. Testing nine recent models, researchers found performance drops of up to 44.5 percentage points from counting to region-conditioned action, while humans achieve 98.8% accuracy.
PerceptionDLM: Multimodal Diffusion Model Achieves Parallel Region Perception
Researchers propose PerceptionDLM, a multimodal diffusion language model optimized for parallel region perception. Built on the state-of-the-art baseline PerceptionDLM-Base, it uses efficient prompting and structured attention masking to generate descriptions for multiple masked regions simultaneously, significantly improving inference efficiency. The team also introduces the ParaDLC-Bench benchmark to evaluate parallelism in visual perception.
Deep Residual Injection Method Enables Full-Spectrum Forensic AI Detection in Multimodal Models
Researchers propose Deep Visual Residual MLLM (Deep-VRM), a method that injects low-level artifact signals into multimodal large language models without disrupting pre-trained semantic knowledge. The approach achieves state-of-the-art detection of AI-generated images across multiple benchmarks.
RAMS: Resource-Adaptive Model Switching for Embedded Edge Perception Under Load
Researchers present RAMS, a runtime controller that monitors device pressure and dynamically selects among three YOLOv8 tiers on embedded hardware, achieving up to 5.6x faster inference than a fixed medium model while retaining 74% of its accuracy. The system introduces a detection-conditioned switching policy and a new scalar metric, SWAS, for offline policy comparison.
Survey on Medical Embodied AI Highlights Integration of Perception, Decision-Making, and Action
A systematic survey of medical embodied AI examines its core components — perception, decision-making, and action — and their coordinated integration for real-world clinical workflows. The paper reviews representative applications, datasets, and challenges, highlighting the need for unified system-level organization beyond individual functional aspects.
DH-V2: Geometry-Based Sampler Achieves 1,433x Compression for Edge Perception
Researchers present Double-Helix Vision (DH-V2), a geometry-based visual sampler that compresses 2D images into compact 1D signals using golden-ratio-inspired spiral trajectories. At 4K resolution, it achieves a 1,433x compression ratio while running in 0.52ms on CPU-only hardware, and includes a JSON-serializable Robotics API for bandwidth-constrained perception.