Researchers introduced VideoMM, an adaptive macro-micro inference framework for video multimodal large language models that decouples region selection from detailed reasoning, according to an arXiv pa…
Researchers introduced UniCAR-RL, an annotation-free reinforcement learning framework that decouples perception and reasoning optimization in Multimodal Large Language Models (MLLMs) to improve comple…
Researchers introduced LACL-GUI, a length-aware contrastive learning framework for GUI agents powered by multimodal large language models, which improves reinforcement learning stability and performan…
A new study identifies Relevant Visual Information Shift (RVIS) during decoding as the primary cause of performance degradation in visual token pruning methods for Multimodal Large Language Models (ML…
Researchers introduce PerceptionBench, a benchmark designed to evaluate atomic visual perception in multimodal large language models (MLLMs), finding that no model reaches 60% accuracy across ten atom…
A new benchmark called MissionBench reveals that even the strongest multimodal large language models (MLLMs) succeed on fewer than 35% of aerial missions, compared to 84.4% human performance, accordin…
Researchers introduced ViSTR-Bench, a benchmark to evaluate whether multimodal large language models (MLLMs) can reason from continuous visual cues in dynamic scenes, finding that current models remai…
Researchers propose ForeAgent, an agentic forensics framework for AI-generated image detection that uses a Perception-Verdict architecture and a Hindsight-Driven Self-Refining strategy to iteratively …
Researchers propose ThinkDeception, a novel framework that uses multimodal large language models and chain-of-thought reasoning to detect deception with interpretability. It introduces a progressive t…