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The paper introduces DECOWAM, a decoupled whole-body world-action model for legged mobile manipulation that improves video and action prediction performance over existing models like FastWAM through dedicated conditional interfaces and a new dataset.
Black Forest Lab's Flux 3 is a new omni-modal AI model capable of generating and predicting images, video, audio, and actions.
This paper introduces memory-augmented speculative execution for LLM agents, using three online memory systems to improve prediction accuracy by 19-39% on action prediction and up to 2.5x on observation prediction, all while being lossless with zero added wall-clock cost.
CLAP proposes a method to convert pretrained vision-language models (VLMs) into vision-language-action models (VLAs) by prepending natural-language action descriptions to action token sequences, preserving semantic capabilities without architectural changes. It achieves 90.8% on LIBERO and improves robustness.
An open model that predicts a robot's actions from a control signal, raising questions about whether it constitutes a world model or just a video generator.
Light-WAM is a lightweight world action model for efficient robot manipulation that uses a compact video backbone and downsampled latent space for future-video supervision, achieving high performance with low inference latency.
RoboSemanticBench is a benchmark that diagnoses semantic grounding in action prediction for vision-language-action models, revealing that while robots can grasp objects, they fail to select semantically correct targets based on instruction semantics.
This paper introduces the PiSAR benchmark for screen-conditioned action prediction and compares supervised fine-tuned models against frontier zero-shot baselines. Key findings show a fine-tuned Qwen3-VL-8B achieves 0.783 semantic similarity, significantly outperforming Claude Opus 4.7 and GPT-5.5 (0.459 and 0.482), but the same fine-tuning recipe on a larger reasoning-tuned Gemma model yields only 0.441, indicating a model-recipe mismatch.
MementoGUI introduces a plug-in agentic memory framework for GUI agents that uses learned controllers for selective memory management and retrieval, improving performance on long-horizon tasks with compressed visual and textual representations.