Researchers introduced Player-1, a framework for learning game control through synthetic experience. They expanded the computer-use action space and created synthetic environments for training. Player-1-9B showed significant improvement in MineDojo-300 compared to baseline models.
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Player-1 can be useful for developing more effective agents capable of playing games using synthetic experience, improving their generalization capabilities.
The study presents AgentSWE, a benchmark evaluating coding agents' ability to build agents aligned with user requirements. The research found that existing agents fail to accurately fulfill tasks, as there is a significant gap between demonstrated and actual performance.
6 days agoDaily PapersThe paper introduces S-Space, a linear subspace in multimodal language models where object-token representations encode continuous spatial coordinates. It demonstrates that S-Space serves as an internal workspace for spatial reasoning, enabling accurate spatial judgments through perception, internal reasoning, and selective verbal access, while highlighting limitations in spatial transformations across viewpoints.
5 days agoDaily PapersThe paper introduces TerminalHorizon, an automated data synthesis engine for long-horizon terminal agents. It constructs 1.5K task environments and fine-tunes Qwen3.5-27B to create TerminalHorizon-27B, achieving performance gains across multiple benchmarks. The model demonstrates improved ability to experiment, refine methods, and retain effective improvements in final results.
6 days agoDaily PapersUrbex is an agentic vision-language framework that reformulates grounding as active spatial search. It represents a city-scale 3D scene as an interactive multi-view environment, enabling a VLM agent to query landmarks, zoom into local regions, and commit grounded bounding boxes. Experiments show Urbex achieves the best Hit@1 and IoU on CityRefer.
2 days agoDaily PapersDetermining whether an image is AI-generated does not establish whether the situation it depicts is factually supported. We formulate Image Factuality Assessment: given an image and a generic verification instruction, a system returns a supported or refuted verdict with an evidence-grounded explanation. We introduce EviLens, an expert-reviewed benchmark of 10,174 supported and refuted images covering twelve factual error types.
5 days agoDaily PapersMulti-Stream Attention Residuals (MSAR) extends the AttnRes method by generalizing the attention mechanism to multi-head depth, achieving significant performance improvements with minimal additional overhead. MSAR leverages multi-scale causal convolutions to draw stream diversity from sequence context, allowing different heads to retrieve historical representations through distinct contextual views.
6 days ago