Apertus 1.5 extends the fully open Apertus family from text to multimodal reasoning. The models underwent continued pretraining on 4T additional tokens for the 8B and 2T for the 70B, adding image and audio understanding and expanding the context window fourfold to 262,144 tokens. The models improved performance on nearly all benchmarks.
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Apertus 1.5 models may be useful for research and development where openness and data accessibility are important, as well as for tasks requiring multimodal analysis.
The 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 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 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 PapersContext can enhance the performance of large language models, but these gains may be lost after context removal. Researchers introduced Cortex, which generates prospective queries to anticipate context usage and distills context-conditioned outputs into mergeable low-rank or structural network updates. Cortex shows effectiveness in consolidation and retention of performance.
yesterdayDaily 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 ago