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Imperial College London
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Reasoning VLMs falter under semantic distractions, often mistaking irrelevant cues for evidence, which can lead to incorrect answers.
Mixed-authorship documents can be harder to detect than purely human or AI-generated texts, challenging existing assumptions about AI-text detection.
Integrating raster and vector data could revolutionize geospatial AI, unlocking richer insights from Earth observation data.
Achieving superior safety alignment with LLMs using only 100 harmful samples, SafeSteer drastically cuts alignment costs while maintaining model performance.
Forget data selection鈥攔eordering your existing dataset using these four simple guidelines can significantly boost LLM training performance and stability.
Achieve stable and accurate robot control with significantly less data by learning the system's energy function directly.
Spatial foundation models aren't as "all-round" as we thought: SpatialBench reveals surprising generalization gaps and the critical importance of domain alignment over naive data scaling.
Over-eagerly adapting VLMs for robot control can actually hurt performance, suggesting the original VLM representation already encodes surprisingly useful action priors.
