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This paper systematically investigates when auxiliary context helps in multi-modal time series forecasting, identifying two key conditions and demonstrating significant performance improvements when those conditions are met.
SoftVTBench presents a synchronized visuo-tactile dataset and deformation-aware benchmark for evaluating physical interaction quality in deformable-object manipulation, including 4,000 demonstrations and a new success metric.
The paper proposes UNIT, a framework that fine-tunes a large language model on the first task to enhance its adaptability for graph continual learning, addressing semantic-structural separation and imbalanced knowledge transfer. Experiments show state-of-the-art performance.