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This paper introduces CGTime, a 4B-parameter computation-grounded time-series-language model that decouples perception from description using deterministic statistics and LLM verbalization, outperforming larger general-purpose models on multivariate understanding tasks.
This paper introduces a self-evolving framework for vision-language models to improve their question-generation capabilities without external supervision, enhancing both question quality and answerer performance.
This paper introduces U-TTT, a U-shaped deep learning model with test-time training layers and dual-domain adaptation for robust PET image denoising under distribution shifts, achieving state-of-the-art performance across different dose levels and scanner types.
This paper introduces TraceLock, a lightweight plug-in controller that learns a token-commitment policy for frozen diffusion language models, improving the quality-step tradeoff across various tasks without retraining.
EVE-Agent introduces a framework for self-evolving search agents that ensure evidence verifiability by generating questions, answers, and evidence spans, and training on marginal accuracy gain of evidence. This improves grounded correctness without human annotations.