National Yang Ming Chiao Tung University

PRISM-Net splits a product image's holistic features into a line pathway and a color pathway, supervises the two routers zero-shot with frozen SigLIP text prompts, and fuses both with an image-adaptive meta-router. In 5-fold cross-validation over 275,454 e-commerce images in 18 categories and full-scale retrieval of 55,091 queries against a 220,363-image gallery, the SwinV2 backbone gains 5.74 points in line mAP@10, while the color-only configuration gains 9.12 points in color mAP@10; on ConvNeXt-V2 and DINOv3, product retrieval and attribute alignment trade off.

PRISM-Net splits a product image's holistic features into a line pathway and a color pathway, supervises the two routers zero-shot with frozen SigLIP text prompts, and fuses both with an image-adaptive meta-router. In 5-fold cross-validation over 275,454 e-commerce images in 18 categories and full-scale retrieval of 55,091 queries against a 220,363-image gallery, the SwinV2 backbone gains 5.74 points in line mAP@10, while the color-only configuration gains 9.12 points in color mAP@10; on ConvNeXt-V2 and DINOv3, product retrieval and attribute alignment trade off.

JuloVelo moves gene kinetics pattern identification ahead of neural network training: each gene is first classified as Circle, Complete induction, or Complete repression from its unspliced–spliced RNA relationship, and training starts from pattern-matched pretrained weights, steering cell-specific kinetics away from biologically implausible parameter regimes. Across eight biological datasets spanning erythrogenesis, neurogenesis, and endocrinogenesis, plus simulated data with known kinetics, it was more stable than constant-rate and cell-specific models, was the only method to resolve all five hippocampal neurogenesis branches, and used promoter chromatin accessibility to process multiome data.

JuloVelo moves gene kinetics pattern identification ahead of neural network training: each gene is first classified as Circle, Complete induction, or Complete repression from its unspliced–spliced RNA relationship, and training starts from pattern-matched pretrained weights, steering cell-specific kinetics away from biologically implausible parameter regimes. Across eight biological datasets spanning erythrogenesis, neurogenesis, and endocrinogenesis, plus simulated data with known kinetics, it was more stable than constant-rate and cell-specific models, was the only method to resolve all five hippocampal neurogenesis branches, and used promoter chromatin accessibility to process multiome data.

The authors propose BLS, an offline zero-shot task-inference method for Behavioral Foundation Models that starts from an ordinary-least-squares (OLS) task vector and refines it with a soft-margin contrastive loss plus a trust-region loss to reduce both reward reconstruction error and successor-measure mismatch; they prove a suboptimality gap upper bound characterized by successor-measure and reward residuals, and empirically BLS outperforms OLS, ZOL, ReLA, and LoLA on average across five feature representations on OGBench (proprioceptive and pixel observations), 20 DMC tasks, and 45 HumEnv humanoid control tasks, with negligible inference overhead.

The authors propose BLS, an offline zero-shot task-inference method for Behavioral Foundation Models that starts from an ordinary-least-squares (OLS) task vector and refines it with a soft-margin contrastive loss plus a trust-region loss to reduce both reward reconstruction error and successor-measure mismatch; they prove a suboptimality gap upper bound characterized by successor-measure and reward residuals, and empirically BLS outperforms OLS, ZOL, ReLA, and LoLA on average across five feature representations on OGBench (proprioceptive and pixel observations), 20 DMC tasks, and 45 HumEnv humanoid control tasks, with negligible inference overhead.