Tootfinder

Scaling Language-Centric Omnimodal Representation Learning
Chenghao Xiao, Hou Pong Chan, Hao Zhang, Weiwen Xu, Mahani Aljunied, Yu Rong
https://arxiv.org/abs/2510.11693 https://…

Scaling Language-Centric Omnimodal Representation Learning
Recent multimodal embedding approaches leveraging multimodal large language models (MLLMs) fine-tuned with contrastive learning (CL) have shown promising results, yet the underlying reasons behind their superiority remain underexplored. This work argues that a crucial advantage of MLLM-based approaches stems from implicit cross-modal alignment achieved during generative pretraining, where the language decoder learns to exploit multimodal signals within a shared representation space for generati…

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Power corrections to the production of a prompt photon in association with a jet in the $N$-jettiness slicing scheme at NLO QCD
Prem Agarwal, Kirill Melnikov, Ivan Pedron
https://arxiv.org/abs/2510.03097

Power corrections to the production of a prompt photon in association with a jet in the $N$-jettiness slicing scheme at NLO QCD
We compute the next-to-leading-power corrections in the $N$-jettiness variable to the production of a prompt photon and a jet at next-to-leading order in perturbative QCD in the $q \bar q$ annihilation channel. We employ the $k_\perp$ jet algorithm and assume that the $N$-jettiness value divided by the jet transverse momentum is the smallest parameter in the problem; in particular it should be small compared to the jet radius $R$.

InfiMed-Foundation: Pioneering Advanced Multimodal Medical Models with Compute-Efficient Pre-Training and Multi-Stage Fine-Tuning
Guanghao Zhu, Zhitian Hou, Zeyu Liu, Zhijie Sang, Congkai Xie, Hongxia Yang
https://arxiv.org/abs/2509.22261

InfiMed-Foundation: Pioneering Advanced Multimodal Medical Models with Compute-Efficient Pre-Training and Multi-Stage Fine-Tuning
Multimodal large language models (MLLMs) have shown remarkable potential in various domains, yet their application in the medical field is hindered by several challenges. General-purpose MLLMs often lack the specialized knowledge required for medical tasks, leading to uncertain or hallucinatory responses. Knowledge distillation from advanced models struggles to capture domain-specific expertise in radiology and pharmacology. Additionally, the computational cost of continual pretraining with lar…

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