Hybrid Inflation from Fermion Condensate

Kavli Affiliate: Misao Sasaki | First 5 Authors: Stephon Alexander, Stephon Alexander, , , | Summary: We investigate how inflation can emerge from four-fermion interactions generated by spacetime torsion, eliminating the need for additional scalar fields beyond the Standard Model. We partition fermions in two sectors and introduce two bound fields. In the effective theory […]


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AMS-L0 silicon ladder assembly with high precision by gantry

Kavli Affiliate: Feng Wang | First 5 Authors: Feng Wang, Feng Wang, , , | Summary: A high-precision silicon microstrip detector assembly methodology based on a gantry system is presented in this paper. The proposed approach has been applied to the mass production of all flight-model detector modules for the L0 tracking detector in the […]


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Agentic Services Computing

Kavli Affiliate: Cheng Peng | First 5 Authors: Shuiguang Deng, Shuiguang Deng, , , | Summary: The rise of large language model (LLM)-powered agents is transforming services computing, moving it beyond static, request-driven functions toward dynamic, goal-oriented, and socially embedded multi-agent ecosystems. We propose Agentic Services Computing (ASC), a paradigm that reimagines services as autonomous, […]


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Experience Paper: Adopting Activity Recognition in On-demand Food Delivery Business

Kavli Affiliate: Wei Gao | First 5 Authors: , , , , | Summary: This paper presents the first nationwide deployment of human activity recognition (HAR) technology in the on-demand food delivery industry. We successfully adapted the state-of-the-art LIMU-BERT foundation model to the delivery platform. Spanning three phases over two years, the deployment progresses from […]


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Search for Distant Hypervelocity Star Candidates Using RR Lyrae Stars

Kavli Affiliate: Huawei Zhang | First 5 Authors: Haozhu Fu, Haozhu Fu, , , | Summary: Hypervelocity stars (HVSs) are stars with velocities exceeding their local escape velocities. Searching for HVSs and studying their origins can be an important way to study the properties of the Milky Way. In this paper, we utilize precise distances […]


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HunyuanImage 3.0 Technical Report

Kavli Affiliate: Li Xin Li | First 5 Authors: Siyu Cao, Siyu Cao, , , | Summary: We present HunyuanImage 3.0, a native multimodal model that unifies multimodal understanding and generation within an autoregressive framework, with its image generation module publicly available. The achievement of HunyuanImage 3.0 relies on several key components, including meticulous data […]


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AutoPrune: Each Complexity Deserves a Pruning Policy

Kavli Affiliate: Ke Wang | First 5 Authors: Hanshi Wang, Hanshi Wang, , , | Summary: The established redundancy in visual tokens within large vision-language models allows pruning to effectively reduce their substantial computational demands. Previous methods typically employ heuristic layer-specific pruning strategies where, although the number of tokens removed may differ across decoder layers, […]


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Probing Scalar-Mediated Sterile Neutrinos with Gravitational Wave and Colliders Signals

Kavli Affiliate: Jia Liu | First 5 Authors: Qi Bi, Qi Bi, , , | Summary: We propose a UV-complete extension of the Standard Model in which a gauge-singlet scalar $S$ acquires a vacuum expectation value, generates a Majorana mass for a sterile neutrino $N$, and mixes with the Higgs field. This framework addresses neutrino […]


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RADAR: A Risk-Aware Dynamic Multi-Agent Framework for LLM Safety Evaluation via Role-Specialized Collaboration

Kavli Affiliate: Zheng Zhu | First 5 Authors: Xiuyuan Chen, Xiuyuan Chen, , , | Summary: Existing safety evaluation methods for large language models (LLMs) suffer from inherent limitations, including evaluator bias and detection failures arising from model homogeneity, which collectively undermine the robustness of risk evaluation processes. This paper seeks to re-examine the risk […]


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AdaPtis: Reducing Pipeline Bubbles with Adaptive Pipeline Parallelism on Heterogeneous Models

Kavli Affiliate: Wei Gao | First 5 Authors: Jihu Guo, Jihu Guo, , , | Summary: Pipeline parallelism is widely used to train large language models (LLMs). However, increasing heterogeneity in model architectures exacerbates pipeline bubbles, thereby reducing training efficiency. Existing approaches overlook the co-optimization of model partition, model placement, and workload scheduling, resulting in […]


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