Reliable high-accuracy error mitigation for utility-scale quantum circuits

Kavli Affiliate: Itai Cohen| First 5 Authors: Dorit Aharonov, Dorit Aharonov, , , | Summary:Error mitigation is essential for unlocking the full potential of quantum algorithms and accelerating the timeline toward quantum advantage. As quantum hardware progresses to push the boundaries of classical simulation, efficient and robust error mitigation methods are becoming increasingly important for […]


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Supernova rates and luminosity functions from ASAS-SN II: 2014-2017 core-collapse supernovae and their subtypes

Kavli Affiliate: Subo Dong | Summary:The volumetric rates and luminosity functions (LFs) of core-collapse supernovae (ccSN) and their subtypes are important for understanding the cosmic history of star formation and the buildup of ccSN products. To estimate these rates, we use data of nearby ccSNe discovered by the All-Sky Automated Survey for Supernovae (ASAS-SN) from […]


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Effective Microstructure

Kavli Affiliate: Emil Martinec | Summary:In AdS$_3$/CFT$_2$ duality, there are large families of smooth, horizonless microstate geometries that correspond to heavy pure states of the dual CFT. The metric and fluxes are complicated functions of up to five coordinates. There are also many duals of heavy pure states that cannot be described in supergravity, but […]


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“Not in My Backyard”: LLMs Uncover Online and Offline Social Biases Against Homelessnes

Kavli Affiliate: Li Xin Li| First 5 Authors: [#item_custom_name[1, [#item_custom_name[2, [#item_custom_name[3, [#item_custom_name[4, [#item_custom_name[5| Summary:Homelessness is a persistent social challenge, impacting millions worldwide. Over 876,000 people experienced homelessness (PEH) in the U.S. in 2025. Social bias is a significant barrier to alleviation, shaping public perception and influencing policymaking. Given that online textual media and offline city […]


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Psyche-R1: Towards Reliable Psychological LLMs through Unified Empathy, Expertise, and Reasoning

Kavli Affiliate: Zhuo Li | First 5 Authors: Chongyuan Dai, Chongyuan Dai, , , | Summary: Amidst a shortage of qualified mental health professionals, the integration of large language models (LLMs) into psychological applications offers a promising way to alleviate the growing burden of mental health disorders. Recent reasoning-augmented LLMs have achieved remarkable performance in […]


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Psyche-R1: Towards Reliable Psychological LLMs through Unified Empathy, Expertise, and Reasoning

Kavli Affiliate: Zhuo Li | First 5 Authors: Chongyuan Dai, Chongyuan Dai, , , | Summary: Amidst a shortage of qualified mental health professionals, the integration of large language models (LLMs) into psychological applications offers a promising way to alleviate the growing burden of mental health disorders. Recent reasoning-augmented LLMs have achieved remarkable performance in […]


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On data-driven robust distortion risk measures for non-negative risks with partial information

Kavli Affiliate: Ran Wang | First 5 Authors: Xiangyu Han, Xiangyu Han, , , | Summary: In this paper, by proposing two new kinds of distributional uncertainty sets, we explore robustness of distortion risk measures against distributional uncertainty. To be precise, we first consider a distributional uncertainty set which is characterized solely by a ball […]


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Driving Accurate Allergen Prediction with Protein Language Models and Generalization-Focused Evaluation

Kavli Affiliate: Ran Wang | First 5 Authors: Brian Shing-Hei Wong, Brian Shing-Hei Wong, , , | Summary: Allergens, typically proteins capable of triggering adverse immune responses, represent a significant public health challenge. To accurately identify allergen proteins, we introduce Applm (Allergen Prediction with Protein Language Models), a computational framework that leverages the 100-billion parameter […]


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Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation

Kavli Affiliate: Zhuo Li | First 5 Authors: Huizhen Shu, Huizhen Shu, , , | Summary: With the rapid proliferation of Natural Language Processing (NLP), especially Large Language Models (LLMs), generating adversarial examples to jailbreak LLMs remains a key challenge for understanding model vulnerabilities and improving robustness. In this context, we propose a new black-box […]


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