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Ancilla-train quantum algorithm for simulating non-Markovian open quantum systems
Hans Michael Christensen, Johannes Agerskov, Frederik Nathan
https://arxiv.org/abs/2509.12717 h…

Ancilla-train quantum algorithm for simulating non-Markovian open quantum systems
We present a quantum algorithm for simulating open quantum systems coupled to Gaussian environments valid for any configuration and coupling strength. The algorithm is for instance applicable to problems with strongly coupled, or non-Markovian, environments; problems with multiple environments out of mutual equilibrium; and problems with time-dependent Hamiltonians. We show that the algorithm can reproduce the true dynamics of such problems at arbitrary accuracy and, for a broad range of proble…

Accuracy Paradox in Large Language Models: Regulating Hallucination Risks in Generative AI
Zihao Li, Weiwei Yi, Jiahong Chen
https://arxiv.org/abs/2509.13345 https://

Accuracy Paradox in Large Language Models: Regulating Hallucination Risks in Generative AI
As Large Language Models (LLMs) permeate everyday decision-making, their epistemic and societal risks demand urgent scrutiny. Hallucinations, the generation of fabricated, misleading, oversimplified or untrustworthy outputs, has emerged as imperative challenges. While regulatory, academic, and technical discourse position accuracy as the principal benchmark for mitigating such harms, this article contends that overreliance on accuracy misdiagnoses the problem and has counterproductive effect: t…

Do LLMs Understand Wine Descriptors Across Cultures? A Benchmark for Cultural Adaptations of Wine Reviews
Chenye Zou, Xingyue Wen, Tianyi Hu, Qian Janice Wang, Daniel Hershcovich
https://arxiv.org/abs/2509.12961

Do LLMs Understand Wine Descriptors Across Cultures? A Benchmark for Cultural Adaptations of Wine Reviews
Recent advances in large language models (LLMs) have opened the door to culture-aware language tasks. We introduce the novel problem of adapting wine reviews across Chinese and English, which goes beyond literal translation by incorporating regional taste preferences and culture-specific flavor descriptors. In a case study on cross-cultural wine review adaptation, we compile the first parallel corpus of professional reviews, containing 8k Chinese and 16k Anglophone reviews. We benchmark both ne…

Rich Vehicle Routing Problem with diverse Vertices allowing Hierarchical and Multimodal Time-Dependant Transhipment of multiple Node- Vehicle- compatible Cargo with Cascaded Time-Minimization Objective for Emergency Decision Support Systems
Santanu Banerjee, Goutam Sen, Siddhartha Mukhopadhyay
https://arxiv.org/abs/2509.13227

Rich Vehicle Routing Problem with diverse Vertices allowing Hierarchical and Multimodal Time-Dependant Transhipment of multiple Node- Vehicle- compatible Cargo with Cascaded Time-Minimization Objective for Emergency Decision Support Systems
A rich vehicle routing problem is considered allowing multiple trips of heterogeneous vehicles stationed at distributed vehicle depots spread across diverse geographies having access to different modes of transportation. The problem arises from the real world requirement of optimizing the disaster response/preparedness time and minimizes the route duration of the vehicles to achieve the solution with the minimum highest-vehicle-route-duration. Multiple diversely-functional vertices are consider…

Adaptive Client Selection via Q-Learning-based Whittle Index in Wireless Federated Learning
Qiyue Li, Yingxin Liu, Hang Qi, Jieping Luo, Zhizhang Liu, Jingjin Wu
https://arxiv.org/abs/2509.13933

Adaptive Client Selection via Q-Learning-based Whittle Index in Wireless Federated Learning
We consider the client selection problem in wireless Federated Learning (FL), with the objective of reducing the total required time to achieve a certain level of learning accuracy. Since the server cannot observe the clients' dynamic states that can change their computation and communication efficiency, we formulate client selection as a restless multi-armed bandit problem. We propose a scalable and efficient approach called the Whittle Index Learning in Federated Q-learning (WILF-Q), which us…

Do Natural Language Descriptions of Model Activations Convey Privileged Information?
Millicent Li, Alberto Mario Ceballos Arroyo, Giordano Rogers, Naomi Saphra, Byron C. Wallace
https://arxiv.org/abs/2509.13316

Do Natural Language Descriptions of Model Activations Convey Privileged Information?
Recent interpretability methods have proposed to translate LLM internal representations into natural language descriptions using a second verbalizer LLM. This is intended to illuminate how the target model represents and operates on inputs. But do such activation verbalization approaches actually provide privileged knowledge about the internal workings of the target model, or do they merely convey information about its inputs? We critically evaluate popular verbalization methods across datasets…

FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale
Boris Bonev, Thorsten Kurth, Ankur Mahesh, Mauro Bisson, Jean Kossaifi, Karthik Kashinath, Anima Anandkumar, William D. Collins, Michael S. Pritchard, Alexander Keller
https://arxiv.org/abs/2507.12144…

FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale
FourCastNet 3 advances global weather modeling by implementing a scalable, geometric machine learning (ML) approach to probabilistic ensemble forecasting. The approach is designed to respect spherical geometry and to accurately model the spatially correlated probabilistic nature of the problem, resulting in stable spectra and realistic dynamics across multiple scales. FourCastNet 3 delivers forecasting accuracy that surpasses leading conventional ensemble models and rivals the best diffusion-ba…

Language models align with brain regions that represent concepts across modalities
Maria Ryskina, Greta Tuckute, Alexander Fung, Ashley Malkin, Evelina Fedorenko
https://arxiv.org/abs/2508.11536

Language models align with brain regions that represent concepts across modalities
Cognitive science and neuroscience have long faced the challenge of disentangling representations of language from representations of conceptual meaning. As the same problem arises in today's language models (LMs), we investigate the relationship between LM--brain alignment and two neural metrics: (1) the level of brain activation during processing of sentences, targeting linguistic processing, and (2) a novel measure of meaning consistency across input modalities, which quantifies how consiste…

Subjective Evaluation Profile Analysis of Science Fiction Short Stories and its Critical-Theoretical Significance
Kazuyoshi Otsuka
https://arxiv.org/abs/2507.11582

Subjective Evaluation Profile Analysis of Science Fiction Short Stories and its Critical-Theoretical Significance
This study positions large language models (LLMs) as "subjective literary critics" to explore aesthetic preferences and evaluation patterns in literary assessment. Ten Japanese science fiction short stories were translated into English and evaluated by six state-of-the-art LLMs across seven independent sessions. Principal component analysis and clustering techniques revealed significant variations in evaluation consistency (α ranging from 1.00 to 0.35) and five distinct evaluation patterns. Ad…