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Multi-Physics: A Comprehensive Benchmark for Multimodal LLMs Reasoning on Chinese Multi-Subject Physics Problems
Zhongze Luo, Zhenshuai Yin, Yongxin Guo, Zhichao Wang, Jionghao Zhu, Xiaoying Tang
https://arxiv.org/abs/2509.15839

Multi-Physics: A Comprehensive Benchmark for Multimodal LLMs Reasoning on Chinese Multi-Subject Physics Problems
While multimodal LLMs (MLLMs) demonstrate remarkable reasoning progress, their application in specialized scientific domains like physics reveals significant gaps in current evaluation benchmarks. Specifically, existing benchmarks often lack fine-grained subject coverage, neglect the step-by-step reasoning process, and are predominantly English-centric, failing to systematically evaluate the role of visual information. Therefore, we introduce \textbf {Multi-Physics} for Chinese physics reasonin…

EmoQ: Speech Emotion Recognition via Speech-Aware Q-Former and Large Language Model
Yiqing Yang, Man-Wai Mak
https://arxiv.org/abs/2509.15775 https://arxiv…

EmoQ: Speech Emotion Recognition via Speech-Aware Q-Former and Large Language Model
The performance of speech emotion recognition (SER) is limited by the insufficient emotion information in unimodal systems and the feature alignment difficulties in multimodal systems. Recently, multimodal large language models (MLLMs) have made progress in SER. However, MLLMs still suffer from hallucination and misclassification problems in complex emotion reasoning. To address these problems, we propose an MLLM-based framework called EmoQ, which generates query embeddings that fuse multimodal…

Apple Watch – eine Uhr für (fast) alle Fälle | Mac & i-Podcast
Vom holprigen Start über Health-Meilensteine und enttäuschte Erwartungen bis zu Alltagsnutzen und Problemstellen: höchste Zeit für eine Apple-Watch-Episode.

Probing EFX via PMMS: (Non-)Existence Results in Discrete Fair Division
Jaros{\l}aw Byrka, Franciszek Malinka, Tomasz Ponitka
https://arxiv.org/abs/2507.14957

Probing EFX via PMMS: (Non-)Existence Results in Discrete Fair Division
We study the fair division of indivisible items and provide new insights into the EFX problem, which is widely regarded as the central open question in fair division, and the PMMS problem, a strictly stronger variant of EFX. Our first result constructs a three-agent instance with two monotone valuations and one additive valuation in which no PMMS allocation exists. Since EFX allocations are known to exist under these assumptions, this establishes a formal separation between EFX and PMMS. We p…

Joint Pilot Allocation and Sequence Design for MIMO-OFDM Systems With Channel Sparsity
Kabuto Arai, Koji Ishibashi, Hiroki Iimori, Yuto Hama, Paulo Valente Klaine, Szabolcs Malomsoky
https://arxiv.org/abs/2509.17916

Joint Pilot Allocation and Sequence Design for MIMO-OFDM Systems With Channel Sparsity
This paper proposes a joint optimization of pilot subcarrier allocation and non-orthogonal sequence for multiple-input-multiple-output (MIMO)-orthogonal frequency-division multiplexing (OFDM) systems under compressed sensing (CS)-based channel estimation exploiting delay and angle sparsity. Since the performance of CS-based approaches depends on a coherence metric of the sensing matrix in the measurement process, we formulate a joint optimization problem to minimize this coherence. Due to the d…

CCL-XCoT: An Efficient Cross-Lingual Knowledge Transfer Method for Mitigating Hallucination Generation
Weihua Zheng, Roy Ka-Wei Lee, Zhengyuan Liu, Kui Wu, AiTi Aw, Bowei Zou
https://arxiv.org/abs/2507.14239

CCL-XCoT: An Efficient Cross-Lingual Knowledge Transfer Method for Mitigating Hallucination Generation
Multilingual Large Language Models(MLLMs) demonstrate strong generalization across languages, yet they remain prone to hallucinations, especially in low-resource languages, due to training data imbalances. These hallucinations, which include inaccurate or fabricated outputs, are particularly problematic in domain-specific generation tasks (Chataigner et al., 2024). To address this challenge, we propose CCL-XCoT(Curriculum-based Contrastive Learning-based Cross-lingual Chain-of-Thought), a two-s…

SOMEONE'S GONNA GET HAAAACKED still unpatched after 6 months...
Original post: #cybersecurity

A Semi-analytic but Biased Uncertainty Assessment Method using Sample Extensions, Analysed for Nonlinear Travel Time Tomography
Xuebin Zhao, Andrew Curtis
https://arxiv.org/abs/2507.16353

A Semi-analytic but Biased Uncertainty Assessment Method using Sample Extensions, Analysed for Nonlinear Travel Time Tomography
Many geophysical problems can be cast as inverse problems that estimate a set of parameter values from observed data. Within a Bayesian framework, solutions to such problems are described probabilistically by the so-called posterior probability distribution functions (pdf's). To obtain robust inference results often requires millions of model parameter value samples to be drawn, and their simulation to be performed; this is a computationally expensive procedure. We investigate the concept of sa…

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COMPASS: A Multi-Dimensional Benchmark for Evaluating Code Generation in Large Language Models
James Meaden, Micha{\l} Jarosz, Piotr Jod{\l}owski, Grigori Melnik
https://arxiv.org/abs/2508.13757

COMPASS: A Multi-Dimensional Benchmark for Evaluating Code Generation in Large Language Models
Current code generation benchmarks focus primarily on functional correctness while overlooking two critical aspects of real-world programming: algorithmic efficiency and code quality. We introduce COMPASS (COdility's Multi-dimensional Programming ASSessment), a comprehensive evaluation framework that assesses code generation across three dimensions: correctness, efficiency, and quality. COMPASS consists of 50 competitive programming problems from real Codility competitions, providing authentic …