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@arXiv_csCL_bot@mastoxiv.page
2025-10-15 10:28:41

From Knowledge to Treatment: Large Language Model Assisted Biomedical Concept Representation for Drug Repurposing
Chengrui Xiang, Tengfei Ma, Xiangzheng Fu, Yiping Liu, Bosheng Song, Xiangxiang Zeng
arxiv.org/abs/2510.12181

From Knowledge to Treatment: Large Language Model Assisted Biomedical Concept Representation for Drug Repurposing
Drug repurposing plays a critical role in accelerating treatment discovery, especially for complex and rare diseases. Biomedical knowledge graphs (KGs), which encode rich clinical associations, have been widely adopted to support this task. However, existing methods largely overlook common-sense biomedical concept knowledge in real-world labs, such as mechanistic priors indicating that certain drugs are fundamentally incompatible with specific treatments. To address this gap, we propose LLaDR, …

@axbom@axbom.me
2025-11-15 17:33:25
In one of my first talks about human rights and AI (2022) I used the widespread printing press reference to emphasise that in many cases it supported colonialism.

https://axbom.com/aihr/

This text by Sonja Drimmer really breaks down common printing press misrepresentation. Love it!

htt…
@arXiv_csAI_bot@mastoxiv.page
2025-09-15 09:19:21

Compartmentalised Agentic Reasoning for Clinical NLI
Ma\"el Jullien, Lei Xu, Marco Valentino, Andr\'e Freitas
arxiv.org/abs/2509.10222

Compartmentalised Agentic Reasoning for Clinical NLI
A common assumption holds that scaling data and parameters yields increasingly structured, generalisable internal representations. We interrogate this assumption in clinical natural language inference (NLI) by adopting a benchmark decomposed into four reasoning families, Causal Attribution, Compositional Grounding, Epistemic Verification, and Risk State Abstraction, and introducing CARENLI, a Compartmentalised Agentic Reasoning for Clinical NLI that separates knowledge access from principled in…

@arXiv_csCV_bot@mastoxiv.page
2025-10-13 10:36:20

PhysToolBench: Benchmarking Physical Tool Understanding for MLLMs
Zixin Zhang, Kanghao Chen, Xingwang Lin, Lutao Jiang, Xu Zheng, Yuanhuiyi Lyu, Litao Guo, Yinchuan Li, Ying-Cong Chen
arxiv.org/abs/2510.09507

PhysToolBench: Benchmarking Physical Tool Understanding for MLLMs
The ability to use, understand, and create tools is a hallmark of human intelligence, enabling sophisticated interaction with the physical world. For any general-purpose intelligent agent to achieve true versatility, it must also master these fundamental skills. While modern Multimodal Large Language Models (MLLMs) leverage their extensive common knowledge for high-level planning in embodied AI and in downstream Vision-Language-Action (VLA) models, the extent of their true understanding of phys…

@arXiv_csPL_bot@mastoxiv.page
2025-10-13 08:33:30

A Multilingual Python Programming Language
Saad Ahmed Bazaz, Mirza Omer Beg
arxiv.org/abs/2510.09591 arxiv.org/pdf/2510.09591

A Multilingual Python Programming Language
All widely used and useful programming languages have a common problem. They restrict entry on the basis of knowledge of the English language. The lack of knowledge of English poses a major hurdle to many newcomers who do not have the resources, in terms of time and money, to learn the English language. Studies show that people learn better in their own language. Therefore, we propose a language transpiler built on top of the Python programming language, called UniversalPython, which allows one…

@arXiv_csRO_bot@mastoxiv.page
2025-10-10 09:44:29

GM3: A General Physical Model for Micro-Mobility Vehicles
Grace Cai, Nithin Parepally, Laura Zheng, Ming C. Lin
arxiv.org/abs/2510.07807 ar…

GM3: A General Physical Model for Micro-Mobility Vehicles
Modeling the dynamics of micro-mobility vehicles (MMV) is becoming increasingly important for training autonomous vehicle systems and building urban traffic simulations. However, mainstream tools rely on variants of the Kinematic Bicycle Model (KBM) or mode-specific physics that miss tire slip, load transfer, and rider/vehicle lean. To our knowledge, no unified, physics-based model captures these dynamics across the full range of common MMVs and wheel layouts. We propose the "Generalized Micro-…

@hex@kolektiva.social
2025-10-30 21:06:59

I'm especially interested in a few things if anyone is willing to help...
1) missing words and simple typos - My ADHD brain skips words coming out and fulls them in when reading, so it's easy for me to make mistakes and hard for me to catch them.
2) questions - I tend to work from a lot of assumed knowledge, collected from all over, and I'm really trying to make my work more accessible. I assume I'm talking about a bunch of stuff most folks don't know, but I don't know which of them come from some rabbit hole I went down and which are more common knowledge.
3) challenges - I get bored when having things explained to me so I have a tendency to keep explanations light... Which can mean I leave out a bunch of critical context or logical steps.

@deabigt@universeodon.com
2025-10-04 23:26:37

When Everyone Knows That Everyone Knows...: Common Knowledge and the Mysteries of Money, Power, and Everyday Life goodreads.com/review/show/7893

@arXiv_statML_bot@mastoxiv.page
2025-10-07 10:10:22

Gini-based Model Monitoring: A General Framework with an Application to Non-life Insurance Pricing
Alexej Brauer, Paul Menzel
arxiv.org/abs/2510.04556

Gini-based Model Monitoring: A General Framework with an Application to Non-life Insurance Pricing
In a dynamic landscape where portfolios and environments evolve, maintaining the accuracy of pricing models is critical. To the best of our knowledge, this is the first study to systematically examine concept drift in non-life insurance pricing. We (i) provide an overview of the relevant literature and commonly used methodologies, clarify the distinction between virtual drift and concept drift, and explain their implications for long-run model performance; (ii) review and formalize common perfo…

@arXiv_csAI_bot@mastoxiv.page
2025-10-10 10:32:09

Measuring What Matters: The AI Pluralism Index
Rashid Mushkani
arxiv.org/abs/2510.08193 arxiv.org/pdf/2510.08193

Measuring What Matters: The AI Pluralism Index
Artificial intelligence systems increasingly mediate knowledge, communication, and decision making. Development and governance remain concentrated within a small set of firms and states, raising concerns that technologies may encode narrow interests and limit public agency. Capability benchmarks for language, vision, and coding are common, yet public, auditable measures of pluralistic governance are rare. We define AI pluralism as the degree to which affected stakeholders can shape objectives, …

@arXiv_csCV_bot@mastoxiv.page
2025-09-29 11:15:27

Integrating Background Knowledge in Medical Semantic Segmentation with Logic Tensor Networks
Luca Bergamin, Giovanna Maria Dimitri, Fabio Aiolli
arxiv.org/abs/2509.22399

Integrating Background Knowledge in Medical Semantic Segmentation with Logic Tensor Networks
Semantic segmentation is a fundamental task in medical image analysis, aiding medical decision-making by helping radiologists distinguish objects in an image. Research in this field has been driven by deep learning applications, which have the potential to scale these systems even in the presence of noise and artifacts. However, these systems are not yet perfected. We argue that performance can be improved by incorporating common medical knowledge into the segmentation model's loss function. To…

@arXiv_csLG_bot@mastoxiv.page
2025-09-29 11:35:27

OFMU: Optimization-Driven Framework for Machine Unlearning
Sadia Asif, Mohammad Mohammadi Amiri
arxiv.org/abs/2509.22483 arxiv.org/pdf/2509…

OFMU: Optimization-Driven Framework for Machine Unlearning
Large language models deployed in sensitive applications increasingly require the ability to unlearn specific knowledge, such as user requests, copyrighted materials, or outdated information, without retraining from scratch to ensure regulatory compliance, user privacy, and safety. This task, known as machine unlearning, aims to remove the influence of targeted data (forgetting) while maintaining performance on the remaining data (retention). A common approach is to formulate this as a multi-ob…

@arXiv_csSE_bot@mastoxiv.page
2025-09-18 09:45:21

Trace Sampling 2.0: Code Knowledge Enhanced Span-level Sampling for Distributed Tracing
Yulun Wu, Guangba Yu, Zhihan Jiang, Yichen Li, Michael R. Lyu
arxiv.org/abs/2509.13852

Trace Sampling 2.0: Code Knowledge Enhanced Span-level Sampling for Distributed Tracing
Distributed tracing is an essential diagnostic tool in microservice systems, but the sheer volume of traces places a significant burden on backend storage. A common approach to mitigating this issue is trace sampling, which selectively retains traces based on specific criteria, often preserving only anomalous ones. However, this method frequently discards valuable information, including normal traces that are essential for comparative analysis. To address this limitation, we introduce Trace Sam…

@UP8@mastodon.social
2025-10-16 13:53:47

🕯️Simple casting and heat treatment yields alloy with both strength and ductility
techxplore.com/news/2025-09-si

Simple casting and heat treatment yields alloy with both strength and ductility
In the world of metals, it has long been considered common knowledge that achieving both strength and ductility (the ability to stretch without breaking) at the same time is nearly impossible. When a metal is made stronger, it typically becomes brittle, and when it is made more ductile, it usually loses strength—like a seesaw, where one property increases as the other decreases.

@arXiv_eessSP_bot@mastoxiv.page
2025-08-29 08:41:11

Towards Automated EEG-Based Detection Using Deep Convolutional Autoencoders
Annika Stiehl, Nicolas Weeger, Christian Uhl, Dominic Bechtold, Nicole Ille, Stefan Gei{\ss}els\"oder
arxiv.org/abs/2508.20535

Towards Automated EEG-Based Detection Using Deep Convolutional Autoencoders
Epilepsy is one of the most common neurological disorders. This disease requires reliable and efficient seizure detection methods. Electroencephalography (EEG) is the gold standard for seizure monitoring, but its manual analysis is a time-consuming task that requires expert knowledge. In addition, there are no well-defined features that allow fully automated analysis. Existing deep learning-based approaches struggle to achieve high sensitivity while maintaining a low false alarm rate per hour (…

@arXiv_csCL_bot@mastoxiv.page
2025-10-02 10:39:31

Exposing the Cracks: Vulnerabilities of Retrieval-Augmented LLM-based Machine Translation
Yanming Sun, Runzhe Zhan, Chi Seng Cheang, Han Wu, Xuebo Liu, Yuyao Niu, Fengying Ye, Kaixin Lan, Lidia S. Chao, Derek F. Wong
arxiv.org/abs/2510.00829

Exposing the Cracks: Vulnerabilities of Retrieval-Augmented LLM-based Machine Translation
\textbf{RE}trieval-\textbf{A}ugmented \textbf{L}LM-based \textbf{M}achine \textbf{T}ranslation (REAL-MT) shows promise for knowledge-intensive tasks like idiomatic translation, but its reliability under noisy retrieval contexts remains poorly understood despite this being a common challenge in real-world deployment. To address this gap, we propose a noise synthesis framework and new metrics to evaluate the robustness of REAL-MT systematically. Using this framework, we instantiate REAL-MT with Q…

@arXiv_csDB_bot@mastoxiv.page
2025-08-26 09:14:36

Views: A Hardware-friendly Graph Database Model For Storing Semantic Information
Yanjun Yang, Adrian Wheeldon, Yihan Pan, Alex Serb
arxiv.org/abs/2508.18123

Views: A Hardware-friendly Graph Database Model For Storing Semantic Information
The graph database (GDB) is an increasingly common storage model for data involving relationships between entries. Beyond its widespread usage in database industries, the advantages of GDBs indicate a strong potential in constructing symbolic artificial intelligences (AIs) and retrieval-augmented generation (RAG), where knowledge of data inter-relationships takes a critical role in implementation. However, current GDB models are not optimised for hardware acceleration, leading to bottlenecks in…

@arXiv_csCY_bot@mastoxiv.page
2025-08-21 08:42:49

An Investigation Into Secondary School Students' Debugging Behaviour in Python
Laurie Gale, Sue Sentance
arxiv.org/abs/2508.14833 arxiv…

An Investigation Into Secondary School Students' Debugging Behaviour in Python
Background and context: Debugging is a common and often frustrating challenge for beginner programmers. Understanding students' debugging processes can help us identify the difficulties and misunderstandings they possess. However, we currently have limited knowledge of how secondary students debug in a text-based language, a medium through which millions of students will learn to program in the future. Objectives: In this paper, we investigate the debugging behaviour of K-12 students learning a…

@arXiv_csCV_bot@mastoxiv.page
2025-09-05 10:28:31

TRUST-VL: An Explainable News Assistant for General Multimodal Misinformation Detection
Zehong Yan, Peng Qi, Wynne Hsu, Mong Li Lee
arxiv.org/abs/2509.04448

TRUST-VL: An Explainable News Assistant for General Multimodal Misinformation Detection
Multimodal misinformation, encompassing textual, visual, and cross-modal distortions, poses an increasing societal threat that is amplified by generative AI. Existing methods typically focus on a single type of distortion and struggle to generalize to unseen scenarios. In this work, we observe that different distortion types share common reasoning capabilities while also requiring task-specific skills. We hypothesize that joint training across distortion types facilitates knowledge sharing and …

@arXiv_physicsaoph_bot@mastoxiv.page
2025-08-26 08:57:16

Learning from nature: insights into GraphDOP's representations of the Earth System
Peter Lean, Mihai Alexe, Eulalie Boucher, Ewan Pinnington, Simon Lang, Patrick Laloyaux, Niels Bormann, Anthony McNally
arxiv.org/abs/2508.18018

Learning from nature: insights into GraphDOP's representations of the Earth System
Through a series of experiments, we provide evidence that the GraphDOP model - trained solely on meteorological observations, using no prior knowledge - develops internal representations of the Earth System state, structure and dynamics as well as the characteristics of different observing systems. Firstly, we demonstrate that the network constructs a unified latent representation of the Earth System state which is common across different observation types. For example, cloud structures maintai…

@arXiv_csCL_bot@mastoxiv.page
2025-08-20 09:45:10

Prediction is not Explanation: Revisiting the Explanatory Capacity of Mapping Embeddings
Hanna Herasimchyk, Alhassan Abdelhalim, S\"oren Laue, Michaela Regneri
arxiv.org/abs/2508.13729

Prediction is not Explanation: Revisiting the Explanatory Capacity of Mapping Embeddings
Understanding what knowledge is implicitly encoded in deep learning models is essential for improving the interpretability of AI systems. This paper examines common methods to explain the knowledge encoded in word embeddings, which are core elements of large language models (LLMs). These methods typically involve mapping embeddings onto collections of human-interpretable semantic features, known as feature norms. Prior work assumes that accurately predicting these semantic features from the wor…

@arXiv_csLG_bot@mastoxiv.page
2025-09-16 12:37:07

Collapse of Irrelevant Representations (CIR) Ensures Robust and Non-Disruptive LLM Unlearning
Filip Sondej, Yushi Yang
arxiv.org/abs/2509.11816

Collapse of Irrelevant Representations (CIR) Ensures Robust and Non-Disruptive LLM Unlearning
Current unlearning techniques and safety training consistently fail to remove dangerous knowledge from language models. We analyze the root causes and propose a highly selective technique which unlearns robustly and without disrupting general performance. We perform PCA on activations and module output gradients to identify subspaces containing common representations, and collapse them before calculating unlearning updates. This way we avoid unlearning general representations, and only target…

@arXiv_csCV_bot@mastoxiv.page
2025-09-22 10:34:51

DistillMatch: Leveraging Knowledge Distillation from Vision Foundation Model for Multimodal Image Matching
Meng Yang, Fan Fan, Zizhuo Li, Songchu Deng, Yong Ma, Jiayi Ma
arxiv.org/abs/2509.16017

DistillMatch: Leveraging Knowledge Distillation from Vision Foundation Model for Multimodal Image Matching
Multimodal image matching seeks pixel-level correspondences between images of different modalities, crucial for cross-modal perception, fusion and analysis. However, the significant appearance differences between modalities make this task challenging. Due to the scarcity of high-quality annotated datasets, existing deep learning methods that extract modality-common features for matching perform poorly and lack adaptability to diverse scenarios. Vision Foundation Model (VFM), trained on large-sc…

@arXiv_csAI_bot@mastoxiv.page
2025-08-27 10:21:23

The Ramon Llull's Thinking Machine for Automated Ideation
Xinran Zhao, Boyuan Zheng, Chenglei Si, Haofei Yu, Ken Liu, Runlong Zhou, Ruochen Li, Tong Chen, Xiang Li, Yiming Zhang, Tongshuang Wu
arxiv.org/abs/2508.19200

The Ramon Llull's Thinking Machine for Automated Ideation
This paper revisits Ramon Llull's Ars combinatoria - a medieval framework for generating knowledge through symbolic recombination - as a conceptual foundation for building a modern Llull's thinking machine for research ideation. Our approach defines three compositional axes: Theme (e.g., efficiency, adaptivity), Domain (e.g., question answering, machine translation), and Method (e.g., adversarial training, linear attention). These elements represent high-level abstractions common in scientific …

@arXiv_csCV_bot@mastoxiv.page
2025-09-19 10:24:21

No Modality Left Behind: Adapting to Missing Modalities via Knowledge Distillation for Brain Tumor Segmentation
Shenghao Zhu, Yifei Chen, Weihong Chen, Shuo Jiang, Guanyu Zhou, Yuanhan Wang, Feiwei Qin, Changmiao Wang, Qiyuan Tian
arxiv.org/abs/2509.15017

No Modality Left Behind: Adapting to Missing Modalities via Knowledge Distillation for Brain Tumor Segmentation
Accurate brain tumor segmentation is essential for preoperative evaluation and personalized treatment. Multi-modal MRI is widely used due to its ability to capture complementary tumor features across different sequences. However, in clinical practice, missing modalities are common, limiting the robustness and generalizability of existing deep learning methods that rely on complete inputs, especially under non-dominant modality combinations. To address this, we propose AdaMM, a multi-modal brain…

@arXiv_csDB_bot@mastoxiv.page
2025-08-18 07:43:00

Tabularis Formatus: Predictive Formatting for Tables
Mukul Singh, Jos\'e Cambronero, Sumit Gulwani, Vu Le, Gust Verbruggen
arxiv.org/abs/2508.11121

Tabularis Formatus: Predictive Formatting for Tables
Spreadsheet manipulation software are widely used for data management and analysis of tabular data, yet the creation of conditional formatting (CF) rules remains a complex task requiring technical knowledge and experience with specific platforms. In this paper we present TaFo, a neuro-symbolic approach to generating CF suggestions for tables, addressing common challenges such as user unawareness, difficulty in rule creation, and inadequate user interfaces. TaFo takes inspiration from component …

@arXiv_csCL_bot@mastoxiv.page
2025-09-26 10:12:41

Who's Laughing Now? An Overview of Computational Humour Generation and Explanation
Tyler Loakman, William Thorne, Chenghua Lin
arxiv.org/abs/2509.21175

Who's Laughing Now? An Overview of Computational Humour Generation and Explanation
The creation and perception of humour is a fundamental human trait, positioning its computational understanding as one of the most challenging tasks in natural language processing (NLP). As an abstract, creative, and frequently context-dependent construct, humour requires extensive reasoning to understand and create, making it a pertinent task for assessing the common-sense knowledge and reasoning abilities of modern large language models (LLMs). In this work, we survey the landscape of computa…

@arXiv_csCV_bot@mastoxiv.page
2025-08-25 07:54:50

Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping
Dexuan He, Xiao Zhou, Wenbin Guan, Liyuan Zhang, Xiaoman Zhang, Sinuo Xu, Ge Wang, Lifeng Wang, Xiaojun Yuan, Xin Sun, Yanfeng Wang, Kun Sun, Ya Zhang, Weidi Xie
arxiv.org/abs/2508.15904

Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping
Rare cancers comprise 20-25% of all malignancies but face major diagnostic challenges due to limited expert availability-especially in pediatric oncology, where they represent over 70% of cases. While pathology vision-language (VL) foundation models show promising zero-shot capabilities for common cancer subtyping, their clinical performance for rare cancers remains limited. Existing multi-instance learning (MIL) methods rely only on visual features, overlooking cross-modal knowledge and compro…