Tootfinder

Forschung: Verschmelzen Menschen und KI zu einem "evolutionären Individuum"?
Mit der Entwicklung von KI-Technik könnte die Menschheit einen großen evolutionären Übergang eingeleitet haben. Das meinen zumindest zwei Evolutionsbiologen.

RIMO: An Easy-to-Evaluate, Hard-to-Solve Olympiad Benchmark for Advanced Mathematical Reasoning
Ziye Chen, Chengwei Qin, Yao Shu
https://arxiv.org/abs/2509.07711 https://…

RIMO: An Easy-to-Evaluate, Hard-to-Solve Olympiad Benchmark for Advanced Mathematical Reasoning
As large language models (LLMs) reach high scores on established mathematical benchmarks, such as GSM8K and MATH, the research community has turned to International Mathematical Olympiad (IMO) problems to push the evaluation frontier. However, existing Olympiad-level benchmarks suffer from practical constraints that introduce grading noise and potential bias, such as heterogeneous answer formats requiring model-based judges and a reliance on potentially flawed solutions. We introduce RIMO, a tw…

Cluster passage driving galaxy kinematic and structural evolution in the SAMI Galaxy Survey
Ryan Bagge, Caroline Foster, Sarah Brough, O\u{g}uzhan \c{C}ak{\i}r, Luca Cortese, Franceso D'Eugenio, Scott Croom, Matt Owers, Jesse van de Sande
https://arxiv.org/abs/2508.07589

Cluster passage driving galaxy kinematic and structural evolution in the SAMI Galaxy Survey
The cluster environment can have a significant impact on galaxy evolution. We study the impact that passage through a cluster has on stellar and ionised gas kinematics for galaxies within the Sydney-AAO Multi Integral field (SAMI) Galaxy Survey. We compute the kinematic asymmetry $v_{\rm asym}$ in the line-of-sight stellar and ionsied gas velocity maps to quantify how the cluster environment disturbs the kinematics of the stars and ionised gas. We find a significantly higher fraction of galaxie…

A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving
Yisong Zhang, Ran Cheng, Guoxing Yi, Kay Chen Tan
https://arxiv.org/abs/2509.08269

A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving
Large Language Models (LLMs), with their strong understanding and reasoning capabilities, are increasingly being explored for tackling optimization problems, especially in synergy with evolutionary computation. Despite rapid progress, however, the field still lacks a unified synthesis and a systematic taxonomy. This survey addresses this gap by providing a comprehensive review of recent developments and organizing them within a structured framework. We classify existing research into two main s…

An expanded evaluation matrix for the entropy-weight TODIM method to reduce the rank reversal probability and its application in selecting energy storage technology
Lisheng Jiang, Tianyu Zhang, Shiyu Yan, Ran Fang
https://arxiv.org/abs/2509.08236

An expanded evaluation matrix for the entropy-weight TODIM method to reduce the rank reversal probability and its application in selecting energy storage technology
The TODIM method (an acronym in Portuguese for interactive and multiple criteria decision-making) with entropy weights is influenced by rank reversal, a phenomenon where the order of two alternatives changes following the addition of another alternative. Research on rank reversal has predominantly focused on single decision-making methods. To the best of our knowledge, the reduction of rank reversal probability in hybrid methods, such as the entropy-weight TODIM method, remains an unresolved ch…

A Benchmark Dataset for Satellite-Based Estimation and Detection of Rain
Simon Pfreundschuh, Malarvizhi Arulraj, Ali Behrangi, Linda Bogerd, Alan James Peixoto Calheiros, Daniele Casella, Neda Dolatabadi, Clement Guilloteau, Jie Gong, Christian D. Kummerow, Pierre Kirstetter, Gyuwon Lee, Maximilian Maahn, Lisa Milani, Giulia Panegrossi, Rayana Palharini, Veljko Petkovi\'c, Soorok Ryu, Paolo San\`o, Jackson Tan

A Benchmark Dataset for Satellite-Based Estimation and Detection of Rain
Accurately tracking the global distribution and evolution of precipitation is essential for both research and operational meteorology. Satellite observations remain the only means of achieving consistent, global-scale precipitation monitoring. While machine learning has long been applied to satellite-based precipitation retrieval, the absence of a standardized benchmark dataset has hindered fair comparisons between methods and limited progress in algorithm development. To address this gap, th…

The Landscape of Fairness: An Axiomatic and Predictive Framework for Network QoE Sensitivity
Zhiyuan Ren, Xinke Jian, Wenchi Cheng, Kun Yang
https://arxiv.org/abs/2509.08551 htt…

The Landscape of Fairness: An Axiomatic and Predictive Framework for Network QoE Sensitivity
Evaluating network-wide fairness is challenging because it is not a static property but one highly sensitive to Service Level Agreement (SLA) parameters. This paper introduces a complete analytical framework to transform fairness evaluation from a single-point measurement into a proactive engineering discipline centered on a predictable sensitivity landscape. Our framework is built upon a QoE-Imbalance metric whose form is not an ad-hoc choice, but is uniquely determined by a set of fundamental…

Accelerating vRAN and O-RAN with SIMD: Architectural Perspectives and Performance Evaluation
Jaebum Park, Chan-Byoung Chae, Robert W. Heath Jr
https://arxiv.org/abs/2510.07843 h…

Accelerating vRAN and O-RAN with SIMD: Architectural Perspectives and Performance Evaluation
The evolution of radio access networks (RANs) toward virtualization and openness creates new opportunities for flexible, cost-effective, and high-performance deployments. Achieving real-time and energy-efficient baseband processing on commercial off-the-shelf platforms, however, remains a critical challenge. This article explores how single instruction multiple data (SIMD) architectures can accelerate RAN workloads. We first outline why key physical-layer functions, such as channel estimation, …

LLMs Don't Know Their Own Decision Boundaries: The Unreliability of Self-Generated Counterfactual Explanations
Harry Mayne, Ryan Othniel Kearns, Yushi Yang, Andrew M. Bean, Eoin Delaney, Chris Russell, Adam Mahdi
https://arxiv.org/abs/2509.09396

LLMs Don't Know Their Own Decision Boundaries: The Unreliability of Self-Generated Counterfactual Explanations
To collaborate effectively with humans, language models must be able to explain their decisions in natural language. We study a specific type of self-explanation: self-generated counterfactual explanations (SCEs), where a model explains its prediction by modifying the input such that it would have predicted a different outcome. We evaluate whether LLMs can produce SCEs that are valid, achieving the intended outcome, and minimal, modifying the input no more than necessary. When asked to generate…

Combined-distance-based score function of cognitive fuzzy sets and its application in lung cancer pain evaluation
Lisheng Jiang, Tianyu Zhang, Shiyu Yan, Ran Fang
https://arxiv.org/abs/2509.08239

Combined-distance-based score function of cognitive fuzzy sets and its application in lung cancer pain evaluation
In decision making, the cognitive fuzzy set (CFS) is a useful tool in expressing experts' complex assessments of alternatives. The distance of CFS, which plays an important role in decision analyses, is necessary when the CFS is applied in solving practical issues. However, as far as we know, the studies on the distance of CFS are few, and the current Minkowski distance of CFS ignores the hesitancy degree of CFS, which might cause errors. To fill the gap of the studies on the distance of CFS, b…