Tootfinder

Online Robust Planning under Model Uncertainty: A Sample-Based Approach
Tamir Shazman, Idan Lev-Yehudi, Ron Benchetit, Vadim Indelman
https://arxiv.org/abs/2509.10162 https://…

Online Robust Planning under Model Uncertainty: A Sample-Based Approach
Online planning in Markov Decision Processes (MDPs) enables agents to make sequential decisions by simulating future trajectories from the current state, making it well-suited for large-scale or dynamic environments. Sample-based methods such as Sparse Sampling and Monte Carlo Tree Search (MCTS) are widely adopted for their ability to approximate optimal actions using a generative model. However, in practical settings, the generative model is often learned from limited data, introducing approxi…

The Hidden Bias: A Study on Explicit and Implicit Political Stereotypes in Large Language Models
Konrad L\"ohr, Shuzhou Yuan, Michael F\"arber
https://arxiv.org/abs/2510.08236

The Hidden Bias: A Study on Explicit and Implicit Political Stereotypes in Large Language Models
Large Language Models (LLMs) are increas- ingly integral to information dissemination and decision-making processes. Given their grow- ing societal influence, understanding potential biases, particularly within the political domain, is crucial to prevent undue influence on public opinion and democratic processes. This work investigates political bias and stereotype propa- gation across eight prominent LLMs using the two-dimensional Political Compass Test (PCT). Initially, the PCT is employed to…

Arrow's Impossibility Theorem as a Generalisation of Condorcet's Paradox
Ori Livson, Mikhail Prokopenko
https://arxiv.org/abs/2510.09076 https://ar…

Arrow's Impossibility Theorem as a Generalisation of Condorcet's Paradox
Arrow's Impossibility Theorem is a seminal result of Social Choice Theory that demonstrates the impossibility of ranked-choice decision-making processes to jointly satisfy a number of intuitive and seemingly desirable constraints. The theorem is often described as a generalisation of Condorcet's Paradox, wherein pairwise majority voting may fail to jointly satisfy the same constraints due to the occurrence of elections that result in contradictory preference cycles. However, a formal proof of t…

Fine-Grained Emotion Recognition via In-Context Learning
Zhaochun Ren, Zhou Yang, Chenglong Ye, Haizhou Sun, Chao Chen, Xiaofei Zhu, Xiangwen Liao
https://arxiv.org/abs/2510.06600

Fine-Grained Emotion Recognition via In-Context Learning
Fine-grained emotion recognition aims to identify the emotional type in queries through reasoning and decision-making processes, playing a crucial role in various systems. Recent methods use In-Context Learning (ICL), enhancing the representation of queries in the reasoning process through semantically similar examples, while further improving emotion recognition by explaining the reasoning mechanisms. However, these methods enhance the reasoning process but overlook the decision-making process…

An Orthogonal Learner for Individualized Outcomes in Markov Decision Processes
Emil Javurek, Valentyn Melnychuk, Jonas Schweisthal, Konstantin Hess, Dennis Frauen, Stefan Feuerriegel
https://arxiv.org/abs/2509.26429

An Orthogonal Learner for Individualized Outcomes in Markov Decision Processes
Predicting individualized potential outcomes in sequential decision-making is central for optimizing therapeutic decisions in personalized medicine (e.g., which dosing sequence to give to a cancer patient). However, predicting potential outcomes over long horizons is notoriously difficult. Existing methods that break the curse of the horizon typically lack strong theoretical guarantees such as orthogonality and quasi-oracle efficiency. In this paper, we revisit the problem of predicting individ…

Understanding Collaboration between Professional Designers and Decision-making AI: A Case Study in the Workplace
Nami Ogawa, Yuki Okafuji, Yuji Hatada, Jun Baba
https://arxiv.org/abs/2509.24718

Understanding Collaboration between Professional Designers and Decision-making AI: A Case Study in the Workplace
The rapid development of artificial intelligence (AI) has fundamentally transformed creative work practices in the design industry. Existing studies have identified both opportunities and challenges for creative practitioners in their collaboration with generative AI and explored ways to facilitate effective human-AI co-creation. However, there is still a limited understanding of designers' collaboration with AI that supports creative processes distinct from generative AI. To address these gaps…

Interpreting Language Models Through Concept Descriptions: A Survey
Nils Feldhus, Laura Kopf
https://arxiv.org/abs/2510.01048 https://arxiv.org/pdf/2510.01…

Interpreting Language Models Through Concept Descriptions: A Survey
Understanding the decision-making processes of neural networks is a central goal of mechanistic interpretability. In the context of Large Language Models (LLMs), this involves uncovering the underlying mechanisms and identifying the roles of individual model components such as neurons and attention heads, as well as model abstractions such as the learned sparse features extracted by Sparse Autoencoders (SAEs). A rapidly growing line of work tackles this challenge by using powerful generator mod…

Enhancing XAI Narratives through Multi-Narrative Refinement and Knowledge Distillation
Flavio Giorgi, Matteo Silvestri, Cesare Campagnano, Fabrizio Silvestri, Gabriele Tolomei
https://arxiv.org/abs/2510.03134

Enhancing XAI Narratives through Multi-Narrative Refinement and Knowledge Distillation
Explainable Artificial Intelligence has become a crucial area of research, aiming to demystify the decision-making processes of deep learning models. Among various explainability techniques, counterfactual explanations have been proven particularly promising, as they offer insights into model behavior by highlighting minimal changes that would alter a prediction. Despite their potential, these explanations are often complex and technical, making them difficult for non-experts to interpret. To a…

LLM4Rec: Large Language Models for Multimodal Generative Recommendation with Causal Debiasing
Bo Ma, Hang Li, ZeHua Hu, XiaoFan Gui, LuYao Liu, Simon Lau
https://arxiv.org/abs/2510.01622

LLM4Rec: Large Language Models for Multimodal Generative Recommendation with Causal Debiasing
Contemporary generative recommendation systems face significant challenges in handling multimodal data, eliminating algorithmic biases, and providing transparent decision-making processes. This paper introduces an enhanced generative recommendation framework that addresses these limitations through five key innovations: multimodal fusion architecture, retrieval-augmented generation mechanisms, causal inference-based debiasing, explainable recommendation generation, and real-time adaptive learni…

Vision Transformers: the threat of realistic adversarial patches
Kasper Cools, Clara Maathuis, Alexander M. van Oers, Claudia S. H\"ubner, Nikos Deligiannis, Marijke Vandewal, Geert De Cubber
https://arxiv.org/abs/2509.21084

Vision Transformers: the threat of realistic adversarial patches
The increasing reliance on machine learning systems has made their security a critical concern. Evasion attacks enable adversaries to manipulate the decision-making processes of AI systems, potentially causing security breaches or misclassification of targets. Vision Transformers (ViTs) have gained significant traction in modern machine learning due to increased 1) performance compared to Convolutional Neural Networks (CNNs) and 2) robustness against adversarial perturbations. However, ViTs rem…

Style Over Story: A Process-Oriented Study of Authorial Creativity in Large Language Models
Donghoon Jung, Jiwoo Choi, Songeun Chae, Seohyon Jung
https://arxiv.org/abs/2510.02025

Style Over Story: A Process-Oriented Study of Authorial Creativity in Large Language Models
Evaluations of large language models (LLMs)' creativity have focused primarily on the quality of their outputs rather than the processes that shape them. This study takes a process-oriented approach, drawing on narratology to examine LLMs as computational authors. We introduce constraint-based decision-making as a lens for authorial creativity. Using controlled prompting to assign authorial personas, we analyze the creative preferences of the models. Our findings show that LLMs consistently emp…

Human-AI Narrative Synthesis to Foster Shared Understanding in Civic Decision-Making
Cassandra Overney, Hang Jiang, Urooj Haider, Cassandra Moe, Jasmine Mangat, Frank Pantano, Effie G. McMillian, Paul Riggins, Nabeel Gillani
https://arxiv.org/abs/2509.19643

Human-AI Narrative Synthesis to Foster Shared Understanding in Civic Decision-Making
Community engagement processes in representative political contexts, like school districts, generate massive volumes of feedback that overwhelm traditional synthesis methods, creating barriers to shared understanding not only between civic leaders and constituents but also among community members. To address these barriers, we developed StoryBuilder, a human-AI collaborative pipeline that transforms community input into accessible first-person narratives. Using 2,480 community responses from an…

eXplainable Artificial Intelligence for RL-based Networking Solutions
Yeison Stiven Murcia, Oscar Mauricio Caicedo, Daniela Maria Casas, Nelson Luis Saldanha da Fonseca
https://arxiv.org/abs/2509.21649

eXplainable Artificial Intelligence for RL-based Networking Solutions
Reinforcement Learning (RL) agents have been widely used to improve networking tasks. However, understanding the decisions made by these agents is essential for their broader adoption in networking and network management. To address this, we introduce eXplaNet - a pipeline grounded in explainable artificial intelligence - designed to help networking researchers and practitioners gain deeper insights into the decision-making processes of RL-based solutions. We demonstrate how eXplaNet can be app…

Fundamental Mechanisms of Human Learning
Scott E. Allen, A. David Redish, Ren\'e F. Kizilcec
https://arxiv.org/abs/2509.17202 https://arxiv.org/pdf/250…

Fundamental Mechanisms of Human Learning
Learning underlies nearly all human behavior and is central to education and education reform. Although recent advances in neuroscience have revealed the fundamental structure of learning processes, these insights have yet to be integrated into research and practice. Specifically, neuroscience has found that decision-making is governed by a structured process of perception, action-selection, and execution, supported by multiple neural systems with distinct memory stores and learning mechanisms.…

Unveiling Interesting Insights: Monte Carlo Tree Search for Knowledge Discovery
Pietro Totis, Alberto Pozanco, Daniel Borrajo
https://arxiv.org/abs/2510.00876 https://

Unveiling Interesting Insights: Monte Carlo Tree Search for Knowledge Discovery
Organizations are increasingly focused on leveraging data from their processes to gain insights and drive decision-making. However, converting this data into actionable knowledge remains a difficult and time-consuming task. There is often a gap between the volume of data collected and the ability to process and understand it, which automated knowledge discovery aims to fill. Automated knowledge discovery involves complex open problems, including effectively navigating data, building models to e…

Quantum-like representation of neuronal networks' activity: modeling "mental entanglement"
Andrei Khrennikov, Makiko Yamada
https://arxiv.org/abs/2509.16253 https:…

Quantum-like representation of neuronal networks' activity: modeling "mental entanglement"
Quantum-like modeling (QLM) - quantum theory applications outside of physics - are intensively developed with applications in biology, cognition, psychology, and decision-making. For cognition, QLM should be distinguished from quantum reductionist models in the spirit of Hameroff and Penrose and well as Umezawa and Vitiello. QLM is not concerned with just quantum physical processes in the brain but also QL information processing by macroscopic neuronal structures. Although QLM of cognition and …

Learning Robust Penetration-Testing Policies under Partial Observability: A systematic evaluation
Raphael Simon, Pieter Libin, Wim Mees
https://arxiv.org/abs/2509.20008 https://…

Learning Robust Penetration-Testing Policies under Partial Observability: A systematic evaluation
Penetration testing, the simulation of cyberattacks to identify security vulnerabilities, presents a sequential decision-making problem well-suited for reinforcement learning (RL) automation. Like many applications of RL to real-world problems, partial observability presents a major challenge, as it invalidates the Markov property present in Markov Decision Processes (MDPs). Partially Observable MDPs require history aggregation or belief state estimation to learn successful policies. We investi…

Swarm Intelligence for Chemical Reaction Optimisation
R\'emi Schlama, Joshua W. Sin, Ryan P. Burwood, Kurt P\"untener, Raphael Bigler, Philippe Schwaller
https://arxiv.org/abs/2509.11798

Swarm Intelligence for Chemical Reaction Optimisation
Chemical reaction optimisation is essential for synthetic chemistry and pharmaceutical development, demanding the extensive exploration of many reaction parameters to achieve efficient and sustainable processes. We report $α$-PSO, a novel nature-inspired metaheuristic algorithm that augments canonical particle swarm optimisation (PSO) with machine learning (ML) for parallel reaction optimisation. Unlike black-box ML approaches that obscure decision-making processes, $α$-PSO uses mechanistical…

Unveiling Interesting Insights: Monte Carlo Tree Search for Knowledge Discovery
Pietro Totis, Alberto Pozanco, Daniel Borrajo
https://arxiv.org/abs/2510.00876 https://

Unveiling Interesting Insights: Monte Carlo Tree Search for Knowledge Discovery
Organizations are increasingly focused on leveraging data from their processes to gain insights and drive decision-making. However, converting this data into actionable knowledge remains a difficult and time-consuming task. There is often a gap between the volume of data collected and the ability to process and understand it, which automated knowledge discovery aims to fill. Automated knowledge discovery involves complex open problems, including effectively navigating data, building models to e…