Tootfinder

An Exploratory Study on AI-driven Visualisation Techniques on Decision Making in Extended Reality
Ze Dong, Binyang Han, Jingjing Zhang, Ruoyu Wen, Barrett Ens, Adrian Clark, Tham Piumsomboon
https://arxiv.org/abs/2507.10981

An Exploratory Study on AI-driven Visualisation Techniques on Decision Making in Extended Reality
The integration of extended reality (XR) with artificial intelligence (AI) introduces a new paradigm for user interaction, enabling AI to perceive user intent, stimulate the senses, and influence decision-making. We explored the impact of four AI-driven visualisation techniques -- `Inform,' `Nudge,' `Recommend,' and `Instruct' -- on user decision-making in XR using the Meta Quest Pro. To test these techniques, we used a pre-recorded 360-degree video of a supermarket, overlaying each technique t…

A Computational Framework and Implementation of Implicit Priors in Bayesian Inverse Problems
Jasper M. Everink, Chao Zhang, Amal M. A. Alghamdi, R\'emi Laumont, Nicolai A. B. Riis, Jakob S. J{\o}rgensen
https://arxiv.org/abs/2509.11781

A Computational Framework and Implementation of Implicit Priors in Bayesian Inverse Problems
Solving Bayesian inverse problems typically involves deriving a posterior distribution using Bayes' rule, followed by sampling from this posterior for analysis. Sampling methods, such as general-purpose Markov chain Monte Carlo (MCMC), are commonly used, but they require prior and likelihood densities to be explicitly provided. In cases where expressing the prior explicitly is challenging, implicit priors offer an alternative, encoding prior information indirectly. These priors have gained incr…

A Dataset for Distilling Knowledge Priors from Literature for Therapeutic Design
Haydn Thomas Jones, Natalie Maus, Josh Magnus Ludan, Maggie Ziyu Huan, Jiaming Liang, Marcelo Der Torossian Torres, Jiatao Liang, Zachary Ives, Yoseph Barash, Cesar de la Fuente-Nunez, Jacob R. Gardner, Mark Yatskar
https://arxiv.org/abs/2508.10899

A Dataset for Distilling Knowledge Priors from Literature for Therapeutic Design
AI-driven discovery can greatly reduce design time and enhance new therapeutics' effectiveness. Models using simulators explore broad design spaces but risk violating implicit constraints due to a lack of experimental priors. For example, in a new analysis we performed on a diverse set of models on the GuacaMol benchmark using supervised classifiers, over 60\% of molecules proposed had high probability of being mutagenic. In this work, we introduce \ourdataset, a dataset of priors for design pr…

On Equivalent Characterizations of NP in Abstract Models of Computation
Jeremy C. Kirn, Lucas Meijer, Tillmann Miltzow, Hans L. Bodlaender
https://arxiv.org/abs/2510.05894 https…

On Equivalent Characterizations of NP in Abstract Models of Computation
We investigate machine models similar to Turing machines that are augmented by the operations of a first-order structure $\mathcal{R}$, and we show that under weak conditions on $\mathcal{R}$, the complexity class $\text{NP}(\mathcal{R})$ may be characterized in three equivalent ways: (1) by polynomial-time verification algorithms implemented on $\mathcal{R}$-machines, (2) by the $\text{NP}(\mathcal{R})$-complete problem $\text{SAT}(\mathcal{R})$, and (3) by existential second-order metafinite …

Evaluating LLM-Generated Legal Explanations for Regulatory Compliance in Social Media Influencer Marketing
Haoyang Gui, Thales Bertaglia, Taylor Annabell, Catalina Goanta, Tjomme Dooper, Gerasimos Spanakis
https://arxiv.org/abs/2510.08111

Evaluating LLM-Generated Legal Explanations for Regulatory Compliance in Social Media Influencer Marketing
The rise of influencer marketing has blurred boundaries between organic content and sponsored content, making the enforcement of legal rules relating to transparency challenging. Effective regulation requires applying legal knowledge with a clear purpose and reason, yet current detection methods of undisclosed sponsored content generally lack legal grounding or operate as opaque "black boxes". Using 1,143 Instagram posts, we compare gpt-5-nano and gemini-2.5-flash-lite under three prompting str…

Base Models Know How to Reason, Thinking Models Learn When
Constantin Venhoff, Iv\'an Arcuschin, Philip Torr, Arthur Conmy, Neel Nanda
https://arxiv.org/abs/2510.07364 https…

Base Models Know How to Reason, Thinking Models Learn When
Why do thinking language models like DeepSeek R1 outperform their base counterparts? Despite consistent performance gains, it remains unclear to what extent thinking models learn entirely new reasoning capabilities or repurpose pre-existing base model ones. In this work, we propose a hybrid model where we activate reasoning mechanisms in base models at the right time to elicit thinking-model-level reasoning chains, implying that thinking models exploit already existing capabilities. To ground o…

Conditioning on PDE Parameters to Generalise Deep Learning Emulation of Stochastic and Chaotic Dynamics
Ira J. S. Shokar, Rich R. Kerswell, Peter H. Haynes
https://arxiv.org/abs/2509.09599

Conditioning on PDE Parameters to Generalise Deep Learning Emulation of Stochastic and Chaotic Dynamics
We present a deep learning emulator for stochastic and chaotic spatio-temporal systems, explicitly conditioned on the parameter values of the underlying partial differential equations (PDEs). Our approach involves pre-training the model on a single parameter domain, followed by fine-tuning on a smaller, yet diverse dataset, enabling generalisation across a broad range of parameter values. By incorporating local attention mechanisms, the network is capable of handling varying domain sizes and re…

Bi-Level Game-Theoretic Planning of Cyber Deception for Cognitive Arbitrage
Ya-Ting Yang, Quanyan Zhu
https://arxiv.org/abs/2509.05498 https://arxiv.org/pd…

Bi-Level Game-Theoretic Planning of Cyber Deception for Cognitive Arbitrage
Cognitive vulnerabilities shape human decision-making and arise primarily from two sources: (1) cognitive capabilities, which include disparities in knowledge, education, expertise, or access to information, and (2) cognitive biases, such as rational inattention, confirmation bias, and base rate neglect, which influence how individuals perceive and process information. Exploiting these vulnerabilities allows an entity with superior cognitive awareness to gain a strategic advantage, a concept re…

Addressing Methodological Uncertainty in MCDM with a Systematic Pipeline Approach to Data Transformation Sensitivity Analysis
Juan B. Cabral, Alvaro Roy Schachner
https://arxiv.org/abs/2509.24996

Addressing Methodological Uncertainty in MCDM with a Systematic Pipeline Approach to Data Transformation Sensitivity Analysis
Multicriteria decision-making methods exhibit critical dependence on the choice of normalization techniques, where different selections can alter 20-40% of the final rankings. Current practice is characterized by the ad-hoc selection of methods without systematic robustness evaluation. We present a framework that addresses this methodological uncertainty through automated exploration of the scaling transformation space. The implementation leverages the existing Scikit-Criteria infrastructure to…

FBMS: An R Package for Flexible Bayesian Model Selection and Model Averaging
Florian Frommlet, Jon Lachmann, Geir Storvik, Aliaksandr Hubin
https://arxiv.org/abs/2509.00753 http…

FBMS: An R Package for Flexible Bayesian Model Selection and Model Averaging
The FBMS R package facilitates Bayesian model selection and model averaging in complex regression settings by employing a variety of Monte Carlo model exploration methods. At its core, the package implements an efficient Mode Jumping Markov Chain Monte Carlo (MJMCMC) algorithm, designed to improve mixing in multi-modal posterior landscapes within Bayesian generalized linear models. In addition, it provides a genetically modified MJMCMC (GMJMCMC) algorithm that introduces nonlinear feature gener…