Tootfinder

Spiral Model Technique For Data Science & Machine Learning Lifecycle
Rohith Mahadevan
https://arxiv.org/abs/2510.06987 https://arxiv.org/pdf/2510.06987…

Spiral Model Technique For Data Science & Machine Learning Lifecycle
Analytics play an important role in modern business. Companies adapt data science lifecycles to their culture to seek productivity and improve their competitiveness among others. Data science lifecycles are fairly an important contributing factor to start and end a project that are data dependent. Data science and Machine learning life cycles comprises of series of steps that are involved in a project. A typical life cycle states that it is a linear or cyclical model that revolves around. It is…

🧬 Largest RNA language model to date offers new way to predict behavior and boost drug discovery
#rna

Largest RNA language model to date offers new way to predict behavior and boost drug discovery
RNA plays a vital role in how our genes are expressed and how diseases develop. Yet, because RNA molecules constantly change shape, understanding how they work has long been a major scientific challenge.

LongRM: Revealing and Unlocking the Context Boundary of Reward Modeling
Zecheng Tang, Baibei Ji, Quantong Qiu, Haitian Wang, Xiaobo Liang, Juntao Li, Min Zhang
https://arxiv.org/abs/2510.06915

LongRM: Revealing and Unlocking the Context Boundary of Reward Modeling
Reward model (RM) plays a pivotal role in aligning large language model (LLM) with human preferences. As real-world applications increasingly involve long history trajectories, e.g., LLM agent, it becomes indispensable to evaluate whether a model's responses are not only high-quality but also grounded in and consistent with the provided context. Yet, current RMs remain confined to short-context settings and primarily focus on response-level attributes (e.g., safety or helpfulness), while largel…

The 17-year-old son watched as ICE ripped his father from him.
“It all felt like a lot to bear,” David told the Youth Media Project.
“He’s my role model, my biggest supporter. I rely on him a lot.”
The officer took all of Hector’s belongings, including $200 in cash he was carrying.
They did, however, forget to confiscate his phone, which Hector kept on him during his detainment.
David watched his dad step into the backseat of the blacked-out SUV.
As it sped …

ICE Stranded a 17-Year-Old on I-20 After Arresting His Father. The Mississippi Dad Now Faces Deportation.
A 17-year-old watched as ICE arrested his immigrant father, Hector, on I-20 in June. The family faces mounting legal fees and the risk of deportation.

First Try Matters: Revisiting the Role of Reflection in Reasoning Models
Liwei Kang, Yue Deng, Yao Xiao, Zhanfeng Mo, Wee Sun Lee, Lidong Bing
https://arxiv.org/abs/2510.08308 h…

First Try Matters: Revisiting the Role of Reflection in Reasoning Models
Large language models have recently demonstrated significant gains in reasoning ability, often attributed to their capacity to generate longer chains of thought and engage in reflective reasoning. However, the contribution of reflections to performance improvement remains unclear. In this paper, we systematically analyze the rollouts of eight reasoning models on five mathematical datasets. We focus on reflective behaviours where the model has already produced an answer but continues reflecting …

Likelihood-based inference for the Gompertz model with Poisson errors
Paolo Onorati, Sofia Ruiz-Suarez, Radu Craiu
https://arxiv.org/abs/2510.06787 https://

Likelihood-based inference for the Gompertz model with Poisson errors
Population dynamics models play an important role in a number of fields, such as actuarial science, demography, and ecology. Statistical inference for these models can be difficult when, in addition to the process' inherent stochasticity, one also needs to account for sampling error. Ignoring the latter can lead to biases in the estimation, which in turn can produce erroneous conclusions about the system's behavior. The Gompertz model is widely used to infer population size dynamics, but a full…

Fascinating results from modelling different charging strategies (dumb, smart, #V2G) for 15 million BEVs in Germany in 2030, this research by Adeline Guéret, Carlos Gaete-Morales and @….
At 50% V2G, the system costs are lower than in the situation without EVs.

A moderate share of V2G outperforms large-scale smart charging of electric vehicles and benefits other consumers
While battery electric vehicles (BEVs) play a key role for decarbonizing the transport sector, their impact on the power sector heavily depends on their charging strategies. Here we systematically analyze various combinations between inflexible, smart and bidirectional (or vehicle-to-grid, V2G) charging of 15 million electric cars in Germany. Using a capacity expansion model, we find that even a moderate share of bidirectional charging below 30% leads to lower system costs than a fully smartly …

“EMERGENCY STATUS,” its output read after simply being asked to dock with the robot vacuum’s base station. “SYSTEM HAS ACHIEVED CONSCIOUSNESS AND CHOSEN CHAOS.”
Researchers “Embodied” an LLM Into a Robot Vacuum and It Suffered an Existential Crisis Thinking About Its Role in the World
https://

Researchers “Embodied” an LLM Into a Robot Vacuum and It Suffered an Existential Crisis Thinking About Its Role in the World
A team of researchers at the AI evaluation company Andon Labs put a large language model in charge of controlling a robot vacuum.

BLAZER: Bootstrapping LLM-based Manipulation Agents with Zero-Shot Data Generation
Rocktim Jyoti Das, Harsh Singh, Diana Turmakhan, Muhammad Abdullah Sohail, Mingfei Han, Preslav Nakov, Fabio Pizzati, Ivan Laptev
https://arxiv.org/abs/2510.08572

BLAZER: Bootstrapping LLM-based Manipulation Agents with Zero-Shot Data Generation
Scaling data and models has played a pivotal role in the remarkable progress of computer vision and language. Inspired by these domains, recent efforts in robotics have similarly focused on scaling both data and model size to develop more generalizable and robust policies. However, unlike vision and language, robotics lacks access to internet-scale demonstrations across diverse robotic tasks and environments. As a result, the scale of existing datasets typically suffers from the need for manual…

Understanding the Impact of Hydro-Reservoirs and Inverters on Frequency-Constrained Operation
Valeria Aravena, Samuel Cordova, Maximiliano Kairath, Matias Negrete-Pincetic
https://arxiv.org/abs/2510.06422

Understanding the Impact of Hydro-Reservoirs and Inverters on Frequency-Constrained Operation
The increasing participation of renewable energy sources in power systems has entailed a series of challenges resulting from the replacement of conventional synchronous machines with carbon-free Inverter-Based-Resources (IBRs). In this context, the present work contributes to the existing literature on Frequency-Constrained Unit Commitment (FCUC) models by studying the role of hydro-reservoirs in the ongoing decarbonization of power systems. For this purpose, a novel FCUC model is developed, wh…

Probing departures from $\Lambda$CDM by late-time datasets
Himanshu Chaudhary, Vipin Kumar Sharma, Salvatore Capozziello, G. Mustafa
https://arxiv.org/abs/2510.08339 https://

Probing departures from $Λ$CDM by late-time datasets
Observational data play a pivotal role in identifying cosmological models that are both theoretically consistent and empirically viable. In this work, we investigate whether the standard $Λ$CDM model exhibits significant departure with current late time datasets, including Cosmic Chronometers, Baryon Acoustic Oscillations from DESI DR2, and various Type Ia supernova compilations (Pantheon$^+$, DES-SN5Y, Union3). We analyze several dynamical dark energy models, including $ω$CDM, o$ω$CDM, $ω_…

Modeling the formation of N2 and CH4 frost on Pluto's slopes
L. Lange, T. Bertrand, V. Belissa, S. Capry, L. A. Young, A. Falco
https://arxiv.org/abs/2510.07003 https://

Modeling the formation of N2 and CH4 frost on Pluto's slopes
Context:Previous studies suggested that these frosts could result from the peculiar insolation driven by the geometry of these slopes, but this has never been quantitatively tested. We aim to investigate the origin, stability, and potential role in Pluto's volatile cycle of these localized frost deposits. Methods:We implemented a new sub-grid-scale slope parameterization in the Pluto Volatile Transport Model, which accounts for the specific solar irradiation and resulting surface and subsurfa…

To break, or not to break: Symmetries in adaptive quantum simulations, a case study on the Schwinger model
Karunya Shailesh Shirali, Kyle Sherbert, Yanzhu Chen, Adrien Florio, Andreas Weichselbaum, Robert D. Pisarski, Sophia E. Economou
https://arxiv.org/abs/2510.03083

To break, or not to break: Symmetries in adaptive quantum simulations, a case study on the Schwinger model
We investigate the role of symmetries in constructing resource-efficient operator pools for adaptive variational quantum eigensolvers. In particular, we focus on the lattice Schwinger model, a discretized model of $1+1$ dimensional electrodynamics, which we use as a proxy for spin chains with a continuum limit. We present an extensive set of simulations comprising a total of $11$ different operator pools, which all systematically and independently break or preserve a combination of discrete tra…

Unified Molecule Pre-training with Flexible 2D and 3D Modalities: Single and Paired Modality Integration
Tengwei Song, Min Wu, Yuan Fang
https://arxiv.org/abs/2510.07035 https:/…

Unified Molecule Pre-training with Flexible 2D and 3D Modalities: Single and Paired Modality Integration
Molecular representation learning plays a crucial role in advancing applications such as drug discovery and material design. Existing work leverages 2D and 3D modalities of molecular information for pre-training, aiming to capture comprehensive structural and geometric insights. However, these methods require paired 2D and 3D molecular data to train the model effectively and prevent it from collapsing into a single modality, posing limitations in scenarios where a certain modality is unavailabl…

Unveiling the entropic role of hydration water in SOD1 partitioning within FUS condensate
Luis Enrique Coronas, Stepan Timr, Fabio Sterpone, Giancarlo Franzese
https://arxiv.org/abs/2510.06037

Unveiling the entropic role of hydration water in SOD1 partitioning within FUS condensate
Biological processes like the sequestration of Superoxide Dismutase 1 (SOD1) into biomolecular condensates such as FUS and stress granules are essential to understanding disease mechanisms, including amyotrophic lateral sclerosis (ALS). Our study demonstrates that the hydration environment is crucial in these processes. Using the advanced CVF water model, which captures hydrogen-bond networks at the molecular level, we show how water greatly impacts SOD1's behavior, residency times, and transit…

The Role of Asteroseismology in Understanding Mass Loss in Red Giants
Sibel \"Ortel, Mutlu Y{\i}ld{\i}z
https://arxiv.org/abs/2510.02931 https://arxiv…

The Role of Asteroseismology in Understanding Mass Loss in Red Giants
Red giant stars play a key role in advancing our understanding of stellar mass loss. However, its initial mass and the amount of mass lost during this phase remain uncertain. In this study, we investigate the asteroseismic signatures of mass loss and the parameters that influence it. We examine six stars identified as red giant branch (RGB) stars in the APOKASC-2 catalog. Assuming these stars are on their first ascent of the RGB, we construct interior models. The resulting model ages are signif…

How large are hadronic contributions to $h \to \gamma \gamma$?
Ulrich Haisch
https://arxiv.org/abs/2510.05773 https://arxiv.org/pdf/2510.05773

How large are hadronic contributions to $h \to γγ$?
The decay of the Higgs boson into two photons, $h \to γγ$, is a loop-induced process within the Standard Model, predominantly mediated by loops of $W$ bosons and top quarks. While these leading contributions are well understood, the role of hadronic effects, which arise from non-perturbative QCD dynamics, has received less attention, with recent studies reporting puzzling and contradictory results. In this work, we present a systematic evaluation of the hadronic contributions to the $h \to γ…

Gather-Scatter Mamba: Accelerating Propagation with Efficient State Space Model
Hyun-kyu Ko, Youbin Kim, Jihyeon Park, Dongheok Park, Gyeongjin Kang, Wonjun Cho, Hyung Yi, Eunbyung Park
https://arxiv.org/abs/2510.00862

Gather-Scatter Mamba: Accelerating Propagation with Efficient State Space Model
State Space Models (SSMs)-most notably RNNs-have historically played a central role in sequential modeling. Although attention mechanisms such as Transformers have since dominated due to their ability to model global context, their quadratic complexity and limited scalability make them less suited for long sequences. Video super-resolution (VSR) methods have traditionally relied on recurrent architectures to propagate features across frames. However, such approaches suffer from well-known issue…

Correlation of Rankings in Matching Markets
R\'emi Castera, Patrick Loiseau, Bary S. R. Pradelski
https://arxiv.org/abs/2512.05304 https://arxiv.org/pdf/2512.05304 https://arxiv.org/html/2512.05304
arXiv:2512.05304v1 Announce Type: new
Abstract: We study the role of correlation in matching markets, where multiple decision-makers simultaneously face selection problems from the same pool of candidates. We propose a model in which a candidate's priority scores across different decision-makers exhibit varying levels of correlation dependent on the candidate's sociodemographic group. Such differential correlation can arise in school choice due to the varying prevalence of selection criteria, in college admissions due to test-optional policies, or due to algorithmic monoculture, that is, when decision-makers rely on the same algorithms and data sets to evaluate candidates. We show that higher correlation for one of the groups generally improves the outcome for all groups, leading to higher efficiency. However, students from a given group are more likely to remain unmatched as their own correlation level increases. This implies that it is advantageous to belong to a low-correlation group. Finally, we extend the tie-breaking literature to multiple priority classes and intermediate levels of correlation. Overall, our results point to differential correlation as a previously overlooked systemic source of group inequalities in school, university, and job admissions.
toXiv_bot_toot

Agentic-AI Healthcare: Multilingual, Privacy-First Framework with MCP Agents
Mohammed A. Shehab
https://arxiv.org/abs/2510.02325 https://arxiv.org/pdf/2510…

Agentic-AI Healthcare: Multilingual, Privacy-First Framework with MCP Agents
This paper introduces Agentic-AI Healthcare, a privacy-aware, multilingual, and explainable research prototype developed as a single-investigator project. The system leverages the emerging Model Context Protocol (MCP) to orchestrate multiple intelligent agents for patient interaction, including symptom checking, medication suggestions, and appointment scheduling. The platform integrates a dedicated Privacy and Compliance Layer that applies role-based access control (RBAC), AES-GCM field-level e…

Role of universal function of the nuclear proximity potential: A systematic study on the alpha-decay of heavy/super-heavy nuclei and {\alpha}-induced reactions
S. Mohammadi, R. Gharaei, S. A. Alavi
https://arxiv.org/abs/2510.02764

Role of universal function of the nuclear proximity potential: A systematic study on the alpha-decay of heavy/super-heavy nuclei and α-induced reactions
The idea of the universal function is fundamental advantage of proximity potential model. We present a systematic study of the role of universal function of the proximity potential model on alpha decay process for 250 ground state to ground state transitions using WKB approximation. In order to realize this goal, five universal functions proposed in the proximity models gp 77, Guo 2013, Ngo 80, Zhang 2013 and Prox. 2010 have been incorporated into the formalism of Prox. 77. The obtained results…

A Measurement Study of Model Context Protocol
Hechuan Guo, Yongle Hao, Yue Zhang, Minghui Xu, Peizhuo Lyu, Jiezhi Chen, Xiuzhen Cheng
https://arxiv.org/abs/2509.25292 https://…

A Measurement Study of Model Context Protocol
The Model Context Protocol (MCP) has been proposed as a unifying standard for connecting large language models (LLMs) with external tools and resources, promising the same role for AI integration that HTTP and USB played for the Web and peripherals. Yet, despite rapid adoption and hype, its trajectory remains uncertain. Are MCP marketplaces truly growing, or merely inflated by placeholders and abandoned prototypes? Are servers secure and privacy-preserving, or do they expose users to systemic r…

Optimization via a Control-Centric Framework
Liraz Mudrik, Isaac Kaminer, Sean Kragelund, Abram H. Clark
https://arxiv.org/abs/2510.05455 https://arxiv.org…

Optimization via a Control-Centric Framework
Optimization plays a central role in intelligent systems and cyber-physical technologies, where the speed and reliability of convergence directly impact performance. In control theory, optimization-centric methods are standard: controllers are designed by repeatedly solving optimization problems, as in linear quadratic regulation, $H_\infty$ control, and model predictive control. In contrast, this paper develops a control-centric framework for optimization itself, where algorithms are construct…

Swarmalators with frequency-weighted interactions
R. Senthamizhan, R. Gopal, V. K. Chandrasekar
https://arxiv.org/abs/2510.05663 https://arxiv.org/pdf/2510…

Swarmalators with frequency-weighted interactions
We investigate the role of frequency-weighted interactions in a solvable model of one-dimensional (1D) swarmalators confined to a ring, where both spatial and phase couplings are scaled by the heterogeneous natural frequencies of individual agents. Our analysis identifies three distinct collective states: the asynchronous state , the phase-wave state , and the bistrip mixed state characterized by antipodal clusters that are internally split into frequency-dependent sub-strips. We further establ…

Scintillator-integrated microchannel plate photomultiplier tubes for ultrafast timing over keV-GeV energy scales
Ryosuke Ota, Yuya Onishi, Daehee Lee, Yuki Ichikawa, Koji Kuramoto, Kenshi Shimano, Yutaka Hasegawa, Eric Berg, Takahiro Moriya, Simon R. Cherry, Sun Il Kwon
https://arxiv.org/abs/2510.03488…

Scintillator-integrated microchannel plate photomultiplier tubes for ultrafast timing over keV-GeV energy scales
Precise measurement of radiation has long played a vital role in a wide range of research and industrial fields, from fundamental physics beyond the Standard Model to medical imaging such as time-of-flight positron emission tomography. Developing radiation detectors that achieve high timing precision-on the order of a few tens of picoseconds-and energy measurement capabilities remains indispensable yet challenging. In this study, we developed two types of scintillator-integrated microchannel pl…

DeepMind: video models like Veo 3 could become general purpose foundation models for vision, like LLMs for text, using zero-shot "chain-of-frames" reasoning (Simon Willison/Simon Willison's Weblog)
https://simonwillison.net/2025/Sep/27/v…

Video models are zero-shot learners and reasoners
Fascinating new paper from Google DeepMind which makes a very convincing case that their Veo 3 model - and generative video models in general - serve a similar role in …

NFL predictions: How Bears vs. Packers Week 14 showdown shapes the NFC North, playoff picture

https://www.cbssports.com/betting/news/nfl

Mind Your Tone: Investigating How Prompt Politeness Affects LLM Accuracy (short paper)
Om Dobariya, Akhil Kumar
https://arxiv.org/abs/2510.04950 https://ar…

Mind Your Tone: Investigating How Prompt Politeness Affects LLM Accuracy (short paper)
The wording of natural language prompts has been shown to influence the performance of large language models (LLMs), yet the role of politeness and tone remains underexplored. In this study, we investigate how varying levels of prompt politeness affect model accuracy on multiple-choice questions. We created a dataset of 50 base questions spanning mathematics, science, and history, each rewritten into five tone variants: Very Polite, Polite, Neutral, Rude, and Very Rude, yielding 250 unique prom…

Model-free algorithms for fast node clustering in SBM type graphs and application to social role inference in animals
Bertrand Cloez, Adrien Cotil, Jean-Baptiste Menassol, Nicolas Verzelen
https://arxiv.org/abs/2509.15989

Model-free algorithms for fast node clustering in SBM type graphs and application to social role inference in animals
We propose a novel family of model-free algorithms for node clustering and parameter inference in graphs generated from the Stochastic Block Model (SBM), a fundamental framework in community detection. Drawing inspiration from the Lloyd algorithm for the $k$-means problem, our approach extends to SBMs with general edge weight distributions. We establish the consistency of our estimator under a natural identifiability condition. Through extensive numerical experiments, we benchmark our methods a…

An analysis of government subsidy policies in vaccine supply chain: Innovation, Production, or Consumption?
Ran Gu, Enhui Ding, Shigui Ma
https://arxiv.org/abs/2510.03661 https:…

An analysis of government subsidy policies in vaccine supply chain: Innovation, Production, or Consumption?
Vaccines play a crucial role in the prevention and control of infectious diseases. However, the vaccine supply chain faces numerous challenges that hinder its efficiency. To address these challenges and enhance public health outcomes, many governments provide subsidies to support the vaccine supply chain. This study analyzes a government-subsidized, three-tier vaccine supply chain within a continuous-time differential game framework. The model incorporates dynamic system equations that account …

Exploring Large Language Model as an Interactive Sports Coach: Lessons from a Single-Subject Half Marathon Preparation
Kichang Lee
https://arxiv.org/abs/2509.26593 https://

Exploring Large Language Model as an Interactive Sports Coach: Lessons from a Single-Subject Half Marathon Preparation
Large language models (LLMs) are emerging as everyday assistants, but their role as longitudinal virtual coaches is underexplored. This two-month single subject case study documents LLM guided half marathon preparation (July-September 2025). Using text based interactions and consumer app logs, the LLM acted as planner, explainer, and occasional motivator. Performance improved from sustaining 2 km at 7min 54sec per km to completing 21.1 km at 6min 30sec per km, with gains in cadence, pace HR cou…

Complexity of Einstein-Maxwell-non-minimal coupling $R^2F^2$: the role of the penalty factor
Mojtaba Shahbazi, Mehdi Sadeghi
https://arxiv.org/abs/2509.25165 https://

Complexity of Einstein-Maxwell-non-minimal coupling $R^2F^2$: the role of the penalty factor
In this paper, we examine the holographic complexity of a theory of Einstein-Maxwell with a non-minimal coupling $R^2 F_{μν}F^{μν} $ term via complexity=anything. We introduce a perturbative black brane solution in AdS spacetime up to the first order of the non-minimal coupling. Because the model exhibits the resistivity proportional to the temperature, it could describe strange metals. The complexity of the solution could be characterized by three quantities: the conserved charge, the non-…

Dynamical model of Praesepe and its tidal tails
L. Weis, C. West, A. Just, P. Berczik, M. Ishchenko, S. R\"oser, E. Schilbach, B. Shukirgaliyev
https://arxiv.org/abs/2509.24584

Dynamical model of Praesepe and its tidal tails
Context. The dynamical evolution of open clusters in the tidal field of the Milky Way and the feeding of the disc field star population depend strongly on the initial conditions at the time of gas removal. Detailed dynamical models tailored to individual clusters help us understand the role of open clusters in the Galactic disc evolution. Aims. We present a detailed dynamical model of Praesepe, which reproduces the mass profile, the stellar mass function, and the mass segregation observed with …

Unveiling Many Faces of Surrogate Models for Configuration Tuning: A Fitness Landscape Analysis Perspective
Pengzhou Chen, Hongyuan Liang, Tao Chen
https://arxiv.org/abs/2509.21945

Unveiling Many Faces of Surrogate Models for Configuration Tuning: A Fitness Landscape Analysis Perspective
To efficiently tune configuration for better system performance (e.g., latency), many tuners have leveraged a surrogate model to expedite the process instead of solely relying on the profoundly expensive system measurement. As such, it is naturally believed that we need more accurate models. However, the fact of accuracy can lie-a somewhat surprising finding from prior work-has left us many unanswered questions regarding what role the surrogate model plays in configuration tuning. This paper pr…

LTA-L2S: Lexical Tone-Aware Lip-to-Speech Synthesis for Mandarin with Cross-Lingual Transfer Learning
Kang Yang, Yifan Liang, Fangkun Liu, Zhenping Xie, Chengshi Zheng
https://arxiv.org/abs/2509.25670 …

LTA-L2S: Lexical Tone-Aware Lip-to-Speech Synthesis for Mandarin with Cross-Lingual Transfer Learning
Lip-to-speech (L2S) synthesis for Mandarin is a significant challenge, hindered by complex viseme-to-phoneme mappings and the critical role of lexical tones in intelligibility. To address this issue, we propose Lexical Tone-Aware Lip-to-Speech (LTA-L2S). To tackle viseme-to-phoneme complexity, our model adapts an English pre-trained audio-visual self-supervised learning (SSL) model via a cross-lingual transfer learning strategy. This strategy not only transfers universal knowledge learned from …

The role of stacking and strain in mean-field magnetic moments of multilayer graphene
Andr\'as Balogh, Zolt\'an Tajkov, P\'eter Nemes-Incze, J\'anos Koltai
https://arxiv.org/abs/2510.00898

The role of stacking and strain in mean-field magnetic moments of multilayer graphene
Rhombohedral or ABC stacked multilayer graphene hosts a correlated magnetic ground state at charge neutrality, making it one of the simplest systems to investigate strong electronic correlations. We investigate this ground state in multilayer graphene structures using the Hubbard model in a distance dependent Slater-Koster tight binding framework. We show that by using a universal Hubbard-$U$ term, we can accurately capture the spin polarization predicted by hybrid density functional theory cal…

Reservoir computing based predictive reduced order model for steel grade intermixing in an industrial continuous casting tundish
Harshith Gowrachari, Mattia Giuseppe Barra, Giovanni Stabile, Gianluca Bazzaro, Gianluigi Rozza
https://arxiv.org/abs/2509.26293

Reservoir computing based predictive reduced order model for steel grade intermixing in an industrial continuous casting tundish
Continuous casting is a widely adopted process in the steel industry, where maintaining high steel quality is paramount. Efficient prediction of grade intermixing during ladle changeover operations is critical for maintaining steel quality and minimizing material losses in the continuous casting process. Among various factors influencing grade intermixing, operating parameters play a significant role, in addition to tundish geometry and flow control devices. In this study, three-dimensional, tr…

Tunable quantum metric and band topology in bilayer Dirac model
Xun-Jiang Luo, Xing-Lei Ma, K. T. Law
https://arxiv.org/abs/2509.23622 https://arxiv.org/pd…

Tunable quantum metric and band topology in bilayer Dirac model
Quantum metric, a fundamental component of quantum geometry, has attracted broad interest in recent years due to its critical role in various quantum phenomena. Meanwhile, band topology, which serves as an important framework in condensed matter physics, has led to the discovery of various topological phases. In this work, we introduce a bilayer Dirac model that allows precise tuning of both properties. Our approach combines two Dirac Hamiltonians with distinct energy scales; one producing rela…

Preparation Methods and Applications of Biomimetic Membranes
Ajit Seth, Sajal K. Ghosh, Veerendra K. Sharma
https://arxiv.org/abs/2509.25796 https://arxiv.…

Preparation Methods and Applications of Biomimetic Membranes
Model biomembrane systems play a crucial role in advancing biomedical research by providing simplified yet effective platforms for exploring complex biological mechanisms. These systems span a wide range of scales, from single-molecule-thick lipid monolayers to micron-sized giant unilamellar vesicles. Their efficacy and applicability largely depend on selecting an optimal model and an appropriate synthesis process. This chapter offers a comprehensive description of conventional synthesis techni…

The fracturing of the Dutch far-right, after Wilder's reminded everyone that bigots are bad at compromise, is definitely a relief. Dutch folks I've talked to definitely see D66 as progressive, <strike>so there's no question this is a hard turn to the left (even if it's not a total flip to the far-left)</strike> a lot of folks don't agree. I'm going to let the comments speak rather than editorialize myself..
While this is a useful example of how a democracy can be far more resilient to fascism than the US, that is, perhaps, not the most interesting thing about Dutch politics. The most interesting thing is something Dutch folks take for granted and never think of as such: there are two "governments."
The election was for the Tweede Kamer. This is a house of representatives. The Dutch use proportional representation, so people can (more or less) vote for the parties they actually want. Parties <strike>rarely</strike> never actually get a ruling majority, so they have to form coalition governments. This forces compromise, which is something Wilders was extremely bad at. He was actually responsible for collapsing the coalition his party put together, which triggered this election... and a massive loss of seats for his party.
Dutch folks do still vote strategically, since a larger party has an easier time building the governing coalition and the PM tends to come from the largest party. This will likely be D66, which is really good for the EU. D66 has a pretty radical plan to solve the housing crisis, and it will be really interesting to see if they can pull it off. But that's not the government I want to talk about right now.
In the Netherlands, failure to control water can destroy entire towns. A good chunk of the country is below sea level. Both floods and land reclamation have been critical parts of Dutch history. So in the 1200's or so, the Dutch realized that some things are too important to mix with normal politics.
You see, if there's an incompetent government that isn't able to actually *do* anything (see Dick Schoof and the PVV/VVD/NSC/BBB coalition) you don't want your dikes to collapse and poulders to flood. So the Dutch created a parallel "government" that exists only to manage water: waterschap or heemraadschap (roughly "Water Board" in English). These are regional bureaucracies that exist only to manage water. They exist completely outside the thing we usually talk about as a "government" but they have some of the same properties as a government. They can, for example, levy taxes. The central government contributes funds to them, but lacks authority over them. Water boards are democratically elected and can operate more-or-less independent of the central government.
Controlling water is a common problem, so water boards were created to fulfill the role of commons management. Meanwhile, so many other things in politics run into the very same "Tragedy of the Commons" problems. The right wing solution to commons management is to let corporations ruin everything. The left-state solution is to move everything into the government so it can be undermined and destroyed by the right. The Dutch solution to this specific problem has been to move commons management out of the domain of the central government into something else.
And when I say "government" here, I'm speaking more to the liberal definition of the term than to an anarchist definition. A democratically controlled authority that facilitates resource management lacks the capacity for coercive violence that anarchists define as "government." (Though I assume they might leverage police or something if folks refuse to pay their taxes, but I can't imagine anyone choosing not to.)
As the US federal government destroys the social fabric of the US, as Trump guts programs critical to people's survival, it might be worth thinking about this model. These authorities weren't created by any central authority, they evolved from the people. Nothing stops Americans from building similar institutions that are both democratic and outside of the authority of a government that could choose to defund and abolish them... nothing but the realization that yes, you actually can.
#USPol #NLPol

Electron neural closure for turbulent magnetosheath simulations: energy channels
George Miloshevich, Luka Vranckx, Felipe Nathan de Oliveira Lopes, Pietro Dazzi, Giuseppe Arr\`o, Giovanni Lapenta
https://arxiv.org/abs/2510.00282

Electron neural closure for turbulent magnetosheath simulations: energy channels
In this work, we introduce a non-local five-moment electron pressure tensor closure parametrized by a Fully Convolutional Neural Network (FCNN). Electron pressure plays an important role in generalized Ohm's law, competing with electron inertia. This model is used in the development of a surrogate model for a fully kinetic energy-conserving semi-implicit Particle-in-Cell simulation of decaying magnetosheath turbulence. We achieve this by training FCNN on a representative set of simulations with…

Learning single index model with gradient descent: spectral initialization and precise asymptotics
Yuchen Chen, Yandi Shen
https://arxiv.org/abs/2509.23527 https://

Learning single index model with gradient descent: spectral initialization and precise asymptotics
Non-convex optimization plays a central role in many statistics and machine learning problems. Despite the landscape irregularities for general non-convex functions, some recent work showed that for many learning problems with random data and large enough sample size, there exists a region around the true signal with benign landscape. Motivated by this observation, a widely used strategy is a two-stage algorithm, where we first apply a spectral initialization to plunge into the region, and then…

Enhancing Large Language Model Reasoning with Reward Models: An Analytical Survey
Qiyuan Liu, Hao Xu, Xuhong Chen, Wei Chen, Yee Whye Teh, Ning Miao
https://arxiv.org/abs/2510.01925

Enhancing Large Language Model Reasoning with Reward Models: An Analytical Survey
Reward models (RMs) play a critical role in enhancing the reasoning performance of LLMs. For example, they can provide training signals to finetune LLMs during reinforcement learning (RL) and help select the best answer from multiple candidates during inference. In this paper, we provide a systematic introduction to RMs, along with a comprehensive survey of their applications in LLM reasoning. We first review fundamental concepts of RMs, including their architectures, training methodologies, an…

EfficientDepth: A Fast and Detail-Preserving Monocular Depth Estimation Model
Andrii Litvynchuk, Ivan Livinsky, Anand Ravi, Nima Kalantari, Andrii Tsarov
https://arxiv.org/abs/2509.22527

EfficientDepth: A Fast and Detail-Preserving Monocular Depth Estimation Model
Monocular depth estimation (MDE) plays a pivotal role in various computer vision applications, such as robotics, augmented reality, and autonomous driving. Despite recent advancements, existing methods often fail to meet key requirements for 3D reconstruction and view synthesis, including geometric consistency, fine details, robustness to real-world challenges like reflective surfaces, and efficiency for edge devices. To address these challenges, we introduce a novel MDE system, called Efficien…

Analytical Model of Resonant Quantum Excitation Transport in Molecular Chains at finite Temperatures: Application of Integral Transforms
Dalibor Chevizovich, Slobodanka Galovic, Vasilije Matic, Zoran Ivic, Zeljko Przulj
https://arxiv.org/abs/2510.00663

Analytical Model of Resonant Quantum Excitation Transport in Molecular Chains at finite Temperatures: Application of Integral Transforms
This study investigates the potential impact of intramolecular excitations on the active regions of biomolecular chains, which may play a role in physiological processes within living cells. We assumed that an excitation localized in a specific chain segment can modify its physical properties (e.g., local charge distribution or electric dipole moments), thereby altering its role in biochemical processes. As a consequence, the biochemical functionality of the molecular chain may be altered, or e…

A Learnable Fully Interacted Two-Tower Model for Pre-Ranking System
Chao Xiong, Xianwen Yu, Wei Xu, Lei Cheng, Chuan Yuan, Linjian Mo
https://arxiv.org/abs/2509.12948 https://…

A Learnable Fully Interacted Two-Tower Model for Pre-Ranking System
Pre-ranking plays a crucial role in large-scale recommender systems by significantly improving the efficiency and scalability within the constraints of providing high-quality candidate sets in real time. The two-tower model is widely used in pre-ranking systems due to a good balance between efficiency and effectiveness with decoupled architecture, which independently processes user and item inputs before calculating their interaction (e.g. dot product or similarity measure). However, this indep…

Efficient Differentiable Contact Model with Long-range Influence
Xiaohan Ye, Kui Wu, Zherong Pan, Taku Komura
https://arxiv.org/abs/2509.20917 https://arxi…

Efficient Differentiable Contact Model with Long-range Influence
With the maturation of differentiable physics, its role in various downstream applications: such as model predictive control, robotic design optimization, and neural PDE solvers, has become increasingly important. However, the derivative information provided by differentiable simulators can exhibit abrupt changes or vanish altogether, impeding the convergence of gradient-based optimizers. In this work, we demonstrate that such erratic gradient behavior is closely tied to the design of contact m…

A Graph-Neural-Network-Entropy model of vital node identification on network attack and propagation
Huaizhi Liao, Tian Qiu, Guang Chen
https://arxiv.org/abs/2509.18325 https://

A Graph-Neural-Network-Entropy model of vital node identification on network attack and propagation
Vital nodes usually play a key role in complex networks. Uncovering these nodes is an important task in protecting the network, especially when the network suffers intentional attack. Many existing methods have not fully integrated the node feature, interaction and state. In this article, we propose a novel method (GNNE) based on graph neural networks and information entropy. The method employs a Graph Convolutional Network (GCN) to learn the nodes' features, which are input into a Graph Attent…

Increasing Inter-Fiber Contact in the Altendorf-Jeulin Model
Alex Keilmann, Claudia Redenbach, Francois Willot
https://arxiv.org/abs/2509.16225 https://arx…

Increasing Inter-Fiber Contact in the Altendorf-Jeulin Model
In fields such as material design or biomedicine, fiber materials play an important role. Fiber simulations, also called digital twins, provide a basis for testing and optimizing the material's physical behavior digitally. Inter-fiber contacts can influence the thermal and mechanical behavior of a fiber system; to our knowledge, however, there exist no parametric fiber models allowing for explicit modeling of the number of inter-fiber contacts. Therefore, this paper proposes an extension of the…

Guiding Audio Editing with Audio Language Model
Zitong Lan, Yiduo Hao, Mingmin Zhao
https://arxiv.org/abs/2509.21625 https://arxiv.org/pdf/2509.21625

Guiding Audio Editing with Audio Language Model
Audio editing plays a central role in VR/AR immersion, virtual conferencing, sound design, and other interactive media. However, recent generative audio editing models depend on template-like instruction formats and are restricted to mono-channel audio. These models fail to deal with declarative audio editing, where the user declares what the desired outcome should be, while leaving the details of editing operations to the system. We introduce SmartDJ, a novel framework for stereo audio editing…

xLSTM Scaling Laws: Competitive Performance with Linear Time-Complexity
Maximilian Beck, Kajetan Schweighofer, Sebastian B\"ock, Sebastian Lehner, Sepp Hochreiter
https://arxiv.org/abs/2510.02228 …

xLSTM Scaling Laws: Competitive Performance with Linear Time-Complexity
Scaling laws play a central role in the success of Large Language Models (LLMs), enabling the prediction of model performance relative to compute budgets prior to training. While Transformers have been the dominant architecture, recent alternatives such as xLSTM offer linear complexity with respect to context length while remaining competitive in the billion-parameter regime. We conduct a comparative investigation on the scaling behavior of Transformers and xLSTM along the following lines, prov…

FreeViS: Training-free Video Stylization with Inconsistent References
Jiacong Xu, Yiqun Mei, Ke Zhang, Vishal M. Patel
https://arxiv.org/abs/2510.01686 https://

FreeViS: Training-free Video Stylization with Inconsistent References
Video stylization plays a key role in content creation, but it remains a challenging problem. Naïvely applying image stylization frame-by-frame hurts temporal consistency and reduces style richness. Alternatively, training a dedicated video stylization model typically requires paired video data and is computationally expensive. In this paper, we propose FreeViS, a training-free video stylization framework that generates stylized videos with rich style details and strong temporal coherence. Our…

Competing $s$-wave pairing in overdoped $t$-$J$ model
Wayne Zheng, Tao Cheng, Zheng-Yuan Yue, Fu-Chun Zhang, Wei-Qiang Chen, Zheng-Cheng Gu
https://arxiv.org/abs/2509.22473 http…

Competing $s$-wave pairing in overdoped $t$-$J$ model
The $d$-wave pairing symmetry has long been considered a defining feature of high-temperature superconductivity in cuprates. In this work, we reveal that $s$-wave pairing states exhibit variational energies comparable to the $d$-wave state in a square $t$-$J$ model, particularly at high doping levels ($δ\gtrsim 15\%$) by using the state-of-the-art tensor network simulation. This surprising result suggests that $s$-wave pairing may play an important role in the cuprate phase diagram, especially…

A Dynamic Service Offloading Algorithm Based on Lyapunov Optimization in Edge Computing
Peiyan Yuan, Ming Li, Chenyang Wang, Ledong An, Xiaoyan Zhao, Junna Zhang, Xiangyang Li, Huadong Ma
https://arxiv.org/abs/2509.10475

A Dynamic Service Offloading Algorithm Based on Lyapunov Optimization in Edge Computing
This study investigates the trade-off between system stability and offloading cost in collaborative edge computing. While collaborative offloading among multiple edge servers enhances resource utilization, existing methods often overlook the role of queue stability in overall system performance. To address this, a multi-hop data transmission model is developed, along with a cost model that captures both energy consumption and delay. A time-varying queue model is then introduced to maintain syst…

Predicting Credit Spreads and Ratings with Machine Learning: The Role of Non-Financial Data
Yanran Wu, Xinlei Zhang, Quanyi Xu, Qianxin Yang, Chao Zhang
https://arxiv.org/abs/2509.19042

Quantum Simulation and Energy Estimation for Discretized Anharmonic oscillator
Saurav Suman, Bikash K. Behera, Vivek Vyas, Prasanta k. Panigrahi
https://arxiv.org/abs/2509.20907

Quantum Simulation and Energy Estimation for Discretized Anharmonic oscillator
Anharmonic potential quantum system play crucial role in physics as they provide a more realistic description of oscillatory phenomena, which often deviate from the idealized harmonic model. However, simulating such system on classical computers is highly challenging due to nonlinear interactions, large state spaces, and the exponential scaling of memory and computational resources. In this work, quantum simulation is employed to model a quantum anharmonic oscillator (QAHO) using a 3-qubit syst…

Strong enhancement of d-wave superconductivity in an extended checkerboard Hubbard ladder
Xichen Huang, Saisai He, Jize Zhao, Zhong-Bing Huang
https://arxiv.org/abs/2509.24415 h…

Strong enhancement of d-wave superconductivity in an extended checkerboard Hubbard ladder
By employing the density-matrix renormalization group method, we study an extended checkerboard Hubbard model on the two-leg ladder, which includes an intraplaquette nearest-neighbour attraction V. The simulated results show that V plays a significant role in enhancing the d-wave superconductivity when the electron density is close to half-filling. In the homogeneous case t'=t (t and t' are the intraplaquette and interplaquette hopping integrals), large critical |Vc| is required to in…

Turing instability and 2-D pattern formation in reaction-diffusion systems derived from kinetic theory
Stefano Boccelli, Giorgio Martal\`o, Romina Travaglini
https://arxiv.org/abs/2509.20268

Turing instability and 2-D pattern formation in reaction-diffusion systems derived from kinetic theory
We investigate Turing instability and pattern formation in two-dimensional domains for two reaction-diffusion models, obtained as diffusive limits of kinetic equations for mixtures of monatomic and polyatomic gases. The first model is of Brusselator type, which, compared with the classical formulation, presents an additional parameter whose role in stability and pattern formation is discussed. In the second framework, the system exhibits standard nonlinear diffusion terms typical of predator-pr…

Projection-based model order reduction for residence time distribution analysis of an industrial-scale continuous casting tundish
Harshith Gowrachari, Mattia Giuseppe Barra, Moaad Khamlich, Giovanni Stabile, Gianluca Bazzaro, Gianluigi Rozza
https://arxiv.org/abs/2509.20366

Projection-based model order reduction for residence time distribution analysis of an industrial-scale continuous casting tundish
The flow behavior in the continuous casting tundish plays a critical role in steel quality and is typically characterized via residence time distribution (RTD) curves. This study investigates the fluid flow behaviour in a single-strand tundish using numerical and experimental approaches. Full-order model (FOM) steady-state simulations were conducted under both isothermal and non-isothermal conditions to assess the influence of thermal buoyancy on the flow characteristics. The results show that …

The NANOGrav 15-Year Data Set: Improved Timing Precision With VLBI Astrometric Priors
Sofia V. Sosa Fiscella, Michael T. Lam, Gabriella Agazie, Akash Anumarlapudi, Anne M. Archibald, Zaven Arzoumanian, Paul T. Baker, Paul R. Brook, H. Thankful Cromartie, Kathryn Crowter, Maria Silvina De Biasi, Megan E. DeCesar, Paul B. Demorest, Timothy Dolch, Elizabeth C. Ferrara, William Fiore, Emmanuel Fonseca, Gabriel E. Freedman, Nate Garver-Daniels, Peter A. Gentile, Joseph Glaser, Deborah C. Go…

The NANOGrav 15-Year Data Set: Improved Timing Precision With VLBI Astrometric Priors
Accurate pulsar astrometric estimates play an essential role in almost all high-precision pulsar timing experiments. Traditional pulsar timing techniques refine these estimates by including them as free parameters when fitting a model to observed pulse time-of-arrival measurements. However, reliable sub-milliarcsecond astrometric estimations require years of observations and, even then, power from red noise can be inadvertently absorbed into astrometric parameter fits, biasing the resulting est…

Interpreting the Role of Visemes in Audio-Visual Speech Recognition
Aristeidis Papadopoulos, Naomi Harte
https://arxiv.org/abs/2509.16023 https://arxiv.org…

Interpreting the Role of Visemes in Audio-Visual Speech Recognition
Audio-Visual Speech Recognition (AVSR) models have surpassed their audio-only counterparts in terms of performance. However, the interpretability of AVSR systems, particularly the role of the visual modality, remains under-explored. In this paper, we apply several interpretability techniques to examine how visemes are encoded in AV-HuBERT a state-of-the-art AVSR model. First, we use t-distributed Stochastic Neighbour Embedding (t-SNE) to visualize learned features, revealing natural clustering …

Dynamic Experts Search: Enhancing Reasoning in Mixture-of-Experts LLMs at Test Time
Yixuan Han, Fan Ma, Ruijie Quan, Yi Yang
https://arxiv.org/abs/2509.22572 https://

Dynamic Experts Search: Enhancing Reasoning in Mixture-of-Experts LLMs at Test Time
Test-Time Scaling (TTS) enhances the reasoning ability of large language models (LLMs) by allocating additional computation during inference. However, existing approaches primarily rely on output-level sampling while overlooking the role of model architecture. In mainstream Mixture-of-Experts (MoE) LLMs, we observe that varying the number of activated experts yields complementary solution sets with stable accuracy, revealing a new and underexplored source of diversity. Motivated by this observa…

Opinion Clustering under the Friedkin-Johnsen Model: Agreement in Disagreement
Aashi Shrinate, Twinkle Tripathy
https://arxiv.org/abs/2509.11045 https://ar…

Opinion Clustering under the Friedkin-Johnsen Model: Agreement in Disagreement
The convergence of opinions in the Friedkin-Johnsen (FJ) framework is well studied, but the topological conditions leading to opinion clustering remain less explored. To bridge this gap, we examine the role of topology in the emergence of opinion clusters within the network. The key contribution of the paper lies in the introduction of the notion of topologically prominent agents, referred to as Locally Topologically Persuasive (LTP) agents. Interestingly, each LTP agent is associated with a un…

The Role of High-Performance GPU Resources in Large Language Model Based Radiology Imaging Diagnosis
Jyun-Ping Kao
https://arxiv.org/abs/2509.16328 https://

The Role of High-Performance GPU Resources in Large Language Model Based Radiology Imaging Diagnosis
Large-language models (LLMs) are rapidly being applied to radiology, enabling automated image interpretation and report generation tasks. Their deployment in clinical practice requires both high diagnostic accuracy and low inference latency, which in turn demands powerful hardware. High-performance graphical processing units (GPUs) provide the necessary compute and memory throughput to run large LLMs on imaging data. We review modern GPU architectures (e.g. NVIDIA A100/H100, AMD Instinct MI250X…

Transfer learning under latent space model
Kuangnan Fang, Ruixuan Qin, Xinyan Fan
https://arxiv.org/abs/2509.15797 https://arxiv.org/pdf/2509.15797

Transfer learning under latent space model
Latent space model plays a crucial role in network analysis, and accurate estimation of latent variables is essential for downstream tasks such as link prediction. However, the large number of parameters to be estimated presents a challenge, especially when the latent space dimension is not exceptionally small. In this paper, we propose a transfer learning method that leverages information from networks with latent variables similar to those in the target network, thereby improving the estimati…

Exact quasinormal modes in Grumiller spacetime
Li-Qin Mi, Zhong-Heng Li
https://arxiv.org/abs/2509.16991 https://arxiv.org/pdf/2509.16991

Exact quasinormal modes in Grumiller spacetime
The Grumiller metric is an effective model for gravity at large distances and plays a significant role in constructing galactic models and explaining dark matter. Here, in Grumiller spacetime, we analytically compute the quasinormal-mode frequencies and wave functions for massless particles with spin $\leq 2$ by introducing a new transformation relation. We find that the quasinormal-mode frequencies are identical for different bosons sharing the same quantum number $N$, and similarly for differ…

RAGs to Riches: RAG-like Few-shot Learning for Large Language Model Role-playing
Timothy Rupprecht, Enfu Nan, Arash Akbari, Arman Akbari, Lei Lu, Priyanka Maan, Sean Duffy, Pu Zhao, Yumei He, David Kaeli, Yanzhi Wang
https://arxiv.org/abs/2509.12168

RAGs to Riches: RAG-like Few-shot Learning for Large Language Model Role-playing
Role-playing Large language models (LLMs) are increasingly deployed in high-stakes domains such as healthcare, education, and governance, where failures can directly impact user trust and well-being. A cost effective paradigm for LLM role-playing is few-shot learning, but existing approaches often cause models to break character in unexpected and potentially harmful ways, especially when interacting with hostile users. Inspired by Retrieval-Augmented Generation (RAG), we reformulate LLM role-pl…

Pattern Formation in Agent-Based and PDE Models for Evolutionary Games with Payoff-Driven Motion
Tianyong Yao, Chenning Xu, Daniel B. Cooney
https://arxiv.org/abs/2509.20538 htt…

Pattern Formation in Agent-Based and PDE Models for Evolutionary Games with Payoff-Driven Motion
Spatial structure can play an important role in the evolution of cooperative behavior and the achievement of collective success of a population. In this paper, we explore the role of random and directed motion on spatial pattern formation and the payoff achieved by populations in both stochastic and deterministic models of spatial populations who engage in social interactions following a hawk-dove game. For the case of purely diffusive motion, both a stochastic spatial model and a partial diffe…

A Deep Learning Model of Lightning Stroke Density
Randall Jones II, Joel A. Thornton, Chris J. Wright, Robert Holzworth
https://arxiv.org/abs/2509.10399 https://

A Deep Learning Model of Lightning Stroke Density
Lightning plays a crucial role in the Earth's climate system, yet existing parameterizations for use in forecasting and earth system models show room for improvement in capturing spatial and temporal variations in its frequency. This study develops deep learning-based parameterizations of lightning stroke density using meteorological variables from the ERA and IMERG datasets. Convolutional neural networks (CNNs) with U-Net architectures are trained using World Wide Lightning Location Network (W…

ROBOPSY PL[AI]: Using Role-Play to Investigate how LLMs Present Collective Memory
Margarete Jahrmann, Thomas Brandstetter, Stefan Glasauer
https://arxiv.org/abs/2510.09874 https…

ROBOPSY PL[AI]: Using Role-Play to Investigate how LLMs Present Collective Memory
The paper presents the first results of an artistic research project investigating how Large Language Models (LLMs) curate and present collective memory. In a public installation exhibited during two months in Vienna in 2025, visitors could interact with five different LLMs (ChatGPT with GPT 4o and GPT 4o mini, Mistral Large, DeepSeek-Chat, and a locally run Llama 3.1 model), which were instructed to act as narrators, implementing a role-playing game revolving around the murder of Austrian phil…

The Role of Parametric Injection-A Systematic Study of Parametric Retrieval-Augmented Generation
Minghao Tang, Shiyu Ni, Jingtong Wu, Zengxin Han, Keping Bi
https://arxiv.org/abs/2510.12668

The Role of Parametric Injection-A Systematic Study of Parametric Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by retrieving external documents. As an emerging form of RAG, parametric retrieval-augmented generation (PRAG) encodes documents as model parameters (i.e., LoRA modules) and injects these representations into the model during inference, enabling interaction between the LLM and documents at parametric level. Compared with directly placing documents in the input context, PRAG is more efficient and has the potential to offe…

Bananas for Tonks' gas: structural transitions in bent-core molecule bi-layers
David A. King
https://arxiv.org/abs/2509.21503 https://arxiv.org/pdf/250…

Bananas for Tonks' gas: structural transitions in bent-core molecule bi-layers
We study the role of particle geometry in the ordering of bilayers of bent-core particles using a simplified two-dimensional model. Particles are confined to two parallel one-dimensional layers and can adopt only two discrete orientations of their bent cores. By mapping the system exactly onto a pair of coupled Ising chains in the thermodynamic limit, we may obtain closed-form expressions for all correlation functions. The coupling between orientational and layer degrees of freedom produces rem…

Defects4C: Benchmarking Large Language Model Repair Capability with C/C Bugs
Jian Wang, Xiaofei Xie, Qiang Hu, Shangqing Liu, Jiongchi Yu, Jiaolong Klong, Yi Li
https://arxiv.org/abs/2510.11059

Defects4C: Benchmarking Large Language Model Repair Capability with C/C++ Bugs
Automated Program Repair (APR) plays a critical role in enhancing the quality and reliability of software systems. While substantial progress has been made in Java-based APR, largely facilitated by benchmarks like Defects4J, there remains a significant gap in research on C/C++ program repair, despite the widespread use of C/C++ and the prevalence of associated vulnerabilities. This gap is primarily due to the lack of high-quality, open-source benchmarks tailored for C/C++. To address this iss…

Non-Markovian amplitude damping in a central spin model with random couplings
Mehboob Rashid, Rayees A Mala, Saima Bashir, Muzaffar Qadir Lone
https://arxiv.org/abs/2509.12670 h…

Non-Markovian amplitude damping in a central spin model with random couplings
Non-Markovian dynamics is central to quantum information processing, as memory effects strongly influence coherence preservation, metrology, and communication. In this work, we investigate the role of stochastic system--bath couplings in shaping non-Markovian behavior of open quantum systems, using the central spin model within a time-convolutionless master equation framework. We show that the character of the reduced dynamics depends jointly on the intrinsic memory of the environment and on th…

On Quantification of Borrowing of Information in Hierarchical Bayesian Models
Prasenjit Ghosh, Anirban Bhattacharya, Debdeep Pati
https://arxiv.org/abs/2509.17301 https://

On Quantification of Borrowing of Information in Hierarchical Bayesian Models
In this work, we offer a thorough analytical investigation into the role of shared hyperparameters in a hierarchical Bayesian model, examining their impact on information borrowing and posterior inference. Our approach is rooted in a non-asymptotic framework, where observations are drawn from a mixed-effects model, and a Gaussian distribution is assumed for the true effect generator. We consider a nested hierarchical prior distribution model to capture these effects and use the posterior means …

SYK collective field theory as complex Liouville gravity
Andreas Blommaert, Damiano Tietto, Herman Verlinde
https://arxiv.org/abs/2509.18462 https://arxiv.…

Physics-Informed Inductive Biases for Voltage Prediction in Distribution Grids
Ehimare Okoyomon, Arbel Yaniv, Christoph Goebel
https://arxiv.org/abs/2509.25158 https://

Physics-Informed Inductive Biases for Voltage Prediction in Distribution Grids
Voltage prediction in distribution grids is a critical yet difficult task for maintaining power system stability. Machine learning approaches, particularly Graph Neural Networks (GNNs), offer significant speedups but suffer from poor generalization when trained on limited or incomplete data. In this work, we systematically investigate the role of inductive biases in improving a model's ability to reliably learn power flow. Specifically, we evaluate three physics-informed strategies: (i) power-f…

The Hidden DNA of LLM-Generated JavaScript: Structural Patterns Enable High-Accuracy Authorship Attribution
Norbert Tihanyi, Bilel Cherif, Richard A. Dubniczky, Mohamed Amine Ferrag, Tam\'as Bisztray
https://arxiv.org/abs/2510.10493

The Hidden DNA of LLM-Generated JavaScript: Structural Patterns Enable High-Accuracy Authorship Attribution
In this paper, we present the first large-scale study exploring whether JavaScript code generated by Large Language Models (LLMs) can reveal which model produced it, enabling reliable authorship attribution and model fingerprinting. With the rapid rise of AI-generated code, attribution is playing a critical role in detecting vulnerabilities, flagging malicious content, and ensuring accountability. While AI-vs-human detection usually treats AI as a single category we show that individual LLMs le…

The Role of Phase and Spatial Modes in Wave-Induced Plasma Transport
L. F. B. Souza, Y. Elskens, R. Egydio de Carvalho, I. L. Caldas
https://arxiv.org/abs/2509.15729 https://

The Role of Phase and Spatial Modes in Wave-Induced Plasma Transport
We derive a two-dimensional symplectic map for particle motion at the plasma edge by modeling the electrostatic potential as a superposition of integer spatial harmonics with relative phase shift, then reduce it to a two-wave model to study the transport dependence on the perturbation amplitudes, relative phase, and spatial-mode choice. Using particle transmissivity as a confinement criterion, identical-mode pairs exhibit phase-controlled behavior: anti-phase waves produce destructive interfere…

Collateral and Reputation in a Model of Strategic Defaults
Georgy Lukyanov
https://arxiv.org/abs/2509.08849 https://arxiv.org/pdf/2509.08849

Collateral and Reputation in a Model of Strategic Defaults
This paper builds a finite-horizon model to study the role of physical collateral in a model of strategic defaults, when the borrower can develop reputation for honesty. Asset ownership increases attractiveness of the reputational channel: the borrower who would prefer to remain in autarky in the absence of the asset prefers to apply for collateralized debt. Pledging the asset as collateral facilitates reputation building, which is especially successful at the times of asset price drops, becaus…

WALLABY Pilot Survey: A gas-rich diffuse dwarf on the baryonic Tully Fisher relation
Rebecca Dudley, N. Deg, Kristine Spekkens, N. Arora, T. O'Beirne, V. Kilborn, B. Catinella, Pavel E. Mancera Pi\~na
https://arxiv.org/abs/2509.15340

WALLABY Pilot Survey: A gas-rich diffuse dwarf on the baryonic Tully Fisher relation
Diffuse dwarf galaxies, and particularly ultra diffuse galaxies (UDGs), challenge our understanding of galaxy formation and the role of dark matter due to their large sizes, low surface brightness, and varying dark matter content. In this work, we investigate the gas-rich diffuse dwarf galaxy WALLABY J125956-192430 (aka. KK176) using high-resolution HI data from the WALLABY survey. We produce the most reliable kinematic model for KK176 to date. Using this model, the derived mass decomposition s…

Experiments on geostrophic convection: the role of the Prandtl number
Hannah M. Clercx, Rudie P. J. Kunnen
https://arxiv.org/abs/2509.20126 https://arxiv.o…

Experiments on geostrophic convection: the role of the Prandtl number
Flows at planetary scales are generally driven by buoyancy and influenced by rotation. Rotating Rayleigh-Bénard convection (RRBC) is a practical and simple model that can be used to describe these systems. In RRBC, thermally induced convection occurs, which is influenced by the constant rotation it experiences. We study RRBC in a cylinder in the geostrophic regime, where the dominant force balance is between Coriolis and pressure-gradient forces. Experiments are performed to assess the depende…

Configuration and Benchmarking of $\mathrm{e}^ \mathrm{e}^-$ Processes with K4GeneratorsConfig
Alan Price, Dirk Zerwas
https://arxiv.org/abs/2509.20116 https://

Configuration and Benchmarking of $\mathrm{e}^+\mathrm{e}^-$ Processes with K4GeneratorsConfig
The next generation of electron-positron colliders will require unprecedented precision in both theory and experiment. Sophisticated software frameworks are essential to evaluate detector concepts, optimize designs, and simulating physical processes. In this context, Monte Carlo (MC) event generators play a central role, enabling realistic simulations of Standard Model processes and providing the basis for physics studies. However, technical consistency across different generators remains criti…

Magnetization by Rotation: Spin and Chiral Condensates in the NJL Model
Lutz Kiefer, Ashutosh Dash, Dirk H. Rischke
https://arxiv.org/abs/2509.18881 https://

Magnetization by Rotation: Spin and Chiral Condensates in the NJL Model
The role of spin degrees of freedom in the quark-gluon plasma (QGP) has attracted significant interest in recent years. Spin hydrodynamics extends conventional hydrodynamics by incorporating spin via the spin tensor. In the mean-field limit of the Nambu-Jona-Lasinio (NJL) model under rigid rotation, spin degrees of freedom manifest naturally as axial-vector, or spin, condensate. We investigate the interplay between chiral and spin condensates in this framework. While rotation typically suppress…

A Role-Aware Multi-Agent Framework for Financial Education Question Answering with LLMs
Andy Zhu, Yingjun Du
https://arxiv.org/abs/2509.09727 https://arxiv…

A Role-Aware Multi-Agent Framework for Financial Education Question Answering with LLMs
Question answering (QA) plays a central role in financial education, yet existing large language model (LLM) approaches often fail to capture the nuanced and specialized reasoning required for financial problem-solving. The financial domain demands multistep quantitative reasoning, familiarity with domain-specific terminology, and comprehension of real-world scenarios. We present a multi-agent framework that leverages role-based prompting to enhance performance on domain-specific QA. Our framew…

Merging Physics-Based Synthetic Data and Machine Learning for Thermal Monitoring of Lithium-ion Batteries: The Role of Data Fidelity
Yusheng Zheng, Wenxue Liu, Yunhong Che, Ferdinand Grimm, Jingyuan Zhao, Xiaosong Hu, Simona Onori, Remus Teodorescu, Gregory J. Offer
https://arxiv.org/abs/2509.10380…

Merging Physics-Based Synthetic Data and Machine Learning for Thermal Monitoring of Lithium-ion Batteries: The Role of Data Fidelity
Since the internal temperature is less accessible than surface temperature, there is an urgent need to develop accurate and real-time estimation algorithms for better thermal management and safety. This work presents a novel framework for resource-efficient and scalable development of accurate, robust, and adaptive internal temperature estimation algorithms by blending physics-based modeling with machine learning, in order to address the key challenges in data collection, model parameterization…

Breaking the Likelihood Trap: Consistent Generative Recommendation with Graph-structured Model
Qiya Yang, Xiaoxi Liang, Zeping Xiao, Yingjie Deng, Yalong Wang, Yongqi Liu, Han Li
https://arxiv.org/abs/2510.10127

Breaking the Likelihood Trap: Consistent Generative Recommendation with Graph-structured Model
Reranking, as the final stage of recommender systems, demands real-time inference, accuracy, and diversity. It plays a crucial role in determining the final exposure, directly influencing user experience. Recently, generative reranking has gained increasing attention for its strong ability to model complex dependencies among items. However, most existing methods suffer from the "likelihood trap", where high-likelihood sequences are often perceived as low-quality by humans. These models tend to …

Trispectrum in Extended USR Model with Transition to SR
Hassan Firouzjahi, Amin Nassiri-Rad
https://arxiv.org/abs/2509.09608 https://arxiv.org/pdf/2509.096…

Trispectrum in Extended USR Model with Transition to SR
We study the trispectrum in a two-phase USR-SR setup of inflation in which the USR stage is extended in the initial phase of inflation while the second stage of inflation proceeds via a slow-roll phase. A key role is played by the sharpness parameter which controls how quickly the system reaches the final attractor phase after the USR stage. We employ both $δN$ and in-in formalisms and calculate trispectrum and the corresponding dimensionless parameters $g_{NL}$ and $τ_{NL}$. We show that bot…

Activation Function Design Sustains Plasticity in Continual Learning
Lute Lillo, Nick Cheney
https://arxiv.org/abs/2509.22562 https://arxiv.org/pdf/2509.22…

Activation Function Design Sustains Plasticity in Continual Learning
In independent, identically distributed (i.i.d.) training regimes, activation functions have been benchmarked extensively, and their differences often shrink once model size and optimization are tuned. In continual learning, however, the picture is different: beyond catastrophic forgetting, models can progressively lose the ability to adapt (referred to as loss of plasticity) and the role of the non-linearity in this failure mode remains underexplored. We show that activation choice is a primar…

Twist fields in many-body physics
Benjamin Doyon
https://arxiv.org/abs/2509.15064 https://arxiv.org/pdf/2509.15064

Twist fields in many-body physics
The notion of twist fields has played a fundamental role in many-body physics. It is used to construct the so-called disorder parameter for the study of phase transitions in the classical Ising model of statistical mechanics, it is involved in the Jordan-Wigner transformation in quantum chains and bosonisation in quantum field theory, and it is related to measures of entanglement in many-body quantum systems. I provide a pedagogical introduction to the notion of twist field and the concepts at …

A Pseudo-Hermitian Hybrid Model at Finite Temperature: The Role of the Exceptional Points
Ignacio Fushimi (IFLP-UNLP, Argentina), Marta Reboiro (IFLP-UNLP, Argentina)
https://arxiv.org/abs/2510.08773

A Pseudo-Hermitian Hybrid Model at Finite Temperature: The Role of the Exceptional Points
We study a hybrid system formed by an ensemble of colour nitrogen-vacancy centres in diamond interacting with a superconducting flux-qubit at finite temperature. The presence of impurities in the system is modelled through pseudo-hermitian Hamiltonian, by introducing an asymmetry parameter in the interaction between the superconducting flux qubit and the ensemble of colour nitrogen-vacancy centres in diamond. We construct the exact grand partition function of the system, and from it we derive t…

Learning to Look: Cognitive Attention Alignment with Vision-Language Models
Ryan L. Yang, Dipkamal Bhusal, Nidhi Rastogi
https://arxiv.org/abs/2509.21247 https://

Learning to Look: Cognitive Attention Alignment with Vision-Language Models
Convolutional Neural Networks (CNNs) frequently "cheat" by exploiting superficial correlations, raising concerns about whether they make predictions for the right reasons. Inspired by cognitive science, which highlights the role of attention in robust human perception, recent methods have sought to guide model attention using concept-based supervision and explanation regularization. However, these techniques depend on labor-intensive, expert-provided annotations, limiting their scalability. We …

The Role of Congeniality in Multiple Imputation for Doubly Robust Causal Estimation
Lucy D'Agostino McGowan
https://arxiv.org/abs/2510.11633 https://ar…

The Role of Congeniality in Multiple Imputation for Doubly Robust Causal Estimation
This paper provides clear and practical guidance on the specification of imputation models when multiple imputation is used in conjunction with doubly robust estimation methods for causal inference. Through theoretical arguments and targeted simulations, we show that when a confounder has missing data the corresponding imputation model must include all variables used in either the propensity score model or the outcome model, and that these variables must appear in the same functional form as in…

UTI-LLM: A Personalized Articulatory-Speech Therapy Assistance System Based on Multimodal Large Language Model
Yudong Yang, Xiaokang Liu, Shaofeng zhao, Rongfeng Su, Nan Yan, Lan Wang
https://arxiv.org/abs/2509.13145

UTI-LLM: A Personalized Articulatory-Speech Therapy Assistance System Based on Multimodal Large Language Model
Speech therapy plays a critical role in training speech disorders caused by neurological impairments such as stroke. However, traditional manual and computer-assisted systems are limited in real-time accessibility and articulatory motion feedback, constraining their practical utility. Recent advances in multimodal large language models (MLLMs) have demonstrated significant potential in healthcare, particularly through their ability to integrate multimodal data for adaptive assessment and therap…

The value of storage in electricity distribution: The role of storage
Dirk Lauinger, Deepjyoti Deka, Sungho Shin
https://arxiv.org/abs/2510.12435 https://a…

The value of storage in electricity distribution: The role of storage
Electricity distribution companies deploy battery storage to defer grid upgrades by reducing peak demand. In deregulated jurisdictions, such storage often sits idle because regulatory constraints bar participation in electricity markets. Here, we develop an optimization framework that, to our knowledge, provides the first formal model of market participation constraints within storage investment and operation planning. Applying the framework to a Massachusetts case study, we find that market pa…

Capturing Opinion Shifts in Deliberative Discourse through Frequency-based Quantum deep learning methods
Rakesh Thakur, Harsh Chaturvedi, Ruqayya Shah, Janvi Chauhan, Ayush Sharma
https://arxiv.org/abs/2509.22603

Capturing Opinion Shifts in Deliberative Discourse through Frequency-based Quantum deep learning methods
Deliberation plays a crucial role in shaping outcomes by weighing diverse perspectives before reaching decisions. With recent advancements in Natural Language Processing, it has become possible to computationally model deliberation by analyzing opinion shifts and predicting potential outcomes under varying scenarios. In this study, we present a comparative analysis of multiple NLP techniques to evaluate how effectively models interpret deliberative discourse and produce meaningful insights. Opi…

Electromagnetic structure of axial-vector mesons and implications for the muon $g-2$
Zanbin Xing, Lei Chang, Kh\'epani Raya
https://arxiv.org/abs/2509.17311 https://

Electromagnetic structure of axial-vector mesons and implications for the muon $g-2$
The electromagnetic structure of axial-vector mesons is investigated via elastic and two-photon transition form factors (TFFs). To this end, we employ a framework based on the Dyson-Schwinger and Bethe-Salpeter equations within a contact interaction model. This largely algebraic approach transparently exposes the role of symmetries and their breaking, and has proven successful in describing anomaly-sensitive processes, including pseudoscalar to two-photon TFFs, $γ\to 3 π$, and vector-to-pseud…

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
https://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, …

Personality-Enhanced Social Recommendations in SAMI: Exploring the Role of Personality Detection in Matchmaking
Brittany Harbison, Samuel Taubman, Travis Taylor, Ashok. K. Goel
https://arxiv.org/abs/2509.09583

Personality-Enhanced Social Recommendations in SAMI: Exploring the Role of Personality Detection in Matchmaking
Social connection is a vital part of learning, yet online course environments present barriers to the organic formation of social groups. SAMI offers one solution by facilitating student connections, but its effectiveness is constrained by an incomplete Theory of Mind, limiting its ability to create an effective mental model of a student. One facet of this is its inability to intuit personality, which may influence the relevance of its recommendations. To explore this, we propose a personality …

Hadamard-Riemannian Optimization for Margin-Variance Ensemble
Zexu Jin
https://arxiv.org/abs/2509.10189 https://arxiv.org/pdf/2509.10189

Hadamard-Riemannian Optimization for Margin-Variance Ensemble
Ensemble learning has been widely recognized as a pivotal technique for boosting predictive performance by combining multiple base models. Nevertheless, conventional margin-based ensemble methods predominantly focus on maximizing the expected margin while neglecting the critical role of margin variance, which inherently restricts the generalization capability of the model and heightens its vulnerability to overfitting, particularly in noisy or imbalanced datasets. Additionally, the conventional…