Tootfinder

ProDiGy: Proximity- and Dissimilarity-Based Byzantine-Robust Federated Learning
Sena Ergisi, Luis Ma{\ss}ny, Rawad Bitar
https://arxiv.org/abs/2509.09534 https://

ProDiGy: Proximity- and Dissimilarity-Based Byzantine-Robust Federated Learning
Federated Learning (FL) emerged as a widely studied paradigm for distributed learning. Despite its many advantages, FL remains vulnerable to adversarial attacks, especially under data heterogeneity. We propose a new Byzantine-robust FL algorithm called ProDiGy. The key novelty lies in evaluating the client gradients using a joint dual scoring system based on the gradients' proximity and dissimilarity. We demonstrate through extensive numerical experiments that ProDiGy outperforms existing defen…

Automated Evidence Extraction and Scoring for Corporate Climate Policy Engagement: A Multilingual RAG Approach
Imene Kolli, Ario Saeid Vaghefi, Chiara Colesanti Senni, Shantam Raj, Markus Leippold
https://arxiv.org/abs/2509.08907

Automated Evidence Extraction and Scoring for Corporate Climate Policy Engagement: A Multilingual RAG Approach
InfluenceMap's LobbyMap Platform monitors the climate policy engagement of over 500 companies and 250 industry associations, assessing each entity's support or opposition to science-based policy pathways for achieving the Paris Agreement's goal of limiting global warming to 1.5°C. Although InfluenceMap has made progress with automating key elements of the analytical workflow, a significant portion of the assessment remains manual, making it time- and labor-intensive and susceptible to human er…

Global Optimization of Stochastic Black-Box Functions with Arbitrary Noise Distributions using Wilson Score Kernel Density Estimation
Thorbj{\o}rn Mosekj{\ae}r Iversen, Lars Car{\o}e S{\o}rensen, Simon Faarvang Mathiesen, Henrik Gordon Petersen
https://arxiv.org/abs/2509.09238

Global Optimization of Stochastic Black-Box Functions with Arbitrary Noise Distributions using Wilson Score Kernel Density Estimation
Many optimization problems in robotics involve the optimization of time-expensive black-box functions, such as those involving complex simulations or evaluation of real-world experiments. Furthermore, these functions are often stochastic as repeated experiments are subject to unmeasurable disturbances. Bayesian optimization can be used to optimize such methods in an efficient manner by deploying a probabilistic function estimator to estimate with a given confidence so that regions of the search…

Iterated sumset expansion in $\mathbb{F}_p^n$
Manik Dhar, Sammy Luo
https://arxiv.org/abs/2510.08857 https://arxiv.org/pdf/2510.08857

Iterated sumset expansion in $\mathbb{F}_p^n$
Given a set $A \subseteq \mathbb{F}_p^n$, what conditions does one need to guarantee that iterated sumsets of the form $A+\cdots+A$ expand quickly (say, within $O(p)$ terms) to the whole space? When only the size of $A$ is known, such expansion results are only possible when $|A|>\frac{1}{p}|\mathbb{F}_p^n|$. However, heuristic considerations suggest that expansion should begin with much smaller sets under just mild ``nondegeneracy'' conditions. In this paper, we confirm thi…

Co-designing a Programmable RISC-V Accelerator for MPC-based Energy and Thermal Management of Many-Core HPC Processors
Alessandro Ottaviano, Andrino Meli, Paul Scheffler, Giovanni Bambini, Robert Balas, Davide Rossi, Andrea Bartolini, Luca Benini
https://arxiv.org/abs/2510.09163

Co-designing a Programmable RISC-V Accelerator for MPC-based Energy and Thermal Management of Many-Core HPC Processors
Managing energy and thermal profiles is critical for many-core HPC processors with hundreds of application-class processing elements (PEs). Advanced model predictive control (MPC) delivers state-of-the-art performance but requires solving an online optimization problem over a thousand times per second (1 kHz control bandwidth), with computational and memory demands scaling with PE count. Traditional MPC approaches execute the controller on the PEs, but operating system overheads create jitter a…

ReviewerToo: Should AI Join The Program Committee? A Look At The Future of Peer Review
Gaurav Sahu, Hugo Larochelle, Laurent Charlin, Christopher Pal
https://arxiv.org/abs/2510.08867

ReviewerToo: Should AI Join The Program Committee? A Look At The Future of Peer Review
Peer review is the cornerstone of scientific publishing, yet it suffers from inconsistencies, reviewer subjectivity, and scalability challenges. We introduce ReviewerToo, a modular framework for studying and deploying AI-assisted peer review to complement human judgment with systematic and consistent assessments. ReviewerToo supports systematic experiments with specialized reviewer personas and structured evaluation criteria, and can be partially or fully integrated into real conference workflo…

Progressive Uncertainty-Guided Evidential U-KAN for Trustworthy Medical Image Segmentation
Zhen Yang, Yansong Ma, Lei Chen
https://arxiv.org/abs/2510.08949 https://

Progressive Uncertainty-Guided Evidential U-KAN for Trustworthy Medical Image Segmentation
Trustworthy medical image segmentation aims at deliver accurate and reliable results for clinical decision-making. Most existing methods adopt the evidence deep learning (EDL) paradigm due to its computational efficiency and theoretical robustness. However, the EDL-based methods often neglect leveraging uncertainty maps rich in attention cues to refine ambiguous boundary segmentation. To address this, we propose a progressive evidence uncertainty guided attention (PEUA) mechanism to guide the m…

Fine-Grained Customized Fashion Design with Image-into-Prompt benchmark and dataset from LMM
Hui Li, Yi You, Qiqi Chen, Bingfeng Zhang, George Q. Huang
https://arxiv.org/abs/2509.09324

Fine-Grained Customized Fashion Design with Image-into-Prompt benchmark and dataset from LMM
Generative AI evolves the execution of complex workflows in industry, where the large multimodal model empowers fashion design in the garment industry. Current generation AI models magically transform brainstorming into fancy designs easily, but the fine-grained customization still suffers from text uncertainty without professional background knowledge from end-users. Thus, we propose the Better Understanding Generation (BUG) workflow with LMM to automatically create and fine-grain customize th…

Rigidifying simplicial complexes and realizing group actions
Cristina Costoya, Rafael Gomes, Antonio Viruel
https://arxiv.org/abs/2509.09646 https://arxiv.…

Rigidifying simplicial complexes and realizing group actions
We show that any action of a finite group on a finitely presentable group arises as the action of the group of self-homotopy equivalences of a space on its fundamental group. In doing so, we prove that any finite connected (abstract) simplicial complex $\mathbf{K}$ can be rigidified -- meaning it can be perturbed in a way that reduces the full automorphism group to any subgroup -- while preserving the homotopy type of the geometric realization $| \mathbf{K} |$.

Guarded Fragments Meet Dynamic Logic: The Story of Regular Guards (Extended Version)
Bartosz Bednarczyk, Emanuel Kiero\'nski
https://arxiv.org/abs/2509.09218 https://…

Guarded Fragments Meet Dynamic Logic: The Story of Regular Guards (Extended Version)
We study the Guarded Fragment with Regular Guards (RGF), which combines the expressive power of the Guarded Fragment (GF) with Propositional Dynamic Logic with Intersection and Converse (ICPDL). Our logic generalizes, in a uniform way, many previously-studied extensions of GF, including (conjunctions of) transitive or equivalence guards, transitive or equivalence closure and more. We prove 2EXPTIME-completeness of the satisfiability problem for RGF, showing that RGF is not harder than ICPDL or …

Dependent-Type-Preserving Memory Allocation
Paulette Koronkevich, William J. Bowman
https://arxiv.org/abs/2509.09059 https://arxiv.org/pdf/2509.09059

Dependent-Type-Preserving Memory Allocation
Dependently typed programming languages such as Coq, Agda, Idris, and F*, allow programmers to write detailed specifications of their programs and prove their programs meet these specifications. However, these specifications can be violated during compilation since they are erased after type checking. External programs linked with the compiled program can violate the specifications of the original program and change the behavior of the compiled program -- even when compiled with a verified comp…

LadderSym: A Multimodal Interleaved Transformer for Music Practice Error Detection
Benjamin Shiue-Hal Chou, Purvish Jajal, Nick John Eliopoulos, James C. Davis, George K. Thiruvathukal, Kristen Yeon-Ji Yun, Yung-Hsiang Lu
https://arxiv.org/abs/2510.08580

LadderSym: A Multimodal Interleaved Transformer for Music Practice Error Detection
Music learners can greatly benefit from tools that accurately detect errors in their practice. Existing approaches typically compare audio recordings to music scores using heuristics or learnable models. This paper introduces \textit{LadderSym}, a novel Transformer-based method for music error detection. \textit{LadderSym} is guided by two key observations about the state-of-the-art approaches: (1) late fusion limits inter-stream alignment and cross-modality comparison capability; and (2) relia…

JWST imaging of omega Centauri: II. Evidence for a split white dwarf cooling sequence in the near-infrared
M. Scalco (Indiana University), M. Salaris (Liverpool John Moores University, INAF-OAAb), L. R. Bedin (INAF-OAPD), S. Blouin (University of Victoria), E. Vesperini (Indiana University), P. Bergeron (University of Montreal), M. Libralato (INAF-OAPD), M. Griggio (STScI), A. Burgasser (UniCA SanDiego), D. Nardiello (UniPD, INAF-OAPD), A. Bellini (STScI), J. Anderson (STScI), R. Geras…

JWST imaging of omega Centauri: II. Evidence for a split white dwarf cooling sequence in the near-infrared
We present a detailed analysis of the white dwarf cooling sequence (WD CS) in omega Centauri based on combined Hubble Space Telescope (HST) and JWST observations. Our analysis confirms the previously reported split - based on HST observations in ultraviolet filters - in the upper part of the WD CS, consistent with the presence of two distinct WD populations, and extends it to a significantly fainter and cooler limit (down to ~8000 K), corresponding to cooling ages of about 1 Gyr. We used artifi…

Personalized Sleep Prediction via Deep Adaptive Spatiotemporal Modeling and Sparse Data
Xueyi Wang (Claudine), C. J. C. (Claudine), Lamoth, Elisabeth Wilhelm
https://arxiv.org/abs/2509.09018

Personalized Sleep Prediction via Deep Adaptive Spatiotemporal Modeling and Sparse Data
A sleep forecast allows individuals and healthcare providers to anticipate and proactively address factors influencing restful rest, ultimately improving mental and physical well-being. This work presents an adaptive spatial and temporal model (AdaST-Sleep) for predicting sleep scores. Our proposed model combines convolutional layers to capture spatial feature interactions between multiple features and recurrent neural network layers to handle longer-term temporal health-related data. A domain …

Weak elastic energy of rectifiable curves in the sphere
Domenico Mucci, Alberto Saracco, Cristian Sopio
https://arxiv.org/abs/2509.09405 https://arxiv.org/…

Weak elastic energy of rectifiable curves in the sphere
We introduce for any exponent $p>1$ the $p$-curvature functional for rectifiable curves in the two-dimensional sphere. We prove that this functional is finite and agrees with the integral of the geodesic curvature raised to the power $p$ on curves whose arc length parameterization is in the Sobolev class $W^{2,p}$.

Psyzkaller: Learning from Historical and On-the-Fly Execution Data for Smarter Seed Generation in OS kernel Fuzzing
Boyu Liu, Yang Zhang, Liang Cheng, Yi Zhang, Junjie Fan, Yu Fu
https://arxiv.org/abs/2510.08918

Psyzkaller: Learning from Historical and On-the-Fly Execution Data for Smarter Seed Generation in OS kernel Fuzzing
Fuzzing has become a cornerstone technique for uncovering vulnerabilities and enhancing the security of OS kernels. However, state-of-the-art kernel fuzzers, including the de facto standard Syzkaller, struggle to generate valid syscall sequences that respect implicit Syscall Dependency Relations (SDRs). Consequently, many generated seeds either fail kernel validation or cannot penetrate deep execution paths, resulting in significant inefficiency. We hypothesize that SDRs can be effectively le…

A Hybrid Hinge-Beam Continuum Robot with Passive Safety Capping for Real-Time Fatigue Awareness
Tongshun Chen, Zezhou Sun, Yanhan Sun, Yuhao Wang, Dezhen Song, Ke Wu
https://arxiv.org/abs/2509.09404

A Hybrid Hinge-Beam Continuum Robot with Passive Safety Capping for Real-Time Fatigue Awareness
Cable-driven continuum robots offer high flexibility and lightweight design, making them well-suited for tasks in constrained and unstructured environments. However, prolonged use can induce mechanical fatigue from plastic deformation and material degradation, compromising performance and risking structural failure. In the state of the art, fatigue estimation of continuum robots remains underexplored, limiting long-term operation. To address this, we propose a fatigue-aware continuum robot with…

Cough Classification using Few-Shot Learning
Yoga Disha Sendhil Kumar, Manas V Shetty, Sudip Vhaduri
https://arxiv.org/abs/2509.09515 https://arxiv.org/pdf…

Cough Classification using Few-Shot Learning
This paper investigates the effectiveness of few-shot learning for respiratory sound classification, focusing on coughbased detection of COVID-19, Flu, and healthy conditions. We leverage Prototypical Networks with spectrogram representations of cough sounds to address the challenge of limited labeled data. Our study evaluates whether few-shot learning can enable models to achieve performance comparable to traditional deep learning approaches while using significantly fewer training samples. Ad…

Scalable extensions to given-data Sobol' index estimators
Teresa Portone, Bert Debusschere, Samantha Yang, Emiliano Islas-Quinones, T. Patrick Xiao
https://arxiv.org/abs/2509.09078

Scalable extensions to given-data Sobol' index estimators
Given-data methods for variance-based sensitivity analysis have significantly advanced the feasibility of Sobol' index computation for computationally expensive models and models with many inputs. However, the limitations of existing methods still preclude their application to models with an extremely large number of inputs. In this work, we present practical extensions to the existing given-data Sobol' index method, which allow variance-based sensitivity analysis to be efficiently performed on…

Remoteness, order, size and connectivity constraints in digraphs
Sufiyan Mallu
https://arxiv.org/abs/2510.08841 https://arxiv.org/pdf/2510.08841

Remoteness, order, size and connectivity constraints in digraphs
Let \( D \) be a strongly connected digraph. The average distance of a vertex \( v \) in \( D \) is defined as the arithmetic mean of the distances from \( v \) to all other vertices in \( D \). The remoteness \( ρ(D) \) of \( D \) is the maximum of the average distances of the vertices in \( D \). In this paper, we provide a sharp upper bound on the remoteness of a strong digraph with given order, size, and vertex-connectivity. We then characterise the extremal digraphs that maximise remote…

What Is Your Agent's GPA? A Framework for Evaluating Agent Goal-Plan-Action Alignment
Allison Sihan Jia, Daniel Huang, Nikhil Vytla, Nirvika Choudhury, John C Mitchell, Anupam Datta
https://arxiv.org/abs/2510.08847

What Is Your Agent's GPA? A Framework for Evaluating Agent Goal-Plan-Action Alignment
We introduce the Agent GPA (Goal-Plan-Action) framework: an evaluation paradigm based on an agent's operational loop of setting goals, devising plans, and executing actions. The framework includes five evaluation metrics: Goal Fulfillment, Logical Consistency, Execution Efficiency, Plan Quality, and Plan Adherence. Logical Consistency checks that an agent's actions are consistent with its prior actions. Execution Efficiency checks whether the agent executes in the most efficient way to achieve …

Image Recognition with Vision and Language Embeddings of VLMs
Illia Volkov, Nikita Kisel, Klara Janouskova, Jiri Matas
https://arxiv.org/abs/2509.09311 https://

Image Recognition with Vision and Language Embeddings of VLMs
Vision-language models (VLMs) have enabled strong zero-shot classification through image-text alignment. Yet, their purely visual inference capabilities remain under-explored. In this work, we conduct a comprehensive evaluation of both language-guided and vision-only image classification with a diverse set of dual-encoder VLMs, including both well-established and recent models such as SigLIP 2 and RADIOv2.5. The performance is compared in a standard setup on the ImageNet-1k validation set and i…

The Negation Of Singer's Conjecture For The Sixth Algebraic Transfer
Dang Vo Phuc
https://arxiv.org/abs/2509.09455 https://arxiv.org/pdf/2509.09455

The Negation Of Singer's Conjecture For The Sixth Algebraic Transfer
Let $\mathscr A$ be the Steenrod algebra over the field of characteristic two, $\mathbb F_2.$ Denote by $GL(q)$ the general linear group of rank $q$ over $\mathbb F_2.$ The algebraic transfer, introduced by W. Singer [Math. Z. 202 (1989), 493-523], is a rather effective tool for unraveling the intricate structure of the (mod-2) cohomology of the Steenrod algebra, ${\rm Ext}_{\mathscr A}^{q,*}(\mathbb F_2, \mathbb F_2).$ The Kameko homomorphism is one of the useful tools to study the dimension o…

Stated Preference for Interaction and Continued Engagement (SPICE): Evaluating an LLM's Willingness to Re-engage in Conversation
Thomas Manuel Rost, Martina Figlia, Bernd Wallraff
https://arxiv.org/abs/2509.09043

Stated Preference for Interaction and Continued Engagement (SPICE): Evaluating an LLM's Willingness to Re-engage in Conversation
We introduce and evaluate Stated Preference for Interaction and Continued Engagement (SPICE), a simple diagnostic signal elicited by asking a Large Language Model a YES or NO question about its willingness to re-engage with a user's behavior after reviewing a short transcript. In a study using a 3-tone (friendly, unclear, abusive) by 10-interaction stimulus set, we tested four open-weight chat models across four framing conditions, resulting in 480 trials. Our findings show that SPICE sharply d…

Crosslisted article(s) found for cs.LO. https://arxiv.org/list/cs.LO/new
[1/1]:
- An MDL-Style Cost Functional KC, Distribution-Preserving Reductions ($A2^d$), and an $AC^0$ log L...
Marko Lela

Towards Verified Compilation of Floating-point Optimization in Scientific Computing Programs
Mohit Tekriwal, John Sarracino
https://arxiv.org/abs/2509.09019 https://

Towards Verified Compilation of Floating-point Optimization in Scientific Computing Programs
Scientific computing programs often undergo aggressive compiler optimization to achieve high performance and efficient resource utilization. While performance is critical, we also need to ensure that these optimizations are correct. In this paper, we focus on a specific class of optimizations, floating-point optimizations, notably due to fast math, at the LLVM IR level. We present a preliminary work, which leverages the Verified LLVM framework in the Rocq theorem prover, to prove the correctnes…

The Sonora Substellar Atmosphere Models VI. Red Diamondback: Extending Diamondback with SPHINX for Brown Dwarf Early Evolution
C. Evan Davis, Jonathan J. Fortney, Aishwarya Iyer, Sagnick Mukherjee, Caroline V. Morley, Mark S. Marley, Michael Line, Philip S. Muirhead
https://arxiv.org/abs/2510.08694…

The Sonora Substellar Atmosphere Models VI. Red Diamondback: Extending Diamondback with SPHINX for Brown Dwarf Early Evolution
We extend the Sonora Diamondback brown dwarf evolution models to higher effective temperatures to treat the evolution of younger, higher mass objects. Due to an upper temperature limit of $T_\mathrm{eff}=$2400 K in the original Sonora Diamondback model grid, high mass objects ($M\geq$ 0.05 $M_\mathrm{\odot}=$ 52.4 $M_\mathrm{J}$) were limited to ages of $\gtrsim$ 100 Myr. To include the early evolution of brown dwarfs at $T_\mathrm{eff}>$ 2400 K, we use existing and new SPHINX cloud-free model …

A note on orbifold regularity of canonical metrics
Henri Guenancia, Chung-Ming Pan, Mihai P\u{a}un
https://arxiv.org/abs/2509.09259 https://arxiv.org/pdf/2…

A note on orbifold regularity of canonical metrics
In this short note, we prove that on a compact Kähler variety $X$ with log terminal singularities and $c_1(X)=0$, any singular Ricci-flat Kähler metric has orbifold singularities in restriction to the orbifold locus of $X$.

CommandSans: Securing AI Agents with Surgical Precision Prompt Sanitization
Debeshee Das, Luca Beurer-Kellner, Marc Fischer, Maximilian Baader
https://arxiv.org/abs/2510.08829 h…

CommandSans: Securing AI Agents with Surgical Precision Prompt Sanitization
The increasing adoption of LLM agents with access to numerous tools and sensitive data significantly widens the attack surface for indirect prompt injections. Due to the context-dependent nature of attacks, however, current defenses are often ill-calibrated as they cannot reliably differentiate malicious and benign instructions, leading to high false positive rates that prevent their real-world adoption. To address this, we present a novel approach inspired by the fundamental principle of compu…

PIPES: A Meta-dataset of Machine Learning Pipelines
Cynthia Moreira Maia, Lucas B. V. de Amorim, George D. C. Cavalcanti, Rafael M. O. Cruz
https://arxiv.org/abs/2509.09512 http…

PIPES: A Meta-dataset of Machine Learning Pipelines
Solutions to the Algorithm Selection Problem (ASP) in machine learning face the challenge of high computational costs associated with evaluating various algorithms' performances on a given dataset. To mitigate this cost, the meta-learning field can leverage previously executed experiments shared in online repositories such as OpenML. OpenML provides an extensive collection of machine learning experiments. However, an analysis of OpenML's records reveals limitations. It lacks diversity in pipeli…

VLA-Adapter: An Effective Paradigm for Tiny-Scale Vision-Language-Action Model
Yihao Wang, Pengxiang Ding, Lingxiao Li, Can Cui, Zirui Ge, Xinyang Tong, Wenxuan Song, Han Zhao, Wei Zhao, Pengxu Hou, Siteng Huang, Yifan Tang, Wenhui Wang, Ru Zhang, Jianyi Liu, Donglin Wang
https://arxiv.org/abs/2509.09372

VLA-Adapter: An Effective Paradigm for Tiny-Scale Vision-Language-Action Model
Vision-Language-Action (VLA) models typically bridge the gap between perceptual and action spaces by pre-training a large-scale Vision-Language Model (VLM) on robotic data. While this approach greatly enhances performance, it also incurs significant training costs. In this paper, we investigate how to effectively bridge vision-language (VL) representations to action (A). We introduce VLA-Adapter, a novel paradigm designed to reduce the reliance of VLA models on large-scale VLMs and extensive pr…

[2025-09-12 Fri (UTC), 3 new articles found for stat.ML Machine Learning]
toXiv_bot_toot

Enabling Regulatory Multi-Agent Collaboration: Architecture, Challenges, and Solutions
Qinnan Hu, Yuntao Wang, Yuan Gao, Zhou Su, Linkang Du
https://arxiv.org/abs/2509.09215 htt…

Enabling Regulatory Multi-Agent Collaboration: Architecture, Challenges, and Solutions
Large language models (LLMs)-empowered autonomous agents are transforming both digital and physical environments by enabling adaptive, multi-agent collaboration. While these agents offer significant opportunities across domains such as finance, healthcare, and smart manufacturing, their unpredictable behaviors and heterogeneous capabilities pose substantial governance and accountability challenges. In this paper, we propose a blockchain-enabled layered architecture for regulatory agent collabor…

Endomorphisms of the Cohomology Algebra of Certain Homogeneous Spaces
Arnab Goswami, Swagata Sarkar
https://arxiv.org/abs/2509.09363 https://arxiv.org/pdf/…

Endomorphisms of the Cohomology Algebra of Certain Homogeneous Spaces
Let $M_{n,k}$ denote the homogeneous space $SO(2n) / U(k) \times SO(2n-2k) $. We study the endomorphisms of the rational cohomology algebra of $M_{n,k}$, where $n-k \neq k-1$.

Can Vision-Language Models Solve Visual Math Equations?
Monjoy Narayan Choudhury, Junling Wang, Yifan Hou, Mrinmaya Sachan
https://arxiv.org/abs/2509.09013 https://

Can Vision-Language Models Solve Visual Math Equations?
Despite strong performance in visual understanding and language-based reasoning, Vision-Language Models (VLMs) struggle with tasks requiring integrated perception and symbolic computation. We study this limitation through visual equation solving, where mathematical equations are embedded in images, variables are represented by object icons, and coefficients must be inferred by counting. While VLMs perform well on textual equations, they fail on visually grounded counterparts. To understand this…

[2025-09-12 Fri (UTC), 2 new articles found for cs.PL Programming Languages]
toXiv_bot_toot

Tracing Planetary Accretion in a 3 Gyr-old Hydrogen-Rich White Dwarf: The Extremely Polluted Atmosphere of LSPM J0207 3331
\'Erika Le Bourdais, Patrick Dufour, Carl Melis, Beth L. Klein, Laura K. Rogers, Antoine B\'edard, John Debes, Ashley Messier, Alycia J. Weinberger, Siyi Xu
https://arxiv.org/abs/2510.08676

Tracing Planetary Accretion in a 3 Gyr-old Hydrogen-Rich White Dwarf: The Extremely Polluted Atmosphere of LSPM J0207+3331
We report the detection of 13 heavy elements (Na, Mg, Al, Si, Ca, Ti, Cr, Mn, Fe, Co, Ni, Cu, and Sr) in the photosphere of LSPM J0207+3331, a ~3 Gyr old hydrogen-rich white dwarf with an effective temperature comparable to that of the Sun. Upper limits on carbon, obtained through the absence of molecular CH, suggest accretion from a carbon-volatile-depleted source. The accreted parent body exhibits slight deficits of Mg and Si relative to Fe but otherwise bulk Earth-like abundance patterns; a …

Post-Quantum Security of Block Cipher Constructions
Gorjan Alagic, Chen Bai, Christian Majenz, Kaiyan Shi
https://arxiv.org/abs/2510.08725 https://arxiv.or…

Post-Quantum Security of Block Cipher Constructions
Block ciphers are versatile cryptographic ingredients that are used in a wide range of applications ranging from secure Internet communications to disk encryption. While post-quantum security of public-key cryptography has received significant attention, the case of symmetric-key cryptography (and block ciphers in particular) remains a largely unexplored topic. In this work, we set the foundations for a theory of post-quantum security for block ciphers and associated constructions. Leveraging o…

AGILOped: Agile Open-Source Humanoid Robot for Research
Grzegorz Ficht, Luis Denninger, Sven Behnke
https://arxiv.org/abs/2509.09364 https://arxiv.org/pdf/…

AGILOped: Agile Open-Source Humanoid Robot for Research
With academic and commercial interest for humanoid robots peaking, multiple platforms are being developed. Through a high level of customization, they showcase impressive performance. Most of these systems remain closed-source or have high acquisition and maintenance costs, however. In this work, we present AGILOped - an open-source humanoid robot that closes the gap between high performance and accessibility. Our robot is driven by off-the-shelf backdrivable actuators with high power density a…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/5]:
- Adapting Vision-Language Models for Neutrino Event Classification in High-Energy Physics
Dikshant Sagar, Kaiwen Yu, Alejandro Yankelevich, Jianming Bian, Pierre Baldi

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Uncertainty Estimation using Variance-Gated Distributions
H. Martin Gillis, Isaac Xu, Thomas Trappenberg

ProgD: Progressive Multi-scale Decoding with Dynamic Graphs for Joint Multi-agent Motion Forecasting
Xing Gao, Zherui Huang, Weiyao Lin, Xiao Sun
https://arxiv.org/abs/2509.09210

ProgD: Progressive Multi-scale Decoding with Dynamic Graphs for Joint Multi-agent Motion Forecasting
Accurate motion prediction of surrounding agents is crucial for the safe planning of autonomous vehicles. Recent advancements have extended prediction techniques from individual agents to joint predictions of multiple interacting agents, with various strategies to address complex interactions within future motions of agents. However, these methods overlook the evolving nature of these interactions. To address this limitation, we propose a novel progressive multi-scale decoding strategy, termed …

BRoverbs -- Measuring how much LLMs understand Portuguese proverbs
Thales Sales Almeida, Giovana Kerche Bon\'as, Jo\~ao Guilherme Alves Santos
https://arxiv.org/abs/2509.08960

BRoverbs -- Measuring how much LLMs understand Portuguese proverbs
Large Language Models (LLMs) exhibit significant performance variations depending on the linguistic and cultural context in which they are applied. This disparity signals the necessity of mature evaluation frameworks that can assess their capabilities in specific regional settings. In the case of Portuguese, existing evaluations remain limited, often relying on translated datasets that may not fully capture linguistic nuances or cultural references. Meanwhile, native Portuguese-language dataset…

How precisely can we measure the ages of subgiant and giant stars?
Cheyanne Shariat, Kareem El-Badry, Soumyadeep Bhattacharjee
https://arxiv.org/abs/2510.08675 https://

How precisely can we measure the ages of subgiant and giant stars?
Precise stellar ages are fundamental to Galactic archaeology. However, obtaining reliable age estimates and uncertainties for field stars has been a long-standing challenge. We test the fidelity of ages from recent catalogs of giants and subgiants using wide binaries, whose components formed at the same time and thus should have consistent inferred ages. We find that subgiant ages based on spectroscopic metallicities from Xiang & Rix (2022) are generally consistent within their reported uncerta…

AgriSentinel: Privacy-Enhanced Embedded-LLM Crop Disease Alerting System
Chanti Raju Mylay, Bobin Deng, Zhipeng Cai, Honghui Xu
https://arxiv.org/abs/2509.09103 https://

AgriSentinel: Privacy-Enhanced Embedded-LLM Crop Disease Alerting System
Crop diseases pose significant threats to global food security, agricultural productivity, and sustainable farming practices, directly affecting farmers' livelihoods and economic stability. To address the growing need for effective crop disease management, AI-based disease alerting systems have emerged as promising tools by providing early detection and actionable insights for timely intervention. However, existing systems often overlook critical aspects such as data privacy, market pricing pow…

OmniEVA: Embodied Versatile Planner via Task-Adaptive 3D-Grounded and Embodiment-aware Reasoning
Yuecheng Liu, Dafeng Chi, Shiguang Wu, Zhanguang Zhang, Yuzheng Zhuang, Bowen Yang, He Zhu, Lingfeng Zhang, Pengwei Xie, David Gamaliel Arcos Bravo, Yingxue Zhang, Jianye Hao, Xingyue Quan
https://arxiv.org/abs/2509.09332

OmniEVA: Embodied Versatile Planner via Task-Adaptive 3D-Grounded and Embodiment-aware Reasoning
Recent advances in multimodal large language models (MLLMs) have opened new opportunities for embodied intelligence, enabling multimodal understanding, reasoning, and interaction, as well as continuous spatial decision-making. Nevertheless, current MLLM-based embodied systems face two critical limitations. First, Geometric Adaptability Gap: models trained solely on 2D inputs or with hard-coded 3D geometry injection suffer from either insufficient spatial information or restricted 2D generalizat…

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis
Md. Sajeebul Islam Sk., Md Jobayer, Md Mehedi Hasan Shawon, Md. Golam Raibul Alam
https://arxiv.org/abs/2509.08961

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis
Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide, underscoring the importance of accurate and scalable diagnostic systems. Electrocardiogram (ECG) analysis is central to detecting cardiac abnormalities, yet challenges such as noise, class imbalance, and dataset heterogeneity limit current methods. To address these issues, we propose FoundationalECGNet, a foundational framework for automated ECG classification. The model integrates a dual-stage denoising by Morlet and…

Mind Meets Space: Rethinking Agentic Spatial Intelligence from a Neuroscience-inspired Perspective
Bui Duc Manh, Soumyaratna Debnath, Zetong Zhang, Shriram Damodaran, Arvind Kumar, Yueyi Zhang, Lu Mi, Erik Cambria, Lin Wang
https://arxiv.org/abs/2509.09154

Mind Meets Space: Rethinking Agentic Spatial Intelligence from a Neuroscience-inspired Perspective
Recent advances in agentic AI have led to systems capable of autonomous task execution and language-based reasoning, yet their spatial reasoning abilities remain limited and underexplored, largely constrained to symbolic and sequential processing. In contrast, human spatial intelligence, rooted in integrated multisensory perception, spatial memory, and cognitive maps, enables flexible, context-aware decision-making in unstructured environments. Therefore, bridging this gap is critical for advan…

Documents Are People and Words Are Items: A Psychometric Approach to Textual Data with Contextual Embeddings
Jinsong Chen
https://arxiv.org/abs/2509.08920 https://

Documents Are People and Words Are Items: A Psychometric Approach to Textual Data with Contextual Embeddings
This research introduces a novel psychometric method for analyzing textual data using large language models. By leveraging contextual embeddings to create contextual scores, we transform textual data into response data suitable for psychometric analysis. Treating documents as individuals and words as items, this approach provides a natural psychometric interpretation under the assumption that certain keywords, whose contextual meanings vary significantly across documents, can effectively differ…

DP-FedLoRA: Privacy-Enhanced Federated Fine-Tuning for On-Device Large Language Models
Honghui Xu, Shiva Shrestha, Wei Chen, Zhiyuan Li, Zhipeng Cai
https://arxiv.org/abs/2509.09097

DP-FedLoRA: Privacy-Enhanced Federated Fine-Tuning for On-Device Large Language Models
As on-device large language model (LLM) systems become increasingly prevalent, federated fine-tuning enables advanced language understanding and generation directly on edge devices; however, it also involves processing sensitive, user-specific data, raising significant privacy concerns within the federated learning framework. To address these challenges, we propose DP-FedLoRA, a privacy-enhanced federated fine-tuning framework that integrates LoRA-based adaptation with differential privacy in a…

RENet: Fault-Tolerant Motion Control for Quadruped Robots via Redundant Estimator Networks under Visual Collapse
Yueqi Zhang, Quancheng Qian, Taixian Hou, Peng Zhai, Xiaoyi Wei, Kangmai Hu, Jiafu Yi, Lihua Zhang
https://arxiv.org/abs/2509.09283

RENet: Fault-Tolerant Motion Control for Quadruped Robots via Redundant Estimator Networks under Visual Collapse
Vision-based locomotion in outdoor environments presents significant challenges for quadruped robots. Accurate environmental prediction and effective handling of depth sensor noise during real-world deployment remain difficult, severely restricting the outdoor applications of such algorithms. To address these deployment challenges in vision-based motion control, this letter proposes the Redundant Estimator Network (RENet) framework. The framework employs a dual-estimator architecture that ensur…

Group Distributionally Robust Machine Learning under Group Level Distributional Uncertainty
Xenia Konti, Yi Shen, Zifan Wang, Karl Henrik Johansson, Michael J. Pencina, Nicoleta J. Economou-Zavlanos, Michael M. Zavlanos
https://arxiv.org/abs/2509.08942

Group Distributionally Robust Machine Learning under Group Level Distributional Uncertainty
The performance of machine learning (ML) models critically depends on the quality and representativeness of the training data. In applications with multiple heterogeneous data generating sources, standard ML methods often learn spurious correlations that perform well on average but degrade performance for atypical or underrepresented groups. Prior work addresses this issue by optimizing the worst-group performance. However, these approaches typically assume that the underlying data distribution…

Anti-Money Laundering Machine Learning Pipelines; A Technical Analysis on Identifying High-risk Bank Clients with Supervised Learning
Khashayar Namdar, Pin-Chien Wang, Tushar Raju, Steven Zheng, Fiona Li, Safwat Tahmin Khan
https://arxiv.org/abs/2509.09127

Anti-Money Laundering Machine Learning Pipelines; A Technical Analysis on Identifying High-risk Bank Clients with Supervised Learning
Anti-money laundering (AML) actions and measurements are among the priorities of financial institutions, for which machine learning (ML) has shown to have a high potential. In this paper, we propose a comprehensive and systematic approach for developing ML pipelines to identify high-risk bank clients in a dataset curated for Task 1 of the University of Toronto 2023-2024 Institute for Management and Innovation (IMI) Big Data and Artificial Intelligence Competition. The dataset included 195,789 c…

Towards Confidential and Efficient LLM Inference with Dual Privacy Protection
Honglan Yu, Yibin Wang, Feifei Dai, Dong Liu, Haihui Fan, Xiaoyan Gu
https://arxiv.org/abs/2509.09091

Towards Confidential and Efficient LLM Inference with Dual Privacy Protection
CPU-based trusted execution environments (TEEs) and differential privacy (DP) have gained wide applications for private inference. Due to high inference latency in TEEs, researchers use partition-based approaches that offload linear model components to GPUs. However, dense nonlinear layers of large language models (LLMs) result in significant communication overhead between TEEs and GPUs. DP-based approaches apply random noise to protect data privacy, but this compromises LLM performance and sem…

Occupancy-aware Trajectory Planning for Autonomous Valet Parking in Uncertain Dynamic Environments
Farhad Nawaz, Faizan M. Tariq, Sangjae Bae, David Isele, Avinash Singh, Nadia Figueroa, Nikolai Matni, Jovin D'sa
https://arxiv.org/abs/2509.09206

Occupancy-aware Trajectory Planning for Autonomous Valet Parking in Uncertain Dynamic Environments
Accurately reasoning about future parking spot availability and integrated planning is critical for enabling safe and efficient autonomous valet parking in dynamic, uncertain environments. Unlike existing methods that rely solely on instantaneous observations or static assumptions, we present an approach that predicts future parking spot occupancy by explicitly distinguishing between initially vacant and occupied spots, and by leveraging the predicted motion of dynamic agents. We introduce a pr…

Safe, Untrusted, "Proof-Carrying" AI Agents: toward the agentic lakehouse
Jacopo Tagliabue, Ciro Greco
https://arxiv.org/abs/2510.09567 https://a…

Safe, Untrusted, "Proof-Carrying" AI Agents: toward the agentic lakehouse
Data lakehouses run sensitive workloads, where AI-driven automation raises concerns about trust, correctness, and governance. We argue that API-first, programmable lakehouses provide the right abstractions for safe-by-design, agentic workflows. Using Bauplan as a case study, we show how data branching and declarative environments extend naturally to agents, enabling reproducibility and observability while reducing the attack surface. We present a proof-of-concept in which agents repair data pip…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/5]:
- CogGuide: Human-Like Guidance for Zero-Shot Omni-Modal Reasoning
Zhou-Peng Shou, Zhi-Qiang You, Fang Wang, Hai-Bo Liu

Beyond Tag Collision: Cluster-based Memory Management for Tag-based Sanitizers
Mengfei Xie, Yan Lin, Hongtao Wu, Jianming Fu, Chenke Luo, Guojun Peng
https://arxiv.org/abs/2509.09089

Beyond Tag Collision: Cluster-based Memory Management for Tag-based Sanitizers
Tag-based sanitizers attach a small "key" to each pointer and a matching "lock" tag to its target memory object, enabling runtime verification of pointer-object consistency and helping developers to detect potential memory violations. However, the limited tag encoding space challenges existing studies in assigning distinct tags to memory objects across temporal and spatial dimensions, leading to potential tag collisions. In this paper, we present ClusterTag, a novel cluster-based memory allocat…

AEOS: Active Environment-aware Optimal Scanning Control for UAV LiDAR-Inertial Odometry in Complex Scenes
Jianping Li, Xinhang Xu, Zhongyuan Liu, Shenghai Yuan, Muqing Cao, Lihua Xie
https://arxiv.org/abs/2509.09141

AEOS: Active Environment-aware Optimal Scanning Control for UAV LiDAR-Inertial Odometry in Complex Scenes
LiDAR-based 3D perception and localization on unmanned aerial vehicles (UAVs) are fundamentally limited by the narrow field of view (FoV) of compact LiDAR sensors and the payload constraints that preclude multi-sensor configurations. Traditional motorized scanning systems with fixed-speed rotations lack scene awareness and task-level adaptability, leading to degraded odometry and mapping performance in complex, occluded environments. Inspired by the active sensing behavior of owls, we propose A…

Titans Revisited: A Lightweight Reimplementation and Critical Analysis of a Test-Time Memory Model
Gavriel Di Nepi, Federico Siciliano, Fabrizio Silvestri
https://arxiv.org/abs/2510.09551

Titans Revisited: A Lightweight Reimplementation and Critical Analysis of a Test-Time Memory Model
By the end of 2024, Google researchers introduced Titans: Learning at Test Time, a neural memory model achieving strong empirical results across multiple tasks. However, the lack of publicly available code and ambiguities in the original description hinder reproducibility. In this work, we present a lightweight reimplementation of Titans and conduct a comprehensive evaluation on Masked Language Modeling, Time Series Forecasting, and Recommendation tasks. Our results reveal that Titans does not …

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/5]:
- ACE: A Security Architecture for LLM-Integrated App Systems
Evan Li, Tushin Mallick, Evan Rose, William Robertson, Alina Oprea, Cristina Nita-Rotaru

LIPM-Guided Reinforcement Learning for Stable and Perceptive Locomotion in Bipedal Robots
Haokai Su, Haoxiang Luo, Shunpeng Yang, Kaiwen Jiang, Wei Zhang, Hua Chen
https://arxiv.org/abs/2509.09106

LIPM-Guided Reinforcement Learning for Stable and Perceptive Locomotion in Bipedal Robots
Achieving stable and robust perceptive locomotion for bipedal robots in unstructured outdoor environments remains a critical challenge due to complex terrain geometry and susceptibility to external disturbances. In this work, we propose a novel reward design inspired by the Linear Inverted Pendulum Model (LIPM) to enable perceptive and stable locomotion in the wild. The LIPM provides theoretical guidance for dynamic balance by regulating the center of mass (CoM) height and the torso orientation…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/5]:
- Convergence Analysis of Asynchronous Federated Learning with Gradient Compression for Non-Convex ...
Diying Yang, Yingwei Hou, Weigang Wu

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/5]:
- On the Relationship Between Adversarial Robustness and Decision Region in Deep Neural Networks
Seongjin Park, Haedong Jeong, Tair Djanibekov, Giyoung Jeon, Jinseok Seol, Jaesik Choi

Kinetostatics and Particle-Swarm Optimization of Vehicle-Mounted Underactuated Metamorphic Loading Manipulators
Nan Mao, Guanglu Jia, Junpeng Chen, Emmanouil Spyrakos-Papastavridis, Jian S. Dai
https://arxiv.org/abs/2509.09093

Kinetostatics and Particle-Swarm Optimization of Vehicle-Mounted Underactuated Metamorphic Loading Manipulators
Fixed degree-of-freedom (DoF) loading mechanisms often suffer from excessive actuators, complex control, and limited adaptability to dynamic tasks. This study proposes an innovative mechanism of underactuated metamorphic loading manipulators (UMLM), integrating a metamorphic arm with a passively adaptive gripper. The metamorphic arm exploits geometric constraints, enabling the topology reconfiguration and flexible motion trajectories without additional actuators. The adaptive gripper, driven en…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/2]:
- Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception
Spyridon Loukovitis, Anastasios Arsenos, Vasileios Karampinis, Athanasios Voulodimos

KoopMotion: Learning Almost Divergence Free Koopman Flow Fields for Motion Planning
Alice Kate Li, Thales C Silva, Victoria Edwards, Vijay Kumar, M. Ani Hsieh
https://arxiv.org/abs/2509.09074

KoopMotion: Learning Almost Divergence Free Koopman Flow Fields for Motion Planning
In this work, we propose a novel flow field-based motion planning method that drives a robot from any initial state to a desired reference trajectory such that it converges to the trajectory's end point. Despite demonstrated efficacy in using Koopman operator theory for modeling dynamical systems, Koopman does not inherently enforce convergence to desired trajectories nor to specified goals -- a requirement when learning from demonstrations (LfD). We present KoopMotion which represents motion f…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/2]:
- A Masked Representation Learning to Model Cardiac Functions Using Multiple Physiological Signals
Seong-A Park, Jong-Eui Chae, Sungdong Kim, Hyung-Chul Lee, Hyun-Lim Yang

Adaptive Science Operations in Deep Space Missions Using Offline Belief State Planning
Grace Ra Kim, Hailey Warner, Duncan Eddy, Evan Astle, Zachary Booth, Edward Balaban, Mykel J. Kochenderfer
https://arxiv.org/abs/2510.08812

Adaptive Science Operations in Deep Space Missions Using Offline Belief State Planning
Deep space missions face extreme communication delays and environmental uncertainty that prevent real-time ground operations. To support autonomous science operations in communication-constrained environments, we present a partially observable Markov decision process (POMDP) framework that adaptively sequences spacecraft science instruments. We integrate a Bayesian network into the POMDP observation space to manage the high-dimensional and uncertain measurements typical of astrobiology missions…

ButterflyQuant: Ultra-low-bit LLM Quantization through Learnable Orthogonal Butterfly Transforms
Bingxin Xu, Zhen Dong, Oussama Elachqar, Yuzhang Shang
https://arxiv.org/abs/2509.09679

ButterflyQuant: Ultra-low-bit LLM Quantization through Learnable Orthogonal Butterfly Transforms
Large language models require massive memory footprints, severely limiting deployment on consumer hardware. Quantization reduces memory through lower numerical precision, but extreme 2-bit quantization suffers from catastrophic performance loss due to outliers in activations. Rotation-based methods such as QuIP and QuaRot apply orthogonal transforms to eliminate outliers before quantization, using computational invariance: $\mathbf{y} = \mathbf{Wx} = (\mathbf{WQ}^T)(\mathbf{Qx})$ for orthogonal…

Adaptive Motion Planning via Contact-Based Intent Inference for Human-Robot Collaboration
Jiurun Song, Xiao Liang, Minghui Zheng
https://arxiv.org/abs/2510.08811 https://…

Adaptive Motion Planning via Contact-Based Intent Inference for Human-Robot Collaboration
Human-robot collaboration (HRC) requires robots to adapt their motions to human intent to ensure safe and efficient cooperation in shared spaces. Although large language models (LLMs) provide high-level reasoning for inferring human intent, their application to reliable motion planning in HRC remains challenging. Physical human-robot interaction (pHRI) is intuitive but often relies on continuous kinesthetic guidance, which imposes burdens on operators. To address these challenges, a contact-inf…

Feasibility-Guided Fair Adaptive Offline Reinforcement Learning for Medicaid Care Management
Sanjay Basu, Sadiq Y. Patel, Parth Sheth, Bhairavi Muralidharan, Namrata Elamaran, Aakriti Kinra, Rajaie Batniji
https://arxiv.org/abs/2509.09655

Feasibility-Guided Fair Adaptive Offline Reinforcement Learning for Medicaid Care Management
We introduce Feasibility-Guided Fair Adaptive Reinforcement Learning (FG-FARL), an offline RL procedure that calibrates per-group safety thresholds to reduce harm while equalizing a chosen fairness target (coverage or harm) across protected subgroups. Using de-identified longitudinal trajectories from a Medicaid population health management program, we evaluate FG-FARL against behavior cloning (BC) and HACO (Hybrid Adaptive Conformal Offline RL; a global conformal safety baseline). We report of…

Functional Groups are All you Need for Chemically Interpretable Molecular Property Prediction
Roshan Balaji, Joe Bobby, Nirav Pravinbhai Bhatt
https://arxiv.org/abs/2509.09619 h…

Functional Groups are All you Need for Chemically Interpretable Molecular Property Prediction
Molecular property prediction using deep learning (DL) models has accelerated drug and materials discovery, but the resulting DL models often lack interpretability, hindering their adoption by chemists. This work proposes developing molecule representations using the concept of Functional Groups (FG) in chemistry. We introduce the Functional Group Representation (FGR) framework, a novel approach to encoding molecules based on their fundamental chemical substructures. Our method integrates two t…

Explaining Concept Drift through the Evolution of Group Counterfactuals
Ignacy St\k{e}pka, Jerzy Stefanowski
https://arxiv.org/abs/2509.09616 https://arxiv…

Explaining Concept Drift through the Evolution of Group Counterfactuals
Machine learning models in dynamic environments often suffer from concept drift, where changes in the data distribution degrade performance. While detecting this drift is a well-studied topic, explaining how and why the model's decision-making logic changes still remains a significant challenge. In this paper, we introduce a novel methodology to explain concept drift by analyzing the temporal evolution of group-based counterfactual explanations (GCEs). Our approach tracks shifts in the GCEs' cl…

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance
Haolan Zheng, Yanlai Chen, Jiequn Han, Yue Yu
https://arxiv.org/abs/2509.09611

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance
We propose a novel data-lean operator learning algorithm, the Reduced Basis Neural Operator (ReBaNO), to solve a group of PDEs with multiple distinct inputs. Inspired by the Reduced Basis Method and the recently introduced Generative Pre-Trained Physics-Informed Neural Networks, ReBaNO relies on a mathematically rigorous greedy algorithm to build its network structure offline adaptively from the ground up. Knowledge distillation via task-specific activation function allows ReBaNO to have a comp…

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…

Graph Alignment via Dual-Pass Spectral Encoding and Latent Space Communication
Maysam Behmanesh, Erkan Turan, Maks Ovsjanikov
https://arxiv.org/abs/2509.09597 https://

Graph Alignment via Dual-Pass Spectral Encoding and Latent Space Communication
Graph alignment-the problem of identifying corresponding nodes across multiple graphs-is fundamental to numerous applications. Most existing unsupervised methods embed node features into latent representations to enable cross-graph comparison without ground-truth correspondences. However, these methods suffer from two critical limitations: the degradation of node distinctiveness due to oversmoothing in GNN-based embeddings, and the misalignment of latent spaces across graphs caused by structura…

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection
Zhanhong Jiang, Md Zahid Hasan, Nastaran Saadati, Aditya Balu, Chao Liu, Soumik Sarkar
https://arxiv.org/abs/2509.09485

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection
Stochastic optimization is a pivotal enabler in modern machine learning, producing effective models for various tasks. However, several existing works have shown that model parameters and gradient information are susceptible to privacy leakage. Although Differentially Private SGD (DPSGD) addresses privacy concerns, its static noise mechanism impacts the error bounds for model performance. Additionally, with the exponential increase in model parameters, efficient learning of these models using s…

CountTRuCoLa: Rule Confidence Learning for Temporal Knowledge Graph Forecasting
Julia Gastinger, Christian Meilicke, Heiner Stuckenschmidt
https://arxiv.org/abs/2509.09474 https…

CountTRuCoLa: Rule Confidence Learning for Temporal Knowledge Graph Forecasting
We address the task of temporal knowledge graph (TKG) forecasting by introducing a fully explainable method based on temporal rules. Motivated by recent work proposing a strong baseline using recurrent facts, our approach learns four simple types of rules with a confidence function that considers both recency and frequency. Evaluated on nine datasets, our method matches or surpasses the performance of eight state-of-the-art models and two baselines, while providing fully interpretable predictio…

AEGIS: An Agent for Extraction and Geographic Identification in Scholarly Proceedings
Om Vishesh, Harshad Khadilkar, Deepak Akkil
https://arxiv.org/abs/2509.09470 https://

AEGIS: An Agent for Extraction and Geographic Identification in Scholarly Proceedings
Keeping pace with the rapid growth of academia literature presents a significant challenge for researchers, funding bodies, and academic societies. To address the time-consuming manual effort required for scholarly discovery, we present a novel, fully automated system that transitions from data discovery to direct action. Our pipeline demonstrates how a specialized AI agent, 'Agent-E', can be tasked with identifying papers from specific geographic regions within conference proceedings and then …

AquaCast: Urban Water Dynamics Forecasting with Precipitation-Informed Multi-Input Transformer
Golnoosh Abdollahinejad, Saleh Baghersalimi, Denisa-Andreea Constantinescu, Sergey Shevchik, David Atienza
https://arxiv.org/abs/2509.09458

AquaCast: Urban Water Dynamics Forecasting with Precipitation-Informed Multi-Input Transformer
This work addresses the challenge of forecasting urban water dynamics by developing a multi-input, multi-output deep learning model that incorporates both endogenous variables (e.g., water height or discharge) and exogenous factors (e.g., precipitation history and forecast reports). Unlike conventional forecasting, the proposed model, AquaCast, captures both inter-variable and temporal dependencies across all inputs, while focusing forecast solely on endogenous variables. Exogenous inputs are f…

Composable Score-based Graph Diffusion Model for Multi-Conditional Molecular Generation
Anjie Qiao, Zhen Wang, Chuan Chen, DeFu Lian, Enhong Chen
https://arxiv.org/abs/2509.09451

Composable Score-based Graph Diffusion Model for Multi-Conditional Molecular Generation
Controllable molecular graph generation is essential for material and drug discovery, where generated molecules must satisfy diverse property constraints. While recent advances in graph diffusion models have improved generation quality, their effectiveness in multi-conditional settings remains limited due to reliance on joint conditioning or continuous relaxations that compromise fidelity. To address these limitations, we propose Composable Score-based Graph Diffusion model (CSGD), the first mo…

Fused Lasso Improves Accuracy of Co-occurrence Network Inference in Grouped Samples
Daniel Agyapong, Briana H. Beatty, Peter G. Kennedy, Toby D. Hocking
https://arxiv.org/abs/2509.09413

Fused Lasso Improves Accuracy of Co-occurrence Network Inference in Grouped Samples
Co-occurrence network inference algorithms have significantly advanced our understanding of microbiome communities. However, these algorithms typically analyze microbial associations within samples collected from a single environmental niche, often capturing only static snapshots rather than dynamic microbial processes. Previous studies have commonly grouped samples from different environmental niches together without fully considering how microbial communities adapt their associations when fac…

Kriging prior Regression: A Case for Kriging-Based Spatial Features with TabPFN in Soil Mapping
Jonas Schmidinger, Viacheslav Barkov, Sebastian Vogel, Martin Atzmueller, Gerard B M Heuvelink
https://arxiv.org/abs/2509.09408

Kriging prior Regression: A Case for Kriging-Based Spatial Features with TabPFN in Soil Mapping
Machine learning and geostatistics are two fundamentally different frameworks for predicting and spatially mapping soil properties. Geostatistics leverages the spatial structure of soil properties, while machine learning captures the relationship between available environmental features and soil properties. We propose a hybrid framework that enriches ML with spatial context through engineering of 'spatial lag' features from ordinary kriging. We call this approach 'kriging prior regression' (KpR…

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

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

MetaLLMix : An XAI Aided LLM-Meta-learning Based Approach for Hyper-parameters Optimization
Mohammed Tiouti, Mohamed Bal-Ghaoui
https://arxiv.org/abs/2509.09387 https://

MetaLLMix : An XAI Aided LLM-Meta-learning Based Approach for Hyper-parameters Optimization
Effective model and hyperparameter selection remains a major challenge in deep learning, often requiring extensive expertise and computation. While AutoML and large language models (LLMs) promise automation, current LLM-based approaches rely on trial and error and expensive APIs, which provide limited interpretability and generalizability. We propose MetaLLMiX, a zero-shot hyperparameter optimization framework combining meta-learning, explainable AI, and efficient LLM reasoning. By leveraging h…

Robust Non-Linear Correlations via Polynomial Regression
Luca Giuliani, Michele Lombardi
https://arxiv.org/abs/2509.09380 https://arxiv.org/pdf/2509.09380

Robust Non-Linear Correlations via Polynomial Regression
The Hirschfeld-Gebelein-Rényi (HGR) correlation coefficient is an extension of Pearson's correlation that is not limited to linear correlations, with potential applications in algorithmic fairness, scientific analysis, and causal discovery. Recently, novel algorithms to estimate HGR in a differentiable manner have been proposed to facilitate its use as a loss regularizer in constrained machine learning applications. However, the inherent uncomputability of HGR requires a bias-variance trade-of…

MoSE: Unveiling Structural Patterns in Graphs via Mixture of Subgraph Experts
Junda Ye, Zhongbao Zhang, Li Sun, Siqiang Luo
https://arxiv.org/abs/2509.09337 https://

MoSE: Unveiling Structural Patterns in Graphs via Mixture of Subgraph Experts
While graph neural networks (GNNs) have achieved great success in learning from graph-structured data, their reliance on local, pairwise message passing restricts their ability to capture complex, high-order subgraph patterns. leading to insufficient structural expressiveness. Recent efforts have attempted to enhance structural expressiveness by integrating random walk kernels into GNNs. However, these methods are inherently designed for graph-level tasks, which limits their applicability to ot…

Data Driven Discovery of Emergent Dynamics in Reaction Diffusion Systems from Sparse and Noisy Observations
Saumitra Dwivedi, Ricardo da Silva Torres, Ibrahim A. Hameed, Gunnar Tufte, Anniken Susanne T. Karlsen
https://arxiv.org/abs/2509.09278

Data Driven Discovery of Emergent Dynamics in Reaction Diffusion Systems from Sparse and Noisy Observations
Data-driven discovery of emergent dynamics is gaining popularity, particularly in the context of reaction-diffusion systems. These systems are widely studied across various fields, including neuroscience, ecology, epidemiology, and several other subject areas that deal with emergent dynamics. A current challenge in the discovery process relates to system identification when there is no prior knowledge of the underlying physics. We attempt to address this challenge by learning Soft Artificial Li…

Harnessing Uncertainty: Entropy-Modulated Policy Gradients for Long-Horizon LLM Agents
Jiawei Wang, Jiacai Liu, Yuqian Fu, Yingru Li, Xintao Wang, Yuan Lin, Yu Yue, Lin Zhang, Yang Wang, Ke Wang
https://arxiv.org/abs/2509.09265

Harnessing Uncertainty: Entropy-Modulated Policy Gradients for Long-Horizon LLM Agents
In long-horizon tasks, recent agents based on Large Language Models (LLMs) face a significant challenge that sparse, outcome-based rewards make it difficult to assign credit to intermediate steps. Previous methods mainly focus on creating dense reward signals to guide learning, either through traditional reinforcement learning techniques like inverse reinforcement learning or by using Process Reward Models for step-by-step feedback. In this paper, we identify a fundamental problem in the learni…

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis
Hanyang Wang, Yuxuan Yang, Hongjun Wang, Lihui Wang
https://arxiv.org/abs/2509.09251 https:/…

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis
The intelligent fault diagnosis of rotating mechanical equipment usually requires a large amount of labeled sample data. However, in practical industrial applications, acquiring enough data is both challenging and expensive in terms of time and cost. Moreover, different types of rotating mechanical equipment with different unique mechanical properties, require separate training of diagnostic models for each case. To address the challenges of limited fault samples and the lack of generalizabilit…

Constructing a Question-Answering Simulator through the Distillation of LLMs
Haipeng Liu, Ting Long, Jing Fu
https://arxiv.org/abs/2509.09226 https://arxiv…

Constructing a Question-Answering Simulator through the Distillation of LLMs
The question-answering (QA) simulator is a model that mimics real student learning behaviors and predicts their correctness of their responses to questions. QA simulators enable educational recommender systems (ERS) to collect large amounts of training data without interacting with real students, thereby preventing harmful recommendations made by an undertrained ERS from undermining actual student learning. Given the QA history, there are two categories of solutions to predict the correctness, …

Vejde: A Framework for Inductive Deep Reinforcement Learning Based on Factor Graph Color Refinement
Jakob Nyberg, Pontus Johnson
https://arxiv.org/abs/2509.09219 https://…

Vejde: A Framework for Inductive Deep Reinforcement Learning Based on Factor Graph Color Refinement
We present and evaluate Vejde; a framework which combines data abstraction, graph neural networks and reinforcement learning to produce inductive policy functions for decision problems with richly structured states, such as object classes and relations. MDP states are represented as data bases of facts about entities, and Vejde converts each state to a bipartite graph, which is mapped to latent states through neural message passing. The factored representation of both states and actions allows …

Identifying Key Features for Establishing Sustainable Agro-Tourism Centre: A Data Driven Approach
Alka Gadakh, Vidya Kumbhar, Sonal Khosla, Kumar Karunendra
https://arxiv.org/abs/2509.09214

Identifying Key Features for Establishing Sustainable Agro-Tourism Centre: A Data Driven Approach
Agro-tourism serves as a strategic economic model designed to facilitate rural development by diversifying income streams for local communities like farmers while promoting the conservation of indigenous cultural heritage and traditional agricultural practices. As a very booming subdomain of tourism, there is a need to study the strategies for the growth of Agro-tourism in detail. The current study has identified the important indicators for the growth and enhancement of agro-tourism. The study…

Incentivizing Safer Actions in Policy Optimization for Constrained Reinforcement Learning
Somnath Hazra, Pallab Dasgupta, Soumyajit Dey
https://arxiv.org/abs/2509.09208 https://…

Incentivizing Safer Actions in Policy Optimization for Constrained Reinforcement Learning
Constrained Reinforcement Learning (RL) aims to maximize the return while adhering to predefined constraint limits, which represent domain-specific safety requirements. In continuous control settings, where learning agents govern system actions, balancing the trade-off between reward maximization and constraint satisfaction remains a significant challenge. Policy optimization methods often exhibit instability near constraint boundaries, resulting in suboptimal training performance. To address t…

Breaking the Statistical Similarity Trap in Extreme Convection Detection
Md Tanveer Hossain Munim
https://arxiv.org/abs/2509.09195 https://arxiv.org/pdf/25…

Breaking the Statistical Similarity Trap in Extreme Convection Detection
Current evaluation metrics for deep learning weather models create a "Statistical Similarity Trap", rewarding blurry predictions while missing rare, high-impact events. We provide quantitative evidence of this trap, showing sophisticated baselines achieve 97.9% correlation yet 0.00 CSI for dangerous convection detection. We introduce DART (Dual Architecture for Regression Tasks), a framework addressing the challenge of transforming coarse atmospheric forecasts into high-resolution satellite bri…

Clip Your Sequences Fairly: Enforcing Length Fairness for Sequence-Level RL
Hanyi Mao, Quanjia Xiao, Lei Pang, Haixiao Liu
https://arxiv.org/abs/2509.09177 https://

Clip Your Sequences Fairly: Enforcing Length Fairness for Sequence-Level RL
We propose FSPO (Fair Sequence Policy Optimization), a sequence-level reinforcement learning method for LLMs that enforces length-fair clipping directly in the importance-sampling (IS) weight space. We revisit sequence-level RL methods and identify a mismatch when PPO/GRPO-style clipping is transplanted to sequences: a fixed clip range systematically reweights short vs. long responses, distorting the effective objective. Theoretically, we formalize length fairness via a Length Reweighting Error…

Quantum Machine Learning, Quantitative Trading, Reinforcement Learning, Deep Learning
Jun-Hao Chen, Yu-Chien Huang, Yun-Cheng Tsai, Samuel Yen-Chi Chen
https://arxiv.org/abs/2509.09176

Quantum Machine Learning, Quantitative Trading, Reinforcement Learning, Deep Learning
The convergence of quantum-inspired neural networks and deep reinforcement learning offers a promising avenue for financial trading. We implemented a trading agent for USD/TWD by integrating Quantum Long Short-Term Memory (QLSTM) for short-term trend prediction with Quantum Asynchronous Advantage Actor-Critic (QA3C), a quantum-enhanced variant of the classical A3C. Trained on data from 2000-01-01 to 2025-04-30 (80\% training, 20\% testing), the long-only agent achieves 11.87\% return over aroun…

Adaptive Pareto-Optimal Token Merging for Edge Transformer Models in Semantic Communication
Omar Erak, Omar Alhussein, Hatem Abou-Zeid, Mehdi Bennis
https://arxiv.org/abs/2509.09168

Adaptive Pareto-Optimal Token Merging for Edge Transformer Models in Semantic Communication
Large-scale transformer models have emerged as a powerful tool for semantic communication systems, enabling edge devices to extract rich representations for robust inference across noisy wireless channels. However, their substantial computational demands remain a major barrier to practical deployment in resource-constrained 6G networks. In this paper, we present a training-free framework for adaptive token merging in pretrained vision transformers to jointly reduce inference time and transmissi…

HISPASpoof: A New Dataset For Spanish Speech Forensics
Maria Risques, Kratika Bhagtani, Amit Kumar Singh Yadav, Edward J. Delp
https://arxiv.org/abs/2509.09155 https://

HISPASpoof: A New Dataset For Spanish Speech Forensics
Zero-shot Voice Cloning (VC) and Text-to-Speech (TTS) methods have advanced rapidly, enabling the generation of highly realistic synthetic speech and raising serious concerns about their misuse. While numerous detectors have been developed for English and Chinese, Spanish-spoken by over 600 million people worldwide-remains underrepresented in speech forensics. To address this gap, we introduce HISPASpoof, the first large-scale Spanish dataset designed for synthetic speech detection and attribut…