Tootfinder

Klear-CodeTest: Scalable Test Case Generation for Code Reinforcement Learning
Jia Fu, Xinyu Yang, Hongzhi Zhang, Yahui Liu, Jingyuan Zhang, Qi Wang, Fuzheng Zhang, Guorui Zhou
https://arxiv.org/abs/2508.05710

Klear-CodeTest: Scalable Test Case Generation for Code Reinforcement Learning
Precise, correct feedback is crucial for effectively training large language models (LLMs) in code reinforcement learning. However, synthesizing high-quality test cases remains a profoundly challenging and unsolved problem. In this work, we present Klear-CodeTest, a comprehensive test case synthesis framework featuring rigorous verification to ensure quality and reliability of test cases. Our approach achieves broad coverage of programming problems via a novel Generator-Validation (G-V) framewo…

Active Membership Inference Test (aMINT): Enhancing Model Auditability with Multi-Task Learning
Daniel DeAlcala, Aythami Morales, Julian Fierrez, Gonzalo Mancera, Ruben Tolosana, Javier Ortega-Garcia
https://arxiv.org/abs/2509.07879

Active Membership Inference Test (aMINT): Enhancing Model Auditability with Multi-Task Learning
Active Membership Inference Test (aMINT) is a method designed to detect whether given data were used during the training of machine learning models. In Active MINT, we propose a novel multitask learning process that involves training simultaneously two models: the original or Audited Model, and a secondary model, referred to as the MINT Model, responsible for identifying the data used for training the Audited Model. This novel multi-task learning approach has been designed to incorporate the au…

Sequential Test for Practical Significance: Truncated Mixture Sequential Probability Ratio Test
Kyu Min Shim
https://arxiv.org/abs/2509.07892 https://arxiv…

Sequential Test for Practical Significance: Truncated Mixture Sequential Probability Ratio Test
We present a sequential testing method to identify a practically significant effect. We build on the existing mixture sequential probability ratio test (mSPRT) that can sequentially test for a non-zero treatment effect by using a truncated mixing distribution to differentiate between effects that are large enough to merit a real world action versus that are non-zero but too small to merit a real world action. We verify the Type-I error control of our method theoretically and empirically. We als…

ADPro: a Test-time Adaptive Diffusion Policy for Robot Manipulation via Manifold and Initial Noise Constraints
Zezeng Li, Rui Yang, Ruochen Chen, ZhongXuan Luo, Liming Chen
https://arxiv.org/abs/2508.06266

ADPro: a Test-time Adaptive Diffusion Policy for Robot Manipulation via Manifold and Initial Noise Constraints
Diffusion policies have recently emerged as a powerful class of visuomotor controllers for robot manipulation, offering stable training and expressive multi-modal action modeling. However, existing approaches typically treat action generation as an unconstrained denoising process, ignoring valuable a priori knowledge about geometry and control structure. In this work, we propose the Adaptive Diffusion Policy (ADP), a test-time adaptation method that introduces two key inductive biases into the …

Sequentially Auditing Differential Privacy
Tom\'as Gonz\'alez, Mateo Dulce-Rubio, Aaditya Ramdas, M\'onica Ribero
https://arxiv.org/abs/2509.07055 https://

Sequentially Auditing Differential Privacy
We propose a practical sequential test for auditing differential privacy guarantees of black-box mechanisms. The test processes streams of mechanisms' outputs providing anytime-valid inference while controlling Type I error, overcoming the fixed sample size limitation of previous batch auditing methods. Experiments show this test detects violations with sample sizes that are orders of magnitude smaller than existing methods, reducing this number from 50K to a few hundred examples, across divers…

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

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

Basis Vector Metric: A Method for Robust Open-Ended State Change Detection
David Oprea, Sam Powers
https://arxiv.org/abs/2509.07308 https://arxiv.org/pdf/2…

Basis Vector Metric: A Method for Robust Open-Ended State Change Detection
We test a new method, which we will abbreviate using the acronym BVM (Basis Vectors Method), in its ability to judge the state changes in images through using language embeddings. We used the MIT-States dataset, containing about 53,000 images, to gather all of our data, which has 225 nouns and 115 adjectives, with each noun having about 9 different adjectives, forming approximately 1000 noun-adjective pairs. For our first experiment, we test our method's ability to determine the state of each n…

R-Horizon: How Far Can Your Large Reasoning Model Really Go in Breadth and Depth?
Yi Lu, Jianing Wang, Linsen Guo, Wei He, Hongyin Tang, Tao Gui, Xuanjing Huang, Xuezhi Cao, Wei Wang, Xunliang Cai
https://arxiv.org/abs/2510.08189

R-Horizon: How Far Can Your Large Reasoning Model Really Go in Breadth and Depth?
Recent trends in test-time scaling for reasoning models (e.g., OpenAI o1, DeepSeek-R1) have led to remarkable improvements through long Chain-of-Thought (CoT). However, existing benchmarks mainly focus on immediate, single-horizon tasks, failing to adequately evaluate models' ability to understand and respond to complex, long-horizon scenarios. To address this incomplete evaluation of Large Reasoning Models (LRMs), we propose R-HORIZON, a method designed to stimulate long-horizon reasoning beha…

PAC Learnability in the Presence of Performativity
Ivan Kirev, Lyuben Baltadzhiev, Nikola Konstantinov
https://arxiv.org/abs/2510.08335 https://arxiv.org/p…

PAC Learnability in the Presence of Performativity
Following the wide-spread adoption of machine learning models in real-world applications, the phenomenon of performativity, i.e. model-dependent shifts in the test distribution, becomes increasingly prevalent. Unfortunately, since models are usually trained solely based on samples from the original (unshifted) distribution, this performative shift may lead to decreased test-time performance. In this paper, we study the question of whether and when performative binary classification problems are…

Effects of magnetic fields on spinning test particles orbiting Kerr-Bertotti-Robinson black holes
Yu-Kun Zhang, Shao-Wen Wei
https://arxiv.org/abs/2510.07914 https://

Effects of magnetic fields on spinning test particles orbiting Kerr-Bertotti-Robinson black holes
In this paper, we study the kinematic effects of spinning test particles orbiting the Kerr-Bertotti-Robinson black hole. Employing with the Mathisson-Papapetrou-Dixon equations, we explore the dynamics of precessing orbits and distinct orbital types, including circular orbits and innermost stable circular orbits. Our results reveal the substantial impact of the magnetic field on the trajectories of spinning particles, particularly in regions characterized by significant radial distances. More i…

Guess your neighbor's input: Quantum advantage in Feige's game
Simon Schmidt, Sigurd A. L. Storgaard, Michael Walter, Yuming Zhao
https://arxiv.org/abs/2510.08484 https:…

Guess your neighbor's input: Quantum advantage in Feige's game
In this article, we study a nonlocal game with two questions and three answers per player, which was first considered by Feige in 1991, and show that there is quantum advantage in this game. We prove that the game is a robust self-test for the $3$-dimensional maximally entangled state. Furthermore, we show that the game can be seen as the "or" of two games that each do not have quantum advantage. Lastly, we investigate the behavior of the game with respect to parallel repetition in the classica…

A Systematic Framework to Test the Resilience of Three-Fold Redundant Sparse Arrays Against Two Sensor Failures and Some Never-Before Findings
Ashish Patwari, Andr\'es Alay\'on Glazunov
https://arxiv.org/abs/2509.07442

A Systematic Framework to Test the Resilience of Three-Fold Redundant Sparse Arrays Against Two Sensor Failures and Some Never-Before Findings
As the field of sparse arrays progressed, numerous array designs have been introduced with a focus on larger apertures and higher degrees of freedom (DOFs), resulting in maximally economic sparse arrays (MESAs) that operate with the least number of sensors required to provide a given aperture while ensuring a hole-free difference coarray (DCA). Consequently, MESAs are least robust to sensor failures and cannot afford the failure of even a single sensor. Multifold redundant sparse arrays (MFRSAs…

A Baseline $T\log^2 T$ Upper Bound for KL-Regularized Prime--Zero Optimal Transport
Zhejun Yang (University of Sydney)
https://arxiv.org/abs/2509.07329 https://

A Baseline $T\log^2 T$ Upper Bound for KL-Regularized Prime--Zero Optimal Transport
We prove that $\mathsf{OT}_η(T)\ll T\log^2 T$ unconditionally via a band-limited test scheme with Fejér averaging. The approach normalizes $\|\widehat f\|_1=Θ(T^{-1})$ to ensure $\|h\|_1\asymp T$ and $\|\widehat h\|_1\ll 1$, and applies an $L^1$-controlled smoothed explicit formula to bound the error terms. As a result, the prime--zero optimal transport distance admits the baseline $T\log^2 T$ upper bound without additional assumptions.

Probing Dark Matter Interactions with Stellar Motion near Sagittarius A*
R. Andrew Gustafson, Ian M. Shoemaker, Volodymyr Takhistov
https://arxiv.org/abs/2510.07387 https://

Probing Dark Matter Interactions with Stellar Motion near Sagittarius A*
Stars orbiting Sgr A* at the Milky Way's center provide a unique laboratory to test gravity and dark matter (DM). We demonstrate that DM interactions in stellar interiors induce a novel momentum transfer force, altering orbits beyond gravitational effects. Using S2's 2000-2019 orbital data we derive the first astrophysical constraints on DM-nucleon scattering, excluding new sub-GeV parameter space. Stellar lifetime constraints over Myr timescales complement these, surpassing some direct detecti…

Rethinking the Sioux Falls Network: Insights from Path-Driven Higher-Order Network Analysis
Chen Zhang, Timothy LaRock, Alben Rome Bagabaldo, J\"urgen Hackl
https://arxiv.org/abs/2508.06234

Rethinking the Sioux Falls Network: Insights from Path-Driven Higher-Order Network Analysis
Benchmark scenarios are widely used in transportation research to evaluate routing algorithms, simulate infrastructure interventions, and test new technologies under controlled conditions. However, the structural and behavioral fidelity of these benchmarks remains largely unquantified, raising concerns about the external validity of simulation results. In this study, we introduce a mathematical framework based on higher-order network models to evaluate the representativeness of benchmark networ…

Execution-Feedback Driven Test Generation from SWE Issues
Toufique Ahmed, Jatin Ganhotra, Avraham Shinnar, Martin Hirzel
https://arxiv.org/abs/2508.06365 https://

Execution-Feedback Driven Test Generation from SWE Issues
A software engineering issue (SWE issue) is easier to resolve when accompanied by a reproduction test. Unfortunately, most issues do not come with functioning reproduction tests, so this paper explores how to generate them automatically. The primary challenge in this setting is that the code to be tested is either missing or wrong, as evidenced by the existence of the issue in the first place. This has held back test generation for this setting: without the correct code to execute, it is diffic…

Probing the Cosmic Distance Duality Relation via Non-Parametric Reconstruction for High Redshifts
Felipe Avila, Fernanda Oliveira, Camila Franco, Maria Lopes, Rodrigo Holanda, Rafael C. Nunes, Armando Bernui
https://arxiv.org/abs/2509.07848

Probing the Cosmic Distance Duality Relation via Non-Parametric Reconstruction for High Redshifts
We test the validity of the cosmic distance duality relation (CDDR) by combining angular diameter distance and luminosity distance measurements from recent cosmological observations. For the angular diameter distance, we use data from transverse baryon acoustic oscillations and galaxy clusters. On the other hand, the luminosity distance is obtained from Type Ia supernovae in the Pantheon+ sample and from quasar catalogs. To reduce the large dispersion in quasar luminosity distances, we apply a …

Particle dynamics in TOI-178 planetary system
J. Boskovic, R. Sfair, C. M. Sch\"afer
https://arxiv.org/abs/2509.07930 https://arxiv.org/pdf/2509.07930…

Particle dynamics in TOI-178 planetary system
The TOI-178 system hosts six planets with five of them locked in a :4:6:9:12 Laplace resonance chain. We perform N-body simulations to investigate the dynamics of test particles in this system. We observe that co-orbital regions around each planet are approximately 30\% wider than predicted by classical theory for planets in the resonance chain, while TOI-178b, which lies outside the chain, shows a 52\% enhancement. The region between TOI-178e and TOI-178f reveals Kirkwood gap-like structures c…

UTM Performance Under Stressing Scenarios
Ian Jessen
https://arxiv.org/abs/2509.08124 https://arxiv.org/pdf/2509.08124

UTM Performance Under Stressing Scenarios
Proliferation of new classes of airspace participants, including uncrewed and advanced aerial mobility vehicles, necessitates the development and deployment of novel airspace management solutions, such as the Unmanned Traffic Management (UTM) system and the Provider of Services to UAM (PSU) Network. The efficacy of such systems has been demonstrated on multiple occasions via real-world deployments in limited test environments, however exploration of system behavior under stressing conditions re…

Beyond Distribution Shifts: Adaptive Hyperspectral Image Classification at Test Time
Xia Yue, Anfeng Liu, Ning Chen, Chenjia Huang, Hui Liu, Zhou Huang, Leyuan Fang
https://arxiv.org/abs/2509.08436

Beyond Distribution Shifts: Adaptive Hyperspectral Image Classification at Test Time
Hyperspectral image (HSI) classification models are highly sensitive to distribution shifts caused by various real-world degradations such as noise, blur, compression, and atmospheric effects. To address this challenge, we propose HyperTTA, a unified framework designed to enhance model robustness under diverse degradation conditions. Specifically, we first construct a multi-degradation hyperspectral dataset that systematically simulates nine representative types of degradations, providing a com…

Development and performance test of p-type Silicon pad array detector
Sawan, G. Tambave, S. Das, A. Chaudhry, R. Gupta, V. K. S. Kashyap, B. Mohanty, M. M. Mondal, S. Mathur, A. Puri, K. P. Sharma, R. Sharma, R. Singh
https://arxiv.org/abs/2508.06100

Development and performance test of p-type Silicon pad array detector
This article reports on the development and comprehensive evaluation of p-type silicon detector arrays fabricated at the Semi-Conductor Laboratory (SCL), Mohali, India. The detectors consist of an 8~$\times$~9 array of 1~$\times$~1~cm$^2$ pads fabricated on 6-inch wafers and read out using the High Granularity Calorimeter Readout Chip (HGCROC). Electrical characterization of the detector through current vs. voltage (IV) and capacitance vs. voltage (CV) measurements demonstrated consistent break…

Validity Verification of the New TOEFL Writing Task Based on Classical Test Theory
Yinyu Zhang
https://arxiv.org/abs/2509.05347 https://arxiv.org/pdf/2509.…

Validity Verification of the New TOEFL Writing Task Based on Classical Test Theory
The TOEFL iBT has introduced the Academic Discussion Task (ADT) to assess test-takers' ability to engage in academic discourse, reflecting the growing emphasis on interactive communication skills in higher education. However, research on the ADT's validity and fairness particularly for culturally and linguistically diverse groups, such as Chinese students, remains limited. This study addresses this gap by employing Classical Test Theory (CTT) to evaluate the psychometric properties of the ADT a…

Artificial Intelligence as an Opportunity for the Science of Consciousness: A Dual-Resolution Framework
Shahar Dror, Dafna Bergerbest, Moti Salti
https://arxiv.org/abs/2509.07001

Artificial Intelligence as an Opportunity for the Science of Consciousness: A Dual-Resolution Framework
The encounter of artificial intelligence with consciousness research is often framed as a challenge: could this science determine whether such systems are conscious? We suggest it is equally an opportunity to expand and test the scope of existing theories of consciousness. Current approaches remain polarized. Computational functionalism emphasizes abstract organization, often realized through neural correlates of consciousness, while biological naturalism insists that consciousness is tied to l…

A Liouville theorem for the $2$-Hessian equation on the Heisenberg group
Wei Zhang, Qi Zhou
https://arxiv.org/abs/2509.08415 https://arxiv.org/pdf/2509.084…

A Liouville theorem for the $2$-Hessian equation on the Heisenberg group
In this paper, we prove a Liouville theorem for the $2$-Hessian equation on the Heisenberg group $\mathbb{H}^n$. The result is obtained by choosing a suitable test function and using integration by parts to derive the necessary integral estimates.

An on-sky investigation into factors limiting the performance of Keck-NIRC2 for conducting infrared high-contrast imaging
Rachel Bowens-Rubin, Ma\"issa Salama, Jayke S. Nguyen, William Thompson, Philip Hinz
https://arxiv.org/abs/2509.07138

An on-sky investigation into factors limiting the performance of Keck-NIRC2 for conducting infrared high-contrast imaging
The most common instrument used by the exoplanet/brown dwarf direct imaging community at the W.M. Keck Observatory is currently the NIRC2 near-infrared imager. We performed on-sky testing to investigate three effects which may be limiting the performance of NIRC2 when conducting high-contrast imaging observations from $3-5μ$m. First, we report the measurements of an on-sky test of the throughput of the L/M vector vortex coronagraph. We quantify the throughput and additional background flux pen…

Is Liller 1 a building block of the Galactic bulge? - Evidence with APOGEE
Anna Liptrott, Ricardo P. Schiavon, Andrew C. Mason, Sebastian Kamann, Borja Anguiano, Roger E. Cohen, Jos\'e G. Fern\'andez-Trincado, Danny Horta, Steven R. Majewski, Dante Minniti, David M. Nataf, Michael J. W. O'Connor, Dominic Wearne
https://arxiv.or…

Is Liller 1 a building block of the Galactic bulge? - Evidence with APOGEE
Liller 1 is a stellar system orbiting within the inner 0.8kpc of the Galactic centre, characterised by a wide spread in age and metallicity, indicating a high mass. Liller 1 has been proposed to be a major contributor to the stellar mass of the Galactic bulge, yet its origin is subject to debate. We employ Sloan Digital Sky Survey IV (SDSS-IV) data from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) to test scenarios proposed to explain the nature of Liller 1. Using a rando…

A two-axes shear cell for rheo-optics
Chiara Marraffa, Stefano Aime
https://arxiv.org/abs/2509.08114 https://arxiv.org/pdf/2509.08114

A two-axes shear cell for rheo-optics
We develop and test a rheo-optical platform based on a two-axes, parallel plates shear cell coupled to an optical microscope and a photon correlation imaging setup, for simultaneous investigation of the rheological response and the microscopic structure and dynamics of soft materials under shear. Each plate of the shear cell is driven by an air bearing linear stage, actuated by a voice coil motor. A servo control loop reading the plate displacement through a contactless linear encoder enables b…

DogFit: Domain-guided Fine-tuning for Efficient Transfer Learning of Diffusion Models
Yara Bahram, Mohammadhadi Shateri, Eric Granger
https://arxiv.org/abs/2508.05685 https://…

DogFit: Domain-guided Fine-tuning for Efficient Transfer Learning of Diffusion Models
Transfer learning of diffusion models to smaller target domains is challenging, as naively fine-tuning the model often results in poor generalization. Test-time guidance methods help mitigate this by offering controllable improvements in image fidelity through a trade-off with sample diversity. However, this benefit comes at a high computational cost, typically requiring dual forward passes during sampling. We propose the Domain-guided Fine-tuning (DogFit) method, an effective guidance mechanis…

Replaced article(s) found for math.ST. https://arxiv.org/list/math.ST/new
[1/1]:
- A Necessary and Sufficient Condition for Size Controllability of Heteroskedasticity Robust Test S...
Benedikt M. P\"otscher, David Preinerstorfer

K2-Think: A Parameter-Efficient Reasoning System
Zhoujun Cheng, Richard Fan, Shibo Hao, Taylor W. Killian, Haonan Li, Suqi Sun, Hector Ren, Alexander Moreno, Daqian Zhang, Tianjun Zhong, Yuxin Xiong, Yuanzhe Hu, Yutao Xie, Xudong Han, Yuqi Wang, Varad Pimpalkhute, Yonghao Zhuang, Aaryamonvikram Singh, Xuezhi Liang, Anze Xie, Jianshu She, Desai Fan, Chengqian Gao, Liqun Ma, Mikhail Yurochkin, John Maggs, Xuezhe Ma, Guowei He, Zhiting Hu, Zhengzhong Liu, Eric P. Xing

K2-Think: A Parameter-Efficient Reasoning System
K2-Think is a reasoning system that achieves state-of-the-art performance with a 32B parameter model, matching or surpassing much larger models like GPT-OSS 120B and DeepSeek v3.1. Built on the Qwen2.5 base model, our system shows that smaller models can compete at the highest levels by combining advanced post-training and test-time computation techniques. The approach is based on six key technical pillars: Long Chain-of-thought Supervised Finetuning, Reinforcement Learning with Verifiable Rewa…

Probing the Preferences of a Language Model: Integrating Verbal and Behavioral Tests of AI Welfare
Valen Tagliabue, Leonard Dung
https://arxiv.org/abs/2509.07961 https://…

Probing the Preferences of a Language Model: Integrating Verbal and Behavioral Tests of AI Welfare
We develop new experimental paradigms for measuring welfare in language models. We compare verbal reports of models about their preferences with preferences expressed through behavior when navigating a virtual environment and selecting conversation topics. We also test how costs and rewards affect behavior and whether responses to an eudaimonic welfare scale - measuring states such as autonomy and purpose in life - are consistent across semantically equivalent prompts. Overall, we observed a no…

Automatic Detection of Inauthentic Templated Responses in English Language Assessments
Yashad Samant, Lee Becker, Scott Hellman, Bradley Behan, Sarah Hughes, Joshua Southerland
https://arxiv.org/abs/2509.08355

Automatic Detection of Inauthentic Templated Responses in English Language Assessments
In high-stakes English Language Assessments, low-skill test takers may employ memorized materials called ``templates'' on essay questions to ``game'' or fool the automated scoring system. In this study, we introduce the automated detection of inauthentic, templated responses (AuDITR) task, describe a machine learning-based approach to this task and illustrate the importance of regularly updating these models in production.

Automated Discovery of Test Oracles for Database Management Systems Using LLMs
Qiuyang Mang, Runyuan He, Suyang Zhong, Xiaoxuan Liu, Huanchen Zhang, Alvin Cheung
https://arxiv.org/abs/2510.06663

Automated Discovery of Test Oracles for Database Management Systems Using LLMs
Since 2020, automated testing for Database Management Systems (DBMSs) has flourished, uncovering hundreds of bugs in widely-used systems. A cornerstone of these techniques is test oracle, which typically implements a mechanism to generate equivalent query pairs, thereby identifying bugs by checking the consistency between their results. However, while applying these oracles can be automated, their design remains a fundamentally manual endeavor. This paper explores the use of large language mode…

Scanned SQUID Microscope with High-speed Electrical Connectivity
Ian W. Haygood, Bochao Xu, John Biesecker, Michael L. Schneider
https://arxiv.org/abs/2509.07137 https://…

Scanned SQUID Microscope with High-speed Electrical Connectivity
We report on a scanned superconducting quantum interference device (SQUID) microscope operating in a cryogen-free cryostat, with the capability of up to forty RF connections with 20 GHz bandwidth to a device under test. The system utilizes planar gradiometric DC SQUIDs, which are fully shielded except for a pair of pickup coils with radii as small as 250 nm and have on chip field coils allowing for susceptometry. The system noise is 1.3 $μΦ_{0}/\sqrt{Hz}$ at the base temperature of 3.3 K. The…

Detection of mean changes in partially observed functional data
\v{S}\'arka Hudecov\'a, Claudia Kirch
https://arxiv.org/abs/2510.07854 https://arxi…

Detection of mean changes in partially observed functional data
We propose a test for a change in the mean for a sequence of functional observations that are only partially observed on subsets of the domain, with no information available on the complement. The framework accommodates important scenarios, including both abrupt and gradual changes. The significance of the test statistic is assessed via a permutation test. In addition to the classical permutation approach with a fixed number of permutation samples, we also discuss a variant with controlled resa…

Accelerating AI Development with Cyber Arenas
William Cashman, Chasen Milner, Michael Houle, Michael Jones, Hayden Jananthan, Jeremy Kepner, Peter Michaleas, Alex Pentland
https://arxiv.org/abs/2509.08200

Accelerating AI Development with Cyber Arenas
AI development requires high fidelity testing environments to effectively transition from the laboratory to operations. The flexibility offered by cyber arenas presents a novel opportunity to test new artificial intelligence (AI) capabilities with users. Cyber arenas are designed to expose end-users to real-world situations and must rapidly incorporate evolving capabilities to meet their core objectives. To explore this concept the MIT/IEEE/Amazon Graph Challenge Anonymized Network Sensor was d…

Benchmarking quantum computers with any quantum algorithm
Stefan K. Seritan, Aditya Dhumuntarao, Aidan Q. Wilber-Gauthier, Kenneth M. Rudinger, Antonio E. Russo, Robin Blume-Kohout, Andrew D. Baczewski, Timothy Proctor
https://arxiv.org/abs/2508.05754

Benchmarking quantum computers with any quantum algorithm
Application-based benchmarks are increasingly used to quantify and compare quantum computers' performance. However, because contemporary quantum computers cannot run utility-scale computations, these benchmarks currently test this hardware's performance on ``small'' problem instances that are not necessarily representative of utility-scale problems. Furthermore, these benchmarks often employ methods that are unscalable, limiting their ability to track progress towards utility-scale applications…

A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulator
Elahe Delavari, Feeza Khan Khanzada, Jaerock Kwon
https://arxiv.org/abs/2509.08221

A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulator
Autonomous-driving research has recently embraced deep Reinforcement Learning (RL) as a promising framework for data-driven decision making, yet a clear picture of how these algorithms are currently employed, benchmarked and evaluated is still missing. This survey fills that gap by systematically analysing around 100 peer-reviewed papers that train, test or validate RL policies inside the open-source CARLA simulator. We first categorize the literature by algorithmic family model-free, model-bas…

DP-SPRT: Differentially Private Sequential Probability Ratio Tests
Thomas Michel, Debabrota Basu, Emilie Kaufmann
https://arxiv.org/abs/2508.06377 https://…

DP-SPRT: Differentially Private Sequential Probability Ratio Tests
We revisit Wald's celebrated Sequential Probability Ratio Test for sequential tests of two simple hypotheses, under privacy constraints. We propose DP-SPRT, a wrapper that can be calibrated to achieve desired error probabilities and privacy constraints, addressing a significant gap in previous work. DP-SPRT relies on a private mechanism that processes a sequence of queries and stops after privately determining when the query results fall outside a predefined interval. This OutsideInterval mecha…

Time-Varying Volatility of Bank Betas
Matt Brigida
https://arxiv.org/abs/2510.07671 https://arxiv.org/pdf/2510.07671

Time-Varying Volatility of Bank Betas
Research has shown banks match interest income and expense betas, and thereby obtain net interest income margins which are insensitive to changes in short-term interest rates. The present analysis extends this research in a number of ways. First, we use state-space methods to estimate time-varying betas and test whether they are matched at each time interval. We find substantial variation in interest income and expense betas, which drives variation in net interest margin beta coefficients. Seco…

New test of modified gravity with gravitational wave experiments
N. M. Jim\'enez Cruz, Flavio C. S\'anchez, Gianmassimo Tasinato
https://arxiv.org/abs/2509.08273 https:/…

New test of modified gravity with gravitational wave experiments
We propose a new strategy to probe non-tensorial polarizations in the stochastic gravitational-wave (GW) background. Averaging over polarization angles, we find that three-point correlations of the GW signal vanish for tensor and vector modes, while scalar modes generically leave a nonzero imprint. This property makes the GW bispectrum a distinctive and robust diagnostic of scalar polarizations predicted in theories beyond General Relativity. We derive the corresponding response functions for g…

Largevars: An R Package for Testing Large VARs for the Presence of Cointegration
Anna Bykhovskaya, Vadim Gorin, Eszter Kiss
https://arxiv.org/abs/2509.06295 https://

Largevars: An R Package for Testing Large VARs for the Presence of Cointegration
Cointegration is a property of multivariate time series that determines whether its non-stationary, growing components have a stationary linear combination. Largevars R package conducts a cointegration test for high-dimensional vector autoregressions of order k based on the large N, T asymptotics of Bykhovskaya and Gorin (2022, 2025). The implemented test is a modification of the Johansen likelihood ratio test. In the absence of cointegration the test converges to the partial sum of the Airy_1 …

The $n^{th}$ centered moments of a large orthogonal family of automorphic $L$-functions
Vorrapan Chandee, Yoonbok Lee, Xiannan Li
https://arxiv.org/abs/2510.07647 https://

The $n^{th}$ centered moments of a large orthogonal family of automorphic $L$-functions
We obtain the $n$th centered moments of one level densities of a large orthogonal family of $L$-functions associated with holomorphic Hecke newforms of level $q$, averaged over $q\sim Q$. We verify the Katz-Sarnak conjecture for these statistics, in the range where the sum of the supports of the Fourier transforms of test functions lies in $(-4, 4)$. In so doing, we need to understand certain phantom oversized terms, which allow us to extract the right off-diagonal contributions. We further nee…

Enhancing Software Vulnerability Detection Through Adaptive Test Input Generation Using Genetic Algorithm
Yanusha Mehendran, Maolin Tang, Yi Lu
https://arxiv.org/abs/2508.05923 …

Enhancing Software Vulnerability Detection Through Adaptive Test Input Generation Using Genetic Algorithm
Software vulnerabilities continue to undermine the reliability and security of modern systems, particularly as software complexity outpaces the capabilities of traditional detection methods. This study introduces a genetic algorithm-based method for test input generation that innovatively integrates genetic operators and adaptive learning to enhance software vulnerability detection. A key contribution is the application of the crossover operator, which facilitates exploration by searching acros…

Scalar Quasinormal modes in Reissner--Nordstr\"om black holes: implications for Weak Gravity Conjecture
Giorgio Di Russo, Anna Tokareva
https://arxiv.org/abs/2510.06813 htt…

Scalar Quasinormal modes in Reissner--Nordström black holes: implications for Weak Gravity Conjecture
Microscopic charged black holes can provide possibilities to test the consistency of the effective field theory (EFT) corrections to Einstein-Maxwell theory. A particularly interesting result is fixing the sign of a certain combination of EFT couplings from the requirement that all charged black holes should be able to evaporate (Weak Gravity Conjecture). In our work, we analysed the EFT corrections to a set of zero-damping quasinormal modes (QNMs) of the scalar wave probe in a nearly extremal …

Revisiting the Question of Information Content of EXAFS Spectra through a Bayesian Approach
Lucy Haddad, Diego Gianolio, Andrei Sapelkin
https://arxiv.org/abs/2509.07950 https:/…

Revisiting the Question of Information Content of EXAFS Spectra through a Bayesian Approach
Over the last several decades the Shannon-Nyquist criterion has been widely used as a measure of the maximum information content in EXAFS spectra and provided an upper limit on the number of parameters used in fitting data. However, the criterion implicitly assumes independent parameters which is never the case in EXAFS analysis. Here we introduce a new criterion to measure the information content in EXAFS based on Bayesian approach that lifts the above condition. We test the new criterion by f…

Neutrinoless double beta decay in a supersymmetric left-right model
Vivek Banerjee, Sasmita Mishra
https://arxiv.org/abs/2510.08090 https://arxiv.org/pdf/2…

Neutrinoless double beta decay in a supersymmetric left-right model
Neutrinoless double beta ($0νββ$) decay, an important low-energy process, serves not only as a potential test of the Majorana nature of neutrinos, but also as a sensitive probe for new physics beyond the Standard Model. In this study, the supersymmetric left-right model is explored to investigate its impact on $0νββ$ decay. Although the process takes place at low energies as compared to the electroweak scale, it carries the potential to provide indirect hints about the parity-breaking sca…

Classification of 24-hour movement behaviors from wrist-worn accelerometer data: from handcrafted features to deep learning techniques
Alireza Sameh, Mehrdad Rostami, Mourad Oussalah, Vahid Farrahi
https://arxiv.org/abs/2509.08606

Classification of 24-hour movement behaviors from wrist-worn accelerometer data: from handcrafted features to deep learning techniques
Purpose: We compared the performance of deep learning (DL) and classical machine learning (ML) algorithms for the classification of 24-hour movement behavior into sleep, sedentary, light intensity physical activity (LPA), and moderate-to-vigorous intensity physical activity (MVPA). Methods: Open-access data from 151 adults wearing a wrist-worn accelerometer (Axivity-AX3) was used. Participants were randomly divided into training, validation, and test sets (121, 15, and 15 participants each). Ra…

IRS-Assisted IoT Activity Detection Under Asynchronous Transmission and Heterogeneous Powers: Detectors and Performance Analysis
Amirhossein Taherpour, Somayeh Khani, Abbas Taherpour, Tamer Khattab
https://arxiv.org/abs/2508.05959

IRS-Assisted IoT Activity Detection Under Asynchronous Transmission and Heterogeneous Powers: Detectors and Performance Analysis
This paper addresses the problem of activity detection in distributed Internet of Things (IoT) networks, where devices employ asynchronous transmissions with heterogeneous power levels to report their local observations. The system leverages an intelligent reflecting surface (IRS) to enhance detection reliability, with optional incorporation of a direct line-of-sight (LoS) path. We formulate the detection problem as a binary hypothesis test and develop four detectors: an optimal detector alongs…

ArenaBencher: Automatic Benchmark Evolution via Multi-Model Competitive Evaluation
Qin Liu, Jacob Dineen, Yuxi Huang, Sheng Zhang, Hoifung Poon, Ben Zhou, Muhao Chen
https://arxiv.org/abs/2510.08569

ArenaBencher: Automatic Benchmark Evolution via Multi-Model Competitive Evaluation
Benchmarks are central to measuring the capabilities of large language models and guiding model development, yet widespread data leakage from pretraining corpora undermines their validity. Models can match memorized content rather than demonstrate true generalization, which inflates scores, distorts cross-model comparisons, and misrepresents progress. We introduce ArenaBencher, a model-agnostic framework for automatic benchmark evolution that updates test cases while preserving comparability. G…

Detection of supernova magnitude fluctuations induced by large-scale structure
A. Nguyen, C. Blake, R. J. Turner, V. Aronica, J. Bautista, J. Aguilar, S. Ahlen, S. BenZvi, D. Bianchi, D. Brooks, A. Carr, T. Claybaugh, A. Cuceu, A. de la Macorra, B. Dey, P. Doel, K. Douglass, S. Ferraro, J. E. Forero-Romero, E. Gazta\~naga, S. Gontcho A Gontcho, G. Gutierrez, J. Guy, K. Honscheid, C. Howlett, D. Huterer, M. Ishak, R. Joyce, R. Kehoe, A. G. Kim, A. Kremin, O. Lahav, M. Landriau, L. Le Gu…

Detection of supernova magnitude fluctuations induced by large-scale structure
The peculiar velocities of supernovae and their host galaxies are correlated with the large-scale structure of the Universe, and can be used to constrain the growth rate of structure and test the cosmological model. In this work, we measure the correlation statistics of the large-scale structure traced by the Dark Energy Spectroscopic Instrument Bright Galaxy Survey Data Release 1 sample, and magnitude fluctuations of type Ia supernova from the Pantheon+ compilation across redshifts z < 0.1. We…

Can LLMs effectively provide game-theoretic-based scenarios for cybersecurity?
Daniele Proverbio, Alessio Buscemi, Alessandro Di Stefano, The Anh Han, German Castignani, Pietro Li\`o
https://arxiv.org/abs/2508.05670

Can LLMs effectively provide game-theoretic-based scenarios for cybersecurity?
Game theory has long served as a foundational tool in cybersecurity to test, predict, and design strategic interactions between attackers and defenders. The recent advent of Large Language Models (LLMs) offers new tools and challenges for the security of computer systems; In this work, we investigate whether classical game-theoretic frameworks can effectively capture the behaviours of LLM-driven actors and bots. Using a reproducible framework for game-theoretic LLM agents, we investigate two ca…

Weakly-Driven Quantum Walks for Memory-Constrained Pauli Channel Learning
Yuan-Zhuo Wang, Yi-Ran Xiao, Ming-Yang Li, Shengjun Wu, Zeng-Bing Chen
https://arxiv.org/abs/2509.07702

Weakly-Driven Quantum Walks for Memory-Constrained Pauli Channel Learning
Accurate characterization of quantum noise, exemplified by the Pauli channel, is a cornerstone for building fault-tolerant quantum computers. A recent protocol (PRX Quantum 6, 020323 (2025)) combining channel concatenation and quantum memory has achieved an exponential reduction in measurement complexity for Pauli channel estimation. This efficiency, however, hinges on using logarithmic quantum memory to suppress hypothesis test errors. In this work, we introduce a mechanism termed the ``weakly…

Scalable Offline Metrics for Autonomous Driving
Animikh Aich, Adwait Kulkarni, Eshed Ohn-Bar
https://arxiv.org/abs/2510.08571 https://arxiv.org/pdf/2510.08…

Scalable Offline Metrics for Autonomous Driving
Real-World evaluation of perception-based planning models for robotic systems, such as autonomous vehicles, can be safely and inexpensively conducted offline, i.e., by computing model prediction error over a pre-collected validation dataset with ground-truth annotations. However, extrapolating from offline model performance to online settings remains a challenge. In these settings, seemingly minor errors can compound and result in test-time infractions or collisions. This relationship is unders…

A Honest Cross-Validation Estimator for Prediction Performance
Tianyu Pan, Vincent Z. Yu, Viswanath Devanarayan, Lu Tian
https://arxiv.org/abs/2510.07649 https://

A Honest Cross-Validation Estimator for Prediction Performance
Cross-validation is a standard tool for obtaining a honest assessment of the performance of a prediction model. The commonly used version repeatedly splits data, trains the prediction model on the training set, evaluates the model performance on the test set, and averages the model performance across different data splits. A well-known criticism is that such cross-validation procedure does not directly estimate the performance of the particular model recommended for future use. In this paper, w…

Numerical effects on the stripping of dark matter and stars in IllustrisTNG galaxy groups and clusters
Mark R. Lovell (ICC Durham, Durham Physics, University of Iceland), Annalisa Pillepich (MPIA), Christoph Engler (MPIA), Dylan Nelson (Heidelberg), Rahul Ramesh (Heidelberg), Volker Springel (MPA), Lars Hernquist (ITP Harvard)
https://arxi…

Numerical effects on the stripping of dark matter and stars in IllustrisTNG galaxy groups and clusters
The stellar haloes and intra-cluster light around galaxies are crucial test beds for dark matter (DM) physics and galaxy formation models. We consider the role that the numerical resolution plays in the modelling of these systems by studying the stripping of satellites in the IllustrisTNG cosmological simulations. We focus on host haloes of total halo mass $M_\mathrm{200c}=10^{12-15}M_{\odot}$ and satellites of stellar mass $>10^{7}$$M_{\odot}$, and compare stellar halo / satellite properties a…

Validity and Power of Heavy-Tailed Combination Tests under Asymptotic Dependence
Lin Gui, Tiantian Mao, Jingshu Wang, Ruodu Wang
https://arxiv.org/abs/2508.05818 https://…

Validity and Power of Heavy-Tailed Combination Tests under Asymptotic Dependence
Heavy-tailed combination tests, such as the Cauchy combination test and harmonic mean p-value method, are widely used for testing global null hypotheses by aggregating dependent p-values. However, their theoretical guarantees under general dependence structures remain limited. We develop a unified framework using multivariate regularly varying copulas to model the joint behavior of p-values near zero. Within this framework, we show that combination tests remain asymptotically valid when the tra…

Replaced article(s) found for physics.ins-det. https://arxiv.org/list/physics.ins-det/new
[1/1]:
- The CMS Phase-2 Fast Beam Condition Monitor prototype test with beam
G. Auzinger, et al.

GenPilot: A Multi-Agent System for Test-Time Prompt Optimization in Image Generation
Wen Ye, Zhaocheng Liu, Yuwei Gui, Tingyu Yuan, Yunyue Su, Bowen Fang, Chaoyang Zhao, Qiang Liu, Liang Wang
https://arxiv.org/abs/2510.07217

GenPilot: A Multi-Agent System for Test-Time Prompt Optimization in Image Generation
Text-to-image synthesis has made remarkable progress, yet accurately interpreting complex and lengthy prompts remains challenging, often resulting in semantic inconsistencies and missing details. Existing solutions, such as fine-tuning, are model-specific and require training, while prior automatic prompt optimization (APO) approaches typically lack systematic error analysis and refinement strategies, resulting in limited reliability and effectiveness. Meanwhile, test-time scaling methods opera…

Black Hole Spectroscopy and Tests of General Relativity with GW250114
The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration
https://arxiv.org/abs/2509.08099

Black Hole Spectroscopy and Tests of General Relativity with GW250114
The binary black hole signal GW250114, the loudest gravitational wave detected to date, offers a unique opportunity to test Einstein's general relativity (GR) in the high-velocity, strong-gravity regime and probe whether the remnant conforms to the Kerr metric. Upon perturbation, black holes emit a spectrum of damped sinusoids with specific, complex frequencies. Our analysis of the post-merger signal shows that at least two quasi-normal modes are required to explain the data, with the most damp…

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR
Zeyu Sun, Jingjing Liang, Weiyi Wang, Chenyao Suo, Junjie Chen, Fanjiang Xu
https://arxiv.org/abs/2510.07815

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR
MLIR (Multi-Level Intermediate Representation) has rapidly become a foundational technology for modern compiler frameworks, enabling extensibility across diverse domains. However, ensuring the correctness and robustness of MLIR itself remains challenging. Existing fuzzing approaches-based on manually crafted templates or rule-based mutations-struggle to generate sufficiently diverse and semantically valid test cases, making it difficult to expose subtle or deep-seated bugs within MLIR's complex…

Beyond Real Data: Synthetic Data through the Lens of Regularization
Amitis Shidani, Tyler Farghly, Yang Sun, Habib Ganjgahi, George Deligiannidis
https://arxiv.org/abs/2510.08095

Beyond Real Data: Synthetic Data through the Lens of Regularization
Synthetic data can improve generalization when real data is scarce, but excessive reliance may introduce distributional mismatches that degrade performance. In this paper, we present a learning-theoretic framework to quantify the trade-off between synthetic and real data. Our approach leverages algorithmic stability to derive generalization error bounds, characterizing the optimal synthetic-to-real data ratio that minimizes expected test error as a function of the Wasserstein distance between t…

Carnegie Supernova Project: Fast-Declining Type Ia Supernovae as Cosmological Distance Indicators
M. M. Phillips, Syed A. Uddin, Christopher R. Burns, Nicholas B. Suntzeff, C. Ashall, E. Baron, L. Galbany, P. Hoeflich, E. Y. Hsiao, Nidia Morrell, S. E. Persson, Maximilian Stritzinger, Carlos Contreras, Wendy L. Freedman, Kevin Krisciunas, S. Kumar, J. Lu, Anthony L. Piro, M. Shahbandeh

Carnegie Supernova Project: Fast-Declining Type Ia Supernovae as Cosmological Distance Indicators
In this paper, the suitability of fast-declining Type Ia supernovae (SNe Ia) as cosmological standard candles is examined utilizing a Hubble Flow sample of 43 of these objects observed by the Carnegie Supernova Project (CSP). We confirm previous suggestions that fast-declining SNe Ia offer a viable method for estimating distances to early-type galaxies when the color-stretch parameter, $s_{BV}$, is used as a measure of the light curve shape. As a test, we employ the Tripp (1998) method, which m…

Test-Time Graph Search for Goal-Conditioned Reinforcement Learning
Evgenii Opryshko, Junwei Quan, Claas Voelcker, Yilun Du, Igor Gilitschenski
https://arxiv.org/abs/2510.07257 h…

Test-Time Graph Search for Goal-Conditioned Reinforcement Learning
Offline goal-conditioned reinforcement learning (GCRL) trains policies that reach user-specified goals at test time, providing a simple, unsupervised, domain-agnostic way to extract diverse behaviors from unlabeled, reward-free datasets. Nonetheless, long-horizon decision making remains difficult for GCRL agents due to temporal credit assignment and error accumulation, and the offline setting amplifies these effects. To alleviate this issue, we introduce Test-Time Graph Search (TTGS), a lightwe…

A galactic tug-of-war: how (not) to simultaneously fit the Milky Way satellite luminosity function and the mass-metallicity relation
Sownak Bose, Alis J. Deason
https://arxiv.org/abs/2509.07066

A galactic tug-of-war: how (not) to simultaneously fit the Milky Way satellite luminosity function and the mass-metallicity relation
The satellite population of the Milky Way is shaped by a range of astrophysical processes including mergers, star formation, feedback, and cosmic reionisation. Determining which processes most strongly influence its properties is challenging and a key test of galaxy formation models. We train a neural network on the GALFORM semi-analytic model and apply a variance-based sensitivity analysis to characterise the influence of 11 astrophysical parameters on two key observables: the satellite lumino…

ARISE: An Adaptive Resolution-Aware Metric for Test-Time Scaling Evaluation in Large Reasoning Models
Zhangyue Yin, Qiushi Sun, Zhiyuan Zeng, Zhiyuan Yu, Qipeng Guo, Xuanjing Huang, Xipeng Qiu
https://arxiv.org/abs/2510.06014

ARISE: An Adaptive Resolution-Aware Metric for Test-Time Scaling Evaluation in Large Reasoning Models
Test-time scaling has emerged as a transformative paradigm for enhancing the performance of large reasoning models, enabling dynamic allocation of computational resources during inference. However, as the landscape of reasoning models rapidly expands, a critical question remains: how can we systematically compare and evaluate the test-time scaling capabilities across different models? In this paper, we introduce ARISE (Adaptive Resolution-aware Scaling Evaluation), a novel metric specifically d…

Decisive Evidence for the First Overtone Mode in the Ringdown Signal of GW231028
Hai-Tian Wang
https://arxiv.org/abs/2509.08657 https://arxiv.org/pdf/2509.…

Decisive Evidence for the First Overtone Mode in the Ringdown Signal of GW231028
The properties of a remnant black hole can be probed by analyzing the gravitational waves emitted during its ringdown phase. This signal provides a direct test of general relativity in the strong-field regime. In this study, we apply a time-domain F-statistic framework to the ringdown of GW231028_153006 and find decisive evidence for the presence of the first overtone mode in the signal. The detection of the $\ell|m|n=221$ mode is statistically significant, achieving a Bayes factor of…

WISE: A Weighted Similarity Aggregation Test for Serial Independence
Qihua Zhu, Mingshuo Liu, Yuefeng Han, Doudou Zhou
https://arxiv.org/abs/2509.05678 https://

WISE: A Weighted Similarity Aggregation Test for Serial Independence
We propose a nonparametric test for serial independence that aggregates pairwise similarities of observations with lag-dependent weights. The resulting statistic is powerful to general forms of temporal dependence, including nonlinear and uncorrelated alternatives, and applies to ultra-high-dimensional and non-Euclidean data. We derive asymptotic normality under both permutation and population nulls, and establish consistency in classical large-sample and high-dimension-low-sample-size (HDLSS) …

TTRV: Test-Time Reinforcement Learning for Vision Language Models
Akshit Singh, Shyam Marjit, Wei Lin, Paul Gavrikov, Serena Yeung-Levy, Hilde Kuehne, Rogerio Feris, Sivan Doveh, James Glass, M. Jehanzeb Mirza
https://arxiv.org/abs/2510.06783

TTRV: Test-Time Reinforcement Learning for Vision Language Models
Existing methods for extracting reward signals in Reinforcement Learning typically rely on labeled data and dedicated training splits, a setup that contrasts with how humans learn directly from their environment. In this work, we propose TTRV to enhance vision language understanding by adapting the model on the fly at inference time, without the need for any labeled data. Concretely, we enhance the Group Relative Policy Optimization (GRPO) framework by designing rewards based on the frequency o…

Disclosing Submillimeter Galaxy Formation: Mergers or Secular Evolution?
Siu-Wang Chan, Yiping Ao, Qinghua Tan
https://arxiv.org/abs/2509.07913 https://arx…

Disclosing Submillimeter Galaxy Formation: Mergers or Secular Evolution?
We analyze the morphology of 125 submillimeter galaxies (SMGs) in the PRIMER-COSMOS field using double Sersic modeling on JWST NIRCam images across six bands (F150W, F200W, F277W, F356W, F410M and F444W), with SMGs being classified by bulge Sersic index (n_bulge) and bulge-to-total luminosity ratio (B/T). The Kolmogorov-Smirnov test between the bright (SFR > 175 M_sun yr^{-1}) and the faint group (SFR < 175 M_sun yr^{-1}) reveals no significant statistical differences in morphology across bands…

New Lorentzian Taub-NUT and Euclidean Eguchi-Hanson Solutions in $f(R)$ gravity
J. G. Fenwick, A. M. Ghezelbash
https://arxiv.org/abs/2509.08033 https://ar…

New Lorentzian Taub-NUT and Euclidean Eguchi-Hanson Solutions in $f(R)$ gravity
A New Taub-NUT black hole in multiple coordinate systems is solved and analyzed. This black-hole is found to be an extension of the Clifton-Barrow metric for the specific case that $δ=-1/2$. We look at physical properties such as test particle orbits, thermodynamics, and various space-time limits. In addition, we show an Eguchi-Hanson type-II $f(R)$ solution related to the found Taub-NUT framework.

On a surprising behavior of the likelihood ratio test in non-parametric mixture models
Yan Zhang, Stanislav Volgushev
https://arxiv.org/abs/2509.05610 https://

On a surprising behavior of the likelihood ratio test in non-parametric mixture models
We study the likelihood ratio test in general mixture models where the base density is parametric, the null is a known fixed mixing distribution, and the alternative is a general mixing distribution supported on a bounded parameter space. For Gaussian location mixtures and Poisson mixtures, we show a surprising result: the non-parametric likelihood ratio test statistic converges to a tight limit if and only if the null distribution is a finite mixture, and diverges to infinity otherwise. We fur…

Tool-Augmented Policy Optimization: Synergizing Reasoning and Adaptive Tool Use with Reinforcement Learning
Wenxun Wu, Yuanyang Li, Guhan Chen, Linyue Wang, Hongyang Chen
https://arxiv.org/abs/2510.07038

Tool-Augmented Policy Optimization: Synergizing Reasoning and Adaptive Tool Use with Reinforcement Learning
Recent advances in large language models (LLMs) have popularized test-time scaling, where models generate additional reasoning tokens before producing final answers. These approaches have demonstrated significant performance improvements on benchmarks involving mathematical reasoning. However, language models relying solely on direct inference still struggle with tasks demanding up-to-date knowledge or computational tools such as calculators and code interpreters for complex arithmetic operatio…

Proofs of No Intrusion
Vipul Goyal, Justin Raizes
https://arxiv.org/abs/2510.06432 https://arxiv.org/pdf/2510.06432…

Proofs of No Intrusion
A central challenge in data security is not just preventing theft, but detecting whether it has occurred. Classically, this is impossible because a perfect copy leaves no evidence. Quantum mechanics, on the other hand, forbids general duplication, opening up new possibilities. We introduce Proofs of No Intrusion, which enable a classical client to remotely test whether a quantum server has been hacked and the client's data stolen. Crucially, the test does not destroy the data being tested, av…

The Majority is not always right: RL training for solution aggregation
Wenting Zhao, Pranjal Aggarwal, Swarnadeep Saha, Asli Celikyilmaz, Jason Weston, Ilia Kulikov
https://arxiv.org/abs/2509.06870

The Majority is not always right: RL training for solution aggregation
Scaling up test-time compute, by generating multiple independent solutions and selecting or aggregating among them, has become a central paradigm for improving large language models (LLMs) on challenging reasoning tasks. While most prior work relies on simple majority voting or reward model ranking to aggregate solutions, these approaches may only yield limited benefits. In this work, we propose to learn aggregation as an explicit reasoning skill: given a set of candidate solutions, we train an…

Zero-shot 3D-Aware Trajectory-Guided image-to-video generation via Test-Time Training
Ruicheng Zhang, Jun Zhou, Zunnan Xu, Zihao Liu, Jiehui Huang, Mingyang Zhang, Yu Sun, Xiu Li
https://arxiv.org/abs/2509.06723

Zero-shot 3D-Aware Trajectory-Guided image-to-video generation via Test-Time Training
Trajectory-Guided image-to-video (I2V) generation aims to synthesize videos that adhere to user-specified motion instructions. Existing methods typically rely on computationally expensive fine-tuning on scarce annotated datasets. Although some zero-shot methods attempt to trajectory control in the latent space, they may yield unrealistic motion by neglecting 3D perspective and creating a misalignment between the manipulated latents and the network's noise predictions. To address these challenge…

LATTA: Langevin-Anchored Test-Time Adaptation for Enhanced Robustness and Stability
Harshil Vejendla
https://arxiv.org/abs/2510.05530 https://arxiv.org/pdf…

LATTA: Langevin-Anchored Test-Time Adaptation for Enhanced Robustness and Stability
Test-time adaptation (TTA) aims to adapt a pretrained model to distribution shifts using only unlabeled test data. While promising, existing methods like Tent suffer from instability and can catastrophically forget the source knowledge, especially with small batch sizes or challenging corruptions. We argue that this arises from overly deterministic updates on a complex loss surface. In this paper, we introduce Langevin-Anchored Test-Time Adaptation (LATTA), a novel approach that regularizes ada…

Combining TSL and LLM to Automate REST API Testing: A Comparative Study
Thiago Barradas, Aline Paes, V\^ania de Oliveira Neves
https://arxiv.org/abs/2509.05540 https://

Combining TSL and LLM to Automate REST API Testing: A Comparative Study
The effective execution of tests for REST APIs remains a considerable challenge for development teams, driven by the inherent complexity of distributed systems, the multitude of possible scenarios, and the limited time available for test design. Exhaustive testing of all input combinations is impractical, often resulting in undetected failures, high manual effort, and limited test coverage. To address these issues, we introduce RestTSLLM, an approach that uses Test Specification Language (TSL) …

Investigating the origin of the Milky Way streams. A revised look at their orbital pole distribution in light of precession effects
Elena Asencio, Pavel Kroupa, Ingo Thies
https://arxiv.org/abs/2508.05733

Investigating the origin of the Milky Way streams. A revised look at their orbital pole distribution in light of precession effects
Stellar streams around the Milky Way (MW) can provide valuable insights into its history and substructure formation. Previous studies have suggested that several MW streams could have an origin related to that of the disc of satellite galaxies (DoS) and the young halo globular clusters of the MW, given that many of these structures present a similar orbital pole orientation. In this work we test the validity of this hypothesis by revising the orbital pole distribution of the MW streams with the…

Verifier-free Test-Time Sampling for Vision Language Action Models
Suhyeok Jang, Dongyoung Kim, Changyeon Kim, Youngsuk Kim, Jinwoo Shin
https://arxiv.org/abs/2510.05681 https:/…

Verifier-free Test-Time Sampling for Vision Language Action Models
Vision-Language-Action models (VLAs) have demonstrated remarkable performance in robot control. However, they remain fundamentally limited in tasks that require high precision due to their single-inference paradigm. While test-time scaling approaches using external verifiers have shown promise, they require additional training and fail to generalize to unseen conditions. We propose Masking Distribution Guided Selection (MG-Select), a novel test-time scaling framework for VLAs that leverages the…

Is it Gaussian? Testing bosonic quantum states
Filippo Girardi, Freek Witteveen, Francesco Anna Mele, Lennart Bittel, Salvatore F. E. Oliviero, David Gross, Michael Walter
https://arxiv.org/abs/2510.07305

Is it Gaussian? Testing bosonic quantum states
Gaussian states are widely regarded as one of the most relevant classes of continuous-variable (CV) quantum states, as they naturally arise in physical systems and play a key role in quantum technologies. This motivates a fundamental question: given copies of an unknown CV state, how can we efficiently test whether it is Gaussian? We address this problem from the perspective of representation theory and quantum learning theory, characterizing the sample complexity of Gaussianity testing as a fu…

Sticker-TTS: Learn to Utilize Historical Experience with a Sticker-driven Test-Time Scaling Framework
Jie Chen, Jinhao Jiang, Yingqian Min, Zican Dong, Shijie Wang, Wayne Xin Zhao, Ji-Rong Wen
https://arxiv.org/abs/2509.05007

Sticker-TTS: Learn to Utilize Historical Experience with a Sticker-driven Test-Time Scaling Framework
Large reasoning models (LRMs) have exhibited strong performance on complex reasoning tasks, with further gains achievable through increased computational budgets at inference. However, current test-time scaling methods predominantly rely on redundant sampling, ignoring the historical experience utilization, thereby limiting computational efficiency. To overcome this limitation, we propose Sticker-TTS, a novel test-time scaling framework that coordinates three collaborative LRMs to iteratively e…

Specification Tests for the Error--Law in Vector Multiplicative Errors Models
\v{S}\'arka Hudecov\'a, Simos G. Meintanis
https://arxiv.org/abs/2509.06732 https://…

Specification Tests for the Error--Law in Vector Multiplicative Errors Models
We suggest specification tests for the error distribution in vector multiplicative error models (vMEM). The test statistic is formulated as a weighted integrated distance between the parametric estimator of the Laplace transform of the null distribution and its empirical counterpart computed from the residuals. Asymptotic results are obtained under both the null and alternative hypotheses. If the Laplace transform of the null distribution is not available in closed form, we propose a test stati…

PTEB: Towards Robust Text Embedding Evaluation via Stochastic Paraphrasing at Evaluation Time with LLMs
Manuel Frank, Haithem Afli
https://arxiv.org/abs/2510.06730 https://

PTEB: Towards Robust Text Embedding Evaluation via Stochastic Paraphrasing at Evaluation Time with LLMs
Current evaluations of sentence embedding models typically rely on static test beds such as the Massive Text Embedding Benchmark (MTEB). While invaluable, repeated tuning on a fixed suite can inflate reported performance and obscure real-world robustness. We introduce the Paraphrasing Text Embedding Benchmark (PTEB), a dynamic protocol that stochastically generates meaning-preserving paraphrases at evaluation time and aggregates results across multiple runs. Using a cost-efficient LLM-based met…

MOSAIC: Minimax-Optimal Sparsity-Adaptive Inference for Change Points in Dynamic Networks
Yingying Fan, Jingyuan Liu, Jinchi Lv, Ao Sun
https://arxiv.org/abs/2509.06303 https://…

MOSAIC: Minimax-Optimal Sparsity-Adaptive Inference for Change Points in Dynamic Networks
We propose a new inference framework, named MOSAIC, for change-point detection in dynamic networks with the simultaneous low-rank and sparse-change structure. We establish the minimax rate of detection boundary, which relies on the sparsity of changes. We then develop an eigen-decomposition-based test with screened signals that approaches the minimax rate in theory, with only a minor logarithmic loss. For practical implementation of MOSAIC, we adjust the theoretical test by a novel residual-bas…

Adapt in the Wild: Test-Time Entropy Minimization with Sharpness and Feature Regularization
Shuaicheng Niu, Guohao Chen, Deyu Chen, Yifan Zhang, Jiaxiang Wu, Zhiquan Wen, Yaofo Chen, Peilin Zhao, Chunyan Miao, Mingkui Tan
https://arxiv.org/abs/2509.04977

Adapt in the Wild: Test-Time Entropy Minimization with Sharpness and Feature Regularization
Test-time adaptation (TTA) may fail to improve or even harm the model performance when test data have: 1) mixed distribution shifts, 2) small batch sizes, 3) online imbalanced label distribution shifts. This is often a key obstacle preventing existing TTA methods from being deployed in the real world. In this paper, we investigate the unstable reasons and find that the batch norm layer is a crucial factor hindering TTA stability. Conversely, TTA can perform more stably with batch-agnostic norm …

High-Performance Imaging in a Dilution Refrigerator
Timo Eikelmann, Mara Brinkmann, Leonie Eggers, Tuncay Ulas, Donika Imeri, Konstantin Beck, Lasse Jens Irrgang, Sunil Kumar Mahato, Rikhav Shah, Ralf Riedinger
https://arxiv.org/abs/2510.07054

High-Performance Imaging in a Dilution Refrigerator
Nanophotonic light-matter interfaces hold great promise for quantum technologies. Enhancing local electromagnetic fields, they enable highly efficient detectors, can help realize optically connected processors, or serve as quantum repeaters. In-situ fiber-coupling at sub-Kelvin temperatures, as required for test and development of new devices, proves challenging as suitable cryogenic microscopes are not readily available. Here, we report on a robust and versatile confocal imaging system integra…

GenAI-based test case generation and execution in SDV platform
Denesa Zyberaj, Lukasz Mazur, Nenad Petrovic, Pankhuri Verma, Pascal Hirmer, Dirk Slama, Xiangwei Cheng, Alois Knoll
https://arxiv.org/abs/2509.05112

GenAI-based test case generation and execution in SDV platform
This paper introduces a GenAI-driven approach for automated test case generation, leveraging Large Language Models and Vision-Language Models to translate natural language requirements and system diagrams into structured Gherkin test cases. The methodology integrates Vehicle Signal Specification modeling to standardize vehicle signal definitions, improve compatibility across automotive subsystems, and streamline integration with third-party testing tools. Generated test cases are executed withi…

AutoDAN-Reasoning: Enhancing Strategies Exploration based Jailbreak Attacks with Test-Time Scaling
Xiaogeng Liu, Chaowei Xiao
https://arxiv.org/abs/2510.05379 https://

AutoDAN-Reasoning: Enhancing Strategies Exploration based Jailbreak Attacks with Test-Time Scaling
Recent advancements in jailbreaking large language models (LLMs), such as AutoDAN-Turbo, have demonstrated the power of automated strategy discovery. AutoDAN-Turbo employs a lifelong learning agent to build a rich library of attack strategies from scratch. While highly effective, its test-time generation process involves sampling a strategy and generating a single corresponding attack prompt, which may not fully exploit the potential of the learned strategy library. In this paper, we propose to…

Pushing Test-Time Scaling Limits of Deep Search with Asymmetric Verification
Weihao Zeng, Keqing He, Chuqiao Kuang, Xiaoguang Li, Junxian He
https://arxiv.org/abs/2510.06135 htt…

Pushing Test-Time Scaling Limits of Deep Search with Asymmetric Verification
Test-time compute can be scaled both sequentially and in parallel. Sequential scaling involves lengthening the generation process, while parallel scaling involves verifying and selecting among multiple candidate outputs. Combining these two strategies has led to the most powerful AI systems, such as Grok 4 Heavy and GPT-5 Pro. In certain contexts (e.g., solving Sudoku puzzles), verifying responses can be substantially easier than generating them. This property, referred to as \emph{asymmetric v…

Test-Time Reinforcement Learning for GUI Grounding via Region Consistency
Yong Du, Yuchen Yan, Fei Tang, Zhengxi Lu, Chang Zong, Weiming Lu, Shengpei Jiang, Yongliang Shen
https://arxiv.org/abs/2508.05615

Test-Time Reinforcement Learning for GUI Grounding via Region Consistency
Graphical User Interface (GUI) grounding, the task of mapping natural language instructions to precise screen coordinates, is fundamental to autonomous GUI agents. While existing methods achieve strong performance through extensive supervised training or reinforcement learning with labeled rewards, they remain constrained by the cost and availability of pixel-level annotations. We observe that when models generate multiple predictions for the same GUI element, the spatial overlap patterns revea…

NEO: No-Optimization Test-Time Adaptation through Latent Re-Centering
Alexander Murphy, Michal Danilowski, Soumyajit Chatterjee, Abhirup Ghosh
https://arxiv.org/abs/2510.05635 h…

NEO: No-Optimization Test-Time Adaptation through Latent Re-Centering
Test-Time Adaptation (TTA) methods are often computationally expensive, require a large amount of data for effective adaptation, or are brittle to hyperparameters. Based on a theoretical foundation of the geometry of the latent space, we are able to significantly improve the alignment between source and distribution-shifted samples by re-centering target data embeddings at the origin. This insight motivates NEO -- a hyperparameter-free fully TTA method, that adds no significant compute compared…

A Parametrized Test of General Relativity for LISA Massive Black Hole Binary Inspirals
Manuel Piarulli, Sylvain Marsat, Elise M. S\"anger, Alessandra Buonanno, Jan Steinhoff, Nicola Tamanini
https://arxiv.org/abs/2510.06330

A Parametrized Test of General Relativity for LISA Massive Black Hole Binary Inspirals
Laser Interferometer Space Antenna (LISA) observations of massive black hole binaries (MBHBs) will provide long duration inspiral signals with high signal-to-noise ratio (SNR) data, ideal for testing general relativity (GR) in the strong-field and high-velocity regime. We present an extension of the Flexible Theory-Independent (FTI) framework, adapted to gravitational waves (GWs) from MBHBs observed with LISA, to perform parametrized inspiral tests of GR. This approach introduces generic deviat…

TaTToo: Tool-Grounded Thinking PRM for Test-Time Scaling in Tabular Reasoning
Jiaru Zou, Soumya Roy, Vinay Kumar Verma, Ziyi Wang, David Wipf, Pan Lu, Sumit Negi, James Zou, Jingrui He
https://arxiv.org/abs/2510.06217

TaTToo: Tool-Grounded Thinking PRM for Test-Time Scaling in Tabular Reasoning
Process Reward Models (PRMs) have recently emerged as a powerful framework for enhancing the reasoning capabilities of large reasoning models (LRMs), particularly in the context of test-time scaling (TTS). However, their potential for supervising LRMs on tabular reasoning domains remains underexplored. Through detailed empirical analyses, we identify that existing PRMs, though widely adopted for supervising text-only reasoning steps, struggle with table-specific operations such as sub-table ret…

ToM-SSI: Evaluating Theory of Mind in Situated Social Interactions
Matteo Bortoletto, Constantin Ruhdorfer, Andreas Bulling
https://arxiv.org/abs/2509.05066 https://

ToM-SSI: Evaluating Theory of Mind in Situated Social Interactions
Most existing Theory of Mind (ToM) benchmarks for foundation models rely on variations of the Sally-Anne test, offering only a very limited perspective on ToM and neglecting the complexity of human social interactions. To address this gap, we propose ToM-SSI: a new benchmark specifically designed to test ToM capabilities in environments rich with social interactions and spatial dynamics. While current ToM benchmarks are limited to text-only or dyadic interactions, ToM-SSI is multimodal and incl…

Embezzlement as a "Self-Test" for Infinite Copies of Entangled States
Li Liu
https://arxiv.org/abs/2509.05036 https://arxiv.org/pdf/2509.05036

Embezzlement as a "Self-Test" for Infinite Copies of Entangled States
We investigate the operator-algebraic structure underlying entanglement embezzlement, a phenomenon where a fixed entangled state (the catalyst) can be used to generate arbitrary target entangled states without being consumed. We show that the ability to embezzle a target state $g$ imposes strong internal constraints on the catalyst state $f$: specifically, $f$ must contain infinitely many mutually commuting, locally structured copies of $g$. This property is formalized using C*-algebraic tools …

When vacuum breaks: a self-consistency test for astrophysical environments in extreme mass ratio inspirals
Lorenzo Copparoni, Rohit S. Chandramouli, Enrico Barausse
https://arxiv.org/abs/2510.06948

When vacuum breaks: a self-consistency test for astrophysical environments in extreme mass ratio inspirals
Gravitational-wave signals are typically interpreted under the vacuum hypothesis, i.e. assuming negligible influence from the astrophysical environment. This assumption is expected to break down for low-frequency sources such as extreme mass ratio inspirals (EMRIs), which are prime targets for the Laser Interferometer Space Antenna (LISA) and are expected to form, at least in part, in dense environments such as Active Galactic Nuclei or dark-matter spikes/cores. Modeling environmental effects p…

Test Case Generation from Bug Reports via Large Language Models: A Cognitive Layered Evaluation Framework
Irtaza Sajid Qureshi (Jack), Zhen Ming (Jack), Jiang
https://arxiv.org/abs/2510.05365

Test Case Generation from Bug Reports via Large Language Models: A Cognitive Layered Evaluation Framework
Large Language Models (LLMs) are increasingly applied to automated software testing, yet their ability to generalize beyond memorized patterns and reason about natural language bug reports remains unclear. We present a systematic evaluation of LLM reasoning in test case generation, structured around the cognitive layers of Bloom's taxonomy: \textit{Remember}, \textit{Understand}, \textit{Apply}, \textit{Analyze}, \textit{Evaluate}, and \textit{Create}, which progressively assess higher levels o…

Generalization of Gibbs and Langevin Monte Carlo Algorithms in the Interpolation Regime
Andreas Maurer, Erfan Mirzaei, Massimiliano Pontil
https://arxiv.org/abs/2510.06028 https…

Generalization of Gibbs and Langevin Monte Carlo Algorithms in the Interpolation Regime
The paper provides data-dependent bounds on the test error of the Gibbs algorithm in the overparameterized interpolation regime, where low training errors are also obtained for impossible data, such as random labels in classification. The bounds are stable under approximation with Langevin Monte Carlo algorithms. Experiments on the MNIST and CIFAR-10 datasets verify that the bounds yield nontrivial predictions on true labeled data and correctly upper bound the test error for random labels. Our …

MatheMagic: Generating Dynamic Mathematics Benchmarks Robust to Memorization
Dayy\'an O'Brien, Barry Haddow, Emily Allaway, Pinzhen Chen
https://arxiv.org/abs/2510.05962

MatheMagic: Generating Dynamic Mathematics Benchmarks Robust to Memorization
Conducting contamination-free evaluation of mathematical capabilities can be difficult for two reasons: models may memorize a test set once it is made public, and current mathematical benchmarks are prone to overfitting due to having limited diversity of symbols and rules, coupled with closed-ended answers. This paper proposes a method to leverage these shortcomings as useful features to a construct dynamic, counterfactual benchmark, which can be used to both reveal overfitting and measure true…