Tootfinder

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…

Knudsen number and universal behavior of collective flows in conformal and non-conformal systems
Vincenzo Nugara (Department of Physics and Astronomy, University of Catania, Catania, INFN-Laboratori Nazionali del Sud, Catania, Italy), Nicolas Borghini (Fakult\"at f\"ur Physik, Universit\"at Bielefeld, D-33615 Bielefeld, Germany), Vincenzo Greco (Department of Physics and Astronomy, University of Catania, Catania, INFN-Laboratori Nazionali del Sud, Catania, Italy), Salvat…

Knudsen number and universal behavior of collective flows in conformal and non-conformal systems
We investigate the role of the Knudsen number (Kn) as a scaling parameter governing the emergence of collective behavior in relativistic heavy-ion collisions. Using the Relativistic Boltzmann Transport approach, we explore different initial conditions for both conformal (massless) and non-conformal (massive) systems with a constant specific shear viscosity $η/s$. Observables such as the time evolution of anisotropic flow coefficients collapse onto universal curves for fixed classes of Knudsen …

A Comparative Analysis of Contextual Representation Flow in State-Space and Transformer Architectures
Nhat M. Hoang, Do Xuan Long, Cong-Duy Nguyen, Min-Yen Kan, Luu Anh Tuan
https://arxiv.org/abs/2510.06640

A Comparative Analysis of Contextual Representation Flow in State-Space and Transformer Architectures
State Space Models (SSMs) have recently emerged as efficient alternatives to Transformer-Based Models (TBMs) for long-sequence processing, offering linear scaling and lower memory use. Yet, how contextual information flows across layers and tokens in these architectures remains understudied. We present the first unified, token- and layer-level analysis of representation propagation in SSMs and TBMs. Using centered kernel alignment, stability metrics, and probing, we characterize how representat…

Redundancy Channels in the Conformal Bootstrap
Stefanos R. Kousvos, Andreas Stergiou
https://arxiv.org/abs/2507.05338 https://arxiv.o…

Redundancy Channels in the Conformal Bootstrap
A method for obstructing symmetry enhancement in numerical conformal bootstrap calculations is proposed. Symmetry enhancement refers to situations where bootstrap studies initialised with a certain symmetry end up allowing theories with higher symmetry. In such cases, it is shown that redundant operators in the less symmetric theory can descend from primary scaling operators of the more symmetric one, motivating the imposition of spectral gaps that are justified in the former but not the latter…

Evaluation and Comparison Semantics for ODRL
Jaime Osvaldo Salas, Paolo Pareti, Semih Yumu\c{s}ak, Soulmaz Gheisari, Luis-Daniel Ib\'a\~nez, George Konstantinidis
https://arxiv.org/abs/2509.05139

Evaluation and Comparison Semantics for ODRL
We consider the problem of evaluating, and comparing computational policies in the Open Digital Rights Language (ODRL), which has become the de facto standard for governing the access and usage of digital resources. Although preliminary progress has been made on the formal specification of the language's features, a comprehensive formal semantics of ODRL is still missing. In this paper, we provide a simple and intuitive formal semantics for ODRL that is based on query answering. Our semantics r…

Privacy Enhancement in Over-the-Air Federated Learning via Adaptive Receive Scaling
Faeze Moradi Kalarde, Ben Liang, Min Dong, Yahia A. Eldemerdash Ahmed, Ho Ting Cheng
https://arxiv.org/abs/2510.03860

Privacy Enhancement in Over-the-Air Federated Learning via Adaptive Receive Scaling
In Federated Learning (FL) with over-the-air aggregation, the quality of the signal received at the server critically depends on the receive scaling factors. While a larger scaling factor can reduce the effective noise power and improve training performance, it also compromises the privacy of devices by reducing uncertainty. In this work, we aim to adaptively design the receive scaling factors across training rounds to balance the trade-off between training convergence and privacy in an FL syst…

Audits Under Resource, Data, and Access Constraints: Scaling Laws For Less Discriminatory Alternatives
Sarah H. Cen, Salil Goyal, Zaynah Javed, Ananya Karthik, Percy Liang, Daniel E. Ho
https://arxiv.org/abs/2509.05627

Audits Under Resource, Data, and Access Constraints: Scaling Laws For Less Discriminatory Alternatives
AI audits play a critical role in AI accountability and safety. One branch of the law for which AI audits are particularly salient is anti-discrimination law. Several areas of anti-discrimination law implicate the "less discriminatory alternative" (LDA) requirement, in which a protocol (e.g., model) is defensible if no less discriminatory protocol that achieves comparable performance can be found with a reasonable amount of effort. Notably, the burden of proving an LDA exists typically falls on…

Scaling Artificial Intelligence for Prostate Cancer Detection on MRI towards Population-Based Screening and Primary Diagnosis in a Global, Multiethnic Population (Study Protocol)
Anindo Saha (on behalf of the PI-CAI, ProCAncer-I, COMFORT, STHLM3-MRI,PRIME consortia), Joeran S. Bosma (on behalf of the PI-CAI, ProCAncer-I, COMFORT, STHLM3-MRI,PRIME consortia), Jasper J. Twilt (on behalf of the PI-CAI, ProCAncer-I, COMFORT, STHLM3-MRI,PRIME consortia), Alexander B. C. D. Ng (on behalf of …

Scaling Artificial Intelligence for Prostate Cancer Detection on MRI towards Population-Based Screening and Primary Diagnosis in a Global, Multiethnic Population (Study Protocol)
In this intercontinental, confirmatory study, we include a retrospective cohort of 22,481 MRI examinations (21,288 patients; 46 cities in 22 countries) to train and externally validate the PI-CAI-2B model, i.e., an efficient, next-generation iteration of the state-of-the-art AI system that was developed for detecting Gleason grade group $\geq$2 prostate cancer on MRI during the PI-CAI study. Of these examinations, 20,471 cases (19,278 patients; 26 cities in 14 countries) from two EU Horizon pro…

Re-assessing the stellar population scaling relations of the galaxies in the Local Universe
D. Mattolini, S. Zibetti, A. R. Gallazzi, L. Scholz-Diaz, J. Pratesi
https://arxiv.org/abs/2509.04570

Re-assessing the stellar population scaling relations of the galaxies in the Local Universe
Local galaxies follow scaling relations between mass and stellar population properties such as age and metallicity, which encode key information on their evolutionary histories. We revise these relations using the largest spectroscopic dataset from SDSS DR7 (0.005

Scaling LLM Multi-turn RL with End-to-end Summarization-based Context Management
Miao Lu, Weiwei Sun, Weihua Du, Zhan Ling, Xuesong Yao, Kang Liu, Jiecao Chen
https://arxiv.org/abs/2510.06727

Scaling LLM Multi-turn RL with End-to-end Summarization-based Context Management
We study reinforcement learning (RL) fine-tuning of large language model (LLM) agents for long-horizon multi-turn tool use, where context length quickly becomes a fundamental bottleneck. Existing RL pipelines can suffer from degraded instruction following, excessive rollout costs, and most importantly, strict context limits. To address these challenges, we introduce summarization-based context management to training. In specific, it periodically compresses the tool using history by LLM-generate…