Tootfinder

Replaced article(s) found for hep-ph. https://arxiv.org/list/hep-ph/new
[1/2]:
- Probing the Higgs Portal at the Fermilab Short-Baseline Neutrino Experiments
Brian Batell, Joshua Berger, Ahmed Ismail

Transformer Classification of Breast Lesions: The BreastDCEDL_AMBL Benchmark Dataset and 0.92 AUC Baseline
Naomi Fridman (Ariel University), Anat Goldstein (Ariel University)
https://arxiv.org/abs/2509.26440

Transformer Classification of Breast Lesions: The BreastDCEDL_AMBL Benchmark Dataset and 0.92 AUC Baseline
The error is caused by special characters that arXiv's system doesn't recognize. Here's the cleaned version with all problematic characters replaced: Breast magnetic resonance imaging is a critical tool for cancer detection and treatment planning, but its clinical utility is hindered by poor specificity, leading to high false-positive rates and unnecessary biopsies. This study introduces a transformer-based framework for automated classification of breast lesions in dynamic contrast-enhanced MR…

Quantile Advantage Estimation for Entropy-Safe Reasoning
Junkang Wu, Kexin Huang, Jiancan Wu, An Zhang, Xiang Wang, Xiangnan He
https://arxiv.org/abs/2509.22611 https://

Quantile Advantage Estimation for Entropy-Safe Reasoning
Reinforcement Learning with Verifiable Rewards (RLVR) strengthens LLM reasoning, but training often oscillates between {entropy collapse} and {entropy explosion}. We trace both hazards to the mean baseline used in value-free RL (e.g., GRPO and DAPO), which improperly penalizes negative-advantage samples under reward outliers. We propose {Quantile Advantage Estimation} (QAE), replacing the mean with a group-wise K-quantile baseline. QAE induces a response-level, two-regime gate: on hard queries …

Lung Cancer Survival Prediction Using Machine Learning and Statistical Methods
Varun Vishwanathan Nair, Victor Miranda Soberanis
https://arxiv.org/abs/2510.01267 https://…

Lung Cancer Survival Prediction Using Machine Learning and Statistical Methods
Lung cancer remains one of the leading causes of cancer-related mortality, yet most survival models rely only on baseline factors and overlook posttreatment variables that reflect disease progression. To address this gap, we applied Cox Proportional Hazards and Random Survival Forests, integrating baseline features with post-treatment predictors such as progression-free interval (PFI.time) and residual tumor status. The Cox model achieved a concordance index (C-index) of 0.90, while the RSF mod…

State-of-the-Art Dysarthric Speech Recognition with MetaICL for on-the-fly Personalization
Dhruuv Agarwal, Harry Zhang, Yang Yu, Quan Wang
https://arxiv.org/abs/2509.15516 https…

State-of-the-Art Dysarthric Speech Recognition with MetaICL for on-the-fly Personalization
Personalizing Automatic Speech Recognition (ASR) for dysarthric speech is crucial but challenging due to training and storing of individual user adapters. We propose a hybrid meta-training method for a single model, excelling in zero-shot and few-shot on-the-fly personalization via in-context learning (ICL). Measuring Word Error Rate (WER) on state-of-the-art subsets, the model achieves 13.9% WER on Euphonia which surpasses speaker-independent baselines (17.5% WER) and rivals user-specific pers…

Replaced article(s) found for nlin.CD. https://arxiv.org/list/nlin.CD/new
[1/1]:
- Context parroting: A simple but tough-to-beat baseline for foundation models in scientific machin...
Yuanzhao Zhang, William Gilpin