arXiv:2511.16976v1 Announce Type: new Abstract: Deep equilibrium models (DEQs) have recently emerged as a powerful paradigm for training infinitely deep weight-tied neural networks that achieve state of the art performance across many modern machine learning tasks. Despite their practical success,…
arXiv:2511.17473v1 Announce Type: cross Abstract: Test-time scaling has been shown to substantially improve large language models’ (LLMs) mathematical reasoning. However, for a large portion of mathematical corpora, especially theorem proving, RLVR’s scalability is limited: intermediate reasoning is crucial, while final…
arXiv:2511.16992v1 Announce Type: new Abstract: Aligning Large Language Models (LLMs) with human values often involves balancing multiple, conflicting objectives such as helpfulness and harmlessness. Training these models is computationally intensive, and centralizing the process raises significant data privacy concerns. Federated…
arXiv:2406.01183v3 Announce Type: replace Abstract: In machine learning practice it is often useful to identify relevant input features. Isolating key input elements, ranked according their respective degree of relevance, can help to elaborate on the process of decision making. Here,…
arXiv:2511.17008v1 Announce Type: new Abstract: Multivariate Time-Series (MTS) clustering discovers intrinsic grouping patterns of temporal data samples. Although time-series provide rich discriminative information, they also contain substantial redundancy, such as steady-state machine operation records and zero-output periods of solar power…
arXiv:2507.10998v3 Announce Type: replace Abstract: Adversarial attacks on tabular data present unique challenges due to the heterogeneous nature of mixed categorical and numerical features. Unlike images where pixel perturbations maintain visual similarity, tabular data lacks intuitive similarity metrics, making it…
arXiv:2509.18057v5 Announce Type: replace Abstract: Can AI based methods help us make advances in complexity theory? We provide evidence towards answering this in the affirmative, using AlphaEvolve (an LLM code mutation agent) to obtain new results in three settings: a)…
arXiv:2409.11078v2 Announce Type: replace Abstract: Artificial Neural Networks (ANNs) have significantly advanced various fields by effectively recognizing patterns and solving complex problems. Despite these advancements, their interpretability remains a critical challenge, especially in applications where transparency and accountability are essential.…
arXiv:2505.10297v2 Announce Type: replace Abstract: Federated learning (FL) remains highly vulnerable to adaptive backdoor attacks that preserve stealth by closely imitating benign update statistics. Existing defenses predominantly rely on anomaly detection in parameter or gradient space, overlooking behavioral constraints that…
arXiv:2511.11381v2 Announce Type: replace-cross Abstract: Wi-Fi Channel State Information (CSI) has been repeatedly proposed as a biometric modality, often with reports of high accuracy and operational feasibility. However, the field lacks a consolidated understanding of its security properties, adversarial resilience,…
