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arXiv:2603.11321v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising paradigm for post-training reasoning models. However, group-based methods such …
arXiv:2603.11327v1 Announce Type: new Abstract: This paper introduces MR-Search, an in-context meta reinforcement learning (RL) formulation for agentic search with self-reflection. Instead of optimizing a …
arXiv:2603.11331v1 Announce Type: new Abstract: Adversarial attacks can reliably steer safety-aligned large language models toward unsafe behavior. Empirically, we find that adversarial prompt-injection attacks can …
arXiv:2603.11355v1 Announce Type: new Abstract: We introduce Teleodynamic Learning, a new paradigm for machine learning in which learning is not the minimization of a fixed …
arXiv:2603.11358v1 Announce Type: new Abstract: Financial fraud detection has emerged as a critical research challenge amid the rapid expansion of digital financial platforms. Although machine …
arXiv:2603.11369v1 Announce Type: new Abstract: Antimicrobial resistance (AMR) poses a global health threat, reducing the effectiveness of antibiotics and complicating clinical decision-making. To address this …
arXiv:2603.11372v1 Announce Type: new Abstract: Mechanical ventilation (MV) is a life-saving intervention for patients with acute respiratory failure (ARF) in the ICU. However, inappropriate ventilator …
arXiv:2603.11395v1 Announce Type: new Abstract: Continual reinforcement learning challenges agents to acquire new skills while retaining previously learned ones with the goal of improving performance …
arXiv:2603.11396v1 Announce Type: new Abstract: Manifold learning is a fundamental task at the core of data analysis and visualisation. It aims to capture the simple …
arXiv:2603.11428v1 Announce Type: new Abstract: Statistical dependence measures like mutual information is ideal for analyzing autoencoders, but it can be ill-posed for deterministic, static, noise-free …
arXiv:2603.11436v1 Announce Type: new Abstract: This study addresses the challenge of automatically detecting semantic column types in relational tables, a key task in many real-world …
arXiv:2603.11456v1 Announce Type: new Abstract: Unsupervised neural combinatorial optimization (NCO) offers an appealing alternative to supervised approaches by training learning-based solvers without ground-truth solutions, directly …