Alignment, interpretability, red-teaming, bias and privacy: the research on what these systems do when they misbehave.
arXiv:2406. 11868v2 Announce Type: replace-cross Abstract: The emergence of foundational models represents a paradigm shift in medical imaging, offering extraordinary capabilities in disease detection, diagnosis, and treatment planning.
arXiv:2608. 07965v1 Announce Type: new Abstract: Gamification is especially effective in learning domains requiring active problem-solving and iterative skill-building, such as cybersecurity education.
arXiv:2608. 08883v1 Announce Type: new Abstract: Recent advances in retrieval-augmented generation (RAG) and large language models (LLMs) enable researchers to integrate AI into scientific workflows.
arXiv:2510. 23389v2 Announce Type: replace-cross Abstract: The behaviour of neural network components must be proven correct before deployment in safety-critical systems.
arXiv:2608. 09230v1 Announce Type: new Abstract: Industrial-safety understanding requires more than detecting workers, equipment, and personal protective equipment.
arXiv:2608. 09119v1 Announce Type: new Abstract: We introduce Motif 3, a decoder-only Mixture-of-Experts language model with 314 billion total parameters and 13.
arXiv:2504. 18587v2 Announce Type: replace-cross Abstract: Reinforcement learning has emerged as a powerful approach for improving the reasoning capabilities of large language models, as demonstrated by systems such as OpenAI's O1~\cite{o1} and DeepSeek-R1~\cite{r1}.
arXiv:2511. 19735v2 Announce Type: replace-cross Abstract: Randomized controlled trials (RCTs)have been the cornerstone of clinical evidence; however, their cost, duration, and restrictive eligibility criteria limit power and external validity.
arXiv:2604. 07282v2 Announce Type: replace-cross Abstract: Automated face recognition has made rapid strides over the past decade due to the unprecedented rise of deep neural network (DNN) models that can be trained for domain-specific tasks.
arXiv:2608. 09278v1 Announce Type: cross Abstract: GUI agents have advanced rapidly, producing a growing body of frameworks, benchmarks, and applications.
arXiv:2604. 22416v2 Announce Type: replace-cross Abstract: Latent variables pose a fundamental obstacle to both causal discovery and inference.
arXiv:2608. 08736v1 Announce Type: new Abstract: Fitness Action Quality Assessment (AQA) is important for intelligent sports training, yet the capabilities of Multimodal Large Language Models (MLLMs) in this setting remain underexplored.
arXiv:2608. 07533v1 Announce Type: new Abstract: An embodied agent is an intelligent entity that interacts with its environment through a physical body.
arXiv:2608. 08453v1 Announce Type: new Abstract: Under the current standard, Agent Skills are SKILL.
arXiv:2608. 07509v1 Announce Type: cross Abstract: LLMs are increasingly used for conversational tutoring, but effective tutoring requires more than correct answers.
arXiv:2608. 08640v1 Announce Type: new Abstract: Large language model agents increasingly rely on reusable skills to extend their capabilities beyond parametric knowl- edge.
arXiv:2604. 11704v2 Announce Type: replace-cross Abstract: Deep Neural Networks are highly susceptible to shortcut learning, frequently memorizing low-dimensional spurious correlations instead of underlying causal mechanisms.
arXiv:2608. 09236v1 Announce Type: new Abstract: Federated learning enables privacy-preserving collaboration across distributed devices without centralizing local data.
arXiv:2604. 26836v3 Announce Type: replace Abstract: Predictive safety filters (PSFs) leverage model predictive control to enforce constraint satisfaction during deep reinforcement learning (RL) exploration, yet their reliance on first-principles models or Gaussian processes limits scalability and broader applicability.
arXiv:2601. 03888v4 Announce Type: replace-cross Abstract: In prior work, we introduced IndexTTS 2, a zero-shot neural text-to-speech foundation model comprising two core components: a transformer-based Text-to-Semantic (T2S) module and a non-autoregressive Semantic-to-Mel (S2M) module, which together enable faithful emotion replication and establish the first autoregressive duration-controllable generative paradigm.