arXiv:2604.10228v1 Announce Type: new
Abstract: Current multimodal models often suffer from shallow reasoning, leading to errors caused by incomplete or inconsistent thought processes. To address this limitation, we propose Self-Verification and Self-…
Adobe Summit serves as a platform for Adobe to introduce new services, capabilities, and enhancements to its portfolio of creative and marketing software and services. The 2026 edition kicks off live in Las Vegas on April 20, wit…
arXiv:2604.08805v1 Announce Type: cross
Abstract: In November 2025, the authors ran a workshop on the topic of what makes a good reinforcement learning (RL) environment for autonomous cyber defence (ACD). This paper details the knowledge shared by par…
arXiv:2507.04736v2 Announce Type: replace
Abstract: Large Language Models have emerged as powerful tools for automating Register-Transfer Level (RTL) code generation, yet they face critical limitations: existing approaches typically fail to simultaneo…
arXiv:2604.07720v2 Announce Type: replace
Abstract: Deep Research (DR) requires LLM agents to autonomously perform multi-step information seeking, processing, and reasoning to generate comprehensive reports. In contrast to existing studies that mainly…
arXiv:2604.07927v2 Announce Type: replace
Abstract: Deep research requires reasoning over web evidence to answer open-ended questions, and it is a core capability for AI agents. Yet many deep research agents still rely on implicit, unstructured search…
arXiv:2601.22160v2 Announce Type: replace-cross
Abstract: Human animation aims to generate temporally coherent and visually consistent videos over long sequences, yet modeling long-range dependencies while preserving frame quality remains challenging….
arXiv:2602.07142v2 Announce Type: replace-cross
Abstract: Collaboration is a cornerstone of 21st-century learning, yet teachers continue to face challenges in supporting productive peer interaction. Emerging generative AI tools offer new possibilities…
arXiv:2604.09089v1 Announce Type: cross
Abstract: Large Language Models (LLMs) for code generation can replicate insecure patterns from their training data. To mitigate this, a common strategy for security hardening is to fine-tune models using superv…
arXiv:2604.09107v1 Announce Type: cross
Abstract: Modern LLM reinforcement learning (RL) workloads require a highly efficient weight transfer system to scale training across heterogeneous computational resources. However, existing weight transfer appr…