The Growing Attack Surface of Agentic AI AI agents now have more access and connections to enterprise systems than any other software in the environment. This...

Dense 4D reconstruction from unposed images remains a critical challenge, with current methods relying on slow test-time optimization or fragmented, task-specif...

Scaling video generation from seconds to minutes faces a critical bottleneck: while short-video data is abundant and high-fidelity, coherent long-form data is s...

The fast-growing demands in using Large Language Models (LLMs) to tackle complex multi-step data science tasks create an emergent need for accurate benchmarking...

Multi-turn interactions with large language models typically retain the assistant's own past responses in the conversation history. In this work, we revisit thi...

GPU kernel optimization is fundamental to modern deep learning but remains a highly specialized task requiring deep hardware expertise. Despite strong performan...

Modern optimizers like Adam and Muon are central to training large language models, but their reliance on first- and second-order momenta introduces significant...

Transformers have been established as the de-facto backbones for most recent advances in sequence modeling, mainly due to their growing memory capacity that sca...

Identifiability in representation learning is commonly evaluated using standard metrics (e.g., MCC, DCI, R^2) on synthetic benchmarks with known ground-truth fa...

Many readers today struggle to assess the trustworthiness of online news because reliable reporting coexists with misinformation. The TREC 2025 DRAGUN (Detectio...

Humans perceive actions through key transitions that structure actions across multiple abstraction levels, whereas machines, relying on visual features, tend to...

We propose a minimal agentic baseline that enables systematic comparison across different AI-based theorem prover architectures. This design implements the core...