Covers advancements in generative modeling (diffusion, flow matching) for creating realistic images, video, audio, 3D assets, and other data types, including control and efficiency methods.
Focuses on understanding, evaluating, and enhancing the core reasoning, generation, and problem-solving abilities of LLMs, including mathematical reasoning, code generation, and instruction following.
Includes techniques for making large models efficient: quantization, pruning, LoRA, MoE, efficient attention, memory optimization (KV Cache), and understanding scaling laws.
Investigates methods for aligning LLMs with human preferences (DPO, RLHF), ensuring safety, robustness against attacks (jailbreaking), detecting harmful content, and improving trustworthiness/fairness.
Encompasses RL algorithms (online/offline, multi-agent), planning strategies, world models, imitation learning, agent frameworks, and embodied AI systems.
Focuses on models integrating multiple modalities (vision, language, audio, video, 3D) for tasks like retrieval, generation, reasoning, and benchmarking.
Covers the creation of new datasets, benchmarks, and evaluation methods to rigorously assess AI model capabilities, limitations, safety, robustness, and specific tasks across diverse domains.
Aims to understand the internal workings of deep learning models, identify features (e.g., using sparse autoencoders), explain predictions, discover circuits, and make models transparent.
Focuses on developing and analyzing optimization algorithms (SGD, Adam, second-order, bilevel), understanding convergence, learning dynamics, implicit bias, and loss landscapes.
Advances the theory, architecture, and application of GNNs and related geometric methods, including expressiveness, robustness, dynamic graphs, graph generation, topological learning, and equivariant networks.
Applies AI for scientific discovery and modeling in biology (genomics, proteins), chemistry (molecules, materials), physics simulation, neuroscience, climate science, and PDE solving.
Addresses model security against adversarial/backdoor attacks, data poisoning, model inversion, defenses, machine unlearning, data privacy (DP), and watermarking in various model types beyond just LLMs.
Focuses on learning meaningful data representations without labels, including contrastive learning, masked autoencoders, information bottleneck, representation geometry, and unsupervised methods.
Includes representing, generating, and understanding 3D data, particularly using techniques like Gaussian Splatting, NeRFs, mesh generation, and their applications in scene reconstruction, view synthesis, object generation, and pose estimation.
Focuses on AI agents interacting with physical or simulated environments, including robot manipulation (dexterous grasping, assembly), navigation, control policies, simulation platforms, and learning from demonstrations/videos.