Research Topics
Browse papers by what people actually search for — LLMs, diffusion, RAG, agents, and more. Cross-cuts the arXiv taxonomy.
Large Language Models (LLMs)
Research on large language models — pretraining, scaling, evaluation, alignment, and inference.
Transformers
Papers on transformer architectures, attention mechanisms, and their variants.
Diffusion Models
Generative diffusion models for images, video, audio, and 3D — DDPM, score-based, latent diffusion, and beyond.
Retrieval-Augmented Generation (RAG)
RAG systems — combining retrieval with generative models for grounded, up-to-date answers.
Mixture of Experts (MoE)
Sparse and dense mixture-of-experts architectures — routing, capacity, and efficient scaling.
RLHF & Preference Learning
Reinforcement learning from human feedback, DPO, and other preference-based alignment methods.
Fine-Tuning & LoRA
Efficient adaptation of pretrained models — LoRA, adapters, prefix tuning, and supervised fine-tuning.
Vision Transformers (ViT)
Transformer architectures applied to vision — ViT, DeiT, Swin, and downstream visual recognition.
AI Agents
Autonomous LLM-powered agents — planning, tool use, multi-step reasoning, and benchmarks.
Reasoning & Chain-of-Thought
Reasoning in language models — chain-of-thought, tree-of-thought, self-consistency, and step-level supervision.
Multimodal Models
Vision-language and multimodal foundation models — CLIP, LLaVA, VLMs, and cross-modal grounding.
World Models
Learned world models for planning, control, and embodied agents — Dreamer, JEPA, and successors.
Scaling Laws
Empirical scaling laws for model size, compute, and data — Chinchilla, emergent capabilities, and predictability.
In-Context Learning
Few-shot and in-context learning in LLMs — prompting, demonstration selection, and mechanistic explanations.
AI Alignment & Safety
Alignment, safety, interpretability, and red-teaming for frontier AI systems.
Quantization & Model Compression
Post-training and quantization-aware compression — INT8/INT4, GPTQ, AWQ, and pruning.
Knowledge Distillation
Distilling large teacher models into smaller, faster students for deployment.
Reinforcement Learning
Reinforcement learning — policy gradients, model-based RL, offline RL, and continuous control.
Graph Neural Networks (GNNs)
GNNs and message-passing — graph attention, GCNs, and applications in chemistry, biology, and recommendation.
Robotic Manipulation & Embodied AI
Learning-based robotic manipulation, dexterity, and embodied agents in physical and simulated worlds.