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.
Code Generation & AI for Code
LLM-based code generation, code completion, debugging, and program synthesis.
Speech & Audio Models
Speech recognition, text-to-speech, music generation, and audio foundation models.
3D Generation & NeRF
3D scene reconstruction and synthesis — NeRF, Gaussian Splatting, and text-to-3D.
Mechanistic Interpretability
Reverse-engineering neural networks — circuits, features, sparse autoencoders, and probing.
LLM Evaluation & Benchmarks
Benchmarks, LLM-as-judge, evaluation frameworks, and methodology for measuring model capability.
Long Context Modeling
Extending context windows — efficient attention, position encodings, retrieval, and long-document tasks.
Tool Use & Function Calling
LLMs calling external tools, APIs, and functions — ReAct, function calling, and tool-augmented reasoning.
Synthetic Data Generation
Generating synthetic training data — distillation from LLMs, data augmentation, and self-improvement loops.
State Space Models (Mamba & SSMs)
Selective state space models and linear-time sequence architectures — Mamba, S4, and SSM variants that rival transformers on long sequences.
Test-Time Compute & Inference Scaling
Scaling reasoning at inference time — test-time compute, search, and deliberate o1-style inference for harder problems.
Federated Learning
Privacy-preserving distributed training across decentralized data — federated optimization, aggregation, and on-device learning.
Continual & Lifelong Learning
Learning without forgetting — continual, lifelong, and incremental learning that resists catastrophic forgetting.
Text Embeddings & Dense Retrieval
Learned text and sentence embeddings for semantic search and dense retrieval — contrastive representation learning and embedding models.
Watermarking & AI Content Provenance
Watermarking and provenance for AI-generated text and images — embedding, detection, robustness, and attribution of model outputs.
AI for Science
Machine learning for scientific discovery — protein structure, molecular and materials modeling, and drug discovery.
Time Series & Forecasting
Time-series modeling and forecasting — deep and foundation models for temporal data, plus anomaly detection.
Video Generation
Generative models for video — text-to-video diffusion, world simulators, and temporally consistent synthesis.
Speculative Decoding
Accelerating LLM inference with speculative and parallel decoding — draft models, verification, and serving speedups.
Model Merging
Combining trained models without retraining — weight averaging, task arithmetic, and merging fine-tuned checkpoints.
Prompt Engineering
Prompting methods for LLMs — prompt design, optimization, chain-of-thought prompting, and automatic prompt search.
Jailbreaks & Red-Teaming
Adversarial attacks on LLMs — jailbreaks, prompt injection, red-teaming, and safety robustness evaluation.
Self-Supervised Learning
Learning representations without labels — pretext tasks, masked modeling, and self-supervised pretraining.
Neural Radiance Fields (NeRF)
Neural scene representations for novel-view synthesis — NeRF and 3D reconstruction.
LoRA & Parameter-Efficient Tuning
Parameter-efficient fine-tuning of large models — LoRA, adapters, and low-rank adaptation methods.
Vision-Language Models (VLMs)
Models that jointly understand images and text — CLIP, captioning, visual question answering, and multimodal LLMs.
Object Detection
Detecting and localizing objects in images and video — DETR, YOLO, and modern detection architectures.
Gaussian Splatting
3D Gaussian Splatting for real-time radiance-field rendering, 3D reconstruction, and novel-view synthesis.
Image Segmentation
Pixel-level scene understanding — semantic, instance, and panoptic segmentation.
Recommender Systems
Recommendation and personalization — collaborative filtering, sequential and LLM-based recommenders, and ranking.
Autonomous Driving
Self-driving perception, planning, and control — autonomous vehicles, end-to-end driving, and trajectory prediction.
Anomaly Detection
Detecting outliers and out-of-distribution inputs — anomaly detection, OOD detection, and novelty detection.
Contrastive Learning
Self-supervised representation learning by contrasting positive and negative pairs — SimCLR, MoCo, and InfoNCE.
Medical Imaging
Deep learning for medical images — segmentation, diagnosis, and analysis of CT, MRI, X-ray, and pathology data.
Protein Structure & Design
Computational protein science — structure prediction, protein folding, and generative protein design.