Research Hub
The Papers That Define Modern AI
AI moves fast. These are the foundational papers every technical leader should know—curated and explained so you can stay informed without reading 50+ pages.
Why This Matters
You don't need to be an ML researcher to lead AI initiatives. But understanding the core ideas—attention mechanisms, retrieval-augmented generation, alignment techniques—helps you evaluate tools, hire talent, and make better architectural decisions.
What You Get
- •Curated essentials — The 10-15 papers that shaped LLMs, agents, and retrieval systems
- •Plain-language summaries — Key insights extracted, no PhD required
- •Ask questions — Chat with our AI to explore concepts deeper
The Essential Collection
Attention Is All You Need
Vaswani et al.
2017TransformerFoundationNLPGPT-4 Technical Report
OpenAI
2023LLMMultimodalBenchmarksLlama 2: Open Foundation and Fine-Tuned Chat Models
Touvron et al. (Meta)
2023Open SourceLLMFine-tuningLanguage Models are Few-Shot Learners
Brown et al.
2020LLMIn-Context LearningGPT-3Training Language Models to Follow Instructions
Ouyang et al.
2022RLHFAlignmentInstruction-followingChain-of-Thought Prompting Elicits Reasoning
Wei et al.
2022ReasoningPromptingChain-of-ThoughtLoRA: Low-Rank Adaptation of Large Language Models
Hu et al.
2021Fine-tuningEfficiencyPEFTDenoising Diffusion Probabilistic Models
Ho et al.
2020GenerativeVisionDiffusionDeep Residual Learning for Image Recognition
He et al.
2015VisionDeep LearningCNNGenerative Adversarial Nets
Goodfellow et al.
2014GenerativeAdversarialFoundational