ResearchMate
Your personalized ML research companion. Get daily paper recommendations, track your interests, and ask questions about your knowledge base.
Features
Personalized Daily Feed
ML-ranked papers and articles from ArXiv, Hacker News, and more — tailored to your interests.
Learns From You
Your own LightGBM model trains on your saves and dismissals. The more you use it, the smarter it gets.
Q&A Over Your Documents
Upload papers and notes. Ask questions and get cited answers from your personal knowledge base.
Your Daily Feed
Papers and articles ranked by your personal ML model
Attention Is All You Need
ArXiv · cs.LG
Introduces the Transformer architecture, replacing recurrence with self-attention for sequence modeling.
Transformers are now the backbone of every major LLM including GPT and BERT — understanding this paper is foundational.
Directly relevant to your interest in NLP and deep learning architectures.
LightGBM: A Highly Efficient Gradient Boosting Decision Tree
ArXiv · cs.LG
Proposes histogram-based algorithms that dramatically speed up gradient boosting with lower memory usage.
LightGBM is the go-to model for tabular data and ranking tasks in production ML systems.
Matches your interests in efficient ML and recommendation systems.
How Retrieval-Augmented Generation Actually Works
Hacker News · 342 points
Explains the full RAG pipeline from document chunking to vector retrieval and LLM augmentation.
RAG is now the standard approach for building Q&A systems over private knowledge bases.
Highly relevant to your work on knowledge base assistants.
↑ Sample content — your real feed is ranked by your own trained model
Q&A Over Your Knowledge Base
Upload papers and documents — then ask anything
Based on your uploaded documents:
RAG (Retrieval-Augmented Generation) retrieves relevant passages from your knowledge base at query time and passes them as context to the LLM. It requires no model training and your knowledge base can be updated instantly.
Fine-tuning trains the model weights directly on your data, baking knowledge into the model itself. It requires GPU resources and retraining whenever your data changes.
When to use each: - Use RAG when your knowledge changes frequently or you need citations - Use fine-tuning when you need the model to adopt a specific style or reasoning pattern
*Sources: "Survey of LLM Adaptation Methods" (uploaded), "RAG vs Fine-tuning Benchmark" (uploaded)*
↑ Sample response — answers are grounded in your uploaded documents