Your AI Research
Intelligence Hub
Discover papers from top AI conferences, extract benchmarks, manage your reading list with AI summaries, and explore a curated library of 12,094+ papers.
12,094+
Papers with AI summaries
8+
Curated collections
10+
Top AI conferences
Daily
Trending updates
What you can do
Three pillars of research intelligence
Research Discovery
Explore New Topics Fast
Jump into any research area and instantly surface relevant papers from top AI conferences, extract benchmark leaderboards, and find research gaps — all in one place.
- Semantic search across NeurIPS, ICLR, ACL, EMNLP and more
- Step-by-step benchmark leaderboard extraction from PDFs
- Filter by AI method, application domain, and research task
- Research gap analysis with streaming AI insights
- Cross-domain knowledge transfer suggestions
Reading List Intelligence
Manage Papers Smartly
Your personal AI-powered research workspace. Save papers into private collections, get instant summaries, and generate research reports and questions from selected papers.
- Organize papers into private collections only you can see
- AI-generated summaries: TLDR, key ideas, contributions
- Semantic search — 'find papers about RAG in time series'
- Select papers and extract a structured research report
- Generate research questions from your saved papers
Curated Library
High-Value Paper Database
A growing, curated library of 12,094+ AI research papers — all with structured AI summaries — plus trending research blogs updated every day to keep you current.
- 12,094+ papers with structured AI summaries
- 8 public collections curated by domain experts
- Trending papers ranked by recency and citation signals
- Daily research blog updates across CV, NLP, RL, and more
- Covers NeurIPS, ICLR, ICML, ACL, EMNLP, CVPR & more
STEP 01
Discover & Search
Enter a topic, pick a conference and year. Get ranked papers, benchmarks, and research gaps in minutes.
STEP 02
Save & Organize
Bookmark papers into private collections. AI auto-generates summaries, key ideas, and contributions.
STEP 03
Extract & Report
Select papers from your list, extract a structured report, generate research questions, and export BibTeX.
Library
Featured Collections
Latest additions
Recently Added Papers
Diff-Instruct++: Training One-step Text-to-image Generator Model to Align with Human Preferences
This paper presents Diff-Instruct++, a one-step text-to-image generator model designed to better align generated images with human preferences. By addressing the limitations of existing models, it enhances the quality and relevance of generated images based on textual descriptions.
Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning
This paper presents a method for scaling automated process verifiers to enhance the reasoning capabilities of large language models (LLMs). By rewarding incremental progress in verification tasks, the approach aims to improve LLM performance in complex reasoning scenarios, which is crucial for their deployment in real-world applications.
Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline Data
This paper presents a method for fine-tuning reinforcement learning agents online without the need to retain offline data. This approach enhances efficiency and reduces the storage burden associated with traditional methods.
Efficient Reinforcement Learning with Large Language Model Priors
This paper presents a method for enhancing reinforcement learning (RL) by incorporating large language model (LLM) priors, which helps improve the efficiency and effectiveness of learning agents. The approach leverages the rich contextual understanding of LLMs to guide decision-making in RL tasks, making it a significant advancement in the field.
Direct Post-Training Preference Alignment for Multi-Agent Motion Generation Model Using Implicit Feedback from Pre-training Demonstrations
This paper presents a method for aligning multi-agent motion generation models with human preferences using implicit feedback derived from pre-training demonstrations. This approach enhances the model's ability to generate more desirable behaviors in robotic applications, making it significant for improving autonomy in complex environments.
HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
This paper introduces HuggingGPT, a framework that integrates ChatGPT with various models from Hugging Face to tackle a range of AI tasks. It highlights the potential of combining different AI models to enhance task performance and flexibility.