Explore MIRIX, a powerful new memory system for LLM-based agents. Learn how its unique 6-component architecture and multi-agent design achieve state-of-the-art results, outperforming methods like LangMem, Mem0, and MemGPT.
A detailed analysis of the HiRA paper for Deep Search. Understand its hierarchical reasoning framework, the roles of the Planner, Coordinator, and Executor agents, and why it outperforms other models.
Discover NotesWriting, a simple yet effective technique to boost LLM performance in Multi-Hop RAG. Learn how it refines retrieved documents to create a clean, focused context, solving key challenges in complex question answering.
Explore AutoMind, a state-of-the-art AI agent designed to master complex data science challenges. This deep dive breaks down its powerful LLM-based framework, which uses an expert knowledge base, agentic tree search, and adaptive coding to achieve top results in Kaggle competitions with remarkable efficiency. Learn how the future of automated data science is being shaped.
Introducing Agentic AI: OctoTools! Understand how OctoTools enhances the performance of LLM Agents on complex tasks through its well-defined and extensible Tool Cards, and the ingenious interplay between its Planner and Executor.
Explore an ICLR 2025 paper on guiding Large Language Models (LLMs) between code execution and textual reasoning. Learn why models like GPT-4o may prefer text-based approaches, sometimes leading to errors, and how combining both reasoning methods yields the best performance.
Discover Pre-Act, an approach enhancing Large Language Model (LLM) agent performance through multi-step planning and reasoning. Learn how Pre-Act overcomes ReAct's limitations in long-term planning by generating and modifying plans at each thinking step, thereby improving acting capabilities.
