We are focusing on building! Very little theory in this one. It's not about tricks and prompts; it's about building systematically and solving hard problems. This is useful for both beginners and experienced builders. The course launches tomorrow!
@dair_ai
-
Building Effective AI Agents: Framework and Training Guide
By
–
Anyone can build useful AI Agents. But it requires having a solid framework to design and improve AI agents. That's what we'll teach in our new training on Building Effective AI Agents. Topics include context engineering, augmenting AI agents, multi-agent systems, and more.
-
Enhancing Medical Reasoning with LLMs: Training and Test-Time
By
–
10. Medical Reasoning in the Era of LLMs This review categorizes techniques for enhancing LLM medical reasoning into training-time (e.g., fine-tuning, RL) and test-time (e.g., prompt engineering, multi-agent systems) approaches, applied across modalities and clinical tasks.
-
LLM Methods for Table Understanding Survey
By
–
9. Tabular Data Understanding with LLMs This survey reviews LLM and MLLM methods for table understanding, outlining a taxonomy of tabular representations and tasks.
-
Comprehensive Taxonomy of LLM Hallucinations: Intrinsic vs Extrinsic Errors
By
–
8. A Comprehensive Taxonomy of Hallucinations Presents a detailed taxonomy of LLM hallucinations, distinguishing intrinsic vs extrinsic errors and factuality vs faithfulness, and covering manifestations from factual mistakes to domain-specific failures.
-
Tool-Augmented RAG Agent Framework for Dynamic AI Search
By
–
7. Tool-Augmented Unified Retrieval Agent for AI Search Presents a production-ready framework that extends the RAG (Retrieval-Augmented Generation) paradigm to support real-time, dynamic, and transactional queries through agentic tool use.
-
Seed Diffusion: Discrete-State LLM for Code Generation
By
–
6. Seed Diffusion A discrete-state diffusion-based LLM optimized for code generation, achieving 2,146 tokens/sec on H20 GPUs while maintaining competitive benchmark performance.
-
CoAct-1: Multi-Agent System Combining GUI Interaction and Code Execution
By
–
5. CoAct-1 Researchers from USC, Salesforce, and UW present CoAct-1, a multi-agent system that combines GUI interaction with direct code execution to improve efficiency and robustness in computer-using agents.
-
Apple Launches Multilingual Foundation Models for On-Device AI
By
–
10. Apple Intelligence Foundation Language Models Apple introduces two multilingual, multimodal foundation models: a 3B-parameter on-device model optimized for Apple silicon and a scalable server model using a novel PT-MoE transformer architecture.
-
MCPEval: Open-Source LLM Agent Evaluation Framework
By
–
9. MCPEval MCPEval is an open-source framework that automates end-to-end evaluation of LLM agents using a standardized Model Context Protocol, eliminating manual benchmarking.