Using the toolkit for this specific use case provides multiple benefits: Config-driven workflows The toolkit helps shift the project from a rigid script to a flexible research platform. Instead of hard-coding the interactions between agents, you define the system’s logic—including personas, tools, and constraints—entirely within a YAML configuration. This modularity makes it trivial to swap models for different tasks. For example, you can assign a high-reasoning model to handle hypothesis generation while using a faster, more cost-effective model for the code agent without modifying the underl
Automating and Optimizing Financial Signal Discovery with Multi-Agent Systems | NVIDIA Technical Blog…Extraction Extraction converts a PDF from “pixels and layout” into structured, queryable units because downstream retrieval and reasoning models can’t reliably operate on raw page coordinates and flattened text without losing…
…Making multilingual sovereign AI scalable with MoE To deliver sovereign-scale intelligence with high efficiency, the Sarvam AI models employ a sophisticated heterogeneous mixture-of-experts (MoE) architecture tailored for deep reasoning…
…Before joining NVIDIA, she worked at Microsoft Research on developing real-time AI agent interactions. View all posts by Seonghee Lee View all posts by Seonghee Lee About Moein Khazraee Moein Khazraee…
…Temporal reasoning : Understands sequences of events across time. Multi-hop reasoning : Connects multiple pieces of information to answer complex queries. Anomaly detection : Identifies unusual patterns or behaviors in video content. Scalability : Handles…
…Join Bryan Catanzaro, vice president of Applied Deep Learning Research at NVIDIA, for an interactive “Ask Me Anything” session covering the latest trends in AI. Discuss (0) Discuss (0) Tags Agentic AI…
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