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Chaos Labs Unveils Multi-Agent System for Prediction Market Resolution
The post Chaos Labs Unveils Multi-Agent System for Prediction Market Resolution appeared on BitcoinEthereumNews.com. Rongchai Wang Nov 06, 2024 19:37 Chaos Labs introduces Edge AI Oracle, leveraging LangChain and LangGraph to revolutionize prediction markets with a multi-agent system for precise and transparent query resolution. Chaos Labs has announced the alpha release of Edge AI Oracle, a sophisticated multi-agent system designed to enhance the effectiveness of prediction markets. This system, which is built using the advanced capabilities of large language models (LLMs), aims to provide precise, traceable, and reliable resolutions for various queries, according to LangChain. How Edge AI Oracle Works The Edge AI Oracle operates through an AI Oracle Council, a decentralized network of agents powered by diverse models from prominent providers including OpenAI, Anthropic, and Meta. This setup ensures that each query is processed objectively and accurately, making it particularly suitable for high-stakes prediction markets. Unlike traditional oracles, this system mitigates the limitations and biases of single-model solutions by offering a multi-perspective approach to query resolution. For example, in the Wintermute Election market, the system requires unanimous agreement with over 95% confidence from each Oracle AI Agent, ensuring a high level of reliability. The consensus requirements can be tailored on a per-market basis, providing flexibility for developers and market creators. Addressing Key Challenges Edge AI Oracle is crafted to address three fundamental challenges faced by truth-seeking oracles: prompt optimization, single model bias, and retrieval augmented generation (RAG). Hosted on the Edge Oracle Network and powered by LangChain and LangGraph, the system uses advanced multi-agent orchestration to enhance the accuracy and reliability of query results. The workflow begins with a research analyst reviewing the query to identify key data points and required sources. It then progresses through a web scraper, a document relevance analyst, a report writer, and a summarizer, before concluding with a classifier that evaluates the summarized output. This sequential execution ensures…
The post Chaos Labs Unveils Multi-Agent System for Prediction Market Resolution appeared on BitcoinEthereumNews.com.
Rongchai Wang Nov 06, 2024 19:37 Chaos Labs introduces Edge AI Oracle, leveraging LangChain and LangGraph to revolutionize prediction markets with a multi-agent system for precise and transparent query resolution. Chaos Labs has announced the alpha release of Edge AI Oracle, a sophisticated multi-agent system designed to enhance the effectiveness of prediction markets. This system, which is built using the advanced capabilities of large language models (LLMs), aims to provide precise, traceable, and reliable resolutions for various queries, according to LangChain. How Edge AI Oracle Works The Edge AI Oracle operates through an AI Oracle Council, a decentralized network of agents powered by diverse models from prominent providers including OpenAI, Anthropic, and Meta. This setup ensures that each query is processed objectively and accurately, making it particularly suitable for high-stakes prediction markets. Unlike traditional oracles, this system mitigates the limitations and biases of single-model solutions by offering a multi-perspective approach to query resolution. For example, in the Wintermute Election market, the system requires unanimous agreement with over 95% confidence from each Oracle AI Agent, ensuring a high level of reliability. The consensus requirements can be tailored on a per-market basis, providing flexibility for developers and market creators. Addressing Key Challenges Edge AI Oracle is crafted to address three fundamental challenges faced by truth-seeking oracles: prompt optimization, single model bias, and retrieval augmented generation (RAG). Hosted on the Edge Oracle Network and powered by LangChain and LangGraph, the system uses advanced multi-agent orchestration to enhance the accuracy and reliability of query results. The workflow begins with a research analyst reviewing the query to identify key data points and required sources. It then progresses through a web scraper, a document relevance analyst, a report writer, and a summarizer, before concluding with a classifier that evaluates the summarized output. This sequential execution ensures…
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