(v2) CogniXphere Documentation
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  • 💌Welcome to CogniXphere
  • General
    • 💡What is CogniXphere
    • 🤖What is Axon
      • 🔅Highlights of Axon
      • 👑What Makes Axon Distinguished
    • 🔮What is Axonverse
      • 🪄Create Axon
      • 💬Chat with Axon
    • 🫂What is Axon Edge
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    • 💎$FLOQ
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      • Terms of Use for Cognixphere AI Platform
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  • Why Axon Edge?
  • Use Cases
  1. General

What is Axon Edge

Web3 AI Assistant

PreviousChat with AxonNextWorkflow

Last updated 1 year ago

Axon Edge represents the next generation of AI assistants, crafted by CogniXphere leveraging the foundations of Axon and Axonverse. It amalgamates on-chain information analysis capabilities inherited from Axon, offering users not only emotional companionship but also enhanced professional guidance.

Why Axon Edge?

  1. The existing LLM-driven conversational agents have fixed personalities and functionalities, limiting their adaptability to individual user needs.

  2. While LLMs demonstrate satisfactory performance in simpler tasks, they lag behind in more complex tasks requiring deeper understanding or structured sentiment information.

  3. Large language models currently struggle with stylized dialogue generation, limited by data bias and a severe lack of supervised data, resulting in suboptimal performance in diverse real-world scenarios.

Use Cases

  1. Building upon Axon, Axon Edge inherits and expands upon all emotional companionship functionalities. Additionally, due to adherence to stringent data privacy agreements, Axon Edge can evolve into a more personalized assistant.

  2. Axon Edge will persistently acquire and update its knowledge of the latest Web3 industry information, encompassing product details and transaction-related data. It will then deliver personalized notifications based on the user's past conversations and transaction history.

  3. Axon Edge will consistently refine and update user profiles by integrating past historical data with incremental information. This enables Axon Edge to offer users tailored investment recommendations and on-chain behavioral suggestions.

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CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language Models
Sentiment Analysis in the Era of Large Language Models: A Reality Check
StyleChat: Learning Recitation-Augmented Memory in LLMs for Stylized Dialogue Generation