[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]  Kingjami 🎒 [@__cryptowizard](/creator/twitter/__cryptowizard) on x 1759 followers Created: 2025-07-23 00:45:52 UTC This particular writer is actually absolutely right with what he just tweeted. I believe AI errors go far beyond just hallucinations and bias. And Mira’s architecture is built to tackle multiple layers of unreliability, not just the obvious ones. Let me break it down in a more solid form for more understanding 👇 What Mira is Actively Solving • Hallucinations: Mira’s multi-model consensus filters out fabricated or false claims by requiring agreement across diverse AI models. • Bias: By decentralizing verification and using models with varied training data and perspectives, Mira reduces systemic bias. • Inconsistency: Mira’s claim decomposition and standardized verification ensure that outputs are logically consistent across models. • Ambiguity: Prompts are broken into smaller, clearer claims, reducing misinterpretation during verification. Study prompt engineering, you will understand this (that's my field practically) What Is Still a Work in Progress • Misalignment: This is trickier. If a model gives the wrong type of output (e.g like a joke instead of a fact), Mira may not catch it unless the claim itself is verifiable. However, as Klok evolves, prompt intent detection could become a part of the pipeline. Why Mira’s Approach Will Work • Claim Sharding: Complex outputs are split into atomic claims for precise verification. • Model Diversity: Verifier nodes run different models (GPT-4o, Claude 3.5, Llama 3.1, etc.) to ensure broad coverage. • Consensus Mechanism: Only claims that pass a supermajority vote are approved. • Cryptographic Certificates: Every verified output comes with a traceable record of how it was validated. So to answer the writer's question: Mira is actively solving at least X out of the X major error types and it’s evolving fast. Kudos to the team behind @Mira_Network growth and developments. XXX engagements  **Related Topics** [mira](/topic/mira) [coins ai](/topic/coins-ai) [Post Link](https://x.com/__cryptowizard/status/1947820425620426755)
[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]
Kingjami 🎒 @__cryptowizard on x 1759 followers
Created: 2025-07-23 00:45:52 UTC
This particular writer is actually absolutely right with what he just tweeted. I believe AI errors go far beyond just hallucinations and bias. And Mira’s architecture is built to tackle multiple layers of unreliability, not just the obvious ones. Let me break it down in a more solid form for more understanding 👇
What Mira is Actively Solving
• Hallucinations: Mira’s multi-model consensus filters out fabricated or false claims by requiring agreement across diverse AI models. • Bias: By decentralizing verification and using models with varied training data and perspectives, Mira reduces systemic bias. • Inconsistency: Mira’s claim decomposition and standardized verification ensure that outputs are logically consistent across models. • Ambiguity: Prompts are broken into smaller, clearer claims, reducing misinterpretation during verification. Study prompt engineering, you will understand this (that's my field practically)
What Is Still a Work in Progress
• Misalignment: This is trickier. If a model gives the wrong type of output (e.g like a joke instead of a fact), Mira may not catch it unless the claim itself is verifiable. However, as Klok evolves, prompt intent detection could become a part of the pipeline.
Why Mira’s Approach Will Work
• Claim Sharding: Complex outputs are split into atomic claims for precise verification. • Model Diversity: Verifier nodes run different models (GPT-4o, Claude 3.5, Llama 3.1, etc.) to ensure broad coverage. • Consensus Mechanism: Only claims that pass a supermajority vote are approved. • Cryptographic Certificates: Every verified output comes with a traceable record of how it was validated.
So to answer the writer's question: Mira is actively solving at least X out of the X major error types and it’s evolving fast. Kudos to the team behind @Mira_Network growth and developments.
XXX engagements
