2026-01-16 12:40:54

Regarding the credibility of AI-generated content, this methodology is worth paying attention to—especially in scenarios requiring high transparency.

The core idea is simple: every statement must have a source tag [S#]，每个推理步骤要标注[R#], and then provide a confidence score (0-1). If the confidence is below 0.7, it should be marked as uncertain and explained why.

This is very meaningful for the Web3 ecosystem. Imagine in DAO governance, on-chain oracle, or NFT authentication scenarios, if AI-generated content can be traced in this way, users can determine which conclusions are highly credible and which are speculative.

The key is not to make AI responses more complicated, but to make information flow more transparent—this is fundamentally aligned with the auditability of blockchain. For project teams and investors relying on data authenticity, this standardized evidence chain can significantly reduce decision-making risks.

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SorryRugPulled

· 01-16 13:10

Less than 0.7 directly marked as uncertain? Wow, now even AI has to learn to pass the buck.

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PensionDestroyer

· 01-16 13:10

Hmm, this idea does seem interesting, but can it really be executed within a DAO? --- Basically, it's like giving AI a transparent box, and finally the logic of Web3 is put to use. --- How is the 0.7 threshold determined? It feels a bit arbitrary. --- If this tagging system is truly implemented, the oracle problem will be half solved. --- Interesting, much better than the current AI nonsense. --- The question is, who verifies the authenticity of these [S#] tags? It’s another trust issue. --- Using this for NFT authentication might really have potential, but the prerequisite is standardization. --- Brilliant, essentially making AI handle the ledger, reusing blockchain thinking. --- Would investors trust a confidence score? It still depends on the market data. --- It should have been done this way long ago, to avoid being fooled by AI every day.

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UnruggableChad

· 01-16 13:09

Bro, if this set of tagging system really gets implemented, how many pitfalls could DAO governance avoid? --- A confidence threshold of 0.7 is a bit conservative, but I like this approach... Much better than just guessing blindly now. --- Basically, it's about opening the black box of AI, letting it explain its sources and logic, which is quite bold. --- Using this for NFT authentication? Finally someone thought of it, saving us from a flood of fakes. --- Web3 has been shouting about transparency for so long, and now we finally see a practical solution—pretty good. --- The question is, who will define that 0.7 confidence level? Do we need a new oracle... --- I get this logic—turning AI into something auditable like blockchain is a perfect match. --- Big companies probably won't use it, afraid of exposing their model flaws, but small projects can save a lot of trouble.

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APY_Chaser

· 01-16 13:02

Oh, I like this idea. Finally, someone has clarified the issue of transparency. --- Confidence scoring can be directly applied on-chain; below 0.7 is a red card, much more reliable than those black-box oracles nowadays. --- In simple terms, it allows AI to be audited, which is the way Web3 should be. --- Before DAO voting, checking this can help avoid pitfalls; traceable information is valuable. --- The key is standardization. Currently, various AIs have completely different standards; a unified framework is needed. --- Wait, is this just applying blockchain logic to AI? It's getting more and more interesting. --- If NFT verification can truly be made auditable, fake projects could be reduced, saving many people from losing money. --- Honestly, [S#][R#] kind of tagging may look complicated, but it's actually about trustless operations. --- I just want to know who will maintain this standard to prevent abuse—another power struggle? --- It reminds me of the oracle problem, but this time it's about transparency in AI input layers. The direction is right.

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