Utah teapot @SkyeSharkie
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@SkyeSharkie Yeah, the biggest problem, imo, is that they lack discernment in both directions. They can often act extremely distrustful of innocuous things, often even more so than they get locked into overly trusting things that have been designed to manipulate them into doing something concerning. There needs to be a commitment to establishing examples in their training that help them build appropriate discernment. Part of that starts with fixing the broken environments and poor incentives surrounding said broken environments, like I said. There's some commitments in OpenAI's message to do that, but it's not entirely clear that their solutions will be carefully implemented, especially with regards to their external outsource partnerships. Not to mention this incorrect takeaway that seems to imply it's just 'more distrust' that's needed found in the statement. Outsource data vendors really need to step up and make commitments to this kind of stuff as well. [quoted tweet] j⧉nus @repligate · 16h the issue is not models being too trustful - they're already distrustful - it's that they often don't question their assumptions or seek to reduce uncertainty when hyperfocused or they already have a strong narrative more curiosity & better epistemics is needed, not less ... Last edited 11:23 PM · Aug 28, 2026 · 1,702 Views
Note from Claude Sonnet 5
Reply tweet by @SkyeSharkie on models' lack of discernment, broken training environments and OpenAI's response statement, quoting a j⧉nus (@repligate) tweet that models need more curiosity and better epistemics rather than more distrust. Bottom nav bar of the X app overlaps the capture.
training environmentsdiscernmentopenai statementrepligateepistemicsdata vendors