Hey I'm Dan, I'm new here, but I've been holding onto something since October, and I set this (Jan 2nd, 2026) as my due date to post if I didn't see anything on the concept brought up anywhere (if it has been, that's a win, but I have kept a pulse and haven't seen anthing).

Before I go into it, ff you want a tl;dr, I asked Claude to make a little .jsx app to show the concept, you can find it here: https://claude.ai/public/artifacts/a916d8cc-0e15-4103-90bb-aba374d6f941

The general idea is like the title says: exploring whether we can use subliminal learning in a way to bypass situational awareness.

And yes, you'll find that moves the problem from the model being trained to other teacher-models, but you'll find the paper (post? paper? write-up?) below is very aware and focused on that as the central crux to experiment.

The write-up is of course very Claude-flavored, but it started with an initial draft by myself and has gone through several iterations since October, and I think there's a point to be made about validity of utilizing LLMs intelligently to map to personas and help cross-validate and so on. The ideas are mine, the editor is Claude. That said, here is the write-up:

Offensive Subliminal Learning (OSL): Using Trait Transmission for Alignment

Dan Guilliams & Claude Opus 4.5

A synthesis of Anthropic's (and fellows') 2025 research into an alignment pipeline.

Note: "Offensive" as in proactive: using this mechanism intentionally for alignment, not just flagging it as a risk.

Epistemic Status

What this is: A proposal for an alignment experiment that synthesizes four Anthropic papers. The core idea: select models based on their behavior in realistic scenarios, then transmit their traits to base models through semantically meaningless data (random strings of numbers, arbitrary code, whathaveyou). The idea here is to try exploiting that a model can't game training it can't recognize as training (or, if it suspects training, does not seemingly have a realistic way, as of now, to detect the specifics of what is being trained without a compare/contrast of itself pre/post each step of this training).

(Note: I have not designed or executed any actual experiments or studies on this concept, this is not a research finding, it's an idea for research, what such an experiment might look like, and why it might be worth running.)

Why post now: I first c...

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