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PaperclipPerfector  8d ago (text post)   25991 thread views

Culture War Roundup for the week of February 2, 2026

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With AI models, you can describe behavior directly in terms of the underlying code

You can't. It's intractable. For example, one of the top 3 organizations pursuing AGI, the current leader in agentic coding, Anthropic, investigating misalignment:

New Anthropic Fellows research: How does misalignment scale with model intelligence and task complexity?

When advanced AI fails, will it do so by pursuing the wrong goals? Or will it fail unpredictably and incoherently—like a "hot mess?"

Finding 2: Scale improves coherence on easy tasks, not hard ones
How does incoherence change with model scale? The answer depends on task difficulty:
Easy tasks: Larger models become more coherent
Hard tasks: Larger models become more incoherent or remain unchanged
This suggests that scaling alone won't eliminate incoherence. As more capable models tackle harder problems, variance-dominated failures persist or worsen.

Why Should We Expect Incoherence? LLMs as Dynamical Systems
A key conceptual point: LLMs are dynamical systems, not optimizers. When a language model generates text or takes actions, it traces trajectories through a high-dimensional state space. It has to be trained to act as an optimizer, and trained to align with human intent. It's unclear which of these properties will be more robust as we scale.
Constraining a generic dynamical system to act as a coherent optimizer is extremely difficult. Often the number of constraints required for monotonic progress toward a goal grows exponentially with the dimensionality of the state space. We shouldn't expect AI to act as coherent optimizers without considerable effort, and this difficulty doesn't automatically decrease with scale.

That's, like, the frontier of interpretability research.

Does this look like looking at the code and saying «Ah I get it, X does A»?

We're in a very similar epistemic position with regard to a tiger and to an LLM. The big difference is that with a tiger we have some very limited observation methods like electrocorticography or tomography or something, and with an LLM we can – in theory – deconstruct any particular causal sequence, every activation, every decoded token. But it won't become comprehensible to humans just because we produce another vast array of zeroes and ones from logging its activity.

They are parameterized conditional probability functions, that are finite-order Markovian models over token sequences. Nothing exists outside their context window. They don't persist across interactions, there is no endogenous memory, and no self-updating parameters during inference

Just a string of non sequiturs.

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