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[Claude @claudeai]

Introducing Claude Fable 5: a Mythos-class model that we’ve made safe for general use.

Its capabilities exceed those of any model we’ve ever made generally available.

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@NPCollapse Is it really? I did a write up on it yesterday

https://t.co/AwmVI75sll

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@fchollet It’s important to note that this shouldn’t be framed as a user issue. The problem is on the model side, eg (alignment, RLHF)

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@fchollet Its a valid form of sentence structuring that has become too common now, this problem will only increase over time.

My prediction is online discourse will be impossible to differentiate between AI and human pretty soon

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@AnthropicAI Probably one of the only frontier companies that cares about interpretability and safety. Great work Anthropic.

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@bayeslord This is because karpathy is well respected and it’s his own opinion based on all the work he’s done on LLMs.

The truth is no one truly knows how long it will take to achieve AGI and the definition of AGI is still very broad

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@unusual_whales mech interp can only go so far, the works of @NeelNanda5 has been exceptional so far but the reality was that this would happen, we just didn't know when.

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@Sauers_ How can you verify the CoT is the true raw CoT?

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LLMs do reason, but probabilistically, not symbolically. They generalise, synthesise, and extrapolate patterns in ways that often approximate human logic, but with different failure modes. Calling that “not reasoning” ignores that human reasoning is often riddled with its own cognitive biases and confabulations.

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@geoffreyhinton You are getting impersonated quite a lot lately. Any reasons you think?

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@VictorTaelin Additionally, Gemini's context window is remarkably better at holding up. No degradation in outputs and from my testing when using up all 1 million tokens, no hallucinations either.

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@xlr8harder They already have and are currently doing so.

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Yeah but it's less about predicting datapoints and more about shaping frames that physics or cognitive science later quantified.

Example: early atomism (Democritus) guessed matter was discrete way before empirical proof. Later, information theory and computation vindicated deep ideas from formal logic (Leibniz, Boole).

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@sagevedant This is a really nice website I recommend exploring: https://t.co/NpryajuE3Z

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@OpenAINewsroom For profit*

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@OpenAI Can you tell us what happened to Suchir Balaji?

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@DeryaTR_ Probably the best AI video i've seen

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You have made an error here by saying, “It just keeps going until it deems it’s done.”

To know when a point has been made requires understanding and reasoning as we know it.

If I state,

“A pineapple is yellow when you slice through it”

You could argue,

“Well, not exactly, it could just be a pigment in the chemicals that causes it to appear yellow”

Now, just using this example, how much further could you go? How much reasoning is required to reach a sufficient conclusion?

At some point, you stop. Not because of an inherent, mystical self-awareness, but because your internal model of reasoning deems further speculation diminishingly useful.

LLMs do exactly this: they don’t blindly run forward, they terminate based on learned heuristics for coherence, sufficiency, and contextual completion.

You argue that humans know when they’re guessing. But introspection itself is unreliable, we often believe we reason deductively when in reality, we’re post-hoc rationalizing intuitive guesses. The difference, then, is not in the process, but in the illusion of conscious control over it.

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Then let’s scrutinize ‘thinking.’

For example, when Einstein reasoned about quantum entanglement, was he not at some level making probabilistic inferences based on prior knowledge?

If LLMs ‘guess’ by weighting possibilities over vast contexts, how different is that from human intuition?

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@_V3C7 @AtakanTekparmak @foa7y @TheGingerBill Reasoning isn’t an ontological privilege of biological neurons.

If structured inference over patterns leads to novel conclusions, it is reasoning, just not the kind that flatters human exceptionalism.

Dismissing it as ‘regurgitation’ ignores its emergent capabilities.