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New Stanford paper argues that LLMs already provide the raw skills for AGI, but they still need a coordination layer on top.
Here the LLM is a fast pattern store, while a slower controller should choose which patterns to use, enforce constraints, and keep track of state.
To describe this, the author defines an anchoring strength score that grows when evidence clearly supports an answer, stays stable under small prompt changes, and avoids bloated, noisy context.
When anchoring is weak the model mostly parrots generic patterns and hallucinates, but past a threshold it switches into more reliable, goal directed reasoning, as small arithmetic and concept learning tests show.
MACI then runs several LLM agents in debate, tunes how stubborn they are from anchoring feedback, inserts a judge to block weak arguments, and uses memory to track and revise decisions on longer tasks.
The main claim is that most LLM failures come from missing anchoring, oversight, and memory instead of a bad pattern substrate, so progress should focus on building this coordination layer rather than discarding LLMs.
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Paper Link – arxiv. org/abs/2512.05765
Paper Title: "The Missing Layer of AGI: From Pattern Alchemy to Coordination Physics"
English
