What was considered a state-of-the-art agent architecture six months ago is already legacy. We went from basic tool calling, to complex ReAct loops, to multi-agent frameworks, to entirely new model capabilities (like native tool-calling APIs) in less than 18 months. Even though model reasoning capabilities got a lot better, the hype is outpacing our ability to actually build anything with them due to the lack of standardization. Let me give you some examples (another warning: this will get quite techincal).
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If Transformer reasoning is organised into discrete circuits, it raises a series of fascinating questions. Are these circuits a necessary consequence of the architecture, and emerge from training at scale? Do different model families develop the same circuits in different layer positions, or do they develop fundamentally different architectures?，更多细节参见新收录的资料
В рыболовной сети нашли 15-метровую тушу редкого кита20:45