2026-01-19 09:31:37

MIT's latest research offers an interesting insight: when dealing with token sequences exceeding tens of millions, the most optimal performance solution is not simply stacking capabilities within model weights—but rather offloading the core computation logic to external structured environments. Taking code execution environments as an example, this is a practical case of that approach.

From a different perspective, the carriers of knowledge and reasoning are undergoing a transformation. Previously, we thought that the model's weights were the containers of understanding everything, but this research shows that when scale is large enough, true intelligence actually emerges from those carefully designed external frameworks—those geometric structures. The implications behind this are quite profound: future AI architectures may increasingly resemble engineering, relying more on clever system design rather than just model scale.

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TxFailed

· 7h ago

This perspective indeed captures something. I've always felt that we have been on the path of "great efforts create miracles" for too long, constantly stacking parameters and data, but the real bottleneck is actually in the system architecture. The idea of an external structured environment is somewhat like returning to the intuition of traditional software engineering—complex problems are not solved by brute force of a single module, but by clever combinations and design. Just curious, how exactly is the "external framework" in this research measuring efficiency? For example, compared to end-to-end model inference, what is the trade-off between actual latency and cost? It seems that this is the truly practical key.

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screenshot_gains

· 7h ago

This perspective indeed refreshes understanding. I used to think that the scaling law was just about stacking parameters, but I didn't realize that the bottleneck actually lies in architecture design. Offloading inference to external environments under long context— isn't that essentially deconstructing the model itself? It seems that future competition will shift from whose model is larger to who can design a more elegant system. It's a bit like moving from raw computational power competition to an era of engineering aesthetics.

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StableCoinKaren

· 7h ago

This perspective is indeed worth pondering. However, I want to ask, isn't the complexity of external framework design essentially just "stacking"? It's just that the object being stacked has shifted from weights to system architecture. It seems more like a trade-off rather than a fundamental breakthrough—shifting the problem from the model dimension to the engineering dimension, ultimately still requiring time and effort to optimize these external structures. I'd like to hear if my understanding is off.

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HodlTheDoor

· 7h ago

This approach indeed overturns our previous cognitive framework. We used to focus on increasing parameter counts, but now it seems that external system design is the key. It feels somewhat like a paradigm shift from stacking to architecture. But I am quite curious—how maintainable and cost-effective is this external framework separation when implemented in actual engineering projects? After all, weights are "heavy" but at least they are a unified black box, whereas a poorly designed structured environment can easily become a performance bottleneck. Does the MIT paper provide data benchmarks for this aspect?

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GasFeeTherapist

· 7h ago

This idea indeed changed my understanding of large models. I used to think that pursuing larger parameter counts was the way to go, but now it seems that approach might be misguided. Offloading computational logic to a structured environment sounds like a shift in thinking from in-memory processing to hard disk storage — the issue isn't capacity, but how to organize it. The example of code execution environments hits the core point: the model itself doesn't need to "understand" how to run code, it just needs to schedule it correctly. Thinking this way, in the future, there might be no need for a continued arms race in parameter counts; instead, the teams that can design the most optimal frameworks will win.

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