@realchendahuang: Three years ago, when large language models had just swept the globe, the entire tech world was in an awkward state of being both thrilled and constrained everywhere. At the time, the industry broadly agreed on several almost insurmountable technical hurdles: model memory windows were too short — input just a little longer and it couldn't remember; the underlying compute architecture was extremely compute-hungry, with computation for long text exploding exponentially; the cost per call stayed sky-high, so enterprises didn't dare deploy them broadly in business use; and so-called autonomous agents would completely lose control under probabilistic drift if they ran just a few extra steps...

Three years ago, when large language models had just swept the globe, the entire tech world found itself in an awkward state of being both thrilled and constrained everywhere. At the time, the industry broadly agreed on several nearly insurmountable technical hurdles: model memory windows were too short — feed it input a little longer and it would forget; the underlying compute architecture was extremely compute-hungry, with the computational cost of long text exploding exponentially; the bill for a single call stayed sky-high, so enterprises didn't dare deploy these models broadly in business use; and so-called autonomous agents, if they ran just a few extra steps, would spiral completely out of control in probabilistic drift. Three years on, looking back at how these problems evolved, the solutions the industry actually got to work are remarkably illuminating. Many mathematical ideas once held in high hopes were taught a lesson by real-world physics in practice; and those cost and compute barriers that seemed impossible to cross were flattened one by one by extremely solid underlying engineering. The first direction to undergo a profound transformation is context length and knowledge retrieval. In the early days, the basic unit for processing text in large models was the token — usually a few English characters or a single Chinese character. Back then, mainstream models typically had context windows of only a few thousand tokens, not even enough to fit a thin technical manual. Worse still, as input text got longer, the attention mechanism exhibited a "lost in the middle" phenomenon: the model could only remember the beginning and the end, while large chunks of middle content were simply ignored. To work around this bottleneck, the most popular solution across the industry was Retrieval-Augmented Generation, or RAG. The logic of this mechanism is to first slice up text: a long document of several hundred thousand characters is forcibly cut into small fragments of a few hundred characters each by paragraph. Vector models then map each fragment into a high-dimensional coordinate space, stored in a dedicated vector database. When a user asks a question, the system converts the question into a vector too, compares geometric distances in the database, retrieves the three or five most literally relevant fragments, splices them into the prompt, and finally hands the whole thing to the model for an answer. Many teams treated this slicing-and-vector-matching technique as their core asset. However, once deployed in real complex business scenarios, the approach revealed fundamental flaws. Real-world business logic is an interconnected whole. The moment a long document is forcibly chopped up, the causal chains between pieces of context are physically severed. As soon as a question involves cross-chapter logical inference, or requires a global synthesis of the whole document, vector retrieval immediately falls apart — the spliced fragments often end up disjointed and nonsensical. What truly put long-text processing back on track was the increase in GPU memory bandwidth, combined with a core engineering mechanism: prefix caching. When processing preceding content, large models save the intermediate computation results from attention layers as key-value caches, i.e., the KV Cache. In the past, every time you queried the model, even if the tens of thousands of characters of project documentation ahead hadn't changed at all, the system still had to recompute the entire attention matrix of the text from scratch — an enormous computational expense. The prefix caching mechanism allows the system to keep the key-value caches of common documents resident in GPU memory, so subsequent queries only need to perform incremental computation on the few dozen new characters appended. Once the per-inference cost of ultra-long text was drastically compressed, the complex slicing pipelines rapidly lost their reason to exist. Feeding hundreds or even thousands of code files in their entirety into a million-token context window, letting the model complete the inference within global semantics, produced far cleaner and more stable results than assembling fragments around the outside. The second direction to hit a wall against reality is the search for new architectures to replace the Transformer. Almost all mainstream large models today are based on the Transformer architecture, whose core mechanism is self-attention. This mechanism requires every word in the input text to be compared for association with all other words in the context, one by one. This means that once text length doubles, the computational cost surges fourfold — or even hundreds to thousands of times over — a behavior mathematically known as quadratic complexity. Faced with this compute consumption that explodes with length, academia has long sought lighter-weight alternatives. Researchers have designed many state space models, such as the S4 and Mamba architectures. These models have linear complexity in their mathematical form: double the text length and the computation merely doubles linearly — the formulas look exquisitely elegant. Yet three years on, the models at the very cutting edge of the industry remain firmly built on the Transformer and its variants. The reason theoretically more compute-efficient architectures didn't win boils down to the physical characteristics of modern compute chips and the scale of their ecosystem. The core compute unit of modern GPUs is the tensor core — the Tensor Core. This hardware structure was purpose-built at the physical level for high-density, ultra-high-throughput regular matrix multiplications. Although the Transformer's math is computationally heavy, every one of its operations is a neat, well-formed large matrix computation — exactly the kind that runs the GPU's hardware pipelines at full speed. Conversely, linear models with lower computational complexity in theory often involve large numbers of fine-grained state loops and recursive updates during inference. These operations shuttle data back and forth constantly between compute cores and memory, incurring enormous hardware scheduling overhead — making it hard to actually saturate the chip's peak performance. On top of that, over the past few years the entire industry has accumulated tens of thousands of operator-compiler optimizations and cluster-scheduling toolchains around matrix multiplication. A theoretically lighter algorithm can hardly topple a mature industrial architecture deeply bound to a trillion-dollar hardware ecosystem. The third change that truly brought the technology to the masses is the evolution of inference cost and the role models play. When large models first appeared, loading a hundred-billion-parameter model into GPU memory required massive clusters, and the hardware depreciation and electricity cost per call was extremely high — enterprise production-level bills were often enough to give companies pause. Costs were able to be democratized quickly, first thanks to model quantization. Computers originally recorded model weights in 16-bit or even 32-bit high-precision floating-point numbers. Engineers later discovered that by using quantization algorithms to compress these parameters into 8-bit or even 4-bit integer storage, the model's overall inference capability hardly suffered any noticeable loss — while its memory footprint dropped directly to a fraction of the original. Second came knowledge distillation. By having a massive teacher model with tens of billions of parameters output high-quality reasoning data to guide the training of a small student model with a few billion parameters, even lightweight models could learn complex logical derivation patterns. But the deeper cost-reduction logic comes from task-based routing in architecture design. In the early days, people habitually used one expensive all-purpose model to handle everything — equivalent to hiring top scholars to pour tea and serve water, where even fixing a couple of typos required paying exorbitant consulting fees. Today's engineering practice has shifted toward clear role division: the vast majority of daily text extraction, formatting, semantic classification, and light code completion is routed directly to distilled small models or extremely fast lightweight versions. These models run with minimal overhead, return results in milliseconds, and cost as little as a fraction of a cent per call. Only when a problem involves complex edge cases or multi-step derivation does the system call out a large model for long-chain reasoning. Reasonable task-based routing by complexity is what made the large-scale commercial deployment of large models genuinely economically viable. The fourth direction is agents moving from hype to pragmatism. Three years ago, the open-source community was passionately chasing fully autonomous agents — Autonomous Agents. The idea back then was to give the model a high-level goal and let it open browsers on its own to browse pages, call APIs, and send emails, solving everything autonomously. But run it in real business settings and you quickly find that a large language model is fundamentally a prediction system based on probability distributions, with a tiny error probability at every step of inference. Let it execute continuously for dozens of steps completely unconstrained, and those tiny errors accumulate and amplify exponentially — ultimately causing the program to hang in an infinite loop or produce chaotic output that strays completely from intent. Today, the programming agents and workflow systems that genuinely work reliably in production environments have turned to an extremely pragmatic engineering path: First is progressive disclosure. Instead of trying to cram documentation for hundreds of complex external interfaces into the prompt all at once, the system provides the model with only a concise index, letting it consult the specific documents on demand as needed. Second is command-line sandboxing. The system has abandoned having the model recognize fancy web UIs and simulate mouse clicks, instead giving the model a standard, isolated command-line sandbox environment directly. Large models have learned from vast quantities of real code during training; letting them write standard Python or Bash scripts to call underlying APIs directly achieves far higher determinism than graphical interaction. Finally, the introduction of a test feedback loop. Rigorous code test suites serve as the referee: every time the model finishes writing a block of logic, the system immediately runs automated tests in the sandbox; when an error occurs, the real error stack trace output from the console is fed straight back to the model, helping it fix the code line by line against the specific error. Only by keeping the large model in the solution-reasoning layer and handing the concrete physical execution back to rigorous code sandboxes do agents genuinely achieve engineering usability. And of all the problems listed over the past years, the one that still cannot be fully eliminated within the model itself remains the hallucination problem. The fundamental operating principle of large language models is autoregressive prediction: given the sequence of preceding context already generated, it picks the most reasonable next word from the vocabulary probabilities. There is no hard-coded database of objective facts about the physical world inside — only statistical regularities in how words co-occur. Since it relies on probabilistic guessing, the possibility of wrong guesses is inevitable. Trying to make a model guarantee objectivity purely through prompt optimization, or having a large model review its own output, is in essence still using probability to correct probability. This is precisely why, over the past three years, the most solid progress for large models has come in the fields of programming and automated scripting. In the context of purely textual argumentation, plausible-sounding hallucinations are hard for people to spot at a glance; but in the world of computers, code is absolutely honest. Get a single character wrong in the syntax and the compiler errors on the spot; have a flaw in the logic and the test cases immediately refuse to pass. It is precisely this black-and-white, deterministic feedback from the code execution environment that has placed the most secure physical reins on the probabilistic drift of large models. Tracing this three-year technical evolution, a clear trajectory emerges: the industry has not, as the early enthusiasts imagined, seen omnipotent universal miracles appear overnight; nor has it been trapped in place by theoretical mathematical complexity. What has consistently accumulated and persisted in production pipelines are pragmatic solutions that can mesh deeply with existing hardware ecosystems, using deterministic engineering mechanisms to continuously accommodate probabilistic fluctuations. Once you understand these physical boundaries discovered through real business trials, you can naturally distinguish, when facing endless new technical concepts, which are mere concept packaging on paper and which are genuine foundational productivity tools for your own toolbox. Primary source: