Critique, analyze, agree, disagree, correct, true or false, improve and the like... What does the latest frontier architecture looks like for LLM, it used to be the model, then RAG attempted to supplement the deficiencies, then vector DB's were introduced, then tools were also introduced, then the system to bring it all together? If we want to build a frontier LLM model what is flowchart, and what is in the flowchart of a query? Need latest info and trade secrets July 2026. Be specific as if I want to build, models, softwares, off the shelf solutions. The report is insightful and practical, allowing us to utilize off the shelf software, be up-to-date and build with a plan. We want you to analyze the advice on the report and base it on producing the most intelligent resulting LLM stack possible. We do not want to use any API or non-local model, instead we'll use open source or open model or hugging face models all locally, no API calls, remove the API calls and make the stack with local models. Can you rewrite the report to be more like step 1 one, step 2 so at the end their it is a build system and can you be simpler and descriptive about what each component does, start with the hardware required and the OS requirements? A website chat front end to communicate with the system is also essential.

In a subsequent report, is there a different architecture for agents, swarms. The jobs are broken up to separate agents and if I want to generate reports like z.ai agents.

Finally, an agent that sets up latent space searches to determine answers for instance to explain latent space search, an LLM playing chess might test different moves in a latent space.

With the LLM stacks being used today, we still see hugely limited intelligence. What is the main reason for the problem of limited intelligence in current systems?

Let me check whether anything material has shifted on this in the last few months before I give you a firm answer.Not scale, not architecture, and not even data volume, though that's the nearest of the conventional answers. The actual bottleneck sits one level upstream: current systems have almost no mechanism for improving from outcomes, only for imitating recorded outcomes — and whatever they do pick up in the moment doesn't persist past that moment.

Nearly everything an LLM can do traces back to one of two training signals. The first is imitation of a fixed corpus: the model learns to predict what a human would plausibly write next. That has a hard ceiling — it can't exceed the distribution of what's already been written down, and that supply is close to tapped out. One estimate puts the usable stock of human-generated public text as effectively exhausted by around 2028 at current growth rates, and throwing more compute at it buys less each round: the underlying math means every further jump in compute produces a smaller gain than the jump before it. That's roughly why raw pretraining scale stopped being the whole story around 2024–2025.

The second signal is reinforcement against a reward, and it doesn't share that ceiling — a model can be scored and pushed past anything in its training data, the way self-play took Go engines past every human game they'd ever seen. The version of this that actually worked for LLMs replaced a human rating of which answer sounds better with a mechanical check of whether the answer is actually correct, a shift DeepSeek-R1 popularized and that's since become close to standard across reasoning models. That's why math and code specifically have jumped so hard these last two years: both have a cheap, automatic way to check correctness — run the proof checker, run the tests.

Almost nothing else does. Everyday judgment, strategy, open-ended writing — most of what general intelligence would actually need to cover — depends on graded, contextual calls rather than a clean pass/fail, so there's no equivalent bootstrap available. The stand-ins being tried — a second model as judge, rubric-scored rewards, a model's own confidence used as its own signal — are live 2026 research, not finished technology, and the judge-model approach specifically carries its own documented distortions, favoring certain answer positions and shifting its verdict depending on how a prompt happens to be worded. That's a fairly precise account of why models hedge, flatter, and get confidently wrong exactly on the questions you can't check against an answer key — it's the shape of the training signal, not a personality quirk.

Stack on top of that the fact that nothing learned inside a session updates the model at all. Once weights are frozen at deployment, there's no way for an agent to fold what it just experienced back into itself — correct it ten times in one conversation and the underlying model hasn't improved; it's carrying ten corrections in context that vanish along with the context. The 2026 work trying to fix this — in-place test-time training and related mechanisms — is careful to describe itself as early progress rather than an arrived solution, and catastrophic forgetting remains the unresolved core obstacle despite years of attention. The model you talk to in December knows exactly as little about your specific problem as it did in January.

The serious rival explanation is Yann LeCun's, and it deserves a real hearing rather than a dismissal. He argues that none of the above is really the point, because next-token prediction over text — however you train it — can't build a genuine model of physical or causal structure, since language is a low-dimensional, compressed trace of a reality the model never actually observes. That conviction is serious enough that he left his role leading AI research at Meta at the end of 2025 specifically to build world-model architectures instead. I'd treat it as compounding the problem above rather than replacing it — and hold it a little loosely, since his earlier claim that no text-only model could infer that pushing a table drags the book sitting on it has already been falsified by later models.

For a fully local, open-weight stack, the practical read is that the checkpoint-size axis is close to flat. The leverage is in whatever you bolt on that supplies a real verifier loop for the domains you actually care about, plus something that persists across sessions instead of resetting to the frozen prior every time.