Karpathy's LLM-Wiki and Research in the Age of Agents

I was preparing for tomorrow's graduate lecture, organizing materials on how students should read and organize papers, and by extension, how to use RAG systems. I had been building a simple RAG pipeline myself, exploring different ways to collect literature and structure it for retrieval. Then, over the weekend, Karpathy shared an idea called LLM-Wiki. Once again, he set the weekend on fire.

Just a few months ago, building RAG systems with Obsidian was getting considerable attention. Embedding personal collections of papers, notes, and code and connecting them to an LLM for question-answering is clearly practical. A related study even appeared in Nature, lending it the air of an established methodology. When you build one yourself, it is genuinely convenient. It quickly retrieves relevant information and generates answers based on your accumulated materials.

The Limits of RAG

But once you actually build and use one, it does not feel particularly useful. The retrieval works well, but thinking does not flow the way you expect. RAG is a "system that retrieves related materials well." What researchers need is closer to understanding relationships between materials, building context, and organizing importance by their own criteria. Some RAG practitioners say this is achievable. But what distinguishes researchers is that the structuring and connecting of knowledge and questions is deeply personalized.

For researchers, this difference is larger than it might seem. A researcher who has completed a PhD carries a knowledge structure built over many years. It includes which papers matter, what results are meaningful in what context, and what is not particularly important. When you bundle new materials through RAG, the result often exists as a separate layer rather than naturally connecting to this existing structure. Sometimes it conflicts, or information you do not consider important keeps surfacing. The problem lies in how knowledge is structured.

LLM-Wiki: A Different Approach

LLM-Wiki delegates the reading, organizing, and connecting of materials to the LLM. It breaks papers and notes into concept-level documents, links them together, and gradually builds a wiki. When you ask a question, you generate an answer atop that already-formed structure. The process of asking and answering is guided by human flow. I spent the weekend trying it, and the reason it felt far more natural than conventional RAG is probably this.

Building Knowledge Rather Than Finding It

This connects directly to university and graduate education. Graduate training is about learning to generate your own questions, explore literature around those questions, and connect disparate results into a coherent framework. It is deeply personal work shaped by each individual's questions and experiences, with no right answers. From this perspective, approaches like LLM-Wiki are quite interesting. If the process of students repeatedly reading and organizing papers accumulates as increasingly structured knowledge, they gain an environment where they can externally observe and refine their own thinking. Perhaps this is where we will find answers.

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Karpathy's Talk on Orchestration

Karpathy's talk went up on YouTube. Many readers of this post are probably spending sleepless nights with his autoresearch. I am too. With tools like Agentic Kit and Claw emerging, these are truly exhilarating days. Here are some thoughts on his talk.

The first thing that struck me while listening was that the layer at which humans participate is what is changing. Karpathy says the word "coding" no longer applies to what he does. Instead, he spends 16 hours a day expressing his will to agents. This expression was intriguing. It may sound exaggerated at first, but on reflection it is precise. What matters now is the ability to define objectives, set constraints, embed context, run multiple agents in parallel, evaluate their outputs, and readjust direction.

His use of "manifesting" is closer to structuring intent so that a desired outcome can emerge in reality, and designing the loop that makes it happen. He makes things get built rather than building them. He showed very clearly the scene of the implementer becoming supervisor, designer, editor, orchestrator.

Already Reality in Software

This shift already looks quite real in software. Karpathy talks about delegating to multiple agents by feature, research, and planning units instead of typing code line by line. One agent writes code, another does research, yet another builds implementation plans. The human moves between them, setting direction, checking quality, ensuring nothing conflicts. He even aims to remove himself from the loop, setting things up once and letting them run autonomously for extended periods. It looks like the basic interface of research and production is changing.

What About Biology Research?

A problem I have long felt in biology research is that not all topics fit inside such a loop. Topics that cannot enter the loop are difficult to scale. They lack throughput. If the loop of hypothesis, experiment, measurement, judgment, and next hypothesis does not close cleanly, the research remains bound by human intuition, tacit knowledge, waiting, and interpretive ambiguity. Biology is particularly prone to this. Measurements are slow and expensive, judgment functions are fuzzy, sample preparation carries heavy tacit knowledge, and failures often do not become structured data. This is why the "agent loop" that works in software does not close easily in biology.

I think this difference will become even more important. Research that scales going forward will likely involve important topics that can be loopified. The ability to translate an idea into a repeatable experiment-judgment loop may matter more than the quality of the idea itself.

Confirmation Bias in the Age of Agents

There is one more thing to consider in this context. Even with high-throughput technology, researchers have always adjusted analysis conditions until the desired result appeared instead of letting the data revise the hypothesis. But in the age of agents, this problem becomes much larger. With agents, confirmation bias can become the objective function. Give the system a target outcome, and it can endlessly explore by changing cohorts, covariates, batch corrections, subgroups, and pathway-level detours. Confirmation bias gets automated and parallelized.

The biggest takeaway from this talk, for me, was the discipline of handling evidence. The age of agents demands the ability to lock down what should and should not be run before anything starts. Define questions in advance, set success and falsification criteria, separate discovery analyses from confirmatory analyses, keep a record of where every exploration path went, and close conclusions with independent data, perturbations, or evidence from different layers.

Research That Enters the Loop, and Research That Stays Outside

I believe biology can shift here, toward a continuously running experiment-judgment loop as the basic unit of research. Stronger labs will be those that design the hypothesis-experiment-measurement-judgment loop faster and more rigorously.

And this change will not arrive uniformly across all of biology. Fields with standardizable perturbations, measurable phenotypes, automatable assays, and designable surrogate metrics will absorb this change first. Fields where judgment is ambiguous and physical preparation carries heavy tacit knowledge will move much more slowly.

What I felt while listening to Karpathy's talk was my questions becoming sharper. People keep asking whether AI will replace humans. The question I now carry is which tasks enter the loop, and which tasks remain outside it. In biology, many of the truly important problems sit right on that boundary.

The researcher of the future will need to do two things simultaneously. One is to hold on to the truly important questions in their field. The other is to translate those questions into loops that machines can run. The former remains squarely in the human domain. The latter will increasingly become the decisive competitive advantage. Strong research of the future will come from the ability to transform good ideas into emergent loops.