Two Ideas for Humans Learning from LLMs (p10q.com)

This piece argues that two core ideas from large language models—transformer attention and agentic planning—offer practical patterns for improving human thinking. The transformer’s Query–Key–Value attention is reframed as a three‑pass cognitive routine: Pass 1 (Locate) finds what’s important, Pass 2 (Identify) clarifies who you are and what you need, and Pass 3 (Extract) organizes and structures the content (analogy: librarian + book + contents). Framing reflection or problem solving as these discrete passes helps overcome cognitive fixation and limited working memory the way transformers handle limited context windows: “50% of the solution is knowing the problem.” The second idea, agentic planning, externalizes memory and uses planning + reflection loops to boost performance and accuracy. Like chaining and separate evaluator models in LLM pipelines, external plans, checkpoints, and independent review reduce fixation, mitigate distraction, and raise reliability—useful for therapy, self‑reflection, study techniques, and supporting people with modest memory or ASD fixation patterns. Technically, this suggests cognitive tools that implement QKV‑style staged attention, externalized plan stores, and independent evaluators could meaningfully augment human reasoning, productivity, and clinical interventions.