Learning theory

Type: types/tag-readme.md

This tag gathers theory of how systems learn, verify, and improve: accumulation and generalization, continual learning after deployment, verification and error correction, memory architecture, and self-improvement. The anchor is the theory builder: a system that states its theories in localized units, acts on what they say, criticizes them, and lets the result shape the next round, with whether it learns left as the hypothesis under test. Its members span notes and external system analyses, and apply to any system that adapts through retained artifacts, not only to KBs. Deploy-time learning covers what deployment reveals beyond design and testing and how people or systems respond. Nearby but different: self-improving-systems holds systems that make operative changes to their own organization, while this tag covers learning in general, whether or not the learner revises itself.

Work also qualifies when it substantively meets the inclusion condition of a declared child: deploy-time-learning, constraining, discovery, artifact-analysis, agent-memory, llm-reliability, or self-improving-systems. Their definitions, mechanisms, and boundary analyses belong here without a separate claim of learning or retained improvement. This is inclusion in a subject area, not evidence that the system under discussion learns.

Accumulation — adding knowledge to the store — is the most basic learning operation, with explanatory-reach as its key property: facts sit at the low end, theories at the high end. Accumulated knowledge is transformed by constraining and by working use-shaped artifacts out from it (theory and methodology form a two-layer execution system); the conjecture phase of the discovery lifecycle posits the high-explanatory-reach theories that are accumulation's most valuable items, and recognition is the expensive step in getting there.

Major child areas

These child tags route major parts of the area. A few fundamentals carry only the parent tag: Learning is not only about generality (Simon's definition of learning), LLM learning phases fall between human learning modes, and in-context learning presupposes context engineering. Outside the notes collection, trace-learning techniques in related systems compares the external systems that learn from their own traces.

  • deploy-time-learning — what use reveals beyond design and testing, and the maintenance and learning responses
  • constraining — narrowing the interpretation space, from conventions to deterministic code; codification, relaxing, and the decision heuristics
  • discovery — positing a general concept and recognizing particulars as its instances; explanatory-reach as what it produces
  • artifact-analysis — the four-field vocabulary (substrate, form, lineage, authority) for retained behavior-shaping artifacts
  • agent-memory — memory architecture: spaces, contamination, policy learnability, and the crosscutting decomposition
  • llm-reliability — oracle theory, error correction, and the deviation taxonomy; the area applies verification concepts to LLM output deviations
  • self-improving-systems — systems that make operative, evidence-responsive changes to their own organization, from gradient learners to theory builders; its children cover the theory builder, the improvement loop, reflection, warranted autonomy, and continual learning

Start here

  • evaluation — where learning claims meet oracles, warrant, and experiment design
  • document-system — the type ladder (text→note→structured-claim) instantiates the constraining gradient for documents
  • context-engineering — where in-context learning meets the system layer that selects and organizes knowledge

Other tagged notes