Learning theory

Type: kb/types/tag-readme.md

How systems learn, verify, and improve. These notes define learning mechanisms, verification gradients, and memory architecture that KB design draws on but that aren't KB-specific — they apply to any system that adapts through durable artifacts.

The area is organized around deploy-time learning as the unifying framework. 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); conjecture posits the high-explanatory-reach theories that are accumulation's most valuable items.

The kinds of notes under this tag

Every note carrying learning-theory also carries at least one of these child tags (enforced by validation — the typed routing below is trustworthy):

• deploy-time-learning — the framework itself: adaptation through durable inspectable artifacts, learning fundamentals, and feedback quality
• 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-interpretation-errors — oracle theory, error correction, and reliability; the error-theory area applies verification concepts to LLM interpretation failures

Start here

• deploy-time learning is the missing middle — the unifying framework: three timescales of system adaptation
• learning is not only about generality — accumulation with explanatory-reach as its key property; Simon's definition grounds the decomposition
• agentic systems interpret underspecified instructions — the underspecification foundation: spec-to-program projection and the constrain/relax cycle
• the verifiability gradient — the ladder deploy-time artifacts sit on
• both a narrowed and a use-shaped artifact trade generality for reliability, speed, and cost — how the two transforming mechanisms relate
• conjecture is seeing the particular as an instance of the general — the third operation, and why recognition is its hard problem

Related Tags

• tags — the hub; applies learning theory to KB architecture and evaluation
• 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

• A compact, refreshable whole-picture narrative can replace infeasible fragment reconciliation - Holistic rewrite shifts reconciliation from each consumer to the author, but only when the whole-picture narrative can fit within effective context and be refreshed before the narrative goes stale
• Abstract an experience into a lesson only when you can state where the lesson stops - Abstract an episode into a lesson only when you can state its boundary, else preserve the instance; an over-generalized lesson is one that drops the condition clause
• Activate Behavior-Changing Memory Before The Mistake - Behavior-changing memory must activate before relevant actions rather than waiting for explicit retrospective search
• Ad hoc prompts extend the system without schema changes - Any system with an LLM agent layer can absorb new requirements through natural language prompts without changing the deterministic base
• Adaptation signals choose pressure; artifact analysis chooses the retained surface - Maps agentic-adaptation signals onto artifact-analysis axes so KB learning records which retained surface changes, what authority it gains, and how to review it
• Agent context is constrained by soft degradation, not hard token limits - Agent context is bounded by silent reliability degradation across volume, complexity, and relevance/interference, not just by provider token limits
• Agent memory is a crosscutting concern, not a separable niche - Memory decomposes into storage (solved), retrieval/activation (context engineering), and learning (learning theory) — treating it as a standalone category hides that the hard problems are at the intersections
• Agent memory needs discoverable, composable, trusted knowledge under bounded context - Frames discoverable, composable, trusted remembered knowledge as the minimal artifact-quality basis for agent memory under bounded context.
• An accepted edit verifies the change, not the rule - Human acceptance of an edit is a strong oracle for 'this change was wanted here' but a weak oracle for 'this generalizes' — mining rules from accepted edits inherits instance-level verification while the generalization step stays oracle-poor
• An agentic KB maximizes contextual competence through discoverable, composable, trusted knowledge - Retired note kept as a backlink target; its general memory-quality claim and KB-specific ingress claim now live in narrower successor notes.
• An LLM's generation confidence tracks typicality, not soundness - An LLM's next-token confidence measures how typical a continuation is, not whether it's true or valid; the two are decoupled, so soundness can't be read off confidence and needs a separate check
• An outcome check licenses replay; a rule needs the process verified - Outcome and process verification are two verify-rung oracles — an outcome check licenses replaying an instance, not abstracting a rule, since only a process check inspects the generalizing mechanism
• Apparent success is an unreliable health signal in framework-owned tool loops - When framework-owned tool loops recover from broken tools via agent workarounds, final success stops being a reliable signal that the underlying scripts and workflows are healthy
• Automated synthesis is missing good oracles - Generating synthesis candidates (cross-note connections, novel combinations) is easy — LLMs do it readily. The hard part is evaluating whether a candidate is genuine insight or noise.
• Axes of artifact analysis - Artifact analysis records retained behavior-shaping artifacts by storage substrate, representational form, lineage, and behavioral authority so review evidence, invalidation, and rollback follow how artifacts actually act
• Behavioral authority - Definition - behavioral authority records who consumes a retained artifact, through which channel, and with what force
• Bottom-up structure inference needs capture at the decision surface, not the state - Bottom-up inference of entities and relations from traces needs decision-shaped capture at the decision surface: the 'why' is cheap to record there and hard-to-impossible to recover from state later
• Brainstorming: how explanatory-reach informs KB design - Deutsch's reach, registered here as explanatory-reach, applied to KB notes — a maintenance risk signal, not a retrieval signal, because high-explanatory-reach revisions break downstream reasoning silently
• Brainstorming: maintainability oracles for agentic development - Explores candidate signals, calibration experiments, authority levels, and workflow placements for evaluating maintainability in agent-generated code
• Changing requirements conflate genuine change with disambiguation failure - Agile's 'changing requirements' hide two distinct phenomena — genuine change (world moved) and late discovery that downstream specs committed to a wrong interpretation of an underspecified upstream spec — short iterations limit interpretation-error propagation, not just change-response latency
• Choosing what to learn requires both validity and learning-value gates - Separates two promotion checks for learning loops: whether a candidate is trustworthy enough to learn from, and whether learning it would improve the current system.
• Codification - Definition — codification is constraining that crosses from natural language into a symbolic artifact with formal semantics or assigned consequences; executable code is the main practical KB case
• Codification and relaxing navigate the bitter lesson boundary - Since you can't identify which side of the bitter lesson boundary you're on until scale tests it, practical systems must codify and relax — with spec mining avoiding the vision-feature failure mode
• Codify-versus-LLM decision heuristics - Four lenses on the codify-vs-LLM decision — spec completeness, oracle strength, interpretation space, pattern stability — collected from across the KB, with evidence they come apart at the edges
• Constraining during deployment is continuous learning - Continuous learning can happen outside of weights; constraining is one symbolic-artifact form where prompts, schemas, tools, and tests accumulate durable adaptive capacity during deployment
• Continual learning's open problem is behaviour, not knowledge - Continual learning's hard part is behaviour change; knowledge accumulation fits ordinary stores, while durable behaviour change comes through weights or readable artifacts
• Designing a Memory System for LLM-Based Agents - Derives agent-memory design pressures and links to a requirements inventory for agents designing or evaluating memory systems
• Diagnostic richness constrains outer-loop learning quality - Outer-loop learning depends on inspectable failure evidence, not only on the oracle used to select winning candidates
• Discovery lifecycle - Definition — the discovery lifecycle is a proposed six-phase evaluation of an ampliative conjecture; the compound is technical, bare 'discovery' stays ordinary English
• Enforcement without structured recovery is incomplete - The enforcement gradient covers detection and blocking but has no recovery column — recovery strategies (corrective → fallback → escalation) are the missing layer, and oracle strength determines which are viable at each level
• Ephemeral computation prevents accumulation - Ephemeral computation — discarding generated artifacts after use — trades accumulation for simplicity, making it the inverse of codification
• Ephemerality is safe where embedded operational knowledge has low explanatory-reach - Kirsch's barriers all mark cases where software carries decisions that must survive into future runs, users, and audits; ephemerality is safe only when that knowledge stays local
• Epiplexity by example: what entropy and complexity miss - ELI5 explanation of epiplexity through encrypted messages, shuffled textbooks, CSPRNGs, and chess notation — contrasting surprise, shortest description, and observer-relative usable structure
• Error messages that teach are a constraining technique - In agent systems the error channel is an instruction channel — making errors teach the fix is nearly free and eliminates the agent's need to diagnose, an orthogonal axis to enforcement strength
• Evaluate Memory By Effects, Not By Existence - Memory should be evaluated by downstream effects on tasks, artifacts, answers, behavior, context efficiency, and lineage alignment
• Evaluation automation is phase-gated by comprehension - Optimization loops require manual error analysis and judge calibration before automation can improve behavior rather than just score
• Feedback-trained memory management is oracle-dependent even when its operations are hand-designed - Fixed and merely runtime-responsive memory rules need no training oracle; outcome-driven updates do, while noisy rankings weaken learning and misaligned ones teach the wrong ordering
• Fixed artifacts split into exact specs and proxy theories - Fixed artifacts are safe when their spec fully captures the problem; they are risky when they encode proxy theories whose components may not compose into the larger capability
• Flat memory predicts specific cross-contamination failures that are empirically testable - Flat memory predicts three cross-contamination failures — search pollution, identity scatter, insight trapping — testable via an observation protocol against real agent systems
• In-context learning presupposes context engineering - In-context learning only works when the right knowledge reaches the context window — the selection machinery that ensures this is itself learned and refined over deployment
• Information value is observer-relative - Information value is observer-relative: prior knowledge, tools, compute, and goals determine extractable structure, grounding use-shaped reshaping and discovery.
• Inspectable artifact, not supervision, defeats the blackbox problem - Chollet frames agentic coding as ML producing blackbox codebases — codification counters this not by requiring human review but by choosing readable artifacts (code, prompts, schemas) that any agent can inspect, diff, test, and verify
• Knowledge artifact - Definition - a knowledge artifact is a retained artifact consumed as evidence, reference, context, explanation, or advice
• Known-target discovery benchmarks show reachability, not discovery closure - Distinguishes backcast and reinvention benchmarks from autonomous discovery: they show that target insights are reachable from supplied ingredients, not that a system can select and verify new discoveries prospectively.
• Legal drafting solves the same problem as context engineering - Legal drafting parallels context engineering because both write ambiguous natural-language specifications for judgment-based interpreters, but law develops constraining more than codification
• Lineage - Definition - lineage records the source dependencies needed to invalidate, regenerate, retire, or review retained behavior-shaping artifacts
• Links encode conditional possibilities, not obligations - Links encode conditional possibilities, not obligations — every label must name a specific reader-need (the condition under which following pays off); content required for all reachable readers should be inlined, not linked
• LLM debugging starts with retry-versus-rewrite triage - The two-phenomena model makes the first LLM debugging question diagnostic — is the failure a bad execution of a good interpretation (retry) or a consistent choice of a bad interpretation (rewrite the spec)? — because the fixes differ and do not substitute
• LLM generation relaxes a goal it can't satisfy and hides the constraint a human writer stalls on - A human writer stalls at the constraint they can't satisfy; an LLM instead ships fluent output that looks solved but silently drops it — hiding the error, so the check falls on the reader
• LLM learning phases fall between human learning modes rather than mapping onto them - Pre-training acquires both structural priors (evolution's role in humans) and world knowledge in one pass — making it and in-context learning intermediate on the evolution-to-reaction spectrum
• LLM↔code boundaries are natural checkpoints - At each LLM↔code transition both semantic underspecification and execution indeterminism collapse simultaneously, making these boundaries natural places to anchor debugging, testing, and refactoring
• Memory design adds operational axes to artifact analysis - Memory design needs operational policy axes (capture, derivation, activation, authority assignment, lifecycle, evaluation) on top of substrate, form, lineage, and behavioral authority
• Methodology enforcement is constraining - Instructions, skills, hooks, and scripts form a constraining gradient for methodology — from underspecified and indeterministic (LLM interprets and may not follow) to fully deterministic (code always runs), with hooks occupying a middle ground of deterministic triggers with indeterministic responses
• Minimum viable vocabulary is the naming set that most reduces extraction cost for a bounded observer - Reframes "minimum viable ontology" as an optimization problem — the vocabulary that, once acquired, maximally reduces a bounded observer's extraction cost for a domain; grounds the pedagogical intuition of "conceptual thresholds" in the KB's information-theoretic framework
• Only explicit retention is currently durable, writable, and addressable at once - Every tacit retention form — in-context conditioning, weights under selection or fine-tuning, human expertise — fails addressability, so governed retention currently runs through explicit artifacts
• Opacity is a scale threshold, not a class property - Opacity is not a representational form; any representation becomes practically opaque at sufficient scale, though distributed-parametric artifacts cross that threshold earliest.
• Operational signals that a component is a relaxing candidate - Operational signals for when a component likely encodes a brittle proxy theory rather than an exact specification and should be relaxed instead of codified harder
• Operative part - Definition - an operative part is the behavior-affecting content, structure, parameterization, or mechanism within a retained artifact or consumption path
• Orchestration strategies and run-state have opposite persistence economics - Inside a host-language scheduler, run-state K is task-specific so it has near-zero cross-task reuse value and should stay ephemeral, while select-strategies recur and are expensive to rediscover so they are the high-value promotion target — RLM discards both, losing the valuable half
• Progressive constraining commits only after patterns stabilize - Constraining via LLM code generation freezes a single projection of the spec in one shot, but progressive constraining observes behavior across many runs and commits only the interpretations that consistently emerge
• Prose has no reliable dereference, so a declared fact must be reinforced where it applies - Code resolves a name to its value everywhere; LLM-read prose has no such dereference, so a fact stated once may not govern where it applies — restate it at the point of use, kept honest by a check
• Psychology-to-agent transfer needs per-principle failure-mode testing - Brainstorming a methodology for evaluating cognitive-science-to-agent transfer — assembled from three existing KB notes and tested against Youssef's five psychology principles as worked examples
• Raw accumulation does not create usable memory - Accumulation preserves material, but usable agent memory requires ingress work that adds handles, scope, relationships, provenance, trust signals, and lifecycle pressure.
• Representational form - Definition - representational form classifies how an operative part is encoded and consumed: prose, symbolic, distributed-parametric, or mixed
• Retained artifact - Definition - a retained artifact is retained state that a later agentic loop can consume in a behavior-shaping way, regardless of storage substrate
• Retaining the episode keeps a distilled rule re-derivable - The episode a lesson was learned in and the rule distilled from it are complementary retention layers: with the episode retained and lineage recorded the rule stays evidence-backed and re-derivable; without it the rule hardens into a bare commitment
• Reverse compression is when LLM output expands without adding information - LLMs can inflate compact seeds into verbose artifacts without adding extractable structure; a KB resists this only when links add epiplexity
• RLM, Tendril, and llm-do place symbolic work at different persistence boundaries - Compares RLM variants, Tendril, and llm-do as placements for symbolic work and interfaces: ephemeral REPL code, typed RLM combinators, workspace-local generated tools, and durable unified callables
• Selective revision needs a faithful rationale, not just a legible one - Revision reaches the premise that broke only through the theory's recorded rationale, so a readable but unfaithful rationale makes repair confidently wrong rather than merely uninformed
• Short composable notes maximize combinatorial discovery - The library's purpose is to produce notes that can be co-loaded for combinatorial discovery — short atomic notes are a consequence of this goal; longer synthesized artifacts belong in workshops or derived instructions
• Silent disambiguation is the semantic analogue of tool fallback - When an agent silently resolves unacknowledged material ambiguity in a spec, final success hides that the contract failed to determine the path — an extension of the tool-fallback observability problem
• Soft-bound traditions as sources for context engineering strategies - Survey of twelve soft-bound traditions as candidate sources for context engineering strategies, with a three-tier assessment of what transfers, what's plausible, and what's blocked
• Spec mining is codification's operational mechanism - Operationalizes codification by extracting deterministic verifiers from observed stochastic behavior — the mechanism that converts blurry-zone components into calculators
• Specification strategy should follow where understanding lives - Among durable artifacts, spec-first, bidirectional spec, and spec mining fit different phases: when understanding is available upfront, discovered during execution, or only visible after observation
• Storage substrate - Definition - storage substrate records where retained state persists, as an operational field distinct from form, lineage, and authority
• Storing LLM outputs is constraining - Keeping a specific LLM output resolves semantic underspecification and freezes one run against execution indeterminism - a constraining move applied to artifacts
• System-definition artifact - Definition - a system-definition artifact is a retained artifact consumed with instruction, enforcement, routing, validation, configuration, evaluation, or learning force
• System-definition artifacts are crystallized reasoning under context scarcity - Heuristic system-definition artifacts (tips, playbooks, rules) are mostly crystallized reasoning; under unbounded context heuristic prose collapses into knowledge artifacts plus read-time derivation, while authority-bearing constraints and symbolic codification persist for other reasons
• Systematic prompt variation serves verification and diagnosis, not explanatory-reach testing - Controlled prompt variation either decorrelates checks or measures brittleness under fixed task semantics; Deutsch's variation test instead changes the explanation to test mechanism and explanatory-reach
• The adaptation survey corroborates memory requirements but misses artifact governance - The agentic-adaptation survey supports the memory requirements map by treating memory and skills as adaptive tools, but it needs substrate, form, lineage, and authority governance to become design guidance
• The bitter lesson selects against unearned reach, not against structure - Scale replaces generalizations whose claimed scope was asserted from source-case fit rather than tested; structure whose reach was earned is what a scalable search converges on
• The four-field record exposes an efficiency, security, and sovereignty risk triad - The four artifact-analysis fields exist to surface three architectural review concerns over retained behavior — efficiency, security, and sovereignty — with sovereignty (owner control to inspect, regenerate, delete, roll back) as the new axis
• The readable-artifact loop is the tractable unit for continual learning - Within substrate coevolution, the readable pair (prose + symbolic) is the tractable unit to build a first automated loop around — shared context, current tempo, and an existing codification boundary make joint optimization clean; the pair is also under-explored relative to distributed-parametric optimization
• Theory-mediated learning may improve sample efficiency under structured shifts - Conjecture: learning that discovers, assesses, and revises addressable theories may need fewer target observations when a shift preserves the structure a theory names
• Three-space agent memory echoes Tulving's taxonomy but the analogy may be decorative - The value of separating knowledge, self, and operational memory is that each has a different lifecycle — accumulation, slow evolution, and high churn; whether the Tulving mapping adds explanatory power beyond different retention policies is open
• Trace-extracted memory earns authority per operation, not at capture - Trace-extracted memory arrives as a record, not knowledge — authority is earned through post-capture operations (verify, abstract, consult) with increasingly hard oracles; stores that stall before verification accumulate guesses masquerading as knowledge
• Treat continual learning as substrate coevolution - Behaviour change spans distributed-parametric, prose, and symbolic forms, so the question is how their improvement loops relate — not which is the real locus of learning
• Underspecification and indeterminism complicate programming for prompts in distinct ways - Indeterminism doubles test runs (statistical testing over distributions); underspecification doubles test targets (spec analysis for ambiguity). Conflating the two leads to misdiagnosis
• Unified calling conventions enable bidirectional refactoring between neural and symbolic - When agents and tools share a calling convention, components can move between neural and symbolic without changing call sites — llm-do demonstrates this with name-based dispatch over a hybrid VM
• Use Trace Extraction As Meta-Learning - Trace extraction is an after-the-fact learning path that must respect signal quality, review, and readable-artifact versus distributed-parametric learning boundaries
• Verification needs a typed target before it needs an oracle - A check's warrant depends on a declared target class, so an unverifiable heterogeneous layer is usually blocked by missing artifact classification, not oracle difficulty — ontology precedes oracle
• Weakly discriminated qualities tend to be underselected - Conjecture separating available model capability from selection: qualities weakly distinguished by the actual acceptance oracle lose to strongly verified objectives