deploy-time-learning

Type: kb/types/tag-readme.md · Status: current

The organizing framework of the learning-theory area: deployed systems adapt through symbolic artifacts — durable, inspectable, verifiable — filling the gap between training and in-context learning. Notes here cover the framework itself, the learning fundamentals it rests on, and the feedback signals that govern its quality. A child of learning-theory.

The framework

• deploy-time learning is the missing middle — three timescales of system adaptation; co-evolving prose and code as agile-style deploy-time learning
• the verifiability gradient — the ladder deploy-time artifacts sit on, from restructured prompts through schemas and evals to deterministic code
• readable-artifact loop is the tractable unit for continual learning — the loop that makes behaviour change cheap: readable system-definition artifacts revised in place
• treat continual learning as substrate coevolution — the system and its knowledge substrate evolve together rather than one training the other

Learning fundamentals

• learning is not only about generality — accumulation as the basic operation, with reach as its key property; capacity decomposes into generality vs a reliability/speed/cost compound
• LLM learning phases fall between human learning modes — warns against literal human-LLM learning analogies
• in-context learning presupposes context engineering — "no continual learning needed" relocates the learning to the system layer rather than eliminating it
• choosing what to learn requires both validity and learning-value gates — accumulation policy: true is necessary but not sufficient

Feedback and signal quality

• changing requirements conflate genuine change with disambiguation failure — short iterations bound interpretation-error propagation, not just change-response latency
• evaluation automation is phase-gated by comprehension — comprehension and specification must precede optimization, or automation amplifies the wrong objective
• diagnostic richness constrains outer-loop learning quality — what the learning loop can learn is bounded by what its diagnostics distinguish
• apparent success is an unreliable health signal — completion without verification teaches the wrong lesson

Related Tags

• constraining — the primary hardening mechanism inside the framework
• distillation — the extraction mechanism inside the framework
• discovery — the operation that produces the framework's highest-reach artifacts

Other tagged notes

• 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
• 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.
• Continual learning's open problem is behaviour, not knowledge - Continual learning splits into knowledge accumulation (solved by ordinary data engineering — DBs, files, vector stores, RAG) and behaviour change (the open problem). Behaviour change depends on behavioral authority, with distributed-parametric updates expensive and readable artifacts cheap but under-addressed
• 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
• 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
• 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