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Didactopus Run/Session Correlation + Learning Animation Layer

This update extends the agent audit / key rotation scaffold with:

run/session correlation for learner episodes
workflow logs tied to learner runs
animation data endpoints for replaying learning progress
a UI prototype that can animate a learner's mastery changes over time

Why this matters

A single audit event is useful, but it does not tell the full story of a learning episode.

For both human learners and AI learners, Didactopus should be able to represent:

when a learning run began
what sequence of actions happened
how mastery estimates changed during the run
how recommendations shifted as competence improved

That makes it possible to:

inspect learner trajectories
debug agentic learning behavior
demonstrate the learning process to users, reviewers, or researchers
create visualizations and animations of learning over time

Added in this scaffold

learner run/session records
workflow event log records
animation frame generation from learner history
API endpoints for run creation, workflow-event logging, and animation playback data
UI prototype for replaying learning progression as an animation

Animation concept

This scaffold uses a simple time-series animation model:

each frame corresponds to a learner-history event
each concept's mastery score is shown per frame
the UI can replay those frames with a timer

Later implementations could support:

graph/network animation
concept unlock transitions
recommendation timeline overlays
side-by-side human vs AI learner comparison