Memory System
A retention layer built for long-term recall instead of repeated cramming.
The memory system exists to keep learning from fading after the first successful session. It uses spaced repetition to decide when review is actually needed, so effort is directed toward retention rather than mechanical over-review.
Inside Akari, this system is not isolated from the rest of study. It receives material from learning and practice, tracks review history, and helps users see memory as the consolidation phase of a broader workflow.
Scheduling Logic
The review system is shaped around evidence-based timing rather than fixed repetition rules.
FSRS Scheduling
Akari uses the FSRS algorithm to estimate better review intervals and support strong long-term retention with lower workload.
Adaptive Intervals
Intervals expand or contract based on performance so the review queue reflects what the learner actually remembers.
Effort Optimization
Review time is treated as a limited resource, which means the system tries to reduce wasted repetition where possible.
Review Surface
The memory system is not only about scheduling; it also needs usable review objects and visible history.
SRS Cards
Cards provide the concrete recall unit that turns learned material into something that can be reviewed deliberately.
Review History
Past performance stays visible so users can understand how memory changes over time instead of treating review as a black box.
Context Continuity
Because the system lives inside the broader workspace, recall tasks remain connected to the notes, readings, and concepts they came from.
Consolidation In Practice
Review matters most when it extends the value of the work that came before it.
After Study
The memory system picks up where structured learning leaves off, turning recent understanding into scheduled reinforcement.
Cross-Device Continuity
Memory work can continue across devices and sessions without losing the thread of what still needs reinforcement.
Flexible Sync
Local storage, official cloud sync, and BYOS paths let review history travel without forcing a single storage model.
Retention deserves its own operating logic
When review timing, card history, and learning context stay connected, memory work becomes lighter and more trustworthy. The goal is not more review sessions, but better recall with less wasted effort.
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