path: root/docs/design/fsrs-spec.org

#+TITLE: FSRS scheduler for org-drill — v0 design spec
#+AUTHOR: Craig Jennings
#+DATE: 2026-05-27

* Status

Draft v0 — pending decisions marked =DECIDE:= inline.  The companion todo
entry is =todo.org=:125 [#B] "FSRS (Free Spaced Repetition Scheduler)
Algorithm".  No implementation has started.

* Context and motivation

org-drill ships three SuperMemo-family schedulers — SM2, SM5, and Simple8.
They share an interval/repeats/EF/failures/meanq/total-repeats state model
inherited from the SuperMemo 2 paper and refined since.  The card-state
struct refactor (#147) bundles those fields into =org-drill-card-state= and
makes plugging a new scheduler into the =cl-case= dispatch a one-arm change
in =org-drill-smart-reschedule= and =org-drill-hypothetical-next-review-date=.

FSRS (Free Spaced Repetition Scheduler) is the algorithm Anki has migrated
to since 2024.  It uses the DSR memory model (Difficulty, Stability,
Retrievability) and a 17-to-21-parameter formula family fit on hundreds of
millions of real reviews.  Published evaluations show materially better
retention than SM* at the same review load.  Adding it as a fourth option
to =org-drill-spaced-repetition-algorithm= lets a user opt in without
disturbing existing SM-family decks.

* Goals

- A fourth value =fsrs= for =org-drill-spaced-repetition-algorithm=.
- The =fsrs= arm of the =cl-case= in =smart-reschedule= and
  =hypothetical-next-review-date= calls =org-drill-determine-next-interval-fsrs=
  with the same shape the SM-family arms now use (a state value + the
  rating quality + algorithm-specific extras).
- Per-card FSRS state persisted to org properties.  Existing SM-style
  properties stay untouched on cards that haven't been switched.
- Test coverage at parity with the SM* test files: Normal / Boundary /
  Error per public function, plus reference-vector tests checking the
  algorithm output against the canonical FSRS implementation.

* Non-goals (v1)

- *No parameter optimizer.*  Anki's FSRS shines because of parameter
  fitting on each user's review history.  v1 ships fixed default
  parameters only; the optimizer becomes its own todo and ticket.  Once
  the algorithm is in the tree, plugging an optimizer in front of it is
  additive.
- *No retro-migration heuristic.*  A card switched from SM* to FSRS
  cold-starts on its next review — FSRS treats it as a fresh first review.
  Anki has heuristics that estimate initial =D= and =S= from prior review
  history; we explicitly defer that as a follow-on.
- *No bulk rescheduling pass.*  Switching algorithms does not retroactively
  rewrite SCHEDULED dates on existing cards.  Cards keep their current
  SCHEDULED until their next review.

* Version pin

=DECIDE:= FSRS-4.5 (recommended) vs FSRS-6.x.

The current stable upstream is FSRS-6.3.1 (March 2026) with 21
parameters.  FSRS-4.5 is the last release whose generic defaults provide
meaningful retention improvement over SM-family scheduling without
optimization.  The FSRS community's own guidance — quoting Expertium's
algorithm explainer, March 2026 — is that "generic v6 defaults offer
minimal improvement over v4.5" because v6's gains come from optimizing
=w[17]..w[20]= on a user's data.

Since v1 ships without an optimizer (above), the pragmatic pin is
*FSRS-4.5*: 17 parameters, well-documented defaults, the most-published
test vectors, and the version with the largest delta over SM* in the
no-optimizer regime.  Upgrading to v6 becomes a separate ticket alongside
the optimizer one — by then there'd be a real reason to take v6's
additional surface.

Reference parameters (FSRS-4.5 defaults, source: =py-fsrs= up to v4 and
the original =fsrs4anki= release notes):

#+begin_src elisp
;; w[0..16], FSRS-4.5
(0.4 0.6 2.4 5.8 4.93 0.94 0.86 0.01 1.49 0.14 0.94 2.18 0.05 0.34 1.26 0.29 2.61)
#+end_src

(=DECIDE:= confirm this exact tuple before code lands.  The 4.5 defaults
were tuned twice during 4.x's life; pinning the final 4.5-stable tuple is
a one-line lookup against the =py-fsrs= 4.x release tag, deferred to
implementation.)

* Algorithm shape (v4.5)

The four-rating scale FSRS uses internally:

| Rating | Name  | Meaning                |
|--------+-------+------------------------|
|      1 | Again | failed recall          |
|      2 | Hard  | recalled with struggle |
|      3 | Good  | recalled normally      |
|      4 | Easy  | trivial recall         |

org-drill uses a 0–5 quality scale.  The mapping respects
=org-drill-failure-quality= (default 2):

| org-drill quality | FSRS rating |
|-------------------+-------------|
| 0, 1, 2           | Again (1)   |
| 3                 | Hard (2)    |
| 4                 | Good (3)    |
| 5                 | Easy (4)    |

=DECIDE:= mapping table.  Above is the natural reading of
=org-drill-failure-quality=, but org-drill historically lets users
remap session keys, and the mapping should respect that or be its
own defcustom.  Simplest: pin the mapping above for v1 and add
=org-drill-fsrs-quality-mapping= as a future enhancement.

** State model

Per-card persisted state:

| Property              | Type   | Meaning                              |
|-----------------------+--------+--------------------------------------|
| =DRILL_FSRS_STABILITY=  | float  | =S=, the memory-stability estimate     |
| =DRILL_FSRS_DIFFICULTY= | float  | =D=, the difficulty estimate (1..10)   |
| =DRILL_FSRS_REVIEWS=    | int    | review count under FSRS (≥ 0)        |
| =DRILL_FSRS_LAPSES=     | int    | failures (rating=Again) under FSRS   |
| =DRILL_LAST_REVIEWED=   | time   | reused from the existing SM property |

The last-reviewed timestamp is already written by the SM path
(=org-drill-store-item-data= writes it via =org-set-property= elsewhere in
the flow); FSRS reads the same property.

A virgin FSRS card has all four =DRILL_FSRS_*= properties absent; the
scheduler treats absence as "first review."

** Update equations (v4.5 form)

First review (no prior FSRS state) with rating R ∈ {1..4}:

#+begin_src elisp
S0 = w[R-1]                            ; initial stability per rating
D0 = clamp (- w[4] (* w[5] (- R 2))) 1 10
#+end_src

Subsequent review at time =elapsed-days= since last review, with current
stability =S=, difficulty =D=, and rating =R=:

#+begin_src elisp
;; Retrievability when the card is shown
R-retrieval = (expt (1+ (/ elapsed-days (* 9 S))) -1)

;; Difficulty update (linear with mean reversion toward w[4])
D-delta    = (* w[6] (- (- R 3)))      ; positive on Again, neutral on Good
D-new-raw  = (+ D D-delta)
D-new      = (+ (* w[7] w[4]) (* (- 1 w[7]) D-new-raw))   ; mean reversion
D-new      = (clamp D-new 1 10)

;; Stability update — success (R ≥ 2)
S-new = S * (1 +
              (exp w[8]) *
              ((11 - D) / 9) *
              (S ^ (-w[9])) *
              ((exp ((1 - R-retrieval) * w[10])) - 1) *
              (R == Hard ? w[15] : 1) *
              (R == Easy ? w[16] : 1))

;; Stability update — lapse (R = 1, Again)
S-new = w[11] *
        D^(-w[12]) *
        ((S + 1)^w[13] - 1) *
        exp((1 - R-retrieval) * w[14])
#+end_src

Next interval given desired retention =DR= and new stability =S=:

#+begin_src elisp
I = (* 9 S (- (expt DR -1) 1))         ; days, rounded down (floor)
#+end_src

Default desired retention is *0.9* (a common Anki default).  Pinned as
=org-drill-fsrs-desired-retention= defcustom.

=DECIDE:= the exact form of the v4.5 update equations above is paraphrased
from Expertium's algorithm walkthrough (March 2026, v6-oriented) and the
=py-fsrs= reference implementation.  Implementation will cross-check the
exact constants and grouping against a tagged =py-fsrs= v4.x release
before writing the function body.

** Default parameters (org-drill defcustoms)

#+begin_src elisp
(defcustom org-drill-fsrs-parameters
  '(0.4 0.6 2.4 5.8 4.93 0.94 0.86 0.01 1.49 0.14 0.94 2.18 0.05 0.34 1.26 0.29 2.61)
  "17-tuple FSRS-4.5 default parameters.  See =docs/design/fsrs-spec.org=.")

(defcustom org-drill-fsrs-desired-retention 0.9
  "Target retention for FSRS scheduling.  Higher = shorter intervals.")
#+end_src

* Integration points

** Public function

#+begin_src elisp
(defun org-drill-determine-next-interval-fsrs (state quality)
  "Return next-interval (in days) plus updated FSRS state for STATE after
QUALITY (0-5).  STATE here is a 5-tuple (S D reviews lapses last-reviewed),
not an `org-drill-card-state' — FSRS stores its own state and treats the SM
slots as opaque.")
#+end_src

The shape =(state quality)= matches the post-#147 scheduler signature
convention.  =state= for FSRS is a *separate* struct =org-drill-fsrs-state=
(or a plist — =DECIDE:= which), populated from the =DRILL_FSRS_*=
properties at the call site.

=DECIDE:= the return shape.  Options:

1. Same shape as the SM* schedulers (a list with =next-interval=
   first plus updated state).  Consistent but the trailing slots are
   irrelevant to FSRS.
2. A small =org-drill-fsrs-result= struct (=next-interval=,
   =new-stability=, =new-difficulty=, =new-reviews=, =new-lapses=).
   Cleaner for FSRS but introduces a return-shape special case in the
   =cl-case= caller.

Recommended: option 2.  =cl-case= already special-cases =new-ofmatrix=
for SM5; one more is fine and the FSRS state shape is genuinely different.

** Caller integration

In =org-drill-smart-reschedule=:

#+begin_src elisp
;; sketch — exact destructure shape TBD per the return-shape DECIDE
(fsrs
 (let ((fsrs-state (org-drill-get-fsrs-state)))
   (org-drill-fsrs-apply-result
    (org-drill-determine-next-interval-fsrs fsrs-state quality))))
#+end_src

Two new helpers parallel the existing item-data round-trip:

- =org-drill-get-fsrs-state= — reads the =DRILL_FSRS_*= properties, returns
  the state struct or nil for a virgin card.
- =org-drill-store-fsrs-state= (or =org-drill-fsrs-apply-result=) — writes
  the updated state back.  The =SCHEDULED= timestamp is set via
  =org-schedule= using the returned =next-interval=, the same way
  =smart-reschedule= already does for SM*.

Same in =hypothetical-next-review-date= (read-only, no store).

** Defcustom entry

=org-drill-spaced-repetition-algorithm= grows a fourth =:option= line:

#+begin_src elisp
- FSRS :: Free Spaced Repetition Scheduler, the DSR-based algorithm
  used in Anki since 2024.  See `docs/design/fsrs-spec.org` for the algorithm
  and the state model.
#+end_src

* Backward compatibility

A card last reviewed under SM* has =DRILL_LAST_INTERVAL=,
=DRILL_REPEATS_SINCE_FAIL=, etc., but no =DRILL_FSRS_*=.  On the first
FSRS review after a switch:

- The FSRS scheduler sees absent =DRILL_FSRS_*= and treats this as a
  virgin first review (initial =S₀=, =D₀= from the rating).
- The SM-style properties are left in place.  If the user switches back,
  the SM scheduler reads them as before.
- The =DRILL_FSRS_*= properties are written from this point forward.
  Cards drilled under both algorithms accumulate both property sets.

The cold-start tradeoff is a known one-time loss of the SM-derived
estimate of card difficulty.  Anki's migration heuristic estimates
initial =D= and =S= from the SM2 ease factor and interval; pulling that in
is its own ticket (=todo:= "FSRS migration heuristic from SM history").

=DECIDE:= whether to also surface a one-line "first FSRS review on this
card — cold-starting" message during the prompt for the first FSRS review.
Probably not — users don't need to know.

* Test strategy

Three categories per public function, matching the existing scheduler
test files:

1. =tests/test-org-drill-determine-next-interval-fsrs.el= — Normal /
   Boundary / Error coverage of =org-drill-determine-next-interval-fsrs=.
   *Reference-vector tests* check =(state, rating) -> (next-interval,
   new-state)= against pre-computed outputs from =py-fsrs= at a tagged
   v4.x release.  About a dozen vectors covering the four ratings × first
   review / nth review / lapse / long-elapsed-time, plus boundary ones
   (=elapsed-days=0=, =D= and =S= at clamp limits).

2. =tests/test-org-drill-fsrs-state-roundtrip.el= — get-fsrs-state /
   store-fsrs-state round-trip parity, mirroring the SM
   item-data-roundtrip test.

3. =tests/test-org-drill-fsrs-integration.el= — end-to-end through
   =smart-reschedule= with =org-drill-spaced-repetition-algorithm= set to
   =fsrs=, covering: virgin card first review, returning card under
   FSRS, switched-from-SM card cold-starts cleanly, both algorithms can
   coexist on the same buffer.

Mapping helpers (org-drill quality 0–5 → FSRS rating 1–4) and the
defcustoms get their own small tests.

The reference vectors get committed alongside the tests as a
machine-checkable expected-output table, generated once from =py-fsrs=
and pinned.  The generation script lives at =tests/fixtures/fsrs-vectors.py=
so re-generation is reproducible.

* Effort estimate

Multi-day, plausibly spanning sessions:

- Algorithm function + helpers: 1 day.
- State round-trip + integration in =smart-reschedule=/=hypothetical=: 0.5
  day.
- Test scaffolding + reference vectors + Normal/Boundary/Error: 1 day.
- Documentation (manual entry, README option list, defcustom docstrings,
  this spec ratified): 0.5 day.

Realistic: a session to implement-and-test the algorithm + state
round-trip with reference-vector tests; a follow-up session for the
integration + docs.  No optimizer.

* Open decisions index

Pinned in one place for the implementation gate:

- =DECIDE:= version pin (FSRS-4.5 vs newer).  Recommended: 4.5.
- =DECIDE:= the exact FSRS-4.5 default-parameter tuple.  One-line lookup
  against the =py-fsrs= 4.x final release.
- =DECIDE:= quality-mapping table (0–5 → 1–4).  Recommended: above.
- =DECIDE:= scheduler return shape — list (SM-shape) or
  =org-drill-fsrs-result= struct.  Recommended: struct.
- =DECIDE:= whether to surface a "first FSRS review on this card"
  message.  Recommended: no.
- =DECIDE:= the exact form of the update equations after cross-checking
  =py-fsrs= 4.x source.

* References

- [[https://github.com/open-spaced-repetition/py-fsrs][py-fsrs]] — the canonical Python reference implementation.  Tagged
  v4.x releases pin FSRS-4.5; v6.x is the current line.
- [[https://github.com/open-spaced-repetition/fsrs4anki][fsrs4anki]] — the Anki integration; its wiki has user-facing notes on
  algorithm history and migration.
- [[https://expertium.github.io/Algorithm.html][Expertium: A technical explanation of FSRS]] (March 2026, v6-oriented)
  — the most accessible English-language walkthrough of the formulas
  and parameter roles.
- [[https://help.remnote.com/en/articles/9124137-the-fsrs-spaced-repetition-algorithm][RemNote: The FSRS Spaced Repetition Algorithm]] — third-party
  implementer's overview, useful as a sanity check on terminology.