The half of "adaptive" that was never built. Difficulty was a filter a learner
could set and nothing the session did on its own, so somebody at 30% on a topic
and somebody at 90% were asked the same questions in the same order.
A question's value is now scaled by how far its level is from the one the
learner's readiness on *that topic* calls for: under 0.45 wants easy, up to
0.78 medium, above that hard. Per topic because strong on growth and weak on
arrhythmias is the normal case and one number across the two describes nobody.
A multiplier and not a filter, for the same reason the reranker is a
permutation: thinning the bank to one level makes it three times smaller, and
on a narrow topic that is the same eight questions every time. One step away
keeps 0.72 of its value, two steps 0.5, and an unlabelled question 0.92 —
unknown is not wrong.
The first band's edge is 0.45 rather than 0.55 so that a topic with no evidence
— which sits at exactly NEUTRAL_RECALL — gets medium questions. Handing
somebody the easiest questions in a subject nobody has measured them on is a
poor way to find out what they know.
Written up in docs/adaptive-sessions.md, which now says five rules.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TqXevQJhxFrM7jJg82cgZN
**Prepared sessions.** Most of this existed: unanswered first, weakest topic
next, wrong-before-right after that, all scaled by what share of the real paper
each topic carries. What it could not do was change with time, say anything
about itself, or be reached without filling in a form.
Evidence now decays on a thirty-day half-life. Exponential rather than a fixed
window because memory has a slope, not a cliff — under a window, 29 days counts
fully and 31 counts for nothing — and because it is memoryless, so an answer's
weight does not shift when unrelated questions are answered, which is what lets
the preview stay a valid forecast. Spring is worth an eighth of last week. Two
things decay: a question's recall probability, drifting towards even rather
than past it, so an old right answer becomes eligible rather than wrong; and a
topic's accuracy, against a prior of two "no idea" answers, which fixes "right
once, known forever".
Strict unanswered-first meant that on a bank of 2,900 nothing was ever
recycled — spaced repetition existed and was unreachable. Review now takes up
to two fifths of a session. And the damping that spread the picks across topics
was applied only to seen material, so a learner with no history was handed the
heaviest domain entire instead of a spread; that was live.
The plan is the product. It is computed, shown, and then the session is built
from that plan's own ids and the plan returned with it, so the two cannot
differ; every figure in it is a tally over the chosen questions rather than a
forecast. No model touches the ranking — a learner asking "why these twenty"
has to get the same answer twice.
**Vision.** The proxy's own `/model/info` says which models can see, so nothing
is hard-coded: 77 report yes, 11 no, and 328 say nothing at all, which means
absent rather than incapable — so those are asked once with an 8px PNG and the
refusal cached. The deployment's main model turns out not to see, and questions
carry figures the learner is looking at, so the tutor was answering about an
image it had never been shown. It routes to a configured tool model now, folds
the description back in as text saying plainly where it came from, and caches
on the bytes because the same figure is re-sent every turn.
Also fixed on the way: `article` was missing from the admin's task list, so
article drafting always ran on the fallback model whatever an administrator
chose; and `.jpx` stem images were sent as JPEG because `mimetypes` guesses
that from the name, so the provider rejected them two hops later.
An administrator must pick a tool model in Settings → AI models. Until then the
tutor says a figure exists that nothing could read, rather than describing one
it cannot see.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TqXevQJhxFrM7jJg82cgZN
analysis is a real tab
Adaptive selection knew what you were weak at and nothing about what the
exam is made of, so being weak at something worth 5% of the paper ranked
the same as being weak at something worth 1%. Every score is now
multiplied by the weight the board publishes for that topic's domain —
the same `exam_blueprints.weight` behind the Relevance column.
A topic the blueprint does not cover takes the median published weight. A
zero would make unmapped material unreachable and the highest would make
it the priority; neither is a claim the blueprint supports. With no study
objective the multiplier is absent and selection is about weakness alone,
exactly as before.
Weight scales weakness, it does not replace it: a topic you are certain of
does not surface because it is worth 5% of the paper, because (1 −
accuracy) is near zero and no multiplier rescues that. docs/adaptive-
sessions.md says all of this, including what is still open.
Session analysis is the third tab rather than a link out of the page —
two of the three used to change what you were reading and the third took
you somewhere else. The tab bar is one component both routes wear,
AnalysisSessionPage's body is a component the tab renders in place, and
the tab lives in the address so a link opens where it says.
Two things that were wrong turned up in that work: a session nobody had
sat showed 0% in the figures and "0% correct" in the donut — two separate
statements of a score on a session that had none — and the old third tab
disappeared entirely for anyone with no attempts, so the strip silently
changed shape.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TqXevQJhxFrM7jJg82cgZN
written down
The yellow was a gradient stripe under the x-height, which reads as an
underline rather than a highlight and all but vanishes on a wrapped line.
It is a solid band now, the way a highlighter leaves one.
A block that ends without warning ends on whatever question you happened
to be reading. Five minutes out it says so — once, because a warning that
returns every second is a warning nobody reads.
And docs/adaptive-sessions.md, because "prioritised by impact" was a
phrase with no written meaning. It says where the code is, what the three
rules are — unanswered first, weakest topic among those, then wrong ones
oldest first with the category damped so twenty questions do not all come
from your worst subject — and, as plainly, what it does not do: it is not
weighted by the exam blueprint. Weakest and most-of-the-paper are
different questions and we answer only the first. The refinement that
would combine them is written down too, with the column that already
holds the weights.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TqXevQJhxFrM7jJg82cgZN
