From the dashboard, use the random-problem action or open the problem library. The problem carries its own rating and tells the client which solving mode and response shape to render.

the three modes

answer-only gives one final answer. guided breaks a solution into marked steps. free-response accepts a full written solution.

3 · submit and review

The timer records solve duration. On submission, the server grades the response, stores the result, and returns feedback plus the solution when grading is complete. Deterministic and eligible non-flagged AI grades can update Elo; flagged or manual-review work waits for review.

the client-facing flow

open problem
  → render sanitized prompt and response UI
  → submit response to the server
  → receive marks, feedback, solution, and Elo result

keep going

Read problem types before your first multi-step problem, then try the sandbox.

introduction

first 5 minutes

A short first session with the parts of the product that matter most.

1 · open the dashboard
Sign in at /welcome and continue to your dashboard.
2 · start a problem
Choose a random problem or open one from the library.
3 · read the response shape
Check whether the problem expects a choice, number, expression, or written work.
4 · show the right work
Guided problems expose ordered steps; free-response problems use the proof editor.
5 · submit once ready
Review marks, feedback, the solution, and any eligible Elo change.

remember

A partial score is still a real score. For guided and rubric-based work, the fraction of marks earned becomes the actual Elo score input rather than a simple win/loss flag.

problems

problem types

Schema v2 keeps the solving experience, the student input, and the server-side evaluation strategy separate.

mode	student sees	response kinds	grading
`answer-only`	One final answer.	Multiple choice, numeric, or expression.	Deterministic.
`guided`	Ordered steps with marks.	Any response kind per step.	Per-step strategy; partial credit.
`free-response`	Full written solution.	Text, including LaTeX.	AI rubric or manual review.

three independent layers

mode selects the solving flow. response.kind selects the input component.grading.strategy selects how the server evaluates it. They are related, but they are not interchangeable fields.

mode · answer-only

answer-only

Single-answer competition problems with immediate deterministic feedback.

mode · guided

M/A/R checkpoints

Structured working with method, answer, and reasoning marks.

mode · free-response

AI rubric

Open-ended work evaluated against a rubric, or held for manual review.

problems · answer-only

answer-only

One response, one deterministic grading result.

response kinds

response.kind	use it for	typical strategy
`multiple-choice`	A/B/C/D/E selection.	`exact`
`numeric`	Integers or decimals, optionally within a tolerance.	`numeric`
`expression`	Algebraic or symbolic answers.	`equivalence`

what the server checks

The public response specification describes the input shape. Correct choices and values live ingrading on the server and are removed before a problem is sent to the client.

public response shape

{
  "mode": "answer-only",
  "response": {
    "kind": "numeric",
    "requireInteger": true
  },
  "marks": 1
}

no hidden answer in the browser

The client receives the prompt and public response shape. The server reads the full grading spec, evaluates the submission, and only then returns correct-answer display data.

problems · guided

M/A/R checkpoints

Guided problems break a worked solution into ordered steps. Each step can award method, answer, or reasoning marks.

the three marks

mark	name	awarded for
M	Method	Using the correct technique or approach.
A	Answer	Getting the correct value at the step.
R	Reasoning	Providing valid justification for a non-obvious step.

step dependencies

Each step has its own prompt, response, grading strategy, mark type, and mark count. OptionaldependsOn and allowFollowThrough fields describe how a later step can build on earlier work.

guided step shape

{
  "id": "evaluate",
  "order": 2,
  "prompt": "Evaluate the expression.",
  "markType": "A",
  "marks": 2,
  "response": { "kind": "numeric" }
}

see it graded

The grading walkthrough shows the marks changing one step at a time.

problems · free-response

AI rubric

Full solutions are graded against an explicit rubric rather than a single scalar answer.

when to use it

Free-response problems are intended for proofs, open-ended derivations, and work that does not fit a fixed sequence of input fields. The usual strategy is rubric-ai; manual is available when a human reviewer must make the final decision.

rubric items

A rubric item describes what the grader should assess, how many marks it carries, its M/A/R type, the criteria, and evidence to look for. Optional examples and common errors help calibrate grading.

rubric item shape

{
  "id": "factorization",
  "description": "Factors n^3 - n correctly",
  "marks": 2,
  "markType": "M",
  "criteria": "Uses n(n - 1)(n + 1).",
  "lookFor": ["consecutive integers"]
}

review states

ai-graded means the AI result was returned without a review flag.
flagged means the result needs human review and does not apply Elo yet.
manually-graded and appealed represent human-review workflows.

sanitized by default

Rubric criteria, grading guidance, correct values, and solutions stay server-side until the grading result is ready to return.

★ interactive · walkthrough

grading walkthrough

Advance through a small guided example and see how each available mark is awarded.

guided example · integration by parts

guided

step 1
MA
Let I = ∫ x·eˣ dx. Apply integration by parts with u = x and dv = eˣ dx.
The technique is correct, so the method mark is awarded. No final value has been found yet.
step 2
A
Then du = dx and v = eˣ, so I = x·eˣ − ∫ eˣ dx.
step 3
M
∫ eˣ dx = eˣ, with the constant carried into the final expression.
step 4
AR
Therefore I = x·eˣ − eˣ + C = eˣ(x − 1) + C.

live score

1/6

17% of available marks

marks earned

legend

M method — correct technique

A answer — correct value

R reasoning — valid justification

★ interactive · playground

solve a problem

A local answer-only sandbox. Click begin to start the timer.

sandbox-001answer-onlyproblem Elo 1480

00:00

Find the smallest positive integer n such that n² + 1 is divisible by 41.

your answer

sandbox only

This page uses the production Elo calculation locally, but it never writes a submission or changes your real profile. See the Elo docs for the formula.

input

LaTeX shortcuts

Mathleagues uses KaTeX for mathematical rendering. The same syntax works in prompts, answer fields, and written work.

syntax basics

Use $x^2$ for inline math and $$x^2$$ for display math. The proof editor also recognizes raw commands entered through its keyboard shortcuts and toolbar.

mixed text and math

Let $f: \mathbb{R} \to \mathbb{R}$ be continuous.
By the intermediate value theorem, there exists
$c \in (a,b)$ such that $f(c)=0$.

rendering an example

This is the same mixed-content renderer used by problem statements: The answer is $\frac{1}{2}$ .

\sum_{i=1}^{n} i = \frac{n(n+1)}{2}

math mode only

KaTeX renders mathematical syntax, not a full LaTeX document. Preambles, packages, and TikZ diagrams are not part of the input format.

★ interactive · cheatsheet

LaTeX live preview

Edit the source on the left, then click a cheatsheet row to load it into the preview.

source

preview

\sum_{i=1}^{n} i = \frac{n(n+1)}{2}

cheatsheet

fractions & roots

source	renders as	note
`\frac{a}{b}`	$\frac{a}{b}$	standard fraction
`\sqrt{x}`	$\sqrt{x}$	square root
`\sqrt[3]{x}`	$\sqrt[3]{x}$	nth root

sums, products, integrals

source	renders as	note
`\sum_{i=1}^{n} i`	$\sum_{i=1}^{n} i$	finite sum
`\prod_{k=1}^{n} k`	$\prod_{k=1}^{n} k$	product
`\int_0^1 x \, dx`	$\int_0^1 x \, dx$	definite integral

limits & calculus

source	renders as	note
`\lim_{x \to 0}`	$\lim_{x \to 0}$	limit
`\frac{dy}{dx}`	$\frac{dy}{dx}$	derivative
`\partial_x f`	$\partial_x f$	partial derivative
`\nabla f`	$\nabla f$	gradient

logic, sets, relations

source	renders as	note
`\forall x \in S`	$\forall x \in S$	for all in a set
`\exists x`	$\exists x$	there exists
`A \subseteq B`	$A \subseteq B$	subset
`a \equiv b \pmod{n}`	$a \equiv b \pmod{n}$	congruence

greek

source	renders as	note
`\alpha \beta \gamma`	$\alpha \beta \gamma$	lowercase
`\Sigma \Pi \Omega`	$\Sigma \Pi \Omega$	uppercase
`\theta \phi \psi`	$\theta \phi \psi$	common variables

rating

Elo system

Mathleagues treats a submission as a match between a user and a problem. Both ratings react to the result.

the formula

expected score

E = \frac{1}{1 + 10^{(R_p - R)/400}}

user update

\Delta R = \operatorname{round}(K(S-E))

R is the user rating, R_p is the problem rating, and S is marks awarded divided by marks available.

user K-factors

user Elo	K	purpose
below 1400	32	Faster calibration for developing ratings.
1400–2000	20	Standard competitive volatility.
above 2000	12	More stable expert ratings.

partial credit and re-attempts

A score of 4/6 is S = 0.667, not a binary loss. When a user submits a problem they have attempted before, their K-factor is halved. The problem uses a fixed K-factor of 10 and updates from 1 − S.

worked example

user Elo = 1200
problem Elo = 1333
score = 4 / 6 = 0.667
K = 32
user delta ≈ +11
problem delta ≈ -4

when Elo applies

Deterministic auto-graded results and non-flagged ai-graded results can update both ratings. Flagged work and manual review defer the Elo change until the review workflow resolves.

rating bounds

User Elo is clamped between 100 and 3000. New users start at 1200.

★ interactive · simulator

Elo simulator

Set a starting user rating, a problem rating, a success rate, and a number of rounds. The simulation uses the same pure Elo functions as production.

parameters

your rating1200problem rating1400success rate60%rounds50treat every round as a re-attempt

initial round

expected score0.240

user K32

on success+24

on failure-8

rating trace · 50 rounds

30 success · 20 failure

final user

1400

user delta

+200

final problem

1344

reading the chart

The gold line is the user rating and the muted line is the problem rating. This simulation changes both ratings after every round; real submissions use the same formulas but are not deterministic by success-rate pattern.

reference

keyboard shortcuts

The shortcuts below are implemented by the current app, not aspirational commands.

docs navigation

/	Focus documentation search.
Ctrl / ⌘ + K	Focus documentation search.

solving

A–E	Select a matching multiple-choice option.
Enter	Create a new proof-editor line.
↑ / ↓	Move between proof-editor lines.
Backspace	Join with the previous proof-editor line at the start of a line.

proof-editor replacements

structure

`//`	`\frac{}{}`	frac
`sq`	`\sqrt{}`	√
`^`	`^{}`	^n
`_`	`_{}`	sub

relation

`<=`	`\leq`	≤
`>=`	`\geq`	≥
`!=`	`\neq`	≠
`~~`	`\approx`	≈
`+-`	`\pm`	±
`**`	`\cdot`	·
`xx`	`\times`	×
`->`	`\to`	→
`=>`	`\implies`	⟹
`iff`	`\iff`	⟺

symbol

`inf`	`\infty`	∞
`...`	`\ldots`	…
`sum`	`\sum_{ }^{ }`	Σ
`prod`	`\prod_{ }^{ }`	Π
`int`	`\int_{ }^{ }`	∫
`lim`	`\lim_{ }`	lim

greek

`alpha`	`\alpha`	α
`beta`	`\beta`	β
`gamma`	`\gamma`	γ
`delta`	`\delta`	δ
`eps`	`\epsilon`	ε
`theta`	`\theta`	θ
`lambda`	`\lambda`	λ
`pi`	`\pi`	π
`sigma`	`\sigma`	σ
`phi`	`\phi`	φ
`omega`	`\omega`	ω

function

`sin`	`\sin`	sin
`cos`	`\cos`	cos
`tan`	`\tan`	tan
`log`	`\log`	log
`ln`	`\ln`	ln

toolbar

The proof editor also exposes a clickable subset of these commands. The current toolbar contains 15 shortcuts.

reference

glossary

The terms used across the docs and the solving interface, in plain language.

answer-only: A problem with one final response, graded deterministically.
guided: A problem split into ordered steps, each with its own response and marks.
free-response: A problem where the student writes a complete solution, usually against a rubric.
response.kind: The input shape rendered for a student: multiple-choice, numeric, expression, or text.
grading.strategy: The server-side evaluation method: exact, numeric, equivalence, rubric-ai, or manual.
M / A / R: Method, Answer, and Reasoning marks used for guided steps and rubric items.
Elo: A rating that estimates a user or problem’s current competitive level.
re-attempt: A later submission for the same problem; the user K-factor is halved.
flagged: A grading result held for human review, with Elo deferred.
sanitized problem: The public problem payload with correct answers, grading specs, and solutions removed.

source of truth

The public docs describe the current schema and behavior. Implementation details may change as the product evolves.

please wait

print view · use Ctrl/Cmd+P to save as PDF

docs · schema v2

prove it.

Mathleagues is a competitive math problem-solving platform with timed problems, structured grading, and Elo ratings for both solvers and problems.

start solving →understand grading

01 · introduction

getting started

Create an account, open the dashboard, and submit your first problem.

02 · problems

problem types

See how modes, response shapes, and grading strategies are separated.

03 · input

LaTeX shortcuts

Write mathematical expressions in the same syntax used by the solver.

04 · rating

Elo system

Understand expected score, K-factors, partial credit, and re-attempts.

try it interactive

★ live

These examples run locally in the page. They do not create submissions or change a real rating.

playground

solve a problem

Try an answer-only round with a timer, hint, and local Elo result.

walkthrough

grade a guided solution

Advance through a worked example and watch M/A/R marks accumulate.

cheatsheet

preview LaTeX

Edit an expression and browse common syntax with a live KaTeX preview.

simulator

simulate Elo

Explore how success rate and rating gaps affect user and problem Elo.

introduction

getting started

The shortest route from a new account to a graded Mathleagues submission.

1 · create an account

Open the welcome page and choose sign in or sign up. Authentication is handled by Firebase; your public profile starts with an Elo rating of 1200.

2 · choose a problem

From the dashboard, use the random-problem action or open the problem library. The problem carries its own rating and tells the client which solving mode and response shape to render.

the three modes

answer-only gives one final answer. guided breaks a solution into marked steps. free-response accepts a full written solution.

3 · submit and review

the client-facing flow

open problem
  → render sanitized prompt and response UI
  → submit response to the server
  → receive marks, feedback, solution, and Elo result

keep going

Read problem types before your first multi-step problem, then try the sandbox.

introduction

first 5 minutes

A short first session with the parts of the product that matter most.

1 · open the dashboard
Sign in at /welcome and continue to your dashboard.
2 · start a problem
Choose a random problem or open one from the library.
3 · read the response shape
Check whether the problem expects a choice, number, expression, or written work.
4 · show the right work
Guided problems expose ordered steps; free-response problems use the proof editor.
5 · submit once ready
Review marks, feedback, the solution, and any eligible Elo change.

remember

A partial score is still a real score. For guided and rubric-based work, the fraction of marks earned becomes the actual Elo score input rather than a simple win/loss flag.

problems

problem types

Schema v2 keeps the solving experience, the student input, and the server-side evaluation strategy separate.

mode	student sees	response kinds	grading
`answer-only`	One final answer.	Multiple choice, numeric, or expression.	Deterministic.
`guided`	Ordered steps with marks.	Any response kind per step.	Per-step strategy; partial credit.
`free-response`	Full written solution.	Text, including LaTeX.	AI rubric or manual review.

three independent layers

mode selects the solving flow. response.kind selects the input component.grading.strategy selects how the server evaluates it. They are related, but they are not interchangeable fields.

mode · answer-only

answer-only

Single-answer competition problems with immediate deterministic feedback.

mode · guided

M/A/R checkpoints

Structured working with method, answer, and reasoning marks.

mode · free-response

AI rubric

Open-ended work evaluated against a rubric, or held for manual review.

problems · answer-only

answer-only

One response, one deterministic grading result.

response kinds

response.kind	use it for	typical strategy
`multiple-choice`	A/B/C/D/E selection.	`exact`
`numeric`	Integers or decimals, optionally within a tolerance.	`numeric`
`expression`	Algebraic or symbolic answers.	`equivalence`

what the server checks

The public response specification describes the input shape. Correct choices and values live ingrading on the server and are removed before a problem is sent to the client.

public response shape

{
  "mode": "answer-only",
  "response": {
    "kind": "numeric",
    "requireInteger": true
  },
  "marks": 1
}

no hidden answer in the browser

The client receives the prompt and public response shape. The server reads the full grading spec, evaluates the submission, and only then returns correct-answer display data.

problems · guided

M/A/R checkpoints

Guided problems break a worked solution into ordered steps. Each step can award method, answer, or reasoning marks.

the three marks

mark	name	awarded for
M	Method	Using the correct technique or approach.
A	Answer	Getting the correct value at the step.
R	Reasoning	Providing valid justification for a non-obvious step.

step dependencies

Each step has its own prompt, response, grading strategy, mark type, and mark count. OptionaldependsOn and allowFollowThrough fields describe how a later step can build on earlier work.

guided step shape

{
  "id": "evaluate",
  "order": 2,
  "prompt": "Evaluate the expression.",
  "markType": "A",
  "marks": 2,
  "response": { "kind": "numeric" }
}

see it graded

The grading walkthrough shows the marks changing one step at a time.

problems · free-response

AI rubric

Full solutions are graded against an explicit rubric rather than a single scalar answer.

when to use it

rubric items

A rubric item describes what the grader should assess, how many marks it carries, its M/A/R type, the criteria, and evidence to look for. Optional examples and common errors help calibrate grading.

rubric item shape

{
  "id": "factorization",
  "description": "Factors n^3 - n correctly",
  "marks": 2,
  "markType": "M",
  "criteria": "Uses n(n - 1)(n + 1).",
  "lookFor": ["consecutive integers"]
}

review states

ai-graded means the AI result was returned without a review flag.
flagged means the result needs human review and does not apply Elo yet.
manually-graded and appealed represent human-review workflows.

sanitized by default

Rubric criteria, grading guidance, correct values, and solutions stay server-side until the grading result is ready to return.

★ interactive · walkthrough

grading walkthrough

Advance through a small guided example and see how each available mark is awarded.

guided example · integration by parts

guided

step 1
MA
Let I = ∫ x·eˣ dx. Apply integration by parts with u = x and dv = eˣ dx.
The technique is correct, so the method mark is awarded. No final value has been found yet.
step 2
A
Then du = dx and v = eˣ, so I = x·eˣ − ∫ eˣ dx.
step 3
M
∫ eˣ dx = eˣ, with the constant carried into the final expression.
step 4
AR
Therefore I = x·eˣ − eˣ + C = eˣ(x − 1) + C.

live score

1/6

17% of available marks

marks earned

legend

M method — correct technique

A answer — correct value

R reasoning — valid justification

★ interactive · playground

solve a problem

A local answer-only sandbox. Click begin to start the timer.

sandbox-001answer-onlyproblem Elo 1480

00:00

Find the smallest positive integer n such that n² + 1 is divisible by 41.

your answer

sandbox only

This page uses the production Elo calculation locally, but it never writes a submission or changes your real profile. See the Elo docs for the formula.

input

LaTeX shortcuts

Mathleagues uses KaTeX for mathematical rendering. The same syntax works in prompts, answer fields, and written work.

syntax basics

Use $x^2$ for inline math and $$x^2$$ for display math. The proof editor also recognizes raw commands entered through its keyboard shortcuts and toolbar.

mixed text and math

Let $f: \mathbb{R} \to \mathbb{R}$ be continuous.
By the intermediate value theorem, there exists
$c \in (a,b)$ such that $f(c)=0$.

rendering an example

This is the same mixed-content renderer used by problem statements: The answer is $\frac{1}{2}$ .

\sum_{i=1}^{n} i = \frac{n(n+1)}{2}

math mode only

KaTeX renders mathematical syntax, not a full LaTeX document. Preambles, packages, and TikZ diagrams are not part of the input format.

★ interactive · cheatsheet

LaTeX live preview

Edit the source on the left, then click a cheatsheet row to load it into the preview.

source

preview

\sum_{i=1}^{n} i = \frac{n(n+1)}{2}

cheatsheet

fractions & roots

source	renders as	note
`\frac{a}{b}`	$\frac{a}{b}$	standard fraction
`\sqrt{x}`	$\sqrt{x}$	square root
`\sqrt[3]{x}`	$\sqrt[3]{x}$	nth root

sums, products, integrals

source	renders as	note
`\sum_{i=1}^{n} i`	$\sum_{i=1}^{n} i$	finite sum
`\prod_{k=1}^{n} k`	$\prod_{k=1}^{n} k$	product
`\int_0^1 x \, dx`	$\int_0^1 x \, dx$	definite integral

limits & calculus

source	renders as	note
`\lim_{x \to 0}`	$\lim_{x \to 0}$	limit
`\frac{dy}{dx}`	$\frac{dy}{dx}$	derivative
`\partial_x f`	$\partial_x f$	partial derivative
`\nabla f`	$\nabla f$	gradient

logic, sets, relations

source	renders as	note
`\forall x \in S`	$\forall x \in S$	for all in a set
`\exists x`	$\exists x$	there exists
`A \subseteq B`	$A \subseteq B$	subset
`a \equiv b \pmod{n}`	$a \equiv b \pmod{n}$	congruence

greek

source	renders as	note
`\alpha \beta \gamma`	$\alpha \beta \gamma$	lowercase
`\Sigma \Pi \Omega`	$\Sigma \Pi \Omega$	uppercase
`\theta \phi \psi`	$\theta \phi \psi$	common variables

rating

Elo system

Mathleagues treats a submission as a match between a user and a problem. Both ratings react to the result.

the formula

expected score

E = \frac{1}{1 + 10^{(R_p - R)/400}}

user update

\Delta R = \operatorname{round}(K(S-E))

R is the user rating, R_p is the problem rating, and S is marks awarded divided by marks available.

user K-factors

user Elo	K	purpose
below 1400	32	Faster calibration for developing ratings.
1400–2000	20	Standard competitive volatility.
above 2000	12	More stable expert ratings.

partial credit and re-attempts

worked example

user Elo = 1200
problem Elo = 1333
score = 4 / 6 = 0.667
K = 32
user delta ≈ +11
problem delta ≈ -4

when Elo applies

Deterministic auto-graded results and non-flagged ai-graded results can update both ratings. Flagged work and manual review defer the Elo change until the review workflow resolves.

rating bounds

User Elo is clamped between 100 and 3000. New users start at 1200.

★ interactive · simulator

Elo simulator

Set a starting user rating, a problem rating, a success rate, and a number of rounds. The simulation uses the same pure Elo functions as production.

parameters

your rating1200problem rating1400success rate60%rounds50treat every round as a re-attempt

initial round

expected score0.240

user K32

on success+24

on failure-8

rating trace · 50 rounds

30 success · 20 failure

final user

1400

user delta

+200

final problem

1344

reading the chart

reference

keyboard shortcuts

The shortcuts below are implemented by the current app, not aspirational commands.

docs navigation

/	Focus documentation search.
Ctrl / ⌘ + K	Focus documentation search.

solving

A–E	Select a matching multiple-choice option.
Enter	Create a new proof-editor line.
↑ / ↓	Move between proof-editor lines.
Backspace	Join with the previous proof-editor line at the start of a line.

proof-editor replacements

structure

`//`	`\frac{}{}`	frac
`sq`	`\sqrt{}`	√
`^`	`^{}`	^n
`_`	`_{}`	sub

relation

`<=`	`\leq`	≤
`>=`	`\geq`	≥
`!=`	`\neq`	≠
`~~`	`\approx`	≈
`+-`	`\pm`	±
`**`	`\cdot`	·
`xx`	`\times`	×
`->`	`\to`	→
`=>`	`\implies`	⟹
`iff`	`\iff`	⟺

symbol

`inf`	`\infty`	∞
`...`	`\ldots`	…
`sum`	`\sum_{ }^{ }`	Σ
`prod`	`\prod_{ }^{ }`	Π
`int`	`\int_{ }^{ }`	∫
`lim`	`\lim_{ }`	lim

greek

`alpha`	`\alpha`	α
`beta`	`\beta`	β
`gamma`	`\gamma`	γ
`delta`	`\delta`	δ
`eps`	`\epsilon`	ε
`theta`	`\theta`	θ
`lambda`	`\lambda`	λ
`pi`	`\pi`	π
`sigma`	`\sigma`	σ
`phi`	`\phi`	φ
`omega`	`\omega`	ω

function

`sin`	`\sin`	sin
`cos`	`\cos`	cos
`tan`	`\tan`	tan
`log`	`\log`	log
`ln`	`\ln`	ln

toolbar

The proof editor also exposes a clickable subset of these commands. The current toolbar contains 15 shortcuts.

reference

glossary

The terms used across the docs and the solving interface, in plain language.

answer-only: A problem with one final response, graded deterministically.
guided: A problem split into ordered steps, each with its own response and marks.
free-response: A problem where the student writes a complete solution, usually against a rubric.
response.kind: The input shape rendered for a student: multiple-choice, numeric, expression, or text.
grading.strategy: The server-side evaluation method: exact, numeric, equivalence, rubric-ai, or manual.
M / A / R: Method, Answer, and Reasoning marks used for guided steps and rubric items.
Elo: A rating that estimates a user or problem’s current competitive level.
re-attempt: A later submission for the same problem; the user K-factor is halved.
flagged: A grading result held for human review, with Elo deferred.
sanitized problem: The public problem payload with correct answers, grading specs, and solutions removed.

source of truth

The public docs describe the current schema and behavior. Implementation details may change as the product evolves.