branching-state-enumeration
Starting from an initial state, a sequence of choices (each from a small fixed set of actions) generates a tree of reachable states. The question asks for the count of distinct outcomes or valid sequences. Strategy: build the tree level by level, merging duplicate states or pruning states that violate a constraint.
How to solve
- Increase step_count from 3 to 4 — tree size grows from 8 to 16 leaves, still listable
- Add a third action (e.g. +1) to expand the tree without changing strategy
- Add a 'never go below 0' constraint to convert a state-counting problem into a Dyck-path-flavored one
Sub-archetype mix (5)
Click a row to see member problems.
- markov-state-probability 26% (11)
The process moves through a small set of abstract states labeled by an invariant; the question asks for the probability of hitting or returning to a target state. Solve by writing transition equations or summing a geometric series over the state space.
- constrained-sequence-path 23% (10)
Count sequences or grid paths of fixed length where a structural rule prunes branches at each step (no two consecutive identical elements, must stay non-negative, must return to start); enumerate level-by-level with dynamic programming.
- numeric-operation-tree 21% (9)
A number is transformed by repeatedly applying one of two or three simple operations; the question asks for the count of distinct reachable values after n steps, the minimum steps to reach a target, or all starting values that map to a given output. Grow the forward or backward BFS tree.
- ordered-placement-count 14% (6)
Build an ordered arrangement slot by slot; at each slot the valid choice count decreases because earlier slots constrain later ones (distinctness, adjacency coloring, parity); multiply per-slot choices, splitting by case on the most-constrained slot when needed.
- rule-governed-process-simulation 16% (7)
A cellular automaton, sign-pyramid, game position, or elimination sequence evolves under a local rule for a fixed number of steps; either count starting configurations achieving a target end state (working backward), or simulate forward to identify the resulting state.
More data (year-over-year, tool fingerprint, grade distribution, all members)
Tool fingerprint (1–17)
Grade distribution
- Gr 2 1
- Gr 3 1
- Gr 4 8
- Gr 5 3
- Gr 6 5
- Gr 7 21
- Gr 8 3