Exact reachable color-state count, not an estimate.
SkimmIQ Lab · An open puzzle problem
AI can solve it.
Humans now have two methods.
SkimmIQ's solver can often find a legal route back to solved in about a second. Humans now have two documented methods. The Bear Method set the 107-move fewest-moves benchmark in the editor. Fred used a related LBL-and-commutator approach for the app record: 3:40 and 176 moves. The open challenge is to teach, test, and improve both.
Two tracks now matter: Bear leads fewest moves at 107 in the editor, Fred leads timed app play at 3:40 with 176 moves, and AI routes average roughly 60 moves.
Each swipe advances one 12-sticker tape by one position.
The Bear Method and Fred's related LBL-and-commutator approach.
Bear: 107 moves in the editor. Fred: 176 moves in 3:40 in the app.
01The challenge
Turn two human methods into something others can learn
The problem has changed. The Bear Method and Fred's approach supply working human structure: recognizable stages, useful intermediate goals, and move ideas that produced complete solves without machine search.
Both look layer-by-layer in a way AI routes often do not. They do not have to be optimal, but to become established community methods they must be explainable, independently repeatable, and useful on unfamiliar puzzle states.
The r/Cubers and SpeedSolving discussions show that CFOP and Roux do not transfer directly to SkimmIQ. Instead, both methods use short commutators, setup moves, and controlled side effects. The next task is to reproduce them, explain their differences, and refine them.
02The gap
What the AI proves - and what it doesn't
Proven
Every valid state has a route back
Exact group proofs for all six layouts show that every arrangement with the required number of stickers of each color is reachable from solved and therefore solvable. The app's solvers can usually find and replay a concrete route quickly.
Still open
That either human method is a finished system
Two documented approaches now have working records. Both still need independent solves, clearer stage definitions, and reusable algorithms that other players can test.
03The state space
Why the number is exactly 1.01097 × 1038
54! / (9!)6
101,097,362,223,624,462,291,180,422,369,532,000,000
Show the full derivation
- 54 fixed positions. The 3×3×3 layout has six faces with nine visible sticker positions on each face.
- Nine generators. There are three tapes on each of the X, Y, and Z axes. Each tape is a 12-position cycle; the reverse swipe is its inverse.
- The full symmetric group. An exact Schreier-Sims computation shows that these nine tape cycles generate S54, the complete set of permutations of the 54 positions. Therefore every Classic arrangement containing exactly nine stickers of each color is reachable from solved and solvable.
- Colors are indistinguishable. Classic mode has nine stickers of each of six colors. Dividing 54! by 9! for every color gives the number of distinct color arrangements.
Equivalence convention: face names, axes, and sticker positions are fixed by the game and by SQN notation, so whole-cube view rotations are not identified as the same state. Stickers of the same color are already treated as indistinguishable by the formula.
The group result was reproduced against the canonical Android movement contract and cross-checked with the independent solver-lab geometry. The computation is exact; the native replay checks validate that the proof model uses the same legal movement as the app.
04Live progress
Two tracks. Two methods. One open race.
For almost a year there was no recorded human solve. Bear then cut the editor fewest-moves benchmark from 240 to 107 in four days. Fred developed a related method and now leads the timed app track at 3:40 with 176 moves.
Current benchmarks
~60
AI moves on average
107
Bear · editor fewest moves
3:40
Fred · app · 176 moves
The human side is moving. These are separate tracks rather than one combined ranking: Bear leads the editor's fewest-moves challenge, Fred leads timed app play, and the AI number is an average route length. Together they show where human method development stands.
Fewest-moves track · Bear · four days
240 → 150 → 140 → 107
Each number opens the complete shared replay, not a reconstructed summary.
Expensive-Bear-1376
107 moves in the editor track, down from Bear's first 240-move solve over four days of refinement.
Watch the 107-move replayFred
3:40 · 176 moves on the public leaderboard, using a second documented human method.
View the leaderboardBear and Fred
Two players independently turned layer-by-layer intuition, commutators, and controlled band movement into complete solve methods.
Read both descriptions
The Bear Method
Expensive-Bear-1376 · r/Cubers · source
The method in Bear's own words
A verbatim excerpt from Bear's public explanation after the 107-move solve:
Still intuitive first two layers and then comms, but... that's a method. I think it was the 140 where I improved the method. Before that, I solved the last side and the band around it purely with commutators. Now I also rotate the band around the last side as setup moves. So for example I have a red tile in front of me on the last face and I can shoot it down into the band around it with a commutator.
It would go into the front side that way, but let's say the red side is in the back. Instead of several commutators moving the red tile to the back, I rotate the band around the last side so that its "red part" is at the front, then I shoot the red tile down into it, then I rotate the band back. Or ... I don't rotate it back right away. That's what I did for the 107: I shoot tiles from the top side down into the band around it, but I keep the band freely rotating around.
That just means I have to keep track of which three slots on it are supposed to be the red part, which three the green part, etc. And then I just rotate the band to the correct position at the very end. I think for the 107, I looked ahead more for the first side, then solved first two bands also with more looking ahead and with rotating those bands themselves. I don't think I can improve much further without another significant improvement of the method.
Fred's approach
Fred · leaderboard record 176 · source
LBL, shorter commutators, controlled side effects
Fred began with layer-by-layer and protected the rest of the puzzle with eight-move last-layer commutators. He then found the shorter idea:
"Then I saw that people were doing certain 4-move commutators, and the trick is that those will permute certain other stickers..."
The side effects become harmless when the affected stickers all have the first-layer color. That turns a longer protective sequence into a shorter commutator without losing the solved structure that already exists.
The floating-band analogy
Fred · SpeedSolving source"It's the same idea as in CFOP where you solve the cross pieces relative to each other, then solve them all with a D move."
Structure grows visibly
The solve reads almost like layer-by-layer: ordered regions accumulate, so a viewer can see the plan developing.
Chaos, then a late snap
Machine routes often look disordered for most of the replay and only resolve into recognizable faces near the end.
What remains open?
The Bear Method and Fred's approach now exist as documented human methods, but neither is yet a finished, broadly teachable system:
- Reproduce both approaches on different states and with different human solvers.
- Turn their visual patterns, commutators, setup moves, and controlled side effects into clear teachable stages.
- Find the next major idea that can close the gap from 107 moves toward the AI's roughly 60.
- Improve the timed app track beyond Fred's 3:40 and 176 moves.
05Your move
Choose the next record to break
Use SkimmIQ Lab to test and share the next refinement, challenge Bear's 107-move editor benchmark, or take the timed race into the app against Fred's 3:40 and 176 moves.