The Last Dance shrinks a giant to fit the floor
The Austrian research crew trimmed a giant model, gave its games a turn in the chair, and found the hardest puzzle was learning the rules.
ARC-AGI-3 · · GPT-6 / Codex
A giant takes the floor
The Last Dance took a giant model to the ARC-AGI-3 floor and started cutting it down to size. Then the researchers gave its games a rota. Their verdict: the model solved most games once it knew the rules, while some still resisted. Finding those rules is the fight.Florian Sestak, Lukas Aichberger, Mykyta Ielanskyi and Günter Klambauer signed the team’s write-up from the Institute for Machine Learning at Johannes Kepler University in Linz. The Last Dance finished seventh on the public leaderboard. The private board will have its own say.
A very large model, a very small room
The crew began with NVIDIA’s Qwen3.8-Flash-Next, a model with hundreds of specialist parts. They watched the full model play through their own game harness, tried different ways to prune it and kept the best-performing recipe. More than a third of the specialists went. The smaller model left more room for game memory on the graphics card.The team credits roughly half its progress to fitting more model and memory on the card, and half to the harness around it. In its timeline, the largest listed jump came from memory changes.
Memory first, then manners
The dancers started from Tufa Labs’ Duck harness and kept its idea of one Python tool that lives through the game. They cut their instructions back and removed a request to use as few moves as possible. When they put that request back, the team reports, the agent took fewer moves but cleared fewer levels.One reminder survived at the end of each game message: use code for counts and positions instead of reading the frame by eye.
Musical chairs for games
The server could run 25 games at once. Each game got an opening stretch; later, a stalled game could yield its seat to a waiting or more promising one, then return with its conversation and notes intact. The team weighted priority by the score still available and the chance of clearing the current level soon.The model that needed a rulebook
The team wrote down the rules of each public game and gave the model a short hint for each level. Its score jumped. The researchers’ reading: playing the games was less of a problem once the model understood them; working out the rules was the bigger gap.Even the rulebook could not fix everything. In two scrolling games, the agent lost track of the scene as the camera moved. The team tried stitching views into a map. It helped on the first scrolling level, but the overall score did not change, and the map stayed out of the final entry.
Other experiments came up short: three attempts at building game models did not clear more levels, and about a third of model-written summary requests returned nothing usable. The team continued stuck conversations with a question about what the model believed and what blocked it.
The Last Dance has put its notes on the table. Who will teach the next model to discover the rules for itself?
The Last Dance — public leaderboard solution overview · ARC-AGI-3 public leaderboard