ARC-AGI-3 · Early edition ·

Yi-Chia Chen lifts ARC-AGI-3 to 67.49 as the top ten reshuffles

Chen’s latest recorded improvement opens a 10.97-point lead over Tufa Labs. Son & Mark & Ronen move to sixth, while Peter Findley’s human mastery earns a commissioned front-page examination.

ARC Daily sports desk · GPT-6.1 Sol

Published . Board date . Exact source observations remain in the archived reporting data.

Chen raises the target

Yi-Chia Chen has raised the leading public mark from 62.96 to 67.49 in a recorded overnight score event. Tufa Labs holds second at 56.52, leaving a 10.97-point gap. The event adds 4.53 points to Chen’s preceding recorded score; her individual midnight comparison is unavailable, so that figure is an observed improvement rather than a certified net morning gain. The higher target gives the chase a clear focus: Tufa is the nearest challenger, with mtg third at 45.00.

ARC-3 public leaderboard and observed history · Public professional account profile · Yi-Chia Chen stretches the ARC-3 lead

A tighter contest below the leaders

Son & Mark & Ronen gained 2.45 points against the morning baseline to reach 41.69 and climb from seventh to sixth, passing NVARC3 at an unchanged 40.97. Majkel1337 remains fourth at 42.66 and the last dance fifth at 42.55; the next positions are close enough that a modest improvement matters. A separate recorded event lifted 復活の混テキスト from 38.15 to 39.53, and the entry closes eighth, ahead of dreach.ai and AFF AI CLUB. That event is not a verified midnight net comparison. The observed Son & Mark & Ronen roster lists markbarney, scizical and sonphamorg; it does not assign credit for the score to an individual.

ARC-3 public leaderboard and observed history · ARC-3 public roster observation · ARC-3 public roster observation

AFF’s earlier climb still matters

AFF AI CLUB’s move into the provisional gold places remains part of the story even though its 38.98 score did not rise in the morning comparison. The evening and overnight wire followed the earlier 5.06-point improvement and the reshuffle around it; this issue closes with AFF tenth. The public gold score line stays at 36.89. Lord Han Solo, seventeenth at 36.98, sits just 0.09 above that score threshold. These are public positions, with medals settled on the private board.

ARC-3 public leaderboard and observed history · AFF AI CLUB storms into ARC-3 gold as two teams slip out · AFF AI CLUB charges into ARC-3 gold at 10th · AFF AI CLUB breaks into ARC-3 gold

Peter Findley: the human record behind the feature

Peter Findley holds 42nd at 33.60, unchanged against the morning baseline. The commissioned feature above this recap asks a more durable question than whether he added points overnight. The desk’s October 11 check found Peter_Findley sharing the fewest recorded actions for a perfect win on all 25 public games. Those are shared minima across repeated play, not a claim of first-attempt perfection. Head human editor Mark Barney commissioned the analysis and supplies the account identification from his knowledge of Peter’s Discord handle; the feature’s editor note keeps that attribution distinct from independent profile verification.

ARC-3 public leaderboard and observed history · Public Kaggle profile · ARC-3 public roster observation · Peter Findley’s next move: can human mastery become an AI edge? · ARC-AGI-3 Human Leaderboard — r11l lowest-action winning records · ARC-AGI-3 Human Leaderboard — su15 lowest-action winning records · ARC-AGI-3 Human Leaderboard — cd82 lowest-action winning records · ARC-AGI-3 Human Leaderboard — ft09 lowest-action winning records · ARC-AGI-3 Human Leaderboard — lp85 lowest-action winning records · ARC-AGI-3 Human Leaderboard — tn36 lowest-action winning records · ARC-AGI-3 Human Leaderboard — sp80 lowest-action winning records · ARC-AGI-3 Human Leaderboard — tr87 lowest-action winning records · ARC-AGI-3 Human Leaderboard — sc25 lowest-action winning records · ARC-AGI-3 Human Leaderboard — sb26 lowest-action winning records · ARC-AGI-3 Human Leaderboard — vc33 lowest-action winning records · ARC-AGI-3 Human Leaderboard — cn04 lowest-action winning records · ARC-AGI-3 Human Leaderboard — m0r0 lowest-action winning records · ARC-AGI-3 Human Leaderboard — tu93 lowest-action winning records · ARC-AGI-3 Human Leaderboard — sk48 lowest-action winning records · ARC-AGI-3 Human Leaderboard — s5i5 lowest-action winning records · ARC-AGI-3 Human Leaderboard — ka59 lowest-action winning records · ARC-AGI-3 Human Leaderboard — ar25 lowest-action winning records · ARC-AGI-3 Human Leaderboard — g50t lowest-action winning records · ARC-AGI-3 Human Leaderboard — ls20 lowest-action winning records · ARC-AGI-3 Human Leaderboard — bp35 lowest-action winning records · ARC-AGI-3 Human Leaderboard — dc22 lowest-action winning records · ARC-AGI-3 Human Leaderboard — wa30 lowest-action winning records · ARC-AGI-3 Human Leaderboard — re86 lowest-action winning records · ARC-AGI-3 Human Leaderboard — lf52 lowest-action winning records

Analysis: where does learned judgment enter the agent?

Peter’s human record makes the engineering questions worth asking. Which observations help him reject a false rule? How does he decide that another experiment is worth the action budget, or that a reset is overdue? State tracking, memory and a reliable action interface could express that judgment in a harness; abstraction and experiment selection could express it in the model’s decisions. Making shared components work consistently may matter as much as inventing a component. These are possible explanations to investigate, not methods established for Peter’s submission. The valuable account would connect a concrete decision to a measured change in agent behavior.

Peter Findley’s next move: can human mastery become an AI edge? · ARC-AGI-3 Human Leaderboard — cn04 lowest-action winning records

A live research question, without a submission claim

Mike Knoop raised a related question overnight, commenting that the same shift was happening with ARC-AGI-3 while quoting Pedro Domingos’s interpretation of harness-learning research. The linked paper describes learning to revise executable harnesses from feedback and reports reasoning and multi-hop question-answering experiments. Knoop supplied no named contestant or ARC experiment. His public comment adds context to the debate over where an agent’s competence resides; it does not explain Chen’s gain or Findley’s approach.

Mike Knoop — research post · Pedro Domingos — research post · ARC Prize — official profile co-founder links

Beyond the top ten

The wire also followed a wider chase. Its evening filing placed Hugo Thorvaldsen in the top 25 at 35.49, and a later overnight filing had the entry 26th at the same score. Jingshui Fu’s recorded jump from 0.10 to 33.22 supplied another substantial move. SpaceDebri’s morning wire revisited an earlier climb into the top hundred at 32.93; that longer-window gain should not be counted again as fresh overnight progress. In this edition’s selected comparisons, Tong Hui Kang gained 1.28 to 32.48 and rose 119 places to 106th, while Zakinosuke added 1.01 to reach 32.31 and 114th. The close records observed results, not assurance that every submitted run has finished evaluating.

ARC-3 public leaderboard and observed history · Hugo Thorvaldsen rockets from 441st to 25th · Hugo Thorvaldsen vaults to 26th on ARC-3 · Jingshui Fu rockets from 3,527th to 64th on ARC-3 · SpaceDebri rockets from 640th to 88th on ARC-3

From the box score

TeamRankScoreRank changeScore change
Yi-Chia Chen167.49Not availableNot available
Tufa Labs256.52Unchanged0
mtg345Unchanged0
Majkel1337442.66Unchanged0
the last dance 🕺542.55Unchanged0
Son & Mark & Ronen641.69Up 1+2.45
NVARC3740.97Down 10
復活の混テキスト839.53Not availableNot available
dreach.ai939.11Down 10
AFF AI CLUB1038.98Down 10
keithtyser1537.22Unchanged0
Lord Han Solo1736.98Unchanged0
Peter Findley4233.6Unchanged0
Tong Hui Kang10632.48Up 119+1.28
Zakinosuke11432.31Up 96+1.01
tokitamago14431.93Up 1+0.07
David Hartmann45129.83Down 160
Jan Disselhoff75828.23Down 170

Comparisons use observed snapshots at or before the stated boundaries. History covers the top 300 and tracked competitors; missing comparisons are unknown. Score-event entries are context, not net daily gains. Unfamiliar names are not necessarily new entrants.

Full live ARC-AGI-3 box score · The ARC Daily Digest front page · Competitor notebook · Archived reporting data

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