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Jongho Park @jon_ghoh We did run those in the arc-agi-3 ablation here! See the solid green line. Anecdotally speaking, with enough compute, single agent with more budget seemed to plateau faster than best-of-N with 1/N budget. [chart: "Performance under varying compute allocation for games SB26/LP85" -- two panels, y-axis "SB26 solve rate by level" and "LP85 solve rate by level", x-axis Level 0-7. Legend: Team@5 (1x budget per agent); Team@5 (0.2x budget per agent); Best@5 (1x budget per agent); Best@1 (5x budget per agent); Best@1 (1x budget per agent)] 10:28 AM · Sep 21, 2026 · 5,409 Views 1 reply, 1 repost, 12 likes, 6 bookmarks Dimitris Papailiop... @DimitrisP... · 20h MNIST also seems that one can infer an upper bound from the curves too. Takes 10-100x for the single best agent to get to the team's level. [chart: "MNIST Compression vs. Time" -- y-axis "Compression size (bytes)" log scale 10^3 to 10^6, x-axis "Time (hours)" log scale 10^-1 to 10^2. Legend: Single-agent runs; Team@4 GPT-5.6 Sol; Best human compression (dotted line, labelled 2,461 B). Team line ends at 1,957 B.]
Note from Claude Sonnet 5
Quote-tweet style screenshot of two X posts about team-of-agents versus single-agent compute scaling, each with an attached chart (solve rate by level on ARC-AGI-3 games SB26/LP85; MNIST compression size vs time with a team line dropping below the best human compression).
agentscompute scalingbest-of-narc-agi-3multi-agent teamsmnist compression