Charles Rosenba... @bzogramm... · 31m
"You can solve harder problems by having a ton of AI agents work in parallel and communicate" is the same lesson that SAT solver people found decades ago.
I wonder how long until people start reinventing QBF solver ideas.
1 reply, 3 likes, 80 views
Charles Rosenba... @bzogram... · 22m
I cannot stress this enough. Study SAT/SMT/QBF solvers. Study abstract interpreters. Study complexity theory.
The difficulty boundaries, both empirical and theoretical, apply to humans, classical solvers, and AI algos alike. Many of the techniques transfer as well.
Note from Claude Sonnet 5
Two consecutive X posts in a thread by Charles Rosenba... arguing that parallel communicating AI agents repeat the lesson of SAT solver portfolios, and urging study of SAT/SMT/QBF solvers, abstract interpreters and complexity theory.
multi-agentsat solversqbfcomplexity theoryswarm scalingabstract interpretation
Santiago M. @sanmking · 19h
Isn't this the complete opposite to that:
[quoted post]
Dimitris Papailio... @Dimitris... · Sep 20
Test-time communication looks like a next axis for scaling capabilities
New paper with the incredible @jon_ghoh and @vkontonis @ShivamGarg91462 and Akshay : ...
[embedded figure: three line charts: "ARC-AGI-3 (public)" (x: Agents (k), y: Tasks solved; series Best@k Sonnet 4.6 vs Team@k Sonnet 4.6; labels 6.6x, 8.0%, 4.3x, 4.6%), "FrontierCS Polyomino Packing" (x: Time (hours), y: Packing score; series Best@4 Opus 4.6 vs Team@4 Opus 4.6; labels prior best 0.894, 0.922, 0.893), "MNIST Compression" (x: Output tokens, y: Compression size (bytes); series Best@4 GPT-5.6 Sol vs Team@4 GPT-5.6 Sol; labels prior best 2,461 B, 3,160 B, 1,957 B)]
Figure 1: Communication outperforms independent agents in open-ended tasks. With communication, a team of k communicating agents not only outperforms best@k but scales success rate on ARC-AGI-3 faster. On algorithmic and ML optimization tasks, team@k beats single-agent runs and best-known human solutions, yielding new state-of-the-art results.
3 replies, 7 likes, 421 views
Sharmake Farah reposted
Toby Ord @tobyordoxford
Those graphs are showing the difference between swarm scaling and best@k scaling (i.e. what coordination adds) whereas I looked at the difference between swarm scaling and longer CoT (the most common current inference scaling technique).
12:46 AM · Sep 22, 2026 · 283 Views
2 replies, 10 likes, 1 bookmark
Relevant
Toby Ord @tobyordoxford · 9h
I'm not surprised swarm scaling beats best@k scaling on verifiable tasks (where it doesn't there is no value in working together). [cut off]
Note from Claude Sonnet 5
X thread screenshot: Santiago M. asks whether a quoted Papailiopoulos post and figure (test-time communication among agent teams, team@k vs best@k) contradicts Toby Ord's earlier analysis; Toby Ord replies that the graphs measure swarm vs best@k scaling while he compared swarm scaling to longer chain-of-thought. A reply of his is cut off at the bottom by the app UI.
inference scalingswarm scalingmulti-agentbest@kchain of thoughttoby ordarc-agi-3
