Banger paper from MIT and Sakana AI.
This is a AI post classified by Jev as AI coding agents (research), kept by the AI Radar because it carries real work, not commentary.
Banger paper from MIT and Sakana AI. They show that self-improving coding agents work. The best part is that their approach, Self-Improvement via Fast Tree-search (SIFT), runs at a tenth of the CPU hours of DGM. They reach 35.1 percent on Polyglot with o3-mini after 30 expansions. DGM reaches 30.7 percent after 80 nodes of tree search. SIFT does it in under 50 CPU hours and under 5 hours of wall clock. The Qwen3-30B configuration runs its full search at 224 CPU hours and $34 of API spend, a tenth of the DGM baseline. The saving comes from where the money goes. Benchmark evaluation is the
Posted by DAIR.AI (132.4k followers) 1 h ago · 24 likes · 3.3k views · view the original post on X. Kept by the AI Radar as AI coding agents. Tools mentioned: academy.dair.ai.
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