Meta paper shows that coding agents get much better when they reuse short summaries of past attempts instead of raw logs.

i.e. stronger coding agents do not just need more attempts， but better ways to remember attempts.

That sounds obvious until you look at what an agent actually produces： not an answer， but a messy trail of file reads， shell commands， errors， partial fixes， and abandoned ideas.

The paper's idea is to turn each full attempt into a compact summary of the main guess， partial progress， and failure points， then use those summaries both to pick the best attempts and to guide new ones.