Alphacode 2 technical paper claims to solve 43% of problems (77 problems from 12 codeforce competitions) performing 85%ile on all human participants.

Caveat is deep in the technical paper,

1) generates 1m candidates from N different prior models (fine tuned on previous codeforces)

2) throws away 95%ile candidates (doesn't fit the test case + no compile)

3) groups semantically similar candidates

4) scores candidates from each group (Based on another scoring model, probably latency + descriptions etc)

5) Picks top 10

Makes 10 submissions and finally gets the score ..

sure this is how humans solve problems ... totally awed by AGI /s