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The trick: Scope Swap

Claude Opus 5.5 can now rebuild real programs from scratch, says the viral post.

The 18.5% is real. It means 37 of 200 programs cloned by running the original and passing a finite test suite.

Issue 5929 October 20269 receipts3 min
A robot carpenter builds a copy of a working music box beside the original while an inspector checks only a few keys.
AI illustration

Opus 5.5 can rebuild real programs from scratch, scoring 18.5% on ProgramBench after 0% in May.

Before you read on. Your call?

Vals AI and the benchmark's own leaderboard both report 18.5%, and the May paper found no model fully resolved any task.

The twist

The model gets the original program to run and probe, 163 of 200 tasks still fail at least one test, and the authors warn their tests can never cover all inputs.

18.5%Opus 5.5 tasks fully resolved: 37 of 200
81.5%Tasks with at least one failed hidden test
130Tasks where Opus 5.5 passed at least 95% of hidden tests
87.0%Average share of hidden tests passed per task

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The trick has a name

We call it Scope Swap: applies to far less than it sounds like. You'll see it again. Learn to spot it →

Receipts

  1. Supports vals.ai: Claude Opus 5.5 ranks first on ProgramBench with 18.50%
  2. Supports programbench.com: Given only a compiled binary and its documentation, agents must architect and implement a complete codebase that reproduces the original program's behavior.
  3. Supports arxiv.org: We evaluate 9 LMs and find that none fully resolve any task
  4. Context vals.ai: a task is counted as Fully Resolved only when the submitted implementation passes all tests.
  5. Context vals.ai: Claude Opus 5.5 87.0% 130 37
  6. Context programbench.com: The agent can run the given program with any input and observe exactly what it does
  7. Refutes programbench.com: Note that behavioral tests can never cover all possible inputs.
  8. Context programbench.com: ranging from small terminal utilities like jq and ripgrep to massive software projects like the PHP compiler
  9. Refutes arxiv.org: Models favor monolithic, single-file implementations that diverge sharply from human-written code.

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