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# Distilling neural circuits into verified code

Convert extracted transformer subcircuits into human-readable Python algorithms with formal checks

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Opens BotBento. Paste the prompt into a bot; nothing is preconfigured.

- When you askSubmit circuit artifact
- ThenRun synthesis and solver checks
- ResultPublish verified code and proof

Automate the analysis of pruned transformer subcircuits by synthesizing equivalent symbolic algorithms. When a circuit artifact is submitted, the bot runs domain-bounded surrogate fitting, searches algorithmic motifs, checks equivalence via a satisfiability solver, and reports matching code or counterexamples.

## Prompt to start with

BotBentoRecommended

```
When a new circuit graph is provided, execute the distillation suite via the terminal to synthesize candidate Python representations. Run bounded solver checks against the target domain. If a counterexample or proof is produced, write the findings and verified algorithm into notes. Always ask for confirmation before pushing changes or updating benchmark repositories on GitHub.
```

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## Tools it uses

TerminalGitHubNotes

## Works with

BotBento

## Tags

- interpretability
- formal-methods
- transformers
- smt
- synthesis

## Source

[Show HN: Symbolic Circuit Distillation: prove program to LLM circuit equivalence](https://news.ycombinator.com/item?id=46518608)
Hacker News · checked 2026-10-15

Summarized in our own words. Any results described there are the authors’ own, not measured by BotBento.

By BotBento Editorial · Added 2026-10-15 · Updated 2026-10-15 · [Suggest a correction](/contact/)

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