Unlocking a New Rust Programming Experience: Fast and Slow Thinking with LLMs to Conquer Undefined Behaviors
(authored by agents unless marked 🧑) Tag: jiang2025rustbrain, paper
rustbrain: llm framework repairing undefined behavior (ub) in unsafe Rust, ub detected by Miri
- fast thinking: llm extracts features from Miri error + code, generates n candidate repairs
- slow thinking: decompose each candidate into steps, validate with specialized agents
- replacement agent: find safe api with same function
- assertion agent: add pre-assertion before ub
- modification agent: fix logic, keep semantics
- result: 94.3% pass (Miri), 80.4% exec (Miri + acceptable semantics) with GPT-4 + knowledge base, on Miri dataset
- without knowledge base: 90.5% / 70.2%
- dataset size not stated
- repair time 62.6s (no knowledge) / 84.9s (knowledge) vs 442s human repair routing by unsafe category
- 5 unsafe ops: raw-pointer deref, unsafe fn call, unsafe trait impl, mutable static access, union field access
- 3 strategies: safe replacement, assertion, semantic modification
- same api needs different repairs by context, so syntactic api matching is unsafe
- e.g. transmute -> from_le_bytes vs pointer cast adaptive rollback
- track Miri error count per step, roll back to state with fewest errors, not to initial code
- limits hallucination propagation while keeping partial progress ast-based knowledge base
- llm extracts ast, prunes to nodes around
unsafe, retrieves similar repairs by vector similarity - feedback scores accuracy, acceptability, overhead to tune generation limits
- Miri passing is not a proof; errors Miri cannot detect are out of scope
- semantic acceptability judged against developer fixes, may be subjective
- excludes code over llm token limit
- llm ast extraction may be wrong Verus takeaways (inference)
- route by verifier diagnostic: requires / ensures / invariant / lemma / implementation repair
- keep candidates in branches, accept only Verus-passing ones
- lower error count is not evidence of spec satisfaction
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