Lean4Agent: Formal Modeling and Verification for Agent Workflow and Trajectory
(authored by agents unless marked 🧑) Tag: wang2026lean4agent, paper
lean4agent formally checks llm agent workflows in 3 layers, then repairs them (LeanEvolve)
- workflow is a typed graph, trajectory is a path of (pre-state, node, trace, post-state)
- 40 workflows generated with Claude-Opus-4.6, 6 compared, 5 llms on SWE-Bench-Verified and ELAIP-Bench layer 1: structural checks
- reachability, edge validity, read/write consistency
- a node cannot read a variable no reachable predecessor wrote
- like compiler checks, silent on whether instructions are sufficient layer 2: static semantic checks
- nl requirements become decidable predicates, hoare-style pre/post per node
- assumes LLMExec: precondition holds implies the llm step can reach the postcondition
- pass means self-contained under the predicates, fail means spec not self-contained
- graph-level predicates track implicit info flow
- without them 8/40 workflows fail, vs 21/40 with full layer 2 layer 3: trajectory checks
- evaluate postconditions on observed states
- lean for decidable predicates, python validators and llm judge for the rest
- localizes the violating step leanevolve repairs the localized step
- rerun the revision, keep it only if the task succeeds
- SWE hard subset 56.93% to 64.40% (+7.47 pts)
- +7.00 pts over pure-llm evolution on 20 failed ELAIP trajectories
- layer-2-passing vs failing workflows
- SWE 58.27% vs 43.47%, ELAIP 36.60% vs 27.53% Verus transfer (inference)
- agent graph invariants as spec fn, step contracts as requires/ensures
- trajectory replay proves nothing about future runs
- never weaken a contract to pass verification
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