Verified Detection and Prevention of Concurrency Anomalies in Multi-Agent Large Language Model Systems
This research addresses concurrency anomalies in multi-agent LLM systems by formalizing four anomaly types using TLA+ and building a mechanically verified consistency hierarchy L0-L4. With 274 Verus proof obligations, the detectors are proven sound and complete. Three Rust runtimes implement L0-L1, and the work reproduces real-world anomalies in ByteDance's deer-flow and LangGraph, providing verified fixes.
[2606.17182] Verified Detection and Prevention of Concurrency Anomalies in Multi-Agent Large Language Model Systems
[Submitted on 15 Jun 2026]
Title:Verified Detection and Prevention of Concurrency Anomalies in Multi-Agent Large Language Model Systems
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Abstract:Multi-agent LLM systems share state through memory stores, vector indices, and tool registries. We model such sharing as long-running read-generate-write operations under deterministic-generation semantics -- the regime durable-execution engines enforce by deterministic replay -- and formalize four concurrency anomalies in TLA+: stale-generation, phantom-tool, causal-cascade, and tool-effect reordering, structural analogues of classical isolation anomalies, each with a TLC counter-example. The exclusion lattice over these anomalies is trivial; the contribution is the mechanically verified realizability and strict separation of one maximal chain within it, $L_0 \subsetneq \cdots \subsetneq L_4$, to our knowledge the first machine-checked consistency hierarchy for such runtimes. A development of 274 Verus obligations (zero assume, zero admit; trust base: two structural axioms and a mutex correspondence) proves the detectors sound and complete against the specifications and each runtime its avoidance set. Three deployed Rust runtimes realize L0-L1 (pessimistic locking, serializable snapshot isolation, default-SI), each verified against stale-generation and refined to its state machine; L2-L4 are exec-mode-verified with dependency-free prevention twins (A3, A6, A2: 0/1000 versus 1000/1000), and L2 is run live across three model families (A3 prevented in all 120 retracted sessions). We reproduce a silent lost update in ByteDance's deer-flow, formalizing its fix as a verified $L_0 \to L_1$ refinement, and exhibit tool-effect reordering in LangGraph's ToolNode on unmodified output, removed by an L3 commit-order sequencer. The verified detector, refinements, and realizability artifacts are the contribution; the phenomena and lattice are classical.
Comments: 32 pages, 2 figures, 6 tables. Verus/TLA+ verification artifact, reference Rust runtime, and Python harnesses, plus a supplementary appendix (Sections A-F, Tables S1-S6), included as ancillary files
Subjects:
Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC); Logic in Computer Science (cs.LO); Multiagent Systems (cs.MA); Programming Languages (cs.PL)
ACM classes: D.2.4; F.3.1; D.1.3; I.2.11
Cite as: arXiv:2606.17182 [cs.LG]
(or arXiv:2606.17182v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.17182
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Sajjad Khan [view email] [v1] Mon, 15 Jun 2026 18:19:34 UTC (285 KB)
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