Hengfeng Wei

Qiuhuan Xiong, Hengfeng Wei, Si Liu et al.

Modern database systems widely support per-transaction isolation levels as a practical means of balancing consistency guarantees and performance. Yet, it remains largely unclear whether their concurrency control protocols correctly enforce the intended isolation guarantees under such mixed-isolation settings. In this paper, we address this semantic conformance question by developing \ourframework, a formal semantic framework for mixed isolation levels. We demonstrate its applicability by establishing the semantic conformance of two concurrency control protocols, one combining two isolation levels and the other three.

Weining Cao, Guangyuan Wu, Yuan Yao et al.

Distributed protocols are notoriously difficult to verify correctly. Proving safety typically requires inductive invariants that both imply the desired property and are preserved by every protocol transition; yet inferring such invariants remains a major bottleneck: existing approaches either restrict the protocol models to a decidable fragment of first-order logic or demand expert-crafted templates. We present IC3Syn, a neuro-symbolic framework that synthesizes inductive invariants by executing an IC3-style process over TLA+ states with the assistance of Large Language Models (LLMs). At large, IC3Syn combines a symbolic IC3 controller, which decomposes invariant synthesis into focused blocking tasks and an LLM which provides protocol-level reasoning that IC3 alone lacks for TLA+ specifications. This integration enables a disciplined yet flexible search for invariants without imposing logical restrictions or requiring manual templates. We evaluate IC3Syn on 29 distributed protocols spanning consensus, reconfiguration and client-server systems, and compare it against Endive, IC3PO, SWISS and DistAI. IC3Syn discovers candidate invariants for all 29 protocols, including MongoLoglessDynamicRaft (MLDR), an industrial-scale Raft-based reconfiguration protocol for which none of the compared tools reports a solution, as well as one complex Paxos variant. In each case, the invariants synthesized on finite instances are shown in TLAPS to be inductive for the full unbounded protocol, thereby establishing safety.

Zhiheng Cai, Si Liu, Hengfeng Wei

Elle is one of the most widely adopted black-box isolation validators. It crucially relies on the unique-value assumption for sound and efficient isolation validation. Yet, transaction workloads with duplicate values are highly relevant in practice: they naturally arise in real database systems, and many isolation bugs manifest only in their presence. In this paper, we extend Elle to handle such workloads by introducing a fine-grained dependency model that enables reasoning about dependencies between individual operations. We establish the soundness and completeness of our approach and implement it in a prototype. We also demonstrate its effectiveness in detecting isolation bugs and its promising performance.

Guangyuan Wu, Weining Cao, Zehui Tan et al.

Loop invariant inference is a fundamental yet challenging problem in program verification. Recent LLM-aided guess-and-check techniques have shown strong performance on single-loop programs, but they often struggle with programs containing multiple interacting loops. This paper presents InvWeaver, a neuro-symbolic framework for synthesizing invariants for such programs. The key idea is to expose inter-loop dependencies and propagate proof obligations through a combination of loop-level abstraction, obligation-guided inference, and weakest-precondition-based refinement. We evaluate InvWeaver on a comprehensive benchmark suite, including a newly curated dataset derived from classic algorithms. Experimental results show that InvWeaver substantially outperforms existing invariant inference methods, solving 72 out of 82 multi-loop benchmark problems and maintaining strong performance on single-loop tasks.