arXiv subjects

Finn Hackett

Publications and source records attributed to Finn Hackett.

3 recordsLinked to original sources

arXiv2026

Specula: Scaling formal specifications for autonomous model checking of system code

Specula is a push-button agentic system that generates high-quality formal specifications for large, complex system code and uses the specifications for highly effective model checking and bug finding. Specula employs large language model (LLM) based coding agents to autonomously develop TLA+ specifications, including invariants that describe correctness properties of the target system and formal models that describe the system implementation with the right level of abstractions. Specula is fully autonomous and thus eliminates the barrier of applying formal methods to real-world system code (as in traditional human-centric approaches). Meanwhile, Specula addresses limitations of LLM-driven techniques like reward hacking and hallucinations through self-evolving loops that iteratively improve specification quality by enabling the agents to deepen their understanding of system code and its behaviors. We have used Specula to check 48 open-source system projects; Specula found 249 bugs including many deep bugs that are hard to find by existing approaches. Specula has been used by several companies and is maintained at https://github.com/specula-org/Specula.

cs.SE↗

arXiv2026

Trace Validation of Unmodified Concurrent Systems with OmniLink

Concurrent systems are notoriously difficult to validate: subtle bugs may only manifest under rare thread interleavings, and existing tools often require intrusive instrumentation or unrealistic execution models. We present OmniLink, a new methodology for validating concurrent implementations against high-level specifications in TLA+. Unlike prior TLA+ based approaches which use a technique called trace validation, OmniLink treats system events as black boxes with a timebox in which they occurred and a meaning in TLA+, solving for a logical total order of actions. Unlike prior approaches based on linearizability checking, which already solves for total orders of actions with timeboxes, OmniLink uses a flexible specification language, and offers a different linearizability checking method based on off-the-shelf model checking. OmniLink offers different features compared existing linearizability checking tools, and we show that it outperforms the state of the art on large scale validation tasks. Our evaluation validates WiredTiger, a state-of-the-art industrial database storage layer, as well as Balanced Augmented Tree (BAT), a state-of-the art lock-free data structure from the research community, and ConcurrentQueue, a popular lock-free queue featuring aggressive performance optimizations. We use OmniLink to improve WiredTiger's existing TLA+ model, as well as develop new TLA+ models that closely match the behavior of the modeled systems, including non-linearizable behaviors. OmniLink is able to find known bugs injected into the systems under test, as well as help discover two previously unknown bugs (1 in BAT, 1 in ConcurrentQueue), which we have confirmed with the authors of those systems.

cs.SE↗

arXiv2025

SysMoBench: Evaluating AI on Formally Modeling Complex Real-World Systems

Formal models are essential to specifying large, complex computer systems and verifying their correctness, but are notoriously expensive to write and maintain. Recent advances in generative AI show promise in generating certain forms of specifications. However, existing work mostly targets small code, not complete systems. It is unclear whether AI can deal with realistic system artifacts, as this requires abstracting their complex behavioral properties into formal models. We present SysMoBench, a benchmark that evaluates AI's ability to formally model large, complex systems. We focus on concurrent and distributed systems, which are keystones of today's critical computing infrastructures, encompassing operating systems and cloud infrastructure. We use TLA+, the de facto specification language for concurrent and distributed systems, though the benchmark can be extended to other specification languages. We address the primary challenge of evaluating AI-generated models by automating metrics like syntactic and runtime correctness, conformance to system code, and invariant correctness. SysMoBench currently includes eleven diverse system artifacts: the Raft implementation of Etcd and Redis, the leader election of ZooKeeper, the Spinlock, Mutex, and Ringbuffer in Asterinas OS, etc., with more being added. SysMoBench enables us to understand the capabilities and limitations of today's LLMs and agents, putting tools in this area on a firm footing and opening up promising new research directions.

cs.AI↗