The Airborne Collision Avoidance System Xu (ACAS-Xu) relies on large certified Look-Up Tables (LUTs) that encode the exact decision logic used in operation. Neural-network-based approximations have been proposed to reduce memory requirements, but they inherently introduce approximation errors and complicate formal verification. This paper presents a symbolic compression approach based on Binary Decision Diagrams (BDDs) that preserves the exact semantics of the ACAS-Xu LUTs. The resulting representation is canonical, deterministic, and fully equivalent to the original tables, enabling sound and exact reasoning over the complete decision logic. By expressing both the system behavior and domain-specific operational properties within a common Boolean framework, verification reduces to efficient BDD operations and emptiness checks, with precise counterexamples generated when properties are violated. We demonstrate that the proposed BDD-based representation significantly reduces memory usage, achieves predictable and low-latency execution, and can be deployed on embedded platforms. These results highlight BDDs as a compelling alternative for exact, verifiable, and embedded deployment of ACAS-Xu decision logic.
Assertion-based Verification (ABV) is essential for ensuring that hardware designs conform to their intended specifications. However, existing automated assertion-generation approaches, such as LLM-based frameworks, often generate large numbers of redundant assertions, which significantly degrade simulation efficiency. To mitigate the simulation overhead caused by redundant assertions, this paper proposes Arcane, an efficient assertion reduction framework. It integrates a two-tier assertion clustering approach for accurate semantic classification of large assertion sets, and employs Monte Carlo Tree Search (MCTS) to explore optimal rule-application sequences for efficient assertion reduction. The experimental results on Assertionbench [20] show that Arcane achieves a reduction of up to 76.2% in the assertion count while fully preserving formal coverage and mutation-detection ability. Further simulation studies demonstrate a speedup of 2.6x to 6.1x speedup in simulation time. The proposed framework is released at https://anonymous.4open.science/r/Arcane1-0A6F/.
State-of-the-art neural network verifiers use the branch-and-bound procedure as their core solving mechanism. We introduce an inprocessing framework for neural network verification driven by the lookahead procedure. Under this framework, lookahead derives new lemmas over the phases of unstable ReLUs, which are collected into an implication graph that is used to prune the search space and vivify boolean cuts. We instantiate the framework in two state-of-the-art verifiers, Marabou and α-β-CROWN, and demonstrate that it improves performance in both, proving up to 34% more instances unsatisfiable.
In this work, we investigate the effect of lookahead branching strategies in neural network verification. We present a general recipe to integrate lookahead into any branch-and-bound verifier and demonstrate how one of the current state-of-the-art branching heuristics, FSB, can be viewed as a special instantiation of the lookahead branching strategy. We also describe how, in addition to improving the quality of branching decisions, lookahead can generate additional lemmas that accelerate verification. We instantiate the method in two representative branch-and-bound-based verifiers (Marabou and α-β-CROWN), and demonstrate that lookahead leads to consistent speedups in verification time and up to 57% more solved instances. Code is available at https://github.com/ai-ar-research/lookahead-branching.