Lookahead Branching for Neural Network Verification

2026-07-19

AI Summary

This work addresses the inefficiency of branching strategies in neural network verification by proposing a general lookahead branching ensemble method that integrates lookahead information into the branch-and-bound framework to optimize branching decisions. The approach not only reveals that the existing FSB heuristic is a special case of this formulation but also automatically generates auxiliary lemmas to accelerate the verification process. Experimental evaluation on the Marabou and α,β-CROWN verifiers demonstrates significant performance improvements on standard verification benchmarks, with up to a 57% increase in the number of solved instances compared to baseline methods.

Abstract

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.

Problem

• neural network verification
• lookahead branching
• branch-and-bound
• branching heuristics
• verification acceleration

Innovation

• lookahead branching
• neural network verification
• branch-and-bound
• FSB heuristic
• lemma generation