Learning Lookahead Lemmas for Neural Network Verification

Lookahead Branching for Neural Network Verification

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 $\alpha$-$\beta$-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.

Branch and Bound for Relational Verification of Neural Networks

A branch-and-bound framework to mitigate the issue of verifying relational specifications against relational specifications, which iteratively splits the problem until all sub-problems are verified, and devise a relational neuron selection strategy based on the dual formulation of the verification problem.

Uncovering the Limits of Proof Sharing for Neural Networks

This study shows that template subsumption rates can vary widely across scenarios, and presents FastCert, a novel technique for automatically distributing templates across neural network layers to increase performance impact, eschewing templates entirely if they are unlikely to produce a speedup.

A Conflict-Aware Learning Approach to SCA Verification for MAC Architectures

VeRe: Verification Guided Fault Localization and Repair Synthesis of Deep Neural Networks

VeRe is proposed, a verification-guided repair framework that leverages linear relaxation to precisely and efficiently estimate the repair significance of neurons and synthesizes ideal intervals that provide sound guarantees for correct behaviors, thereby facilitating surgical and targeted adjustments of neuron parameters to rectify faulty behaviors.

PPDL: LLM-Based Flows as Probabilistic Programs

Building reliable applications that leverage large language models (LLMs) remains a significant challenge. While LLMs offer impressive capabilities across diverse tasks, their outputs often lack accuracy and provide no clear measure of confidence. This uncertainty compounds in flows of multiple calls to LLMs and other tools, making it difficult for developers and end-users to trust the results. This paper introduces a probabilistic language for programming LLM-based flows. It enables developers to quantify and propagate uncertainty throughout the application's flow, and experiment with different inference scaling techniques without adding a single line of code beyond the flow's logic. We present an experimental study to demonstrate this capability, and a case study building a theorem proving agent for the Rocq theorem prover.