AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A novel technique for post-hoc explainability queries in GNNs is introduced by focusing on the semifactual reasoning, and a novel learning architecture for addressing their computation is proposed.
This work formalizes explanations as Halpern-Pearl actual causes, modeling input dependencies using Boolean Structural Causal Models (SCMs), and compute HP causes by applying bound propagation and branch-and-bound techniques, while providing formal guarantees of completeness and minimality.
Real-world decision-making in public health and social science can greatly benefit from predictive models, yet translating predictions into effective interventions requires explaining the model behavior. While Graph Neural Networks (GNNs) are well-suited for modeling relational data, existing explanation methods largely operate at the node level and fall short of supporting actionable, network-level intervention design. Existing counterfactual GNN explainers, such as CF-GNNExplainer and CF$^2$, rely on continuous mask optimization over features and edges, which implicitly assume feasible edge manipulation, may allocate effort to immutable or non-actionable attributes, and incur substantial computational overhead. Further, the method of arriving at the explanation itself is difficult to explain to a domain specialist who is not an AI expert. Can simple methods generate good explanations? To explore this, we reframe counterfactual explanation as an intervention design problem. At the local level, we generate counterfactuals via a greedy search that directly identifies minimal, actionable changes to node features and neighbor-level conditions. We derive conditions under which the greedy search provides guarantees, and empirically show that these conditions are approximately met. These counterfactuals are converted into interpretable rules suitable for real-world intervention. At the network level, we formulate intervention selection as a Disjunctive Normal Form (DNF) coverage problem under a budget constraint, which is nondecreasing and approximately submodular, enabling a greedy algorithm with theoretical guarantees. Experiments on synthetic graphs and real-world suicide risk networks demonstrate that our approach produces scalable, cost-effective intervention strategies with significantly improved efficiency over mask-based counterfactual methods.
This work proposes Graph Diffusion Counterfactual Explanation via Inversion (GDCE-I), a discrete denoising diffusion model with a novel discrete inversion scheme that enables distribution-aware edits leveraging the whole domain edit space and qualitatively shows that GDCE-I attains interpretable in-distribution solutions.
This paper proposes Concept-guided Counterfactual Subgraph Retrieval (CCSGR), a dataset-grounded formulation that retrieves from a large graph subgraphs that are structurally and semantically similar to a query but induce different predictions under the same model, yielding domain-valid and verifiable counterfactual explanations.
Causal Discovery (CD) from observational data faces two fundamental challenges. First, purely statistical methods often lack the power to resolve structural ambiguities in low-sample regimes. Second, although LLM-assisted hybrid approaches improve structure recovery through semantic reasoning, the influence of that reasoning on individual edge decisions remains largely opaque. Consequently, existing hybrid methods fail to satisfy a fundamental requirement: explaining why a particular edge is included or excluded in the learned directed acyclic graph (DAG). This is critical in real-world applications, where no ground-truth DAG exists and every structural decision must be independently justified. We formalize this requirement as decision traceability, requiring every inferred edge to be supported by auditable statistical evidence, Markov Blanket consistency, or explicit domain reasoning. We propose GENESIS, an explainable hybrid CD framework that decomposes graph construction into interpretable decision points. GENESIS first identifies and scores three-node structural motifs, including chains, forks, and colliders, to establish transparent structural priors, then progressively refines the graph by integrating these priors with observational evidence, invoking domain knowledge only when statistical evidence is insufficient. By design, every edge decision is resolved through an auditable source of evidence. Experiments show that GENESIS achieves 100% decision traceability across all settings, establishing explainability as a first-class objective in causal discovery. Despite this additional requirement, GENESIS consistently outperforms purely statistical CD methods on the majority of benchmark datasets across all sample regimes in terms of Structural Hamming Distance (SHD), while achieving performance comparable to state-of-the-art LLM-assisted approaches.
The rapid evolution of Large Language Models (LLMs) has brought unprecedented capabilities across reasoning, coding, and multimodal tasks. However, as performance scales, their opaque ''black-box'' nature raises a critical challenge: How can we trace the origins of emergent intelligence, and more importantly, how can we leverage these internal mechanisms to guide model optimization? This tutorial provides a comprehensive, end-to-end view of LLM interpretability, transitioning from microscopic neural analysis to macroscopic application and deployment. It is systematically organized into five core sections: i) Unlocking the Black Box: We begin with the evolution of LLM interpretability and highlight recent breakthroughs from leading research teams. ii) Methodology: We present a rigorous overview of foundational theories (e.g., mathematical framework for transformer, biological mechanisms in LLMs) and essential methods (e.g., path patching, logit lens, and neuron description). iii) Anatomy of LLMs: Using advanced techniques to decode internal semantic features, neural circuits, and complex behaviors, we interpret how models perform reasoning, factual recall, and in-context learning. iv) Applications: We show how to transfer interpretability insights into actionable improvements across the LLM pipeline, including interpretability-guided data synthesis (data value scoring, corpus filtering, and activation-based data diagnosis). We also present Pinpoint Training and Steering for precise capability gains, and Pinpoint Quantization for extreme low-bit compression with minimal capability loss. v) Advanced Topics: We conclude by exploring how these interpretability paradigms scale and inspire the design of frontier architectures, agentic systems, and thinking models. In this tutorial, researchers and engineers will gain the theoretical frameworks and practical engineering toolkits needed to understand, steer, and efficiently deploy LLMs in real-world production environments.
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