Identifiability in structural causal models remains a persistent challenge in high dimensional nonstationary environments where latent shocks are obscured by information asymmetries and regime shifts. Traditional econometric methods often rely on rigid recursive assumptions or sign restrictions that falter during periods of extreme volatility. This paper proposes Adaptive Event Driven Labeling (AEDL), a novel neurosymbolic framework that synthesizes unstructured semantic data with formal causal inference to disentangle simultaneous supply and demand shocks. We introduce a Heterogeneous Multiagent Discussion (HAD) architecture, wherein Large Language Model agents with distinct analytical personas engage in dialectic consensus protocols to generate continuous intensity weighted narrative instruments. To mitigate hallucination and enforce economic coherence, the framework integrates a symbolic verification layer and a Reflexion mechanism that iteratively updates causal priors based on posterior market deviations. Empirical validation on global energy market data from 2020 to 2025 demonstrates that the proposed framework significantly outperforms standard set identification techniques, achieving a supply shock identification F1 score of 0.89. Specifically, the model successfully decomposed the February 2021 Texas Freeze by identifying a supply shock magnitude of 0.8 and a demand shock magnitude of 0.7, which attributed 60.0% of the immediate price spike to supply constraints. Furthermore, the Reflexion mechanism corrected initial physical supply estimates for the 2022 Ukraine invasion from 0.9 down to 0.4, while accurately isolating a geopolitical uncertainty premium of 0.95. By rigorously bridging natural language reasoning with time series econometrics, this work advances the state of the art in automated causal discovery for macro scale complex systems.
Financial transaction networks face a persistent threat from strategic adversarial drift, in which sophisticated actors manipulate graph structure to bypass detection. Conventional temporal graph neural networks tend to fail in this setting because they forget historical patterns when retrained and generalise poorly to novel structural perturbations. We address this gap with Game Theoretic Anticipatory Continual Graph Learning (GT-ACGL), a framework that casts fraud detection as a continuous Stackelberg game between a defender and an adaptive adversary. The framework combines three components: a bilevel anticipatory optimisation step that trains the defender against simulated future attacks, an Adversarial Motif Memory that retains topologically significant historical patterns without redundancy, and a predictive smoothing module that preserves temporal fidelity during high throughput batched training. We evaluate the approach on three large dynamic graph datasets. On the financial benchmark, Elliptic Temporal, GT-ACGL improves F1 by 11.0 percentage points over the strongest baseline under adaptive attack, with smaller but consistent gains on two behavioural interaction benchmarks. The framework also reduces the observed forgetting rate to below 6 percentage points and incurs only a
$$1.45\times$$
training overhead relative to a standard temporal graph network. By modelling the cost of evasion inside a Stackelberg training objective, GT-ACGL encourages decision boundaries that remain comparatively stable under strategic structural perturbation. These results are empirical observations on the studied benchmarks, obtained against the specified edge addition threat model realised by our own attack generator. They are not guarantees of equilibrium behaviour, of the economic infeasibility of attack, or of robustness to the full range of real world fraud adaptations.
Huijie Fan, Yanan Jiao, M. Wang et al.· Scientific Reports· 0 citations
