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Related Articles from SNS
Graph Cascades: Contagion-Based Mesoscopic Rewiring for Structure-Aware Graph Machine Learning
arXiv:2606.05046v1 Announce Type: new Abstract: We introduce Graph Cascades, a mesoscopic rewiring strategy for Graph Neural Networks (GNNs) and Graph Transformers (GTs) that captures intermediate-scale graph structure beyond purely local edges or fully global attention. Using contagion-based diffusion processes, Graph Cascades constructs, in O(|V|+|E|) time, an auxiliary graph where node pairs supported by repeated multi-hop reinforcement are promoted to direct neighbors. We theoretically...
Network Recovery from Cascade Data: A Debiased Jacobian-Based Machine Learning Approach
arXiv:2606.07483v1 Announce Type: new Abstract: Many important outcomes unfold as dynamic cascades, including product adoption, disease spread, financial distress, and information diffusion. A central challenge is to recover the hidden influence network behind these cascades.
Cascading Hallucination in Agentic RAG: The CHARM Framework for Detection and Mitigation
Announce Type: new Abstract: Multi-step agentic retrieval-augmented generation (RAG) pipelines have demonstrated significant capability for complex reasoning tasks, yet remain vulnerable to a class of failure that existing hallucination detection mechanisms systematically miss: cascading hallucination, where errors introduced at early pipeline stages propagate and amplify across successive reasoning steps, producing confident but factually incorrect final outputs. To address this...
Cascades in the Kinetic Equation for the Majda-McLaughlin-Tabak model
arXiv:2606.07763v1 Announce Type: new Abstract: The Majda-McLaughlin-Tabak (MMT) family of models has proven to be an efficient ground for benchmarking wave turbulence theory, thanks to the low computational cost required to test theoretical ideas and the possibility of tuning nonlinearity and dispersive properties of the equations. Here, we study numerically the wave kinetic equation (WKE) associated with the MMT model and perform simulations to study turbulent cascades. We confirm...
MinMax Recurrent Neural Cascades
arXiv:2605.06384v3 Announce Type: replace Abstract: We introduce MinMax Recurrent Neural Cascades (MinMax RNCs), a class of recurrent neural networks built from a novel form of recurrence over the MinMax algebra. We show that MinMax RNCs enjoy key properties that are difficult to obtain simultaneously: strong formal expressivity, efficient evaluation, stable dynamics, and non-vanishing state gradients. First, their formal expressivity corresponds to the regular languages, arguably the...
Online Pandora's Box for Contextual LLM Cascading
Announce Type: new Abstract: Motivated by Large Language Model (LLM) cascading, we propose an online contextual Pandora's Box model for adaptively querying and selecting LLM APIs. In each period, a decision-maker observes a request context and faces a two-phase decision problem. In the query phase, the decision-maker sequentially queries APIs, where each query reveals a generated output and the decision-maker incurs an (output-dependent) cost.
Diffractive cascades for polychromatic hard X-ray focusing
arXiv:2605.15526v2 Announce Type: replace Abstract: Diffractive focusing of hard X-rays has traditionally required structures with large aspect ratios due to the limited interaction of most materials with X-rays. This has increased the complexity of fabricating diffractive X- ray lenses, restricting their widespread deployment.
Hallucination Cascade: Analyzing Error Propagation in Multi-Agent LLM Systems
arXiv:2606.07937v1 Announce Type: new Abstract: Large Language Models (LLMs) generate fluent text but remain vulnerable to hallucinations, producing unsupported, inconsistent, and factually incorrect claims. Most prior work treats hallucination as a static property of isolated outputs. In multi-agent LLM systems, however, responses are exchanged across agents, revised through sequential stages, and reused as context for later reasoning.
CASCADE Conformal Prediction: Uncertainty-Adaptive Prediction Intervals for Two-Stage Clinical Decision Support
arXiv:2605.20468v2 Announce Type: replace Abstract: Effective medication management in Parkinson's Disease (PD) is challenging due to heterogeneous disease progression, variable patient response, and medication side effects. While AI models can forecast levodopa equivalent daily dose (LEDD) as a measure of medication needs, standard uncertainty quantification often fails to communicate the reliability of these predictions, treating high and low confidence clinical decisions identically. We...
Energy spectra and cascade in the spin turbulence of a driven spinor Bose-Einstein condensate
arXiv:2606.00766v1 Announce Type: cross Abstract: We investigate the spin-interaction energy spectrum of spin turbulence in a driven spinor Bose-Einstein condensate. Continuous spin driving of a spin-1 condensate produces a nonequilibrium steady state with spatially fluctuating magnetization. We observe a power-law scaling consistent with the $-7/3$ exponent predicted for spin-wave turbulence, which persists across our full range of drive strengths despite substantial changes in the spectral...