DSR
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Related Articles from SNS
The Dynamic-Probabilistic Consistency Gap in Chaotic Surrogate Modeling
arXiv:2605.31547v1 Announce Type: new Abstract: Dynamical systems reconstruction (DSR) aims to learn surrogate models that capture the dynamics underlying time-series data. Reliably deploying these surrogates requires uncertainty estimates consistent with the learned dynamics. We expose a dynamic-probabilistic consistency (DPC) gap: the pursuit of finite-horizon probabilistic objectives can degrade dynamics or decouple predictive uncertainty from the local tangent dynamics it ought to reflect.
Location-Invariant Assessment of Flexibility Potential under Distribution System Reconfiguration
Announce Type: replace Abstract: The growing integration of renewable and decentralized generation increases the need for flexibility in distribution systems. This flexibility, typically represented in a PQ capability curve, is constrained by network limits and topology. Distribution system reconfiguration (DSR) introduces additional degrees of freedom through switching actions.
Position: A Dynamical Systems Perspective is Needed to Advance Time Series Modeling
Announce Type: replace Abstract: Time series (TS) modeling has come a long way from early statistical, mainly linear, approaches to the current trend in TS foundation models. With a lot of hype and industrial demand in this field, it is not always clear how much progress there really is. To advance TS forecasting and analysis to the next level, here we argue that the field needs a dynamical systems (DS) perspective.
Benign Inputs, Harmful Outputs: Cross-Modal Jailbreaking via Distributed Semantic Recomposition
arXiv:2606.01837v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have recently demonstrated remarkable capabilities in content synthesis and autonomous reasoning. Previous safety guardrails are primarily designed for unimodal textual input interception, leaving them vulnerable to cross-modal jailbreak attacks. However, regardless unimodal textual attack or cross-modal jailbreak, typically inclusive part of explicit harmful or sensitive content at the input level,...
Shortcut to Nowhere: Demystifying Deep Spurious Regression
arXiv:2606.01723v1 Announce Type: new Abstract: Real-world regression often exhibits shortcuts: attributes that are spuriously correlated with continuous targets in training, yet unreliable under deployment shifts; regressing targets using such shortcuts may fail catastrophically at test time. Existing studies on spurious correlations focus primarily on classification, where labels are categorical and groups are naturally defined. However, many real-world tasks require continuous prediction,...
Can LLMs Reason Structurally? Benchmarking via the Lens of Data Structures
Announce Type: replace Abstract: Large language models (LLMs) are deployed on increasingly complex tasks that require multi-step decision-making. Understanding their algorithmic reasoning abilities is therefore crucial. However, we lack a diagnostic benchmark for evaluating these capabilities.
REFLECTOR: Internalizing Step-wise Reflection against Indirect Jailbreak
arXiv:2605.20654v2 Announce Type: replace Abstract: While Large Language Models (LLMs) demonstrate remarkable capabilities, they remain susceptible to sophisticated, multi-step jailbreak attacks that circumvent conventional surface-level safety alignment by exploiting the internal generation process. To address these vulnerabilities, we propose Reflector, a principled two-stage framework that internalizes self-reflection within the generation trajectory. Reflector first leverages...
Hyperspectral Smoke Segmentation via Mixture of Prototypes
arXiv:2602.10858v2 Announce Type: replace Abstract: Smoke segmentation is critical for wildfire management and industrial safety applications. Traditional visible-light-based methods face limitations due to insufficient spectral information, particularly struggling with cloud interference and semi-transparent smoke regions.