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Optimal Wiener-Filter Solutions for Denoising of Graph Signals on Directed Graphs
Electrical Engineering and Systems Science > Signal Processing [Submitted on 5 Jun 2026] Title:Optimal Wiener-Filter Solutions for Denoising of Graph Signals on Directed Graphs View PDF HTML (experimental)Abstract:Graph signal processing has opened new avenues to the canonical denoising problem in interesting settings.
The Amplifying Mirror: Locating and Steering the Partisan Direction inside a Large Language Model
Announce Type: new Abstract: Large language models are rapicly replacing search engines as the primary interface between people and information. Unlike search engines, which retrieve existing content, LLMs generate novel text shaped by internal representations learned during training. Here we show that partisan political identity is encoded in the model's activation space, and that this direction directly shapes generation.
The Case for Model Science: Verify, Explore, Steer, Refine
arXiv:2606.01189v1 Announce Type: new Abstract: We argue that the AI community is now ready to move beyond benchmarking and consolidate scattered efforts in model analysis into a systematic discipline, a direction we term Model Science. Complex AI models now serve billions of users, yet our understanding of how they work lags far behind our ability to deploy them. Decades of benchmark-driven research have delivered remarkable progress: extensive leaderboards, a wide range of performance...
MPC for nonlinear systems: a comparative review of discretization methods
Electrical Engineering and Systems Science > Systems and Control [Submitted on 4 Jun 2026] Title:MPC for nonlinear systems: a comparative review of discretization methods View PDF HTML (experimental)Abstract:This work provides a comparative review of three different numerical methods generally used to discretize continuous-time non-linear equations appearing in model predictive control problems: direct multiple shooting, direct collocation and successive linearizations. An overview of the...
Comparing LLM-Based Conversational and Graphical Interfaces for Industrial Decision Tasks: An Exploratory Mixed-Methods Study
Announce Type: new Abstract: The use of Generative AI Conversational User Interfaces (CUI) as a new way to access and analyze data is growing in all sectors, and the industrial one is no exception. There, large amounts of data produced by IoT devices are flowing through user interfaces and may require them a new adaptation to the new analyses needs of decision-makers. LLM-based CUIs are promising a new way to directly interact with those data through the directness of natural language and...
A Numerical Experiment on Oscillatory Magnetic Reconnection in a Laboratory Plasma System Driven by Alternating Currents
arXiv:2606.09745v1 Announce Type: new Abstract: Using the open source MPI-AMRVAC framework, we study oscillatory reconnection in a laboratory plasma, which occurs when a magnetic null is perturbed by incoming fast magnetoacoustic waves driven by an alternating current. The magnetic null region collapses to first form a $y$-directed current sheet that later changes its orientation to the $x$-direction.
LAP: An Agent-to-Instrument Protocol for Autonomous Science
Announce Type: new Abstract: Autonomous science is moving from demonstration to infrastructure. Large language model agents now plan experiments, and self-driving laboratories execute them. Yet every such system rebuilds the link between the reasoning agent and the physical instrument from scratch, against fragmented vendor SDKs and standards built for deterministic software clients rather than probabilistic, goal-directed agents.
REGAIN: REconciliation GAIN-driven Auxiliary Direction Learning
arXiv:2606.04380v1 Announce Type: cross Abstract: Forecast reconciliation usually starts from a fixed measurement system and asks how forecasts should be projected onto a coherent space. We ask a different question: which additional linear measurements should be forecast and included in the reconciliation system? We propose REGAIN, a reconciliation-gain framework that learns normalized auxiliary directions, forecasts the induced series with a frozen forecasting oracle, and selects directions...