Progressive Advances
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Sanders-Backed Progressive Advances in California Swing District Against Valadao
Randy Villegas, a progressive backed by Senator Bernie Sanders of Vermont, defeated Dr. Jasmeet Bains in the Democratic primary in California’s 22nd Congressional District. He will face Representative David Valadao, the Republican incumbent.
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.
Nithya Raman edges out Spencer Pratt to face Karen Bass in LA mayoral runoff
Progressive challenger to face incumbent mayor in November as former reality star Pratt trails behindNithya Raman, a progressive Los Angeles city council member, has advanced to the November runoff for LA mayor, edging out former reality TV villain Spencer Pratt for the chance to face incumbent mayor Karen Bass. Pratt, who decided to run for mayor after his Pacific Palisades home burned down in the 2025 wildfires, held a lead over Raman for days. But as ballot processing from last week’s...
The Rising Dominance of Methods Across Science
Announce Type: new Abstract: Scientific progress is traditionally narrated through the interplay of theoretical insights and experimental findings. Yet this view of science underplays a third and central pillar of progress: the methods that underlie both conceptual advances and empirical evidence. By analysing more than 3 million articles across science published between 1980 and 2019, we find that science has undergone a fundamental structural transition.
From Holo Pockets to Electron Density: GPT-style Drug Design with Density
arXiv:2605.08767v2 Announce Type: replace Abstract: Recent advances in generative modeling have enabled significant progress in structure-based drug design (SBDD). Existing methods typically condition molecule generation on empty binding pockets from holo complexes, overlooking informative components such as the filler (ligands and solvent). Here, we leverage low-resolution electron density (ED) derived from the filler as a physically grounded condition for \textit{de novo} drug design.
TIDE: Task-Isolated Diffusion for Unified Video Editing and Generation
new Abstract: Recent advances in Diffusion Transformers have driven rapid progress in video generation and editing, yet these capabilities are still handled by separate, task-specific models. Building a unified framework that supports diverse video tasks remains an open challenge: existing unified attempts either require dedicated auxiliary encoders or lack explicit mechanisms to distinguish heterogeneous conditioning tokens, struggling when the number and type of visual conditions vary...
The Fundamental Limits of Fraud Detection in Card Payment Networks
Announce Type: replace Abstract: Card payment fraud detection is usually framed as a supervised classification problem. Although this approach has generated practical progress, improvement has remained incremental despite major advances in model architecture. We argue that this is not mainly a failure of function approximation or optimization, but a consequence of structural information impairments inherent to the payment ecosystem.
DecMem: Towards Minute-Long Consistent World Generation with Decoupled Memory
Announce Type: new Abstract: Recent advances in video generative models have promoted rapid progress in controllable world models. However, maintaining fine-grained spatio-temporal consistency under long-horizon reasoning remains a key challenge. In this work, we move beyond explicit 3D memory and coarse frame-level implicit modeling, and propose a fine-grained, learnable, and scalable memory for consistent world generation.
Benchmarking Counterfactual Prediction in Epidemic Time Series with Time-Varying Interventions
arXiv:2606.05692v1 Announce Type: new Abstract: Deep learning has enabled significant advances in time-series causal inference, yet progress remains constrained by the lack of realistic benchmarks with observable counterfactual outcomes. Existing datasets either rely on real-world observations without ground-truth counterfactuals or on simplified simulations that fail to capture complex causal dynamics. To address this gap, we develop a large-scale benchmark for counterfactual prediction in...
pTNAS: Progressive Neural Architecture Search for Tabular Data
Announce Type: replace Abstract: Recent advances have shifted the paradigm of tabular learning toward tabular foundation models, yet their accuracy relies on a heavy inference cost that scales poorly with context size. Deep neural networks remain a highly competitive and more efficient modeling paradigm when equipped with well-designed architectures; however, identifying such architectures in a data-adaptive and budget-aware manner remains challenging. We propose pTNAS, the first progressive...