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Ensemble Score Filtering for Real-Data Energy Consumption Forecast Correction
arXiv:2605.29072v2 Announce Type: replace Abstract: Accurate estimation and forecasting of energy consumption are important for power-system operation, planning, and demand-side management. In practice, however, complete and timely measurements may not always be available, and the observed data can be partial, noisy, or delayed. This motivates the use of learned forecasting models for predicting the evolving consumption state, together with data assimilation methods for sequential forecast...
Proper Scoring Rules for Right-Censored Survival Data
arXiv:2606.06393v1 Announce Type: new Abstract: Proper scoring rules provide a rigorous theoretical basis for the training and evaluation of probabilistic forecasts. However, in the presence of right censoring, the event time is only partially observed, rendering conventional scoring rules inapplicable in their standard form. We propose a framework for proper scoring of right-censored survival outcomes based on a simple idea: first, map the predictive distribution through the censoring...
Pairwise Reference Alignment as a Model-Level Ordinal Observable
Announce Type: new Abstract: Pairwise preference data is widely used in language-model evaluation and alignment, often for model ranking, reward modeling, or preference optimization. This note formulates a more basic measurement question: given a reference distribution of pairwise preferences, what model-level quantity is estimated when we test whether a model ranks preferred responses above rejected responses? We define pairwise reference alignment as an ordinal observable induced by a...
Nvidia, Meta and Schlumberger rank among top companies adopting AI, new study says
Seemingly every company is obsessed with artificial intelligence these days, whether it's how the technology is transforming their industry or the effects it's having on employees and customers. But the degree to which companies are utilizing AI tools internally and adapting to a rapidly changing reality varies dramatically. A new study from AI-Driven Enterprise Institute (AIDE) breaks down how well S&P 500 companies — and their leaders — are adopting AI compared to their peers.
Samsung's updated Health app unsurprisingly comes with new AI-powered features
Samsung's updated Health app unsurprisingly comes with new AI-powered features The new app was designed to showcase the upcoming Galaxy Watches’ capabilities. Samsung will start rolling out an update on June 8 that will make its Health app more useful in everyday life. The company says that updated app will translate "complex biometric data — from overnight sleep to daily activity — into simple, actionable guidance."
Layerwise Terminal Discrepancy in Chen's Reverse-Heat Coupling on the Boolean Cube
arXiv:2606.04573v1 Announce Type: cross Abstract: We isolate a layerwise refinement of the terminal testing-discrepancy step in Chen's perturbed reverse-heat approach~\cite{Chen2026} to Talagrand's convolution conjecture on the Boolean cube. Built on the joint-filtration martingale formulation of Chen's coupling, and on Chen's approximate monotonicity and conditional squared-score estimates being available in the joint-filtration form stated below, we prove the localized testing estimate \[...
Relative Energy Learning for LiDAR Out-of-Distribution Detection
arXiv:2511.06720v3 Announce Type: replace Abstract: Out-of-distribution (OOD) detection is a critical requirement for reliable autonomous driving, where safety depends on recognizing road obstacles and unexpected objects beyond the training distribution. Despite extensive research on OOD detection in 2D images, direct transfer to 3D LiDAR point clouds has been proven ineffective. Current LiDAR OOD methods struggle to distinguish rare anomalies from common classes, leading to high...
Inverting Data Transformations via Diffusion Sampling
arXiv:2602.08267v2 Announce Type: replace Abstract: We study the problem of transformation inversion on general Lie groups: a datum is transformed by an unknown group element, and the goal is to recover an inverse transformation that maps it back to the original data distribution. Such unknown transformations arise widely in machine learning and scientific modeling, where they can significantly distort observations. We take a probabilistic view and model the posterior over transformations as...
Improving Diffusion Planners by Self-Supervised Action Gating with Energies
arXiv:2603.02650v2 Announce Type: replace Abstract: Diffusion planners are a strong approach for offline reinforcement learning, but they can fail when value-guided selection favours trajectories that score well yet are locally inconsistent with the environment dynamics, resulting in brittle execution. We propose Self-supervised Action Gating with Energies (SAGE), an inference-time re-ranking method that penalises dynamically inconsistent plans using a latent consistency signal. SAGE trains...
Closing the Prior-Posterior Loop: Self-Reflective Molecular Design with Analysis-Driven LLM Iteration
arXiv:2606.09520v1 Announce Type: cross Abstract: Can a general-purpose large language model design molecules with the precision of a seasoned chemist? Current LLM-based frameworks answer this question with scalar feedback loops-generate, score, reject-that amount to informed trial-and-error. Here we show that replacing a single number with the full physicochemical rationale from first-principles calculations transforms the LLM from a stochastic sampler into a causal reasoner.