Chronos
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Noise budget of Cryogenic sub-Hz cROss torsion bar detector with quantum NOn-demolition Speed meter (CHRONOS)
arXiv:2604.05840v2 Announce Type: replace Abstract: CHRONOS is a proposed gravitational-wave detector designed to operate in the sub-Hz frequency range (0.1 to 10 Hz), a largely unexplored band due to strong noise sources that hamper ground-based detectors. It employs cryogenic operation, a cross torsion-bar configuration, a triangular Sagnac interferometer, and a speed meter readout scheme to overcome key noise limitations, targeting a strain sensitivity of $h \sim 10^{-18} Hz^{-1/2}$...
Zero and Few Shot Load Forecasting with Large Language Models
arXiv:2411.11350v2 Announce Type: replace Abstract: Deep learning models have shown strong performance in load forecasting, but they generally require large amounts of data for model training before being applied to new scenarios, which limits their effectiveness in data-scarce scenarios. Inspired by the great success of pre-trained language models (LLMs) in natural language processing, this paper proposes a zero and few shot load forecasting approach using an advanced LLM framework denoted...
Is Grep All You Need? How Agent Harnesses Reshape Agentic Search
Computer Science > Computation and Language [Submitted on 14 May 2026] Title:Is Grep All You Need? How Agent Harnesses Reshape Agentic Search View PDF HTML (experimental)Abstract:Recent advances in Large Language Model (LLM) agents have enabled complex agentic workflows where models autonomously retrieve information, call tools, and reason over large corpora to complete tasks on behalf of users.
Time series Foundation Models based on Physics-Informed Synthetic Histories for Cold-Start Photovoltaic Forecasting
arXiv:2606.07457v1 Announce Type: new Abstract: At commissioning time, Photovoltaic (PV) operators must forecast production before target-site observations are available, limiting the direct use of standard supervised forecasters. This cold-start setting is addressed with a zero-shot pipeline that generates a synthetic production history from plant metadata and meteorological covariates, enabling time-series foundation models (TSFMs) to forecast through inference-time conditioning. Five...
HEPA: A Self-Supervised Horizon-Conditioned Event Predictive Architecture for Time Series
arXiv:2605.11130v4 Announce Type: replace Abstract: Critical events in multivariate time series, from turbine failures to cardiac arrhythmias, demand accurate prediction, yet labeled data is scarce because such events are rare and costly to annotate. We introduce HEPA (Horizon-conditioned Event Predictive Architecture), built on two key principles. First, a causal Transformer encoder is pretrained via a Joint-Embedding Predictive Architecture (JEPA): a horizon-conditioned predictor learns to...
ISOMORPH: A Supply Chain Digital Twin for Simulation, Dataset Generation, and Forecasting Benchmarks
arXiv:2605.12768v2 Announce Type: replace-cross Abstract: Open time-series forecasting (TSF) benchmarks cover retail, energy, weather, and traffic, but supply-chain logistics remains underserved. We introduce ISOMORPH, the first public digital twin of a multi-echelon logistics network with interpretable, user-configurable parameters and modular topology, demand, and control rules. The simulator advances a directed routing graph in discrete time: demand is served from inventory or recorded as...
Amazon Luna adds Hollow Knight to its catalog for June
Amazon Luna adds Hollow Knight to its catalog for June It’s also offering Mafia III and some remastered Tomb Raider as free PC titles. We may receive a commission on purchases made from links. Amazon Luna has dropped its additions for June, and there are some solid titles joining the lineup for the game streaming service.
ChronoPhyBench: Do MLLMs Truly Understand the World or Merely Exploit Language Priors?
arXiv:2606.07962v1 Announce Type: new Abstract: Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in open-world reasoning and understanding. However, a critical ambiguity persists: it remains unclear whether these models genuinely synthesize cross-modal information to construct physically grounded reasoning chains, or if they merely exploit strong language priors to mask single-modality reliance, thereby hallucinating advanced multimodal...
Internalizing Temporal Consistency in Video Object-Centric Learning without Explicit Regularization
arXiv:2605.31508v1 Announce Type: new Abstract: Video Object-Centric Learning (OCL) aims to represent objects as \textit{slot} vectors and maintain their consistency across frames. Slot-Slot Contrastive (SSC) loss has become the cornerstone for state-of-the-art (SOTA) video OCL methods. While highly effective, SSC relies on one-to-one object correspondence across frames and introduces an extra loss.
Lost in the Non-convex Loss Landscape: How to Fine-tune the Large Time Series Model?
Announce Type: new Abstract: Recently, large time series models (LTSMs) have gained increasing attention due to their similarities to large language models, including flexible context length, scalability, and task generality, outperforming advanced task-specific models. However, prior studies indicate that pre-trained LTSMs may exhibit a poorly conditioned non-convex loss landscape, leading to limited trainability. As a result, direct fine-tuning tends to cause overfitting and suboptimal...