forecasting

This arXiv paper proposes a protocol to measure the predictive credit of scientific explanations for experimental forecasts, finding that matched explanations did not significantly improve prediction accuracy across Tox21 and OpenML benchmarks, though some gains appeared under certain model replays.

CruxBench is a new benchmark that evaluates LLMs on their ability to discover valuable information — decomposing forecasting questions into informative subquestions ("cruxes") graded by Value of Information. Evaluations on 293 forecasting questions show VOI strongly correlates with model capability (r=0.90), yet frontier models still barely beat a random-timing baseline.

The article describes applying intelligence analysis methodologies, specifically Continuous Probabilistic Foresight, to college football assessment using the Hinsley AI/human hybrid platform.

Forecast-Dojo is a replayable environment for benchmarking and training LLM forecasting agents, combining resolved prediction-market questions with dated news to enable repeated evaluation and learning from outcomes.

The paper proposes VINTAGE-TS, a revision-aware adaptation of a time-series foundation model that distinguishes observation time from information-availability time, and provides a framework for evaluating forecasts with data revisions.

A finance benchmark named DAYJOB by Surge AI evaluates AI agents on completing financial forecasting tasks, with detailed criteria for pass/fail responses focusing on errors in net sales calculations and revenue growth projections.

This paper introduces Event Signature Transfer (EST), a training-free, model-agnostic operator that constructs forecast scenarios by transferring event signatures from historical events onto time-series forecasts.

This paper examines how sentiment arcs in ECB and Fed press conferences predict policy rate changes and inflation expectations, showing that the sequencing of sentiment carries significant policy signals.

Prolonged summer-like temperatures in the US are attributed to El Niño and climate change, with forecasters predicting above-normal heat through September.

CoRe proposes a model-agnostic learning objective for multivariate time-series forecasting that uses frequency coherence and relational graph losses to improve prediction accuracy over standard methods.

Artificial intelligence systems have outperformed some of the best human forecasters in predictive tasks.

This paper introduces Solar Intelligence, a hybrid retrieval-augmented framework that unifies solar data analytics, evidence-grounded scientific question answering, and machine learning forecasting to support decision-making in solar energy.

In 2025, experts estimated a 10% chance that AI would solve or substantially assist in solving a Millennium Prize Problem by 2027.

This paper introduces competence-gated pooling to determine when language models should influence forecasts that already have external predictions, improving accuracy by learning domain-specific weights and deferring when external sources are stronger.

IBM released the Granite Time Series PatchTST-FM-r2 model, a 385M-parameter foundation model for zero-shot time-series forecasting with top performance on the GIFT-Eval benchmark and a commercial-friendly Apache 2.0 license.

NOAH introduces a generative transformer model for comprehensive representation and forecasting of longitudinal multimodal patient data, enabling tasks like zero-shot classification and counterfactual simulation in clinical settings.

A discussion checking whether ai-2027.com's month-by-month AGI and superintelligence forecasting timeline, released by the AI Futures Project in April 2025, remains accurate and what its continued validity would imply.

This paper proposes a two-stage integrated forecasting model using XGBoost to predict CPU workload in private clouds by first forecasting customer service requests, achieving high accuracy with SMAPE below 7% for most applications.

Google DeepMind and Google Research introduce WeatherNext 3, an advanced AI weather model that uses real-time satellite data for high-resolution, hourly forecasts integrated across Google products.

OutageDiT is a generative foundation model for power outage forecasting that uses a Diffusion Transformer architecture to generate seven-day outage trajectories, improving forecast accuracy and enabling zero-shot transfer to new regions.