arXiv AI June 18, 2026 By Jaeho Lee, Nick Merrill, Ezra Karger

ForecastBench-Sim: A Simulated-World Forecasting Benchmark

arXiv:2606. 18686v1 Announce Type: new Abstract: Forecasting benchmarks for general-purpose AI systems usually inherit the constraints of the real world: outcomes resolve slowly, tail events are rare, and counterfactual questions are difficult to score.

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benchmarks

arXiv AI

Jun 11

WorldReasoner: Evaluating Whether Language Model Agents Forecast Events with Valid Reasoning

arXiv:2606. 11816v1 Announce Type: cross Abstract: Forecasting real-world events requires language-model agents to reason under uncertainty from incomplete, time-bounded information.

By Yizhou Chi, Eric Chamoun, Zifeng Ding, Andreas Vlachos

llms agents benchmarks

Hugging Face Trending Papers

Aug 31

A Human-in-the-Loop Autonomous Agent for Industry Time Series Forecasting

Real-world time-series forecasting is rarely a one-shot model invocation: practitioners must formulate tasks, connect data and models, incorporate domain expertise, assess prediction plausibility, and...

llms agents

arXiv Machine Learning

Sep 1

A Human-in-the-Loop Autonomous Agent for Industry Time Series Forecasting

arXiv:2608.30976v1 Announce Type: new Abstract: Real-world time-series forecasting is rarely a one-shot model invocation: practitioners must formulate tasks, connect data and models, incorporate doma...

By Xiaoyu Tao, Mingyue Cheng, Ze Guo, Bokai Pan, Qi Liu, Shijin Wang, Enhong Chen

llms agents

arXiv AI

Aug 13

AutoWorldModel-Bench: A State-Centric Benchmark for Automated World-Model Research

arXiv:2608. 11216v1 Announce Type: new Abstract: World modeling is an unsettled field: architectures, training objectives, and state representations interact in complex ways, and no single recipe dominates across environments.

By Marjan Moodi, Xuankang Zhu, Fernando De Mesentier Silva, Harold Chaput, Mohammad Reza Taesiri

llms agents benchmarks

arXiv AI

Jul 28

LLM-SoccerArena: Benchmarking LLMs on Real-World Predictions in Sports

arXiv:2607. 24573v1 Announce Type: new Abstract: Large language models (LLMs) increasingly support decisions about uncertain future events, yet evaluating their ability to forecast real-world outcomes remains difficult.

By Jonas Schr\"oder, Jonas Schweisthal, Oliver M\"uller, Markus Weinmann, Stefan Feuerriegel

llms benchmarks

arXiv AI

Jun 2

TimeSage-MT: A Multi-Turn Benchmark for Evaluating Agentic Time Series Reasoning

arXiv:2606. 01498v1 Announce Type: cross Abstract: Time series data inform critical decisions across many real-world domains.

By Yaxuan Kong, Qingren Yao, Yuqi Nie, Yichen Li, Yilei Shao, Stefan Zohren, Anna Vettoruzzo, Joaquin Vanschoren, Ming Jin, Qingsong Wen

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WorldReasoner: Evaluating Whether Language Model Agents Forecast Events with Valid Reasoning

A Human-in-the-Loop Autonomous Agent for Industry Time Series Forecasting

A Human-in-the-Loop Autonomous Agent for Industry Time Series Forecasting

AutoWorldModel-Bench: A State-Centric Benchmark for Automated World-Model Research

LLM-SoccerArena: Benchmarking LLMs on Real-World Predictions in Sports

TimeSage-MT: A Multi-Turn Benchmark for Evaluating Agentic Time Series Reasoning