Back to The Future: Evaluating AI Agents on Predicting Future Events

Beyond Forecasting: The Belief-to-Trade Layer in Prediction-Market Agents

arXiv:2607. 03015v1 Announce Type: new Abstract: Forecasting future events has attracted growing attention as a testbed for general-purpose AI.

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.

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.

Beyond Next-Observation Prediction: Agent-Authored World Modeling for Sequential Decision Making

Recent studies on world modeling for Large Language Model (LLM) agents typically formulate the learning objective as next-observation prediction. However, this objective ties supervision to what a transition happens to reveal, which may omit the dynamics most relevant to the agent's current decision.

Scientific reasoning does not reliably translate into scientific forecasting in frontier AI

arXiv:2605. 22681v2 Announce Type: replace Abstract: AI systems are increasingly used to support forward-looking scientific judgment, but it remains unclear whether they can form reliable expectations about future scientific advances.

AI Agents Are Here. What Now?

Self-Evolving World Models for LLM Agent Planning

arXiv:2606. 30639v1 Announce Type: new Abstract: World models offer a principled way to equip long-horizon LLM agents with foresight: predictions of action consequences before execution.

LLM-based Agents for Forecasting and Prediction: Methods, Training, Evaluation, and Applications

arXiv:2608.23058v1 Announce Type: new Abstract: Large language models (LLMs) now support forecasting systems that combine language-based reasoning with temporal data, evidence retrieval, external too...

ForeSci: Evaluating LLM Agents for Forward-Looking AI Research Judgment

arXiv:2606. 00644v1 Announce Type: new Abstract: AI research often requires decisions before future evidence exists: which bottleneck to attack, which direction to pursue, or where a project should be positioned.

Bridging the Last Mile of Time Series Forecasting with LLM Agents

arXiv:2606. 02497v1 Announce Type: new Abstract: Time series forecasting has advanced rapidly, especially with the emergence of foundation models that show strong zero-shot performance on numerical extrapolation.

ForeDreamer: A Self-Evolving Dual-Agent Memory Architecture for Future Event Prediction

arXiv:2608.20920v1 Announce Type: new Abstract: Open-web future event prediction requires agents to distill reliable signals from noisy, redundant, and incomplete evidence. Existing retrieval/memory...

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.