FutureSim: Replaying World Events to Evaluate Adaptive Agents

Shashwat Goel, Nikhil Chandak, Arvindh Arun, Ameya Prabhu, Steffen Staab, Moritz Hardt, Maksym Andriushchenko, Jonas Geiping · May 14, 2026 · Citations: 0

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How to use this page

Provisional trust

This page is a lightweight research summary built from the abstract and metadata while deeper extraction catches up.

Best use

Background context only

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Evidence quality

Provisional

Derived from abstract and metadata only.

Abstract

AI agents are being increasingly deployed in dynamic, open-ended environments that require adapting to new information as it arrives. To efficiently measure this capability for realistic use-cases, we propose building grounded simulations that replay real-world events in the order they occurred. We build FutureSim, where agents forecast world events beyond their knowledge cutoff while interacting with a chronological replay of the world: real news articles arriving and questions resolving over the simulated period. We evaluate frontier agents in their native harness, testing their ability to predict world events over a three-month period from January to March 2026. FutureSim reveals a clear separation in their capabilities, with the best agent's accuracy being 25%, and many having worse Brier skill score than making no prediction at all. Through careful ablations, we show how FutureSim offers a realistic setting to study emerging research directions like long-horizon test-time adaptation, search, memory, and reasoning about uncertainty. Overall, we hope our benchmark design paves the way to measure AI progress on open-ended adaptation spanning long time-horizons in the real world.

Abstract-only analysis — low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

This page is still relying on abstract and metadata signals, not a fuller protocol read.

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Should You Rely On This Paper?

Signal extraction is still processing. This page currently shows metadata-first guidance until structured protocol fields are ready.

Best use

Background context only

Use if you need

A provisional background reference while structured extraction finishes.

Main weakness

This page is still relying on abstract and metadata signals, not a fuller protocol read.

Trust level

Provisional

Usefulness score

Unavailable

Eval-fit score is unavailable until extraction completes.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

Usefulness for eval research

Provisional (processing)

Extraction confidence 0%

If you are doing eval pipeline work, start here:

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What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

provisional (inferred)

None explicit

No explicit feedback protocol extracted.

"AI agents are being increasingly deployed in dynamic, open-ended environments that require adapting to new information as it arrives."

Evaluation Modes

provisional (inferred)

Automatic metrics, Long Horizon tasks

Includes extracted eval setup.

"AI agents are being increasingly deployed in dynamic, open-ended environments that require adapting to new information as it arrives."

Quality Controls

provisional (inferred)

Not reported

No explicit QC controls found.

"AI agents are being increasingly deployed in dynamic, open-ended environments that require adapting to new information as it arrives."

Benchmarks / Datasets

provisional (inferred)

Not extracted

No benchmark anchors detected.

"AI agents are being increasingly deployed in dynamic, open-ended environments that require adapting to new information as it arrives."

Reported Metrics

provisional (inferred)

Accuracy

Useful for evaluation criteria comparison.

"FutureSim reveals a clear separation in their capabilities, with the best agent's accuracy being 25%, and many having worse Brier skill score than making no prediction at all."

Rater Population

provisional (inferred)

Unknown

Rater source not explicitly reported.

"AI agents are being increasingly deployed in dynamic, open-ended environments that require adapting to new information as it arrives."

Human Feedback Details

This page is using abstract-level cues only right now. Treat the signals below as provisional.

Potential human-data signal: No explicit human-data keywords detected.
Potential benchmark anchors: No benchmark names detected in abstract.
Abstract highlights: 3 key sentence(s) extracted below.

Evaluation Details

Evaluation fields are inferred from the abstract only.

Potential evaluation modes: Automatic metrics, Long-horizon tasks
Potential metric signals: Accuracy
Confidence: Provisional (metadata-only fallback).

Research Brief

Metadata summary

AI agents are being increasingly deployed in dynamic, open-ended environments that require adapting to new information as it arrives.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

AI agents are being increasingly deployed in dynamic, open-ended environments that require adapting to new information as it arrives.
To efficiently measure this capability for realistic use-cases, we propose building grounded simulations that replay real-world events in the order they occurred.
We build FutureSim, where agents forecast world events beyond their knowledge cutoff while interacting with a chronological replay of the world: real news articles arriving and questions resolving over the simulated period.

Researcher Actions

Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
Validate inferred eval signals (Automatic metrics, Long-horizon tasks) against the full paper.
Use related-paper links to find stronger protocol-specific references.

Caveats

Generated from abstract + metadata only; no PDF parsing.
Signals below are heuristic and may miss details reported outside the abstract.

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Accuracy

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