arXiv — cs.AI preprintsInternational12 October 2026
JANUS: Enhancing Long-Horizon Autonomous Research with Co-Evolving World Models
This is an official announcement record
Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2610.13194v1 Announce Type: new Abstract: Long-horizon autonomous research has shown promise in accelerating scientific progress through sustained cycles of hypothesis generation, implementation, and validation. However, this Generate-Execute-Feedback paradigm relies on reactive trial-and-error, where costly and time-consuming executions introduce severe feedback delays, throttling evolutionary progress under explosively expanding search space. To address this challenge, we propose JANUS, a framework that unifies future outcomes forecasting and past experience reflection through a Predic
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- Faithfulness Without Anchors: A Position Paper on the Structural Misalignments of Explainable AI Faithfulness Metrics for Feature Importance12 October 2026
- EliteTable: Self-Improving Program Search for Executable Control Policies12 October 2026
- Equal-Budget Monte Carlo Tree Search versus Randomized-Restart List Scheduling for DAG Workflows: A Controlled Equal-Budget Computational Study12 October 2026
- Know What You Don't Know: Agentic Data Synthesis by Evolving Generation Space for Closed-Loop Learning12 October 2026
- SpireBench: Evaluating Long-Horizon Strategic Play of Agentic Models with Slay the Spire12 October 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.