GameHorizon Suite: Multi-Horizon Data and Evaluation in Gameplay

arXiv:2609.25001 · cs.CV, cs.AI · Submitted 2026-09-21 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-21

Updated: 2026-09-21

Comments: We will release our dataset, annotator, and benchmark to facilitate future research. Github Repo: https://github.com/TencentARC/GameHorizon & Project Page: https://gamehorizon-suite.github.io

Code: https://github.com/TencentARC/GameHorizon

Project page: https://gamehorizon-suite.github.io

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 27× in this paper · explained in 2 episodes
• Accuracy — 24× in this paper · explained in 1 episode
• Consistency — 10× in this paper · explained in 3 episodes
• Abstraction — 8× in this paper · explained in 1 episode
• APEX — 4× in this paper · explained in 1 episode
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Embodied AI — 2× in this paper · explained in 2 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Pseudo-labels — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Action Decoder — 1× in this paper · explained in 1 episode
• Agent Harness — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Causal Reasoning — 1× in this paper · explained in 2 episodes
• Computer Vision and Pattern Recognition — 1× in this paper
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• Task Planning — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• Scaling Behavior Cloning Improves Causal Reasoning: An Open Model for Real-Time Video Game Playing
• Lumine: An Open Recipe for Building Generalist Agents in 3D Open Worlds
• Game-TARS: Pretrained Foundation Models for Scalable Generalist Multimodal Game Agents
• MineStudio: A Streamlined Package for Minecraft AI Agent Development
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
• Gemini: A Family of Highly Capable Multimodal Models
• GPT-4 Technical Report
• SIMA 2: A Generalist Embodied Agent for Virtual Worlds
• UI-TARS: Pioneering Automated GUI Interaction with Native Agents
• UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning
• Voyager: An Open-Ended Embodied Agent with Large Language Models
• Cradle: Empowering Foundation Agents Towards General Computer Control
• RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
• OpenVLA: An Open-Source Vision-Language-Action Model
• GameVerse: Can Vision-Language Models Learn from Video-based Reflection?
• Kimi K3: Open Frontier Intelligence
• Qwen3 Technical Report
• SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture
• Ovis-U1 Technical Report
• InternVL-U: Democratizing Unified Multimodal Models for Understanding, Reasoning, Generation and Editing

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