GameCommBench is a unified benchmark for AI-generated game commentary that covers board games, sports, and esports, aligning commentary with diverse game contexts and annotating it by type. The authors introduce Type-Aware Commentary Evaluation (TACE), a structured framework that evaluates different commentary types, and validate its reliability and human agreement. Using TACE, they benchmark representative AI commentators and find that live observation and strategic analysis are major bottlenecks, revealing non-uniform capability profiles.
arXiv:2506. 17294v3 Announce Type: replace-cross Abstract: The advent of artificial intelligence has propelled AI-Generated Game Commentary (AI-GGC) into a rapidly expanding research area, offering advantages such as scalable availability and personalized narration.
arXiv:2609.25001v1 Announce Type: new Abstract: Modern video games provide a measurable testbed for AI models, combining abilities of visual understanding, instruction decomposition, goal planning, a...
arXiv:2607. 00527v1 Announce Type: new Abstract: Generative AI now enables games to produce dialogue, quests, characters, images, and worlds at runtime.
Game Arena is an open, continuously expanding platform that evaluates large language models through competitive games, allowing head‑to‑head matchups in structured environments. Unlike static benchmarks, it prevents performance saturation by increasing gameplay difficulty as models improve. The report outlines the infrastructure and presents three pilot games—Chess, Poker, and Werewolf—covering perfect information, imperfect information, and multiplayer settings, and details evaluation metrics and competition results.
The paper introduces LM Fight Arena, a new benchmark that pits large multimodal models against each other in the fighting game Mortal Kombat II to evaluate real‑time visual understanding and sequential decision‑making. Six leading open‑ and closed‑source models were tested in a controlled tournament where each controlled the same character, ensuring a fair comparison. The framework offers a fully automated, reproducible, and objective assessment of an LMM’s strategic reasoning in a dynamic setting.
arXiv:2608.09200v3 Announce Type: replace Abstract: Live basketball commentary generation requires determining when an event is sufficiently observable and describing it before subsequent events unfo...
SWE-Game is a benchmark comprising 247 tasks based on 41 Godot games across 13 gameplay categories, testing coding agents on tasks such as brief-to-game, design-document implementation, skeleton completion, fault repair, and Godot-to-Unity porting. Evaluation uses engine-state checks, replay of certified reference inputs, and agent-authored demonstrations to judge mechanic correctness, playability, and post-repair behavior, supplemented by vision‑language rubrics for presentation. Across six models, Opus5 leads but overall scores stay below 60/100, highlighting common issues like omitted requirements and gameplay logic errors.
arXiv:2608.23435v1 Announce Type: cross Abstract: Understanding a basketball game requires recognizing events, localizing actions, identifying players, and relating these to structured game knowledge...
The paper introduces DECE, a unified arena that spans ten Eurogames with shared interfaces and evaluation protocols to assess large language model agents in competitive settings. It evaluates 12 model-harness configurations across 480 three-player matches, revealing consistent cross-game ranking agreement but also game-specific strengths and variations due to execution harnesses. The study highlights that competitive performance does not always align with token or cost efficiency, underscoring the importance of considering resource usage when comparing agent configurations.
arXiv:2603.02655v2 Announce Type: replace-cross Abstract: Real-time video commentary generation provides textual descriptions of ongoing events in videos. It supports accessibility and engagement in...
arXiv:2608. 04240v1 Announce Type: cross Abstract: Superhuman game engines in domains like chess have made expert-level evaluations easily accessible, yet they communicate what is true without the natural-language explanations that make such expertise educationally useful to experts and non-experts alike.
arXiv:2608.21833v1 Announce Type: new Abstract: Recent large language models (LLMs) can operate as coding agents that build complete games from natural language requests. Game development is especial...