Oops, Not Now: PEARL, a RAG-Based Support Agent for Gameplay and What Players Want from AI Help
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
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arXiv:2404. 02039v5 Announce Type: replace Abstract: Game environments provide rich, controllable settings that stimulate many aspects of real-world complexity.
arXiv:2607. 00527v1 Announce Type: new Abstract: Generative AI now enables games to produce dialogue, quests, characters, images, and worlds at runtime.
The paper presents the first dataset that captures how humans learn and use mechanisms in unfamiliar environments, using ARC‑AGI‑3 as a testbed. It includes 144 trajectories from 64 participants across 25 public games, each paired with time‑synchronized actions and think‑aloud audio, and 250 trajectories from large language model agents with and without harnesses. The dataset is annotated for mechanism triggers, action purposes, errors, and recovery, enabling a comparison of human and LLM cognition, highlighting differences in hypothesis testing, exploration, and use of qualitative representations.
arXiv:2609.16679v1 Announce Type: new Abstract: Foundation models, alongside advances in learned game-world models, are reshaping AI across the game lifecycle. Beyond playing games, recent systems mo...
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.
The paper introduces a text-based, multi-agent version of the board game Clue to test multi-step deductive reasoning in large language models (LLMs). Six LLM-based agents (GPT‑4o‑mini and Gemini‑2.5‑Flash) play turn‑based games, and a tool‑augmented approach uses a structured possibility matrix to convert implicit game state into explicit remaining possibilities, thereby offloading memory and deductive constraints from the agents. The study compares this tool‑augmented method against a baseline to assess its impact on reasoning quality and task success in a strategic reasoning environment.