DixitWorld：基于多智能体Dixit游戏评估视觉-语言模型中的多模态溯因推理
DixitWorld: Evaluating Multimodal Abductive Reasoning in Vision-Language Models with Multi-Agent Dixit Gameplay
AI总结：
本文提出多智能体Dixit游戏评估套件DixitWorld，包含动态环境DixitArena与静态基准DixitBench，揭示视觉-语言模型在多模态溯因推理中生成创造力与判别理解的关键权衡。
摘要：
多模态溯因推理——即从部分观察中生成并选择解释性假设——是智能的基石。当前对视觉-语言模型（VLMs）中这一能力的评估主要局限于静态、单智能体任务。受Dixit游戏启发，我们引入了DixitWorld，这是一个旨在解构该挑战的综合评估套件。DixitWorld包含两个核心组件：DixitArena，一个动态、多智能体环境，评估在不完全信息下的假设生成（“讲述者”制作隐晦线索）和假设选择（“听者”从干扰项中选择目标图像）；以及DixitBench，一个静态问答基准，隔离听者任务以进行高效、可控的评估。DixitArena的结果揭示了明显的角色依赖行为：较小的开源模型常作为富有创造力的讲述者表现出色，生成富有想象力但区分度较低的线索，而较大的专有模型展现出更优的整体性能，尤其作为听者时。在DixitBench上的表现与DixitArena中的听者结果高度相关，验证了其作为假设选择可靠代理的有效性。我们的发现揭示了多模态溯因推理中生成创造力与判别理解之间的关键权衡，这是开发更平衡、更强大的视觉-语言智能体的核心挑战。
英文摘要：
Multimodal abductive reasoning--the generation and selection of explanatory hypotheses from partial observations--is a cornerstone of intelligence. Current evaluations of this ability in vision-language models (VLMs) are largely confined to static, single-agent tasks. Inspired by Dixit, we introduce DixitWorld, a comprehensive evaluation suite designed to deconstruct this challenge. DIXITWORLD features two core components: DixitArena, a dynamic, multi-agent environment that evaluates both hypothesis generation (a "storyteller" crafting cryptic clues) and hypothesis selection ("listeners" choosing the target image from decoys) under imperfect information; and DixitBench, a static QA benchmark that isolates the listener's task for efficient, controlled evaluation. Results from DixitArena reveal distinct, role-dependent behaviors: smaller open-source models often excel as creative storytellers, producing imaginative yet less discriminative clues, whereas larger proprietary models demonstrate superior overall performance, particularly as listeners. Performance on DixitBench strongly correlates with listener results in DixitArena, validating it as a reliable proxy for hypothesis selection. Our findings reveal a key trade-off between generative creativity and discriminative understanding in multimodal abductive reasoning, a central challenge for developing more balanced and capable vision-language agents.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
