超越当前观察：在可控非马尔可夫游戏中评估多模态大语言模型
Beyond the Current Observation: Evaluating Multimodal Large Language Models in Controllable Non-Markov Games
摘要
将多模态基础模型作为闭环策略进行应用时，往往需要将行为决策与那些已无法被观察到的信息相结合。然而，现有的评估基准要么会展示完整的状态信息，要么会将隐藏状态与其他智能体能力混为一谈，或者只在游戏结束之后才进行测试。我们提出了RNG-Bench这一评估基准体系，旨在评估基础模型在多步骤交互过程中还原过去信息并据此采取行动的能力。RNG-Bench包含两种互补的游戏：匹配对子游戏，其中在特定位置短暂显示的卡片信息必须被重新回忆起来；以及3D迷宫游戏，其中以自我为中心的视角必须被整合到空间模型中。这两种游戏都通过三个控制性指标进行评估：网格大小、视觉模式以及观测方式。该基准还引入了面对面对的对抗机制来减少不同实例之间的差异，同时还有记忆差距指标，以区分遗忘现象与糟糕的决策行为。最困难的场景需要大约128K个标记和每集350张图像作为输入，这些配置远远没有被前沿的多模态基础模型所覆盖。记忆差距分析表明，大部分误差源于对早期信息的遗忘，而非决策不当。最后，对Qwen3.5-9B模型进行微调，使其能够在最优策略执行和过滤后的模型演示中表现更好，从而提升其在RNG-Bench上的性能，同时不会降低其整体多模态能力。
English Abstract
Deploying multimodal foundation models as closed-loop policies increasingly requires conditioning actions on observations that are no longer visible. However, existing benchmarks either expose the full state, conflate hidden-state reconstruction with other agent skills, or test recall only after an episode has ended. We introduce RNG-Bench (Reconstructive Non-Markov Games), a benchmark suite designed to isolate a base model's ability to reconstruct past observations and act on them during multi-step interaction. RNG-Bench includes two complementary games: Matching Pairs, where card identities briefly revealed at specific locations must later be recalled, and 3D Maze, where egocentric views must be integrated into a spatial map. Both games are evaluated under a unified harness with three controlled difficulty axes: grid size, visual pattern, and observation modality. The benchmark further introduces a head-to-head duel protocol to control for instance-level variance and a Memory Gap metric that disentangles forgetting from poor action selection. The hardest configurations require contexts of roughly 128K tokens and 350 image inputs per episode, and remain far from saturated by frontier MLLMs. Memory Gap analysis shows that most residual errors stem from forgetting earlier observations rather than from suboptimal decision making. Finally, fine-tuning Qwen3.5-9B on optimal-policy rollouts and filtered model demonstrations improves performance on RNG-Bench and transfers to existing benchmarks without degrading general multimodal capability.