Recent work leverages **Large Language Models (LLMs)** to generate executable code for pedagogical animations using libraries such as **Manim**. However, ensuring spatial correctness and visual legibility remains challenging, as existing frameworks emphasize pedagogical content while overlooking geometric occlusions. We propose the **Symbolic Geometric Agent (SGA)**, a plug-and-play module for code-centric animation pipelines that intercepts LLM-generated code, performs partial execution to extract symbolic scene graphs, and applies targeted refinement when spatial conflicts are detected. We further introduce the **Manim Visual Quality Score (MVQS)**, a deterministic rendering-free proxy for spatial integrity. Experiments on the **MMMC-Code** benchmark across four LLM backbones and two agentic pipelines show that SGA achieves a peak MVQS of **73.11** (**Code2Video + GPT-5.1**), corresponding to a **16.1%** relative improvement over the raw baseline, and improves MVQS in **7 of 8** backbone × pipeline configurations. Additionally, we conduct a human evaluation showing that these improvements translate into human preference, with raters preferring SGA over the raw baseline in **84.4%** of comparisons and over a VLM-based critic in **65.0%**.

Can generative agents be trusted in multimodal environments? Despite recent advances, agents remain limited in their ability to reason about safety, coherence, and trust across modalities. We introduce a reproducible simulation framework to evaluate generative agents in three aspects: (1) safety improvement over time via iterative plan revision in multimodal scenarios; (2) detection of unsafe activities across social contexts; and (3) social dynamics, measured through interaction and acceptance rates. These multimodal agents are evaluated using metrics that quantify plan revisions and unsafe-to-safe conversions. Experiments show that while agents detect direct multimodal contradictions, they often fail to align local revisions with global safety, achieving only a 55% success rate in correcting unsafe plans. We release a dataset of 1,000 multimodal plans, yielding more than 600,000 simulation steps. Notably, 45% of unsafe actions are accepted when paired with misleading visual cues, revealing a strong tendency to overtrust visual content. Code is available at https://github.com/AdonaiVera/X-CASE