Synthesizing Ephemeral Biomes: A Transformer-Based Architecture for Generating Ecologically Plausible yet Fictional Ecosystems and Their Human Interpretive Frameworks

Speculative worldbuilding and generative modeling have historically evolved along separate trajectories: computational ecology relies on deterministic differential equations, while contemporary artificial intelligence excels in unconstrained semantic synthesis. In this paper, we introduce BiomeFormer, a novel transformer-based framework capable of generating ecologically plausible yet entirely fictional biomes, designated here as ephemeral biomes. The architecture couples a graph-conditioned autoregressive decoder with thermodynamic trophic-cascade constraints, guaranteeing that synthetic taxa exhibit mass-energy balance, niche differentiation, and metabolic coherence across trophic tiers. Alongside the generative model, we establish a structured human interpretive framework to assess how domain experts in ecology and creative practitioners decode, contextualize, and derive aesthetic resonance from synthetic ecologies. Evaluating 100 synthesized biomes across a dual cohort of 48 evaluators (24 systems ecologists, 24 speculative worldbuilders), we demonstrate that BiomeFormer significantly outperforms unconstrained autoregressive baselines in trophic viability (87.4% adherence vs. 31.2%) while maintaining high speculative novelty. This research bridges generative deep learning and computational xenobiology, offering an interdisciplinary paradigm for speculative simulation, interactive environmental pedagogy, and human-AI collaborative creativity.