Tabular foundation models like TabPFN and TabICL are often augmented with frozen large language model (LLM) embeddings when tables contain free-form text. Whether this helps is highly case-dependent: across 732 dataset–backbone–encoder configurations, 45.9% improve, 30.9% stay neutral, and 23.2% degrade. Using a fixed, outcome-free raw score, we define high- and low-readability strata before applying target-covariance Gaussian sampling, row–text shuffling, and pre-projection whitening. Each intervention reduces the mean gain in the high-readability stratum to near zero, whereas the paired intervention effects in the low-readability stratum remain within sampling noise. The first two interventions show that neither a Gaussian sample from the target covariance nor row-misaligned text reproduces the high-stratum gain; whitening further shows sensitivity to broad pre-projection geometry. Together, the results support a dependence on row-aligned embedding structure without isolating the covariance spectrum as its unique cause. The high-stratum Gaussian-replacement effect replicates on a second backbone. A within-dataset encoder ranking, Decision-Boundary Alignment (DBA), also shows stronger rank alignment with gain than the Massive Text Embedding Benchmark (MTEB) rank in our eight-dataset diagnostic.

Multi-agent systems (MAS) are increasingly used for open-ended idea generation, driven by the expectation that collective interaction will broaden the exploration diversity. However, when and why such collaboration truly expands the solution space remains unclear. We present a systematic empirical study of diversity in MAS-based ideation across three bottom-up levels: model intelligence, agent cognition, and system dynamics. At the model level, we identify a compute efficiency paradox, where stronger, highly aligned models yield diminishing marginal diversity despite higher per-sample quality. At the cognition level, authority-driven dynamics suppress semantic diversity compared to junior-dominated groups. At the system level, group-size scaling yields diminishing returns and dense communication topologies accelerate premature convergence. We characterize these outcomes as collective failures emerging from structural coupling, a process where interaction inadvertently contracts agent exploration and triggers diversity collapse. Our analysis shows that this collapse arises primarily from the interaction structure rather than inherent model insufficiency, highlighting the importance of preserving independence and disagreement when designing MAS for creative tasks. Our code is available at https://github.com/Xtra-Computing/MAS_Diversity.