Universal multimodal embedders enable retrieval across text, image, and combined queries, but their dense representations incur high memory and inference costs. Post-hoc sparsification could reduce these costs but remains underexplored for multimodal retrieval. We introduce PUMA, a sparse autoencoder recipe that maps universal multimodal embeddings to compact sparse codes without retraining the backbone: a pretraining stage preserves dense dot-product geometry, after which the sparse encoder is fine-tuned for retrieval. We evaluate on five benchmarks covering text-to-image and composed image retrieval. On Qwen3-VL-Embedding-2B, PUMA is statistically indistinguishable from or improves over dense retrieval on four of five datasets. We further identify two failure modes of post-hoc sparsification: insufficient pre-TopK support and retrieval-misaligned active support. PUMA reduces per-vector storage by 8-16x (FP32) and runs up to 25x faster over exact dense scoring on larger candidate pools, offering a practical approach to storage- and latency-efficient multimodal retrieval.

Food safety demands timely detection, regulation, and public communication, yet the lack of structured datasets hinders Natural Language Processing (NLP) research. We present and release a new dataset of human-written and Large Language Model (LLM)-generated summaries of food safety documents, plus food safety related metadata. We evaluate its utility on three NLP tasks directly reflecting food safety practices: multilabel classification for organizing documents into domain-specific categories; document retrieval for accessing regulatory and scientific evidence; and question answering via retrieval-augmented generation that improves factual accuracy.We show that LLM summaries perform comparably or better than human ones across tasks. We also demonstrate clustering of summaries for event tracking and compliance monitoring. This dataset enables NLP applications that support core food safety practices, including the organization of regulatory and scientific evidence, monitoring of compliance issues, and communication of risks to the public.