Contrastive fine-tuning of a single embedding model across heterogeneous retrieval tasks is challenging when the tasks differ in label structure. In product-compliance retrieval, some tasks have multi-label supervision — every item carries a set of compliance categories, and many in-batch items share at least one category with the query, producing frequent in-batch false negatives. Other tasks (e.g., cross-catalog product matching) have no category labels: each query has one canonical match. Existing multi-label contrastive methods require every batch item to carry comparable labels, so they cannot serve a single embedding model trained on both label-bearing and label-free tasks. We propose label-aware false-negative exclusion: a zero-cost binary mask on the InfoNCE denominator that drops same-label candidates from the negative set rather than including them to positives. The mask has no effect on label-free items, so the same loss handles both task types. A 0.6 B-parameter embedding model fine-tuned this way handles four retrieval tasks simultaneously, lifting average Recall by 49% (0.489 → 0.729) and matching the 13× larger off-the-shelf Qwen3-Embedding-8B at 32× lower embedding dimensionality. Fine-tuning further enables learned query fusion: concatenating regulatory text with an example product, which hurts retrieval before fine-tuning, becomes the strongest query type after, beating either alone by 6-11%.

In this paper, we study the problem of generating structured objects that conform to a complex schema, with intricate dependencies between the different components (facets) of the object. The facets of the object (attributes, fields, columns, properties) can be a mix of short, structured facts, or long natural-language descriptions. The object has to be self-consistent between the different facets in the redundant information it carries (relative consistency), while being grounded with respect to world knowledge (absolute consistency). We frame the problem as a Language Modeling problem (Structured Object Language Modeling) and train an LLM to perform the task natively, without requiring instructions or prompt-engineering. We propose a self-supervised denoising method to train the model from an existing dataset of such objects. The input query can be the existing object itself, in which case the system acts as a regenerator, completing, correcting, normalizing the input, or any unstructured blurb to be structured. We show that the self-supervised denoising training provides a strong baseline, and that additional supervised fine-tuning with small amount of human demonstrations leads to further improvement. Experimental results show that the proposed method matches or outperforms prompt-engineered general-purpose state-of-the-art LLMs (Claude 3, Mixtral-8x7B), while being order-of-magnitude more cost-efficient.