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 vector storage by 8-16x (FP32) and is up to 25x faster than exact dense scoring on larger candidate pools, enabling efficient multimodal retrieval.
Matteo Attimonelli, Alessandro De Bellis, F. M. Nardini et al.· 0 citations
The RecSys Challenge 2026 studies conversational music recommendation as a joint item recommendation and response generation problem: given a multi-turn dialogue, systems must retrieve relevant tracks from a large catalog and produce a grounded natural-language response. This paper presents the challenge task, dataset, evaluation protocol, and official results. Beyond the leaderboard, we analyze the 16 accepted systems through a common retrieve–rerank–generate framework and examine how recommendation performance varies across users, requests, and dialogue contexts. Strong systems commonly combine heterogeneous candidate sources and preserve source-specific evidence for learned reranking. Across the system papers and our organizer-side analysis, robust design also means 1) grounding cold-start retrieval in multi-turn conversation and item signals, 2) using intent detectors, and 3) modeling the full multi-turn context rather than the current query alone. We further identify limitations of the benchmark and evaluation protocol, including single-ground-truth relevance and teacher-forced evaluation of synthetic dialogues. Together, these findings provide practical guidance for future conversational recommender systems and shared evaluation efforts.
Seungheon Doh, Sergio Oramas, B. Sguerra et al.· Proceedings of the Workshop...· 0 citations
Fashion complementary image generation (CIG) aims to create garments that stylistically match a seed item based on user intent, making it a natural multimodal grounding problem where models must interpret language in visual context. Existing CIG benchmarks rely on rigid template prompts (e.g.,"a photo of a skirt"), failing to reflect natural user queries and obscuring model behavior across levels of linguistic specificity. We introduce fashion complementary image generation with free-form instructions, a multimodal language-grounding setting where a model generates a compatible garment from a seed image and a natural-language instruction. To this end, we enrich three CIG benchmarks with low-, medium-, and high-specificity instructions generated by a vision-language model and validated by human annotators. We instantiate the task with StyleFlow, a Rectified Flow Matching model that jointly conditions on the seed image and instruction within a single multimodal transformer. Across image quality metrics, catalog-alignment analysis, ablations, and human evaluation, StyleFlow consistently produces instruction-aligned and stylistically coherent garments while reducing architectural complexity and inference cost relative to auxiliary-module approaches.
Matteo Attimonelli, Claudio Pomo, A. D. Bellis et al.· 0 citations
Graph models that manipulate the frequency spectrum of user-item interactions to separate preference signals from noise often report significant improvements, but concerns about evaluation rigor and reproducibility persist. We conduct a reproducibility and replicability study that examines three major families: (i) spectral denoising methods, (ii) graph signal processing (GSP) models, and (iii) spectral propagation approaches. Reproducing published pipelines reveals a polarized landscape: while several works are fully reproducible, others rely on flawed metric implementations and incomplete hyperparameter disclosures. In particular, we observe systematic inflation of Recall in the spectral denoising methods due to an implementation error, and theoretically invalid ranking metrics in GSP models due to unordered prediction lists; conversely, the graph filtering models are consistently reproducible. Beyond reproduction, we establish a unified evaluation protocol on four datasets with consistent splits and hyperparameter optimization for all baselines, showing that strong classical methods (e.g., SLIM, Item-kNN) remain highly competitive and that no single spectral model dominates across domains. We further analyze robustness under varying data sparsity and assess beyond-accuracy properties, finding that spectral filtering often improves catalog exploration even when accuracy gains are marginal. Our code is available at https://github.com/sisinflab/Mind_the_Metric_SIGIR-26.
Domenico de Gioia, Claudio Pomo, Ludovico Boratto et al.· Annual International ACM SIG...· 0 citations
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