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LLM agent progress increasingly hinges on better external cognitive infrastructure, not just stronger models.
Federated recommendation systems can learn more robust item embeddings, and thus perform better, by using sharpness-aware minimization to combat data heterogeneity and sparsity.
Forget task-specific fine-tuning: TSEmbed unlocks SOTA multimodal embeddings by disentangling task objectives with a Mixture-of-Experts and a novel expert-aware negative sampling strategy.
Personal photo retrieval isn't just about visual similarity; PhotoBench reveals that current models fail to leverage the rich context of our lives鈥攖ime, place, people鈥攏eeded to truly understand our search intent.
Current multimodal retrieval systems fall flat when faced with realistic visual streams where context is distributed across time, motivating a new agentic paradigm for context-aware image retrieval.
