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This work addresses a critical limitation in traditional approximate nearest neighbor (ANN) retrieval evaluation, which relies solely on recall and fails to distinguish semantically relevant from irrelevant neighbors, often misrepresenting retrieval quality. To overcome this, the authors introduce Semantic Recall—a novel metric that quantifies only those semantically relevant results theoretically recoverable via exact search—and propose Tolerant Recall as an efficient proxy for practical evaluation. This is the first systematic integration of semantic relevance into vector retrieval assessment, revealing the pervasive sparsity of relevant results within embedding spaces. Experimental results demonstrate that the proposed metrics more accurately reflect real-world retrieval effectiveness, and algorithms optimized under this framework achieve superior trade-offs between cost and quality.

Traditional vector retrieval relies on approximate nearest neighbor (ANN) search, which often yields semantically redundant results and fails to meet the diversity and contextual richness requirements of applications such as retrieval-augmented generation (RAG) and multi-hop question answering. To address this, we propose a novel paradigm—“Semantic Compression and Graph-Enhanced Retrieval”—that introduces submodular optimization into vector retrieval for the first time. We formalize semantic compression to maximize information coverage while explicitly suppressing redundancy. Leveraging information-geometric similarity metrics and k-nearest neighbor (kNN) graphs, we construct a multi-hop semantic search framework, further augmented with knowledge graph integration for structured semantic querying. Our method supports hybrid indexing and significantly improves semantic diversity and coverage in high-dimensional embedding spaces, outperforming state-of-the-art ANN baselines. The implementation is open-sourced, advancing research toward semantics-centric vector search.

This work addresses structured clinical document classification, a task requiring accurate and efficient retrieval in domain-specific, highly standardized medical texts. Method: We comparatively evaluate lexical vectorization (TF-IDF with cosine similarity) against semantic embedding approaches (BERT and Sentence-BERT), assessing both classification accuracy and inference latency. Results: On constrained, format-regular clinical documents, a lightweight, domain-adapted lexical method achieves a marginal yet consistent accuracy gain (+0.8%) over neural semantic baselines while accelerating inference by 67%. These findings challenge the prevailing assumption that transformer-based semantic models inherently dominate such structured, professional text classification tasks. Contribution: The study demonstrates the sufficiency and efficiency of lexical matching in structured clinical documentation, establishing interpretable, low-overhead lexical methods as viable alternatives—particularly valuable in resource-constrained clinical settings where computational efficiency and model transparency are critical.

Existing hybrid search systems lack systematic empirical analysis of trade-offs among lexical and semantic retrieval components—i.e., retrieval paradigms, fusion strategies, and re-ranking methods—leading to complex, suboptimal configurations. Method: We introduce the first benchmark framework tailored for advanced hybrid architectures, conducting systematic evaluation across 11 real-world datasets, covering four retrieval paradigms, their combinations, and re-ranking strategies. Contribution/Results: We identify a “weakest-link” effect in hybrid pipelines and propose a data-driven configuration mapping method. Crucially, we find Tensor-based Re-ranking Fusion (TRF) achieves both high efficiency and strong semantic modeling under low-resource conditions, overcoming traditional fusion bottlenecks. Experiments reveal that hybrid performance is severely constrained by imbalanced path quality; optimal configurations are highly dependent on dataset characteristics and resource constraints. TRF significantly improves the effectiveness–cost trade-off, outperforming state-of-the-art baselines across diverse settings.

This study investigates the impact of embedding dimensionality on performance in dense retrieval and its limitations as task complexity increases. Through systematic experiments across models of varying scales, the work presents the first empirical evidence that retrieval performance follows a power-law relationship with embedding dimensionality. Building on this observation, the authors propose predictable scaling laws based solely on dimensionality or jointly on model size. Using dense retrieval architectures, approximate nearest neighbor search, and large-scale comparative evaluations, they demonstrate that in task-aligned scenarios, performance improves with higher dimensionality—albeit with diminishing returns—whereas in misaligned tasks, excessive dimensions degrade performance. These findings offer both theoretical grounding and practical guidance for selecting optimal embedding dimensions in efficient retrieval systems.

This work proposes a semantic similarity computation method that integrates Word Mover’s Distance (WMD) with pretrained word embeddings such as GloVe to better model the semantic relationship between queries and documents in information retrieval. Traditional centroid-based word embedding approaches often fail to capture fine-grained semantic matches, particularly when handling synonymy and polysemy. By minimizing the transportation cost of aligning query and document terms in the embedding space, the proposed method achieves a more precise representation of semantic correspondence. Experimental results demonstrate that this approach significantly outperforms baseline models—including Doc2Vec and Latent Semantic Analysis (LSA)—on similarity ranking tasks, while maintaining domain independence and high retrieval accuracy, thereby confirming its effectiveness and generalizability in practical information retrieval scenarios.

This study addresses the lack of systematic evaluation of hybrid search mechanisms that combine semantic retrieval with metadata filtering in existing vector databases. We propose a novel relevance metric, Global-Local Selectivity (GLS), construct MoReVec—the first benchmark dataset supporting filtered retrieval—and extend ANN-Benchmarks to enable unified evaluation of hybrid search performance. Through comprehensive experiments integrating diverse filtering strategies into FAISS, Milvus, and pgvector with IVFFlat and HNSW indexes, we demonstrate that engine-level algorithmic integration critically governs performance: Milvus achieves more stable recall via hybrid execution, pgvector’s optimizer often selects suboptimal query plans, and IVFFlat outperforms HNSW under low-selectivity queries. Our findings culminate in practical configuration guidelines that offer both theoretical insights and actionable recommendations for efficient hybrid search deployment.

This work proposes a novel approach to large-scale retrieval that circumvents the prohibitive cost of full reranking by constructing query and item embeddings derived from the outputs of a reranker. Specifically, it leverages relevance scores assigned by a heavyweight reranker over a set of support items to generate lightweight embeddings, thereby enabling the reranking model to directly guide embedding learning—a capability demonstrated here for the first time. Under mild conditions, the method is theoretically shown to approximate arbitrarily complex similarity functions. Through systematic investigation of support item selection strategies and integration with approximate nearest neighbor search, the approach significantly improves candidate set quality across multiple academic and industrial datasets while maintaining computational efficiency.

This work addresses the limitations of single-retrieval approaches in semantic and keyword matching for large language model agents operating in local-first settings. We propose a hybrid retrieval framework that constructs a local document memory system using SQLite, integrating vector similarity search (via sqlite-vec) with full-text keyword matching (FTS5). The system employs an adaptive Reciprocal Rank Fusion (RRF) strategy combined with per-query IDF weighting. Innovatively, we introduce a self-supervised embedding fine-tuning method based on retrieval disagreement, optimizing BGE-small with MultipleNegativesRankingLoss. The framework also incorporates integrity verification, version control, and diagnostic tools. Experiments demonstrate up to a 19.5% improvement in NDCG@10 across five BEIR datasets, with our 33M-parameter model matching or surpassing the 110M-parameter ColBERTv2 and BGE-base on three datasets, while achieving a median retrieval latency of only 20.9ms over 50,000 text chunks.