arXiv:2607. 23507v1 Announce Type: cross Abstract: Choosing the right text embedding model is one of the most consequential -- and most frequently under-examined -- decisions in building a retrieval or search system, yet the model that tops a leaderboard is rarely the best choice for a given deployment.
arXiv:2607. 04071v1 Announce Type: cross Abstract: Portuguese remains underrepresented in text embedding evaluation, despite being one of the most widely spoken languages in the world.
arXiv:2608. 02112v1 Announce Type: new Abstract: Embedding benchmarks measure standalone model quality, but they do not establish whether a low-cost retriever contributes complementary ranking information once lexical and transformer-based retrieval are already combined.
arXiv:2607. 18785v2 Announce Type: replace Abstract: As large language model agents gain access to increasingly large skill libraries, retrieving the right skill becomes critical to reliable capability selection and execution.
arXiv:2607. 18785v1 Announce Type: new Abstract: As large language model agents gain access to increasingly large skill libraries, retrieving the right skill becomes critical to reliable capability selection and execution.
VoiceTrace introduces a new benchmark, VoiceTrace-Bench, for hybrid speech retrieval that combines a textual query specifying "what" to retrieve with a reference speech specifying "who" to retrieve. The authors propose a two‑stage framework: VoiceTrace‑Emb, which learns unified audio‑text embeddings for efficient large‑scale retrieval, and VoiceTrace‑Reranker, which fine‑grains relevance by jointly examining query‑candidate pairs. Experiments show VoiceTrace outperforms existing methods on both traditional semantic speech retrieval benchmarks and the new hybrid setting.
arXiv:2603. 14558v3 Announce Type: replace Abstract: Recruiters and job seekers rely on search systems to navigate labor markets, making candidate matching engines critical for hiring outcomes.
Q2D-Web is a new large‑scale benchmark for agentic Retrieval‑Augmented Generation (RAG) systems, featuring a 190 million‑document web corpus and 70 k machine‑reformulated search queries in ten languages. It supplies three sets of relevance judgments—agent citations, production rankings, and a combined set enriched with LLM‑based labels—to evaluate first‑stage retrievers. Experiments on 13 retrievers show consistent ranking across judgment sets but significant variation across domains, languages, and query types, and demonstrate that a carefully sampled sub‑corpus can approximate full‑corpus evaluation with minimal loss in Recall@1000.
The study evaluates six retrieval methods for ranking computer science faculty as potential academic advisors based on graduate applicants’ research interest statements. Using a new dataset of 768 faculty profiles from nine U.S. universities and 162 graded relevance judgments across five queries, the reranked hybrid approach achieved the highest mean NDCG@10 (0.477). Ablation experiments showed that faculty biographies alone outperform the full model, and adding arXiv abstracts actually decreased performance, leading to a late‑fusion design. All code, data, and labels are publicly released.
arXiv:2608.30044v1 Announce Type: new Abstract: Language models are commonly compared by averaging scores across a benchmark list with equal weight. Such lists grow through publication outside an exp...
The paper introduces DEPT, a method that trains a single decoder-only large language model to both expand queries and encode documents for retrieval. By preserving document embeddings close to their initial cached values while allowing gradients to flow through the generator, DEPT stabilizes retrieval targets and enables efficient index reuse and online hard‑negative mining. Experiments on the BEIR benchmark with Qwen3‑4B‑Instruct‑2507 and LLaMA‑3.2‑3B‑Instruct show that DEPT outperforms training‑free, independently trained, and staged unified baselines, with ablations confirming the benefits of preservation, whitening, end‑to‑end expansion training, and online negatives.
arXiv:2605.05726v2 Announce Type: replace Abstract: As LLM agents are increasingly deployed with large libraries of reusable skills, selecting the right skill for a user request has become a critical...