Topic - Edition
arXiv:2602.03422v2 Announce Type: replace Abstract: Large language models (LLMs) are strong zero-shot pointwise rankers, but lag behind pairwise and listwise methods. Beyond missing comparative signals, we identify a…
arXiv:2609.21547v1 Announce Type: cross Abstract: Research on explainability in recommender systems largely centers on end users, overlooking the perspectives of those who build and maintain these systems and their…
arXiv:2510.27141v5 Announce Type: replace-cross Abstract: The increasing prevalence of hybrid vector and relational data necessitates efficient, general support for queries that combine high-dimensional vector search…
arXiv:2609.21018v1 Announce Type: cross Abstract: Recent visual document retrieval (VDR) systems such as ColPali use multi-vector page embeddings, in which patch-level vectors enable fine-grained evidence matching but…
arXiv:2609.21281v1 Announce Type: new Abstract: Embedding-based retrieval on user-generated content at the trillion-document scale exposes a sharp conflict between two production demands: deep, expressive…
arXiv:2608.09634v2 Announce Type: replace Abstract: Multi-task learning over heterogeneous data is fundamental to modern recommendation, while generative models are emerging as the backbone of next-generation…
arXiv:2603.13099v3 Announce Type: replace-cross Abstract: We introduce CRYSTAL (Clear Reasoning via Yielded Steps, Traceability, and Logic), a diagnostic benchmark with 6,372 instances that evaluates multimodal…
arXiv:2609.09556v2 Announce Type: replace-cross Abstract: Neural networks can leverage feature superposition to encode more concepts than dimensions, but cross-feature interference constrains the linear accessibility of…
arXiv:2602.11664v2 Announce Type: replace Abstract: Next Point of Interest (POI) recommendation is essential for modern mobility and location-based services. To provide a smooth user experience, models must understand…
arXiv:2609.22056v1 Announce Type: new Abstract: Multi-hop retrieval failures are not uniformly distributed across queries: they cluster in structurally predictable subpopulations. We prove two results formalizing this…
arXiv:2609.21475v1 Announce Type: new Abstract: Personalized fashion complementary recommendation requires jointly modeling user preferences and item compatibility under sparse and multimodal data conditions. Existing…
arXiv:2603.27922v2 Announce Type: replace-cross Abstract: Procedural knowledge in algorithm design is embedded in source code and rebuilt for each new domain. We introduce Generative Executable Algorithm Knowledge…
arXiv:2606.27401v2 Announce Type: replace-cross Abstract: Semantic code search and clone detection are essential for software development, maintenance, and reuse. This paper evaluates the effectiveness, efficiency, and…
arXiv:2609.21863v1 Announce Type: cross Abstract: Empirical evaluation is central to recommender-systems (RecSys) research, but turning experimental designs into executable code remains a manual and error-prone task. We…
arXiv:2609.21308v1 Announce Type: new Abstract: Auto-bidding is a key component of modern advertising systems that provides a personalized bidding strategy for each advertiser. By characterizing each individual,…
arXiv:2607.17582v2 Announce Type: replace-cross Abstract: Approximate Nearest Neighbor Search (ANNS) plays a pivotal role in modern deep learning pipelines. Recently, many ANNS systems have been proposed to provide…
arXiv:2609.21257v1 Announce Type: new Abstract: Large language model (LLM) agents can propose, implement, and evaluate model changes. Autoresearch loops demonstrate this capability through minutes-scale iterations on a…
arXiv:2609.21595v1 Announce Type: new Abstract: In-context learning using Large Language Models (LLMs) offers a compelling path to training-free post-OCR correction, yet its effectiveness for Devanagari script remains…
