Topic - Edition
arXiv:2611.06956v1 Announce Type: new Abstract: Multi-agent LLM systems explore in parallel by spawning sub-agents with isolated context windows, whereas single-agent systems append every search and verification step…
arXiv:2611.07899v1 Announce Type: cross Abstract: Conversational agents can retrieve information directly or delegate retrieval to a subagent. This design choice affects how retrieval history is maintained, what context…
arXiv:2605.21987v2 Announce Type: replace Abstract: Recent conversational recommender systems have begun to unify recommendation and response generation within autoregressive models, with semantic IDs providing a…
arXiv:2611.07393v1 Announce Type: new Abstract: Recent work in machine learning has demonstrated that transformer-based language model agents can communicate state not only through text, but also directly in latent…
arXiv:2611.07591v1 Announce Type: new Abstract: Re-ranking aims to present users with an ordered list that maximizes sequence-level utility. Industrial deployments widely adopt the Generator--Evaluator framework, where…
arXiv:2611.08228v1 Announce Type: new Abstract: Information Retrieval (IR) is much more than ad hoc web search: a large portion of the information seeking activities takes place in specific domains, often addressing…
arXiv:2611.07859v1 Announce Type: cross Abstract: Deep research agents answer complex questions through repeated search. They rely on a harness that runs their tool calls and returns evidence to their context. This…
arXiv:2606.28780v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) are widely applied to visual document understanding. However, comprehending long documents remains an issue by the limited…
arXiv:2611.07971v1 Announce Type: cross Abstract: Some Python workflows need transactional reads and writes over documents, traversals over a graph, and similarity search over vector embeddings. In practice these are…
arXiv:2603.02999v4 Announce Type: replace Abstract: The end-to-end generative paradigm is revolutionizing advertising recommendation systems, driving a shift from traditional cascaded architectures towards unified…
arXiv:2610.13277v2 Announce Type: replace Abstract: Memory-augmented assistants retrieve evidence from stored conversations and documents to answer queries. Most embedding-based retrievers compare single-vector…
arXiv:2512.12084v3 Announce Type: replace Abstract: Existing Text-to-SQL benchmarks primarily focus on single-table queries or limited joins in general-purpose domains, and thus fail to reflect the complexity of…
arXiv:2606.28925v3 Announce Type: replace-cross Abstract: Language-model agent systems must map natural-language requests to one or more specialized agents before planning or tool execution. This decision is often…
arXiv:2611.07631v1 Announce Type: new Abstract: Long-horizon LLM agents require memory systems that preserve useful information across sessions and retrieve it when needed. Existing systems treat these as separate…
arXiv:2610.37973v2 Announce Type: replace Abstract: LLM-based generative recommendation reads long, text-rich interaction histories but generates only short semantic identifiers (SIDs). This asymmetry makes supervised…
arXiv:2611.06323v2 Announce Type: replace Abstract: Generative retrieval predicts semantic identifiers for candidate entities. In local retail and food delivery, candidates must also be serviceable for a particular user…
arXiv:2611.06777v2 Announce Type: replace Abstract: Algorithm-driven services, including search, recommendation, and advertising, rely on multi-stage model pipelines. While research prioritizes model optimization,…
arXiv:2611.08014v1 Announce Type: new Abstract: Dense similarity supplies reusable corpus representations, while probabilistic relevance decisions offer query-dependent document scoring. We compare dense-only BGE-M3…
arXiv:2605.12887v3 Announce Type: replace Abstract: Web-enabled LLM agents are changing how online information influences search outcomes. Existing Generative Engine Optimization (GEO) studies mainly focus on individual…
arXiv:2611.08306v1 Announce Type: new Abstract: We present \textsc{Ginger}, a state-of-the-art open-source C++ library for graph-based approximate nearest neighbor search (ANNS). We developed \textsc{Ginger} at Google…
38 items available
