Applied & Human-Centered · Computer Science & AI
Information Retrieval
The newest papers in Information Retrieval that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
979 papers, from 4 September 2026 to 5 November 2026: 979 arXiv preprints.
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Newest papers
Reducing Representational Interference in Retrieval Embeddings with Trainable Prefix Tokens
Embedding models compress variable-length text into fixed-size vector representations, but this compression can entangle semantic content with residual token-level information, such as artifacts…CacheGraft: Enhancing Retrieval-Augmented Generation via Latent-Space Document Enrichment
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 space via key-value (KV) caches.BRIDGE: Bridging the Semantic Quantization Gap for Generative Recommendation
Generative recommendation retrieves items by autoregressively generating Semantic IDs(SIDs), where an RQ-VAE quantizes each item into a hierarchical SID sequence that the decoder generates level by…MEvince: Knowledge-Constrained LLM Reasoning for Technique Identification from Malware Reports
Mapping behavioral intelligence in malware analysis reports to ATT&CK techniques supports threat analysis and defense, but reliable identification remains challenging.OptST: Optimizing Semantic Tokenization for LLM-Enhanced Recommendation
Semantic information can enrich the identifiers and structured fields used by recommender systems.What Gets Hacked in a Collaborative Reward for LLM Recommenders: The Part That Does Not Depend on the User
Collaborative-filtering (CF) scores provide graded, personalized rewards for training large language model (LLM) recommenders through reinforcement learning.Beyond Independent Predictions: Learning the Browsing Journey for Sequence-Level Re-ranking
Re-ranking aims to present users with an ordered list that maximizes sequence-level utility.MEMOIR: A Training-Free Memory Lifecycle for Long-Horizon LLM Agents
Long-horizon LLM agents require memory systems that preserve useful information across sessions and retrieve it when needed.Tasteply: Externalizing Stable and Situational Preference as Manipulable Spatial Objects
Most recommender systems treat user preferences as a single signal to infer and optimize, yet human preferences are a mix of a stable preference and situational preferences that turn on and off…CORE: Coarse-to-Fine Search with Context Management for Deep Research
Deep research agents answer complex questions through repeated search.Persistent Retrieval for Conversational Agents: A Study of Retrieval Efficiency
Conversational agents can retrieve information directly or delegate retrieval to a subagent.ZeroRAG: Adaptive Semantic Peer Routing for Decentralized Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) is a standard approach to enhance the reliability of large language model (LLM) outputs by retrieving documents relevant to a query and supplying them in the LLM…ArcadeDB in Python: An In-Process Multi-Model Database
Some Python workflows need transactional reads and writes over documents, traversals over a graph, and similarity search over vector embeddings.Probabilistic Relevance Decisions for Semantic Retrieval
Dense similarity supplies reusable corpus representations, while probabilistic relevance decisions offer query-dependent document scoring.Domain-specific information retrieval
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 work-related tasks.GINGER: Spicing Up Graph-Based Vector Search
We present \textsc{Ginger}, a state-of-the-art open-source C++ library for graph-based approximate nearest neighbor search (ANNS).Decision Models as Retrieval Gates Against Injection and Pollution
Retrieved web text can carry instructions aimed at the language model, or synthetic pages written to mislead.Item Representations Matter: Bridging Hard Semantic IDs and Soft Prototype Mixtures for Generative Retrieval
Modern recommender systems rely on generative retrieval techniques to predict the next item either as discrete codes or as a continuous vector.SciToT: Multimodal Tip-of-the-Tongue Retrieval for Scientific Papers
Researchers frequently encounter difficulties re-finding scientific papers when they remember aspects of the content but cannot recall reliable identifiers, such as titles or author names.Weaver: Weaving Preference Evidence through Incremental Reasoning for Personalized Retrieval-Augmented Generation
Personalized retrieval-augmented generation requires extracting and applying user preferences from extended interaction histories.