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, page 19
Whistle and Wrong Lyrics: Synthetic Queries Are Not Enough for Real-World Query-by-Melody
People look up a song by humming it, but also by whistling it or singing it with the wrong words.MemDerive: A Diagnostic Benchmark for Agent Memory over Evolving Data
Long-running agents rely on memory of their past interactions for task state, such as what has been completed and which conditions still hold.Weakly Supervised Adaptation of Scientific Embeddings for Exploratory Literature Mapping
Scientific document embeddings such as SPECTER2 are optimized for retrieval over large, heterogeneous corpora.Selling in the Wild: Benchmark and Randomized Field Evidence for a Sales Agent Trained on Commercial Conversations
We test whether Javo, a sales agent fine-tuned on 4.6 million consented, de-identified commercial conversations, sells better than a frontier general-purpose model and better than human sales…Benchmark for Structured Retrieval-Augmented Generation in Perovskite Photovoltaics
While retrieval-augmented language models increasingly serve as literature assistants in the physical sciences, existing benchmarks evaluate them primarily on qualitative text overlap or through…The Bottleneck Is Retrieval, Not Reading: Diagnosing Evidence-Based Generation with RAG for Long Context Question Answering
Retrieval-augmented generation (RAG) is the dominant paradigm for grounding large language models in external corpora, but its similarity-driven, chunked pipeline has structural blind spots: it…Optimal Fusion Weights Form Intervals, Not Points: A Latency-Aware Study of Query-Adaptive WRRF
Reciprocal Rank Fusion (RRF) is widely used to combine sparse and dense retrievers in hybrid information retrieval, but a critical limitation is that it assigns a fixed, uniform weight to each…DuoLM: Fast and Effective Personalized Recommendation with Language Models
Language models can make personalized recommendations from rich item content, user history, and feedback.RAGit: Turning Document Preprocessing into an Experimental Variable for RAG
Document preprocessing choices can substantially affect retrieval in Retrieval-Augmented Generation (RAG) pipelines, yet they are often selected heuristically and are difficult to compare under…MemRevise: A Self-Revising Memory for Conversational Search
In conversational search, users correct misun-derstandings, revise their requests and return to earlier topics, so part of the history no longer reflects what they want.VisR-RAG-Fin: Benchmarking Multimodal RAG over Visually Rich Financial Documents
Enterprise documents such as financial reports are increasingly processed by automated systems, yet they are primarily designed for human reading and exhibit complex visual structure, combining…Learning to Program Retrievals: Composing Search Primitives via Reinforcement Learning
Retrieval-augmented language models increasingly use multi-step search to solve knowledge-intensive tasks, yet existing systems typically expose retrieval through fixed pipelines or collections of…Expert-Guided Rubrics as Dense Reward Signals for Reinforcement Learning in Non-Verifiable Domains
Large language models (LLMs) are increasingly deployed as autonomous agents in non-verifiable domains such as financial services, where they must interpret user intent, rewrite queries, and retrieve…GRaSP: Latency-Aware Optimization for Geo-Distributed Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) is increasingly served across geo-distributed cloud and edge infrastructures, where wide-area latency, cross-region egress cost, and data-residency rules compete…Reasoning Reranking with Semantic IDs as the Sole Item Representation
Reasoning rerankers write a rationale before ranking a candidate slate, and existing systems read item metadata to do so, even though generative recommenders increasingly represent items as compact…Code Skill Server: A Large-Scale Search Engine for Code Generation Skills
Agent skills have proven to be an effective technique for improving agent ability on specific tasks, including software engineering, natural science, and mathematics.Jev-as-a-Judge for Brazilian Portuguese Information Retrieval: Relevance Assessment and Reranking
This study investigates Jev, a System One model designed for fast, structured decision-making, in information retrieval.QD-Rec: Open-Ended Evolution of Recommendations via Quality-Diversity
Open-ended search combined with foundation models has produced advances such as faster algorithms, new results on open mathematical problems, and self-improving agents.LSNR: Learned Sparse News Recommendations with Attentive User Modeling
Current learned sparse approaches for recommender systems, such as STAR, represent a user by concatenating their interaction history into a single query string.Humanity's Last Choice: A Common Evaluation Standard for Generative Engine Optimization
Humanity's Last Choice describes the idea that the last choice people need to make is which AI system they entrust with the decisions that follow.