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 36
Retrieved but Not Delivered: Multimodal Memory Delivery for Long-Term Agents
Work on memory for multimodal agents optimizes what is written, updated and retrieved.Concepts Complement Dense Semantics: Learning Compact Sparse Spaces for Text-Image Retrieval
Cross-modal retrieval has been advanced by vision-language pre-trained models that encode images and texts into a shared dense embedding space.RandSlot: Learning Compact Visual Document Representations with Random Soft Tokens
Visual document retrieval requires expressive representations to match queries with evidence distributed across text, tables, and page layouts.Mend the Measurement Gap: Latent User Preference Modeling for Short-Form Video Recommendation
Recommender systems rely heavily on heterogeneous behavioral feedback to infer user preference.PILAR: A Page-Grounded Unified Evidence Representation via an Entity-Linked Assertion Graph for Open-Domain QA Agents over Multimodal Document Corpora
Open-domain question answering (ODQA) over multimodal document corpora requires linking evidence scattered across text, tables, and figures.Backdoor in the Loop: Compromising Agentic Search via Malicious Retrievers
Agentic retrieval-augmented generation (RAG) interleaves reasoning with repeated retrieval, giving the retriever influence over both the evidence an agent observes and its subsequent search decisions.Component Benchmark: Hierarchical Model Profiling for Large-scale Recommendation Systems
Large-scale recommendation models pose distinct, under-explored profiling challenges.Recommendation World Models for Future-State Control
Sequential recommendation optimizes which items to rank, while each displayed slate also shapes subsequent feedback and user state.RecToolBench: Benchmarking Recommendation-Specific Tool Orchestration under Fuzzy User Intent
Recent advances in agentic recommender systems are shifting recommender systems from passive filtering engines to instruction-following agents that use external tools to resolve user intent.QReason: Query-Focused Decoupled Chain-of-Thought for Efficient Passage Reranking
Passage reranking plays a crucial role in information retrieval by refining the ordering of candidate passages to better reflect relevance.KuaFu: Compressing Long User Behavior into Understanding at Billion Scale
Conversational agents, generative recommenders, and personalized advertising all rest on one capability: understanding each user from raw behavior.CG-Probes: Recovering Guardrail Directions from Patient Query Embeddings
Patient-facing AI assistants promise valuable support to patients, but incoming queries can pose medical risks.SPADE: Escaping the Popularity-Similarity Frontier to Measure Serendipitous Recommendations
Recommender systems engineer serendipity to foster active exploration and break predictable consumption cycles.AgentRecommender: LLM Agents Enable Customizable Recommender Systems on the User Side
Recommender systems have traditionally been developed for platforms.Enriching Sequential Recommendation with Graph Laplacian Positional Embeddings
Sequential recommenders typically rely on learnable positional embeddings to encode the order of user interactions.Retail Product Search: A Practical Approach at Target
Search is one of the most important features in e-commerce, directly driving customer engagement and business growth.FARE: Deep Reinforcement Learning For Fair Exposure Constrained Uncertainty Aware Financial Content Personalization
Content personalization systems in financial services must ensure fair exposure across diverse offerings-a requirement driven by contractual obligations and the need to prevent "rich-get-richer"…Overview of the TREC 2025 Million Large Language Models track
Agentic AI envisions ecosystems of intelligent agents collaboratively solving complex tasks with minimal human intervention.Recipe-Matching, Not Equivalence
MathNet-Retrieve asks a retriever to find, for a math problem, a document stating the same problem.Amnesia by Design, Memory By Necessity: Persistent State for Document Intelligence
Modern Document AI reads contracts, extracts fields, reasons over tables, and grounds answers to page regions, then forgets everything.