Retrieval-augmented generation

Retrieval pipelines, vector search, chunking and reranking: how models are grounded in a corpus instead of their weights.

6,643 stories · RSS feed

Hugging Face Trending Papers

Jun 13

Defending against Adaptive Prompt Injection Attacks via Reasoning-enabled Task Alignment

Indirect prompt injection attacks hijack LLM-based agents by embedding malicious instructions in third-party data that the agent retrieves during task execution. Existing defenses report near-zero attack success rate on static benchmarks, yet recent adaptive evaluations show that these results collapse once the attacker is allowed to optimize against the deployed defense.

llms rag agents reinforcement-learning multimodal benchmarks safety

Towards Data Science

Jun 13

Larger Context Windows Don’t Fix RAG — So I Built a System That Does

Increasing context size in RAG systems doesn’t improve accuracy for aggregation tasks—it makes errors harder to detect. In this article, I benchmark retrieval-based pipelines against a deterministic full-scan engine across 100,000 rows and show why computation queries must be routed away from RAG entirely.

By Emmimal P Alexander

rag benchmarks

Towards Data Science

Jun 13

Parse PDFs for RAG Locally with Docling: Rich Tables, No Cloud Upload

Enterprise Document Intelligence [Vol. 1 #5ter] - Table cells, OCR, captions, headings: cloud-grade structure, running on your own machine.

By Kezhan Shi

rag computer-vision

Towards Data Science

Jun 12

When PyMuPDF Can’t See the Table: Parse PDFs for RAG with Azure Layout

Enterprise Document Intelligence [Vol. 1 #5bis] - The same relational tables.

By Kezhan Shi

rag computer-vision

arXiv AI

Jun 12

Learning to Reason by Analogy via Retrieval-Augmented Reinforcement Fine-Tuning

arXiv:2606. 13680v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) has become a standard mechanism for grounding language models in external knowledge, yet conventional retrieval based on lexical or semantic similarity is poorly suited for complex reasoning tasks: a semantically similar problem may demand an entirely different solution strategy, while a superficially different problem may share the same underlying reasoning pattern.

By Zilin Xiao, Qi Ma, Chun-cheng Jason Chen, Xintao Chen, Avinash Atreya, Hanjie Chen, Vicente Ordonez

llms rag fine-tuning efficiency benchmarks

arXiv AI

Jun 12

Nous: An Attempt to Extract and Inject the Cognition Behind Prediction-Market Behavior

arXiv:2606. 13038v1 Announce Type: new Abstract: As LLM agents proliferate in prediction markets and collective decision-making, they risk a cognitive monoculture: agents built on shared foundation models produce correlated forecasts, and recent measurement finds frontier-model errors correlated at r ~ 0.

By Haowei Qian

llms rag agents fine-tuning

arXiv AI

Jun 12

DCD: Domain-Oriented Design for Controlled Retrieval-Augmented Generation

arXiv:2604. 07590v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) is widely used to ground large language models in external knowledge sources.

By Valerii Kovalskii, Nikita Belov, Nikita Miteyko, Igor Reshetnikov, Maksim Maksimov

llms rag safety

arXiv AI

Jun 12

Proto-LeakNet: Towards Signal-Leak Aware Attribution in Synthetic Human Face Imagery

arXiv:2511. 04260v3 Announce Type: replace-cross Abstract: The growing sophistication of synthetic image and deepfake generation models has turned source attribution and authenticity verification into a critical challenge for modern computer vision systems.

By Claudio Giusti, Luca Guarnera, Sebastiano Battiato

rag diffusion computer-vision benchmarks

arXiv AI

Jun 12

PlaceRep: Geospatial Place Representation Learning from Large-Scale Point-of-Interest Data

arXiv:2507. 02921v4 Announce Type: replace-cross Abstract: Learning effective representations of urban environments requires capturing spatial structure beyond fixed administrative boundaries.

By Mohammad Hashemi, Hossein Amiri, Andreas Zufle

rag benchmarks

arXiv AI

Jun 12

SMSR: Certified Defence Against Runtime Memory Poisoning in Persistent LLM Agent Systems

arXiv:2606. 12703v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) agents increasingly run with persistent memory that accumulates across user sessions.

By Tarun Sharma

llms rag agents benchmarks safety

arXiv AI

Jun 12

SkMTEB: Slovak Massive Text Embedding Benchmark and Model Adaptation

arXiv:2606. 13647v1 Announce Type: cross Abstract: We introduce SkMTEB, the first comprehensive MTEB-style text embedding benchmark for Slovak, a low-resource West Slavic language, comprising 31 datasets across 7 task types -- nearly 4$\times$ the depth of existing multilingual benchmark coverage for Slovak.

By Marek \v{S}uppa, Andrej Ridzik, Daniel Hl\'adek, Nat\'alia K\v{n}a\v{z}ekov\'a, Vikt\'oria Ondrejov\'a

rag fine-tuning benchmarks

arXiv AI

Jun 12

Towards More General Control of Diffusion Models Using Jeffrey Guidance

arXiv:2606. 13240v1 Announce Type: cross Abstract: A key strength of diffusion models lies in their flexibility, since their outputs can be controlled at sampling time through guidance.

By Rapha\"el Razafindralambo, R\'emy Sun, Fr\'ed\'eric Precioso, Jes Frellsen, Pierre-Alexandre Mattei

rag diffusion safety

arXiv AI

Jun 12

Fin-RATE: A Real-world Financial Analytics and Tracking Evaluation Benchmark for LLMs on SEC Filings

arXiv:2602. 07294v4 Announce Type: replace-cross Abstract: With the increasing deployment of Large Language Models (LLMs) in the finance domain, LLMs are increasingly expected to parse complex regulatory disclosures.

By Yidong Jiang, Junrong Chen, Eftychia Makri, Jialin Chen, Peiwen Li, Ali Maatouk, Leandros Tassiulas, Eliot Brenner, Bing Xiang, Rex Ying

llms rag benchmarks safety

arXiv AI

Jun 12

scLLM-DSC: LLM-Knowledge Enhanced Cross-Modal Deep Structural Clustering for Single-Cell RNA Sequencing

arXiv:2606. 13007v1 Announce Type: cross Abstract: Clustering is fundamental to scRNA-seq analysis, serving as a cornerstone for identifying cell populations and resolving tissue heterogeneity.

By Ping Xu, Pengjiang Li, Tian Du, Zaitian Wang, Jiawei Gu, Ziyue Qiao, Pengfei Wang, Yuanchun Zhou

llms rag diffusion multimodal benchmarks safety

arXiv AI

Jun 12

ToolSense: A Diagnostic Framework for Auditing Parametric Tool Knowledge in LLMs

arXiv:2606. 12451v1 Announce Type: new Abstract: Large language models deployed as agents over large tool catalogs face a critical tool-retrieval bottleneck.

By Ashutosh Hathidara, Sai Shruthi Sistla, Sebastian Schreiber, Sahil Bansal

llms rag agents fine-tuning benchmarks

arXiv AI

Jun 12

From Verdict to Process: Agentic Reinforcement Learning for Multi-Stage Fact Verification

arXiv:2606. 13262v1 Announce Type: new Abstract: Recent approaches combining Large Language Models (LLMs) with retrieval-augmented reasoning have shown promise for automated fact verification.

By Rongxin Yang, Shenghong He, Siyuan Zhu, Chao Yu

llms rag agents reinforcement-learning

arXiv AI

Jun 12

Fantastic Scientific Agents and How to Build Them: AgentBuild for Rietveld Refinement

arXiv:2606. 12834v1 Announce Type: new Abstract: As scientific workflows shift from deterministic executables to LLM-based agents, the development practices on offer, such as fine-tuning, reinforcement learning, and prompt-and-go, bury the scientist's judgment.

By Woong Shin, Craig A. Bridges, Marshall T. McDonnell, Rafael Ferreira da Silva

llms rag agents reinforcement-learning fine-tuning

arXiv AI

Jun 12

OCOO-T : A Simple and Scalable Virtual Cell Model for Transcriptional Perturbation Response Prediction

arXiv:2606. 12838v1 Announce Type: cross Abstract: Predicting single-cell transcriptional responses to genetic, chemical and cytokine perturbations is a fundamental challenge in computational biology and AI Virtual Cell (AIVC) modeling, with direct implications for drug discovery and the elucidation of gene regulatory networks.

By Danning Jiang, Zheming An, Yalong Zhao, Lipeng Lai

llms rag benchmarks

arXiv AI

Jun 12

Mod-Guide: An LLM-based Content Moderation Feedback System to Address Insensitive Speech toward Indigenous Ethnic and Religious Minority Communities

arXiv:2606. 13397v1 Announce Type: cross Abstract: Language operates as a mechanism of both marginalization and resistance, especially for minority communities navigating insensitive and harmful speech online.

By Dipto Das, Achhiya Sultana, Ankit Singh Chauhan, Saadia Binte Alam, Mohammad Shidujaman, Shion Guha, Sunandan Chakraborty, Syed Ishtiaque Ahmed

llms rag

arXiv AI

Jun 12

Multi-Field Hybrid Retrieval-Augmented Generation for Maritime Accident Root Cause Analysis

arXiv:2606. 13249v1 Announce Type: new Abstract: Maritime accident adjudication reports contain critical tribunal findings for root cause analysis (RCA), yet retrieving relevant precedents and drafting consistent reports from decades of records remains labor-intensive.

By Seongjin Kim, Sungil Kim

llms rag safety

Get the daily briefing