I don't see any threads on this model. Is it because it's dense and/or without-reasoning?
#retrieval-augmented
28 items
Is Granite-4.1-30b Overshadowed by Qwen3.6 & Gemma4 models? (www.reddit.com) Every RAG-based localization pipeline has the same blind spot (lingo.dev via hn) If a localization pipeline uses retrieval augmented generation to inject glossary terms into the model's context window, it has a retrieval recall problem that has never been measured. The pattern is universal: embed the input text, cosine…
RAG pipelines, leaking PII into vector databases and nobody's talking about it (comply-tech.co.uk via hn) Your RAG Pipeline Is Leaking Customer Data Into Vector Embeddings If you're building a RAG (Retrieval Augmented Generation) system on internal documents such as customer support history, knowledge base articles, or internal comms, there's…
Seeing Through the MiRAGE: Evaluating Multimodal Retrieval Augmented Generation (arxiv.org) We introduce MiRAGE, an evaluation framework for retrieval-augmented generation (RAG) from multimodal sources. As audiovisual media becomes a more prevalent source of information online, RAG systems must integrate such media into generatio…
Knowledge-Graph Based Augmentation versus Retrieval Augmented Generation for Cultural-Related Question Answering (arxiv.org) Large language models (LLMs) suffer from a long-tail deficit: culturally specific facts, particularly those concerning underrepresented regions such as Latin America, appear too rarely in pretraining corpora to be reliably memorized. Retri…
STAIR (STructure Aware Information Retriever): A novel dataset and LLM based retriever for document structure augmentation (arxiv.org) Retrieval Augmented Generation (RAG) is a key component for generating accurate and hallucination free answers using Large Language Models (LLMs). LLMs are improving at handling long context, but still suffer from "lost in the middle" prob…
Beyond-RAG: Question Identification and Answer Generation in Real-Time Conversations (arxiv.org) In customer contact centers, human agents often struggle with long average handling times (AHT) due to the need to manually interpret queries and retrieve relevant knowledge base (KB) articles. While retrieval augmented generation (RAG) sy…
Addressing Corpus Knowledge Poisoning Attacks on RAG Using Sparse Attention (arxiv.org) Retrieval Augmented Generation (RAG) is a highly effective paradigm for keeping LLM-based responses up-to-date and reducing the likelihood of hallucinations. Yet, RAG was recently shown to be quite vulnerable to corpus knowledge poisoning:…
DSPrompt: Dynamic Soft Prompt Defense Against M-RAG Corruption (arxiv.org) Multimodal Retrieval Augmented Generation (M-RAG) is increasingly vulnerable to adversarial attacks where malicious data are crafted to produce embeddings that align with benign entries in the vector space, deceiving retrieval and inducing…
SAG: SQL-Retrieval Augmented Generation with Query-Time Dynamic Hyperedges (arxiv.org) While retrieval-augmented generation (RAG) has proven effective at giving LLMs access to external knowledge, mainstream dense-retrieval implementations remain inherently limited in handling structured constraints and multi-hop reasoning. G…
Self-Knowledge Retrieval Augmented Generation Framework for Patent Matching (arxiv.org) Patent retrieval and matching based on large language models (LLMs) play a vital role in intellectual property protection. However, due to the complex structure of patent documents, dense technical terminology, and multi-modal information,…
TS-RAG: Retrieval Augmented Generation for Time Series Forecasting (arxiv.org) While deep learning models, particularly transformer-based architectures, have shown impressive performance in time series forecasting, the application of retrieval-augmented generation (RAG) in this domain remains limited. Since RAG has p…
Retrieval Augmented Biomedical Question Answering with Weak Question Recovery and Neural Reranking for BioASQ Task 14b (arxiv.org) This work presents DS@GT ARC BioASQ team's work for a biomedical question answering pipeline, integrating multi-source query expansion, neural reranking, retrieval refinement, and OpenBioLLM-assisted answer generation. The system combines…
Optimization and Constraint Modeling using LLMs with a Retrieval Augmented Generation Process (arxiv.org) Both optimization modeling and constraint modeling are non-trivial problems requiring deep domain expertise and proficiency in modeling formalism languages. Despite their importance across logistics, healthcare, and supply chain management…
Beyond Factual Accuracy: Evaluating Global Reasoning Integrity in RAG Systems with LogicScore (arxiv.org) Current evaluation methods for Retrieval Augmented Generation (RAG) suffer from \textit{factual myopia}: they relentlessly emphasize factual accuracy yet neglect global logical integrity in long-form answer generation. This drives models t…
Real-time Spatial Retrieval Augmented Generation for Urban Environments (arxiv.org) The proliferation of Generative Artificial Ingelligence (AI), especially Large Language Models, presents transformative opportunities for urban applications through Urban Foundation Models. However, base models face limitations, as they on…
TopoGuard: Graph Theory Based Defenses Against Split-Knowledge Attacks on RAG (arxiv.org) Production Retrieval Augmented Generation (RAG) systems rely on aggregating multiple external documents to answer complex queries. However, the retrieved documents introduce a new threat surface that can be exploited to launch split-knowle…
ReDiTT: Retrieval Augmented Conditional Diffusion Transformers for Asynchronous Time Series (arxiv.org) We present a diffusion based model for asynchronous time series prediction, where the goal is to predict the next inter event time and event type. To address the inherent uncertainty of future events, we introduce ReDiTT, a retrieval augme…
5ting at SemEval-2026 Task 8: Strong End-to-End Multi-Turn RAG via LLM-Based Reranking and Faithfulness Control (arxiv.org) We introduce 5ting, our system for the SemEval2026 Task 8 (MTRAGEval), which evaluates multi-turn Retrieval Augmented Generation (RAG) systems. Multi turn RAG involves context drift, under specification, and hallucination risk.
LLM Program Optimization via Retrieval Augmented Search (arxiv.org) Recent work has demonstrated the potential of large language models (LLMs) for program optimization, a key challenge in programming languages. We propose a blackbox adaptation method called Retrieval Augmented Search (RAS) that performs be…
CausalRAG2: Hierarchical Causal Knowledge Graph Design for RAG (arxiv.org) Retrieval augmented generation (RAG) has enhanced large language models by enabling access to external knowledge, with graph-based RAG emerging as a powerful paradigm for structured retrieval and reasoning. However, existing graph-based me…
Predict the Retrieval! Test time adaptation for Retrieval Augmented Generation (arxiv.org) Retrieval-Augmented Generation (RAG) has emerged as a powerful approach for enhancing large language models' question-answering capabilities through the integration of external knowledge. However, when adapting RAG systems to specialized d…
Uncertainty-Aware Hybrid Retrieval for Long-Document RAG (arxiv.org) Retrieval augmented generation (RAG) depends critically on the quality and granularity of retrieved evidence. Large retrieval units preserve context but often introduce irrelevant content, which can dilute answer bearing evidence and worse…
RACT: Retrieval Augmented Column-Table Learning and Prediction for Multi-Table Schema Matching (arxiv.org) ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generation (arxiv.org) Retrieval Augmented Generation Framework for the Nepali Legal Domain Question Answering (arxiv.org) HypRAG: Hyperbolic Dense Retrieval for Retrieval Augmented Generation (arxiv.org) Embedding geometry plays a fundamental role in retrieval quality, yet dense retrievers for retrieval-augmented generation (RAG) remain largely confined to Euclidean space. However, natural language exhibits hierarchical structure from broa…
Retrieval Augmented Generation with Huggingface Transformers and Ray (huggingface.co)