arXiv:2604. 09552v2 Announce Type: replace-cross Abstract: Engineering rulebooks and technical standards contain multimodal information like dense text, tables, and illustrations that are challenging for retrieval augmented generation (RAG) systems.
arXiv:2609.23769v1 Announce Type: new Abstract: The disambiguation of semantically similar statutory text across jurisdictions is a retrieval problem that existing methods do not solve. This inter-co...
arXiv:2604. 04969v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) mitigates hallucinations in Multimodal Large Language Models (MLLMs), yet existing systems struggle with complex cross-modal reasoning.
arXiv:2608. 11244v1 Announce Type: new Abstract: Construction standards are critical for building safety and sustainability.
The paper introduces TOPOGRAPHRAG-BENCH, a benchmark for evaluating multimodal Graph Retrieval-Augmented Generation (GraphRAG) systems on layout‑grounded evidence reasoning. It contains 2,024 questions derived from 201 long, visually rich documents, designed around three controlled topologies—single‑hop retrieval, bridge‑chain reasoning, and multi‑source synthesis—while employing counterfactual validation to ensure question integrity. Experiments show that multimodal GraphRAG outperforms text‑only and page‑level visual retrieval models, yet still struggles with incomplete visual‑textual alignment and multi‑unit composition, highlighting the need for GraphRAG systems that explicitly model document layouts and cross‑modal evidence alignment.
arXiv:2608. 15056v1 Announce Type: new Abstract: Multimodal retrieval-augmented generation (RAG) systems often rely on long unstructured contexts or aggressively expanded evidence graphs, which can introduce noisy evidence, weaken multi-hop reasoning, and increase unsupported generation.
arXiv:2606. 26458v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) over knowledge graphs has emerged as a promising approach for grounding large language models, yet existing benchmarks largely overlook the challenges of retrieval in multimodal knowledge graph RAG (MKG-RAG).
arXiv:2609.05518v1 Announce Type: cross Abstract: Despite the strong capabilities of multimodal large language models (MLLMs), their parametric knowledge remains incomplete and difficult to update, m...
arXiv:2606. 01613v2 Announce Type: replace-cross Abstract: This paper presents an agentic multimodal retrieval-augmented generation (RAG) framework for domain-specific literature reasoning, instantiated on a curated corpus of several thousand papers in intelligent tires, vehicle dynamics, vehicle control, sensing, estimation, and machine learning.
PlanSightRAG is a visual-first multimodal retrieval‑augmented generation system designed to automate question answering and compliance checking of civil standard plans. It processes plan imagery directly, using a ColNomic‑3B multi‑vector retrieval engine, an agentic Planner‑Retriever‑Auditor‑Synthesizer, and MaxSim heatmaps to provide an evidence trail. The system achieves high recall on a new 4,056‑pair benchmark from five state DOTs, and demonstrates near‑perfect verdict accuracy on synthetic compliance drawings when a rule threshold is supplied, outperforming OCR‑based baselines.
arXiv:2503. 10677v3 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has gained significant attention in recent years for its potential to enhance natural language understanding and generation by combining large-scale retrieval systems with generative models.
The paper surveys recent advances in Retrieval Augmented Generation (RAG), a technique that integrates external retrieval into language model generation to reduce hallucinations and keep knowledge current. It introduces a four‑axis taxonomy—efficiency, defense, interactivity, and reasoning—to organize contemporary RAG research, covering retrieval methods, fusion strategies, embedding optimizations, and reinforcement learning policies. The survey also reviews evaluation practices, domain‑specific applications, and architectural variants, while highlighting ongoing challenges such as retrieval quality, reliability, domain adaptation, scalability, and explainability.