arXiv AI

Edge-Based Agentic Retrieval-Augmented Generation for Autonomous FHWA Bridge Inspection Compliance

BridgeGuard is an air‑gapped, agentic Retrieval‑Augmented Generation system designed to automate compliance verification for the Federal Highway Administration’s bridge inspection requirements. It combines vector search over the FHWA Recording and Coding Guide with structured SQL queries against National Bridge Inventory data, guided by a stateful multi‑step ReAct planning loop that runs locally on commodity edge hardware. The system achieves near‑perfect classification and citation accuracy on large bridge inventories while operating without external network access, and ablation studies show that both vector search and the agentic loop are essential for correct compliance reasoning.

arXiv AI

Jun 4

Cascading Hallucination in Agentic RAG: The CHARM Framework for Detection and Mitigation

arXiv:2606. 04435v1 Announce Type: new Abstract: Multi-step agentic retrieval-augmented generation (RAG) pipelines have demonstrated significant capability for complex reasoning tasks, yet remain vulnerable to a class of failure that existing hallucination detection mechanisms systematically miss: cascading hallucination, where errors introduced at early pipeline stages propagate and amplify across successive reasoning steps, producing confident but factually incorrect final outputs.

By Saroj Mishra

rag agents safety

arXiv AI

Jun 11

Automating Geometry-Intensive Compliance Checking in BIM: Graph-Based Semantic Reasoning Framework

arXiv:2606. 12065v1 Announce Type: new Abstract: Automating compliance check for geometry-intensive regulations remains a significant technical bottleneck in Building Information Modeling (BIM), primarily due to the semantic disparity between high-level regulatory logic and structured IFC data.

By Zixuan Xiao, Pei Troh Koh, Jun Ma, Jack C. P. Cheng

agents multimodal safety

arXiv AI

Jul 2

Bayesian Uncertainty Propagation for Agentic RAG Pipelines: A Proof-of-Concept Study on Multi-Hop Question Answering

arXiv:2607. 00972v1 Announce Type: new Abstract: Trustworthy deployment of Agentic Retrieval-Augmented Generation (RAG) systems requires mechanisms for estimating when multi-stage reasoning pipelines may fail.

By Louis Donaldson, Connor Walker, Koorosh Aslansefat, Yiannis Papadopoulos

llms rag agents nlp

Hugging Face Trending Papers

Jun 10

Automating Geometry-Intensive Compliance Checking in BIM: Graph-Based Semantic Reasoning Framework

Automating compliance check for geometry-intensive regulations remains a significant technical bottleneck in Building Information Modeling (BIM), primarily due to the semantic disparity between high-level regulatory logic and structured IFC data. Existing methods, often reliant on static rule templates, struggle to traverse multi-hop reasoning chains or resolve latent spatial dependencies across multiple building entities.

agents multimodal safety

arXiv AI

Aug 11

ConMem: Contribution-Aware Memory for Long-Horizon Manufacturing Inspection Logs

arXiv:2607. 28126v2 Announce Type: replace Abstract: Long-horizon steel-equipment inspection requires reasoning over heterogeneous records accumulated across repeated inspection cycles.

By Bingchen Liu, Yuanyuan Fang, Lei Liu, Guangyuan Dong, Xing Fu, Yuanyuan Gao, Shuyue Wei, Xin Li, Xiangtian Meng

llms rag computer-vision benchmarks

arXiv AI

Jun 3

FORGE: Multi-Agent Graduated Exploitation and Detection Engineering

arXiv:2606. 03453v1 Announce Type: cross Abstract: Vulnerability disclosure volumes now far exceed organizational assessment capacity, yet three adjacent research communities (proof-of-concept generation, vulnerability prioritization, and detection rule engineering) operate largely in isolation.

By Farooq Shaikh

llms agents

arXiv AI

Jul 7

AgentLTL: A Trace-Verification Framework for Measuring, Enforcing, and Training Procedural Compliance in Tool-Using LLM Agents

arXiv:2607. 02599v1 Announce Type: cross Abstract: Tool-using LLM agents are usually evaluated by final-answer correctness or LLM judges.

By La\"ila Elkoussy (LRE, EPITA), Julien Perez (LRE)

llms agents fine-tuning benchmarks safety

arXiv Computation and Language

Oct 22

HybridResearcher: Bridging the Structured-Unstructured Boundary for Deep Research Agents

arXiv:2610.24899v2 Announce Type: replace-cross Abstract: While LLM agents are increasingly capable of gathering and synthesizing evidence from a single homogeneous source, such as structured tables...

By Soyoung Yoon, Peiran Xu, Han Wang, Ruofan Wu, Xiaodong Yu, Feng Yan, Mohit Bansal, Debiao Li, Seung-Won Hwang, Yuxiong He, Zhewei Yao

llms agents reinforcement-learning fine-tuning benchmarks data-engineering

Hugging Face Trending Papers

Jul 30

ConMem: Contribution-Aware Memory for Long-Horizon Manufacturing Inspection Logs

Long-horizon steel-equipment inspection requires reasoning over heterogeneous records accumulated across repeated inspection cycles. Existing retrieval-augmented generation systems treat historical logs as a static corpus and retain records without estimating their diagnostic value, failing to report early risk.

llms rag computer-vision benchmarks

arXiv Machine Learning

Oct 8

A Vehicle-Integrated Approach to Digital Twin Deployment for Bridges Through Drive-By Sensing

arXiv:2610.08822v1 Announce Type: new Abstract: Ageing bridge infrastructure is a growing global concern, yet conventional Structural Health Monitoring (SHM) systems are costly and difficult to scale...

By Zihao Liu, Daigo Kawabe, Jiaji Wang, Chul-Woo Kim, Mehrisadat Makki Alamdari

llms safety

arXiv AI

Aug 11

RAG-Based Auto-Configuration for Industrial Fieldbus Devices

arXiv:2608. 08618v1 Announce Type: cross Abstract: Industrial device commissioning requires engineers to manually extract hundreds of protocol-specific parameters from heterogeneous PDF manuals and transcribe them into supervisory control systems, a time-intensive, error-prone workflow.

By Aadil Gani Ganie, Saad Ezzini, Naveed Farooz Marazi

llms rag robotics benchmarks

arXiv Machine Learning

Jul 27

Encoding Invisible Causation for Bridge Diagnostic Agents: Triple-Guided Retrieval-Augmented Fine-Tuning with QLoRA

arXiv:2607. 21680v1 Announce Type: new Abstract: Bridge infrastructure deteriorates gradually, yet its root causes---salt intrusion, freezing, fatigue cracking, and others---remain invisible to the naked eye.

By Takato Yasuno

llms rag agents fine-tuning efficiency benchmarks

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