arXiv Machine Learning June 19, 2026 By Ali Asaria, Tony Salomone, Deep Gandhi

Train, Retrieve, or Both? A Four-Arm Head-to-Head for Correct Statutory Citation on the Ontario Residential Tenancies Act

Read the original on arXiv Machine Learning →

arXiv:2606. 20359v1 Announce Type: new Abstract: Self-represented tenants, landlords, and help-desk staff need to be pointed at the provision of law that actually governs a question, with a correct statutory citation.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Aug 11

Temporal Misgrounding in Legal RAG: A Versioned-Corpus Benchmark for French Tax Law

arXiv:2608. 09393v1 Announce Type: cross Abstract: We identify and quantify temporal misgrounding: the systematic retrieval and citation of the currently in-force version of a legal article when the applicable version is an earlier or future one.

By Rose Cymbler, Daniel Guez, Laurent Fabre

llms rag nlp benchmarks safety

arXiv Machine Learning

Jul 27

SURE-RAG: Sufficiency and Uncertainty-Aware Evidence Verification for Selective Retrieval-Augmented Generation

arXiv:2605. 03534v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) grounds answers in retrieved passages, yet relevance does not guarantee sufficiency: a topical passage may still fail to justify the answer.

By Jingxi Qiu, Zeyu Han, Cheng Huang

llms rag benchmarks safety

arXiv AI

Jul 17

L-MARS: Legal Multi-Agent System with Agentic Search and Citation-Faithfulness Audit

arXiv:2509. 00761v4 Announce Type: replace Abstract: Large language models are increasingly deployed for legal question answering, where evaluations typically focus on multiple-choice accuracy.

By Boqin Yuan, Ziqi Wang

llms rag agents nlp

arXiv AI

Jul 1

RARE: Redundancy-Aware Retrieval Evaluation Framework for High-Similarity Corpora

arXiv:2604. 19047v2 Announce Type: replace-cross Abstract: Existing QA benchmarks typically assume distinct documents with minimal overlap, yet real-world retrieval-augmented generation (RAG) systems operate on corpora such as financial reports, legal codes, and patents, where information is highly redundant and documents exhibit strong inter-document similarity.

By Hanjun Cho, Jay-Yoon Lee

llms rag benchmarks

arXiv AI

Aug 7

HERALD: Counterfactual Audits and Minimal Repairs for Proof-of-Retrieval Rewards

arXiv:2608. 06012v1 Announce Type: new Abstract: Search-agent rewards mix answer quality, citation grounding, tool cost, and anti-hacking terms; a high score therefore need not imply that cited evidence was retrieved, and added penalties can cancel.

By Zhuowen Liu, Bohan Cui, YinShang Guo, Yuting Wang, Hao Li

agents multimodal benchmarks

arXiv AI

1d ago

Think Inside the Chunk: RegulaRAG for Regulation-Compliant Scenario Generation using LLMs: A Case Study of UN Regulation No. 152

arXiv:2608. 16394v1 Announce Type: new Abstract: Generating regulation-compliant test scenarios is essential for validating safety-critical automotive systems, yet Large Language Models (LLMs) struggle to ground outputs in long, hierarchical standards.

By Vahid Zolfaghari, Nenad Petrovic, Andr\'E Schamschurko, Alois Knoll

llms rag safety

Related stories

Temporal Misgrounding in Legal RAG: A Versioned-Corpus Benchmark for French Tax Law

SURE-RAG: Sufficiency and Uncertainty-Aware Evidence Verification for Selective Retrieval-Augmented Generation

L-MARS: Legal Multi-Agent System with Agentic Search and Citation-Faithfulness Audit

RARE: Redundancy-Aware Retrieval Evaluation Framework for High-Similarity Corpora

HERALD: Counterfactual Audits and Minimal Repairs for Proof-of-Retrieval Rewards

Think Inside the Chunk: RegulaRAG for Regulation-Compliant Scenario Generation using LLMs: A Case Study of UN Regulation No. 152