arXiv AI August 7, 2026 By Zhuowen Liu, Bohan Cui, YinShang Guo, Yuting Wang, Hao Li

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 8

More Convincing, Not More Correct: Self-Play Reward Hacking of Reference-Free LLM Judges

arXiv:2607. 05904v1 Announce Type: new Abstract: Training a language model against its own reference-free judgments (the premise of self-rewarding, self-play, and LLM-as-a-judge pipelines) assumes a model's verdict on a shown answer tracks correctness.

By Chenyu Zhou

llms reinforcement-learning

arXiv Computation and Language

Sep 15

CiteShade: Citation Laundering in Multi-Source Retrieval-Augmented Generation and Its Counterfactual Defense

arXiv:2609.15660v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) grounds a language model's answers on retrieved external knowledge and returns each answer with citations that i...

By Guo Fuzheng

llms rag nlp

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 Computation and Language

Sep 22

Re:CAP - Auditing Retrieval Coverage in Production RAG Pipelines

Re:CAP is a reference‑free audit loop for retrieval‑augmented generation (RAG) pipelines that probes for missing documents instead of enumerating all relevant ones. It identifies covered topics, generates probing questions, retrieves candidate documents, and uses an LLM judge to keep only those that add new information. On several benchmarks, Re:CAP recovers a significant portion of gold documents that flat BM25 or hybrid retrieval misses, and human evaluation shows most of these documents add new information.

By Aviral Joshi, Hanoz Bhathena, Max Nelson, Saket Sharma

llms rag benchmarks

arXiv Machine Learning

Jul 30

RAGuard: A Layered Defense Framework for Retrieval-Augmented Generation Systems Against Data Poisoning

arXiv:2607. 26339v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems ground large language models (LLMs) in external corpora, but this reliance exposes them to corpus poisoning: maliciously injected passages that manipulate retrieved evidence.

By Pushkal Kumar, Tucker Nielson, Tanish Kolhe, Shubham Zala, Vincent Li

llms rag safety

arXiv AI

Aug 18

What Do Compliance Detectors Read? An Audit of Activation Probes and Guard Models

arXiv:2608. 16852v1 Announce Type: new Abstract: Regulatory compliance monitoring in deployed language models is increasingly implemented as a legal and audit control, checking model outputs against written rules spanning data protection, healthcare, financial regulation, and platform policy.

By Saisab Sadhu, Aadit Sengupta, Vinay Kumar Sankarapu, Pratinav Seth

llms benchmarks