SCOPE is a multi‑agent system that profiles news media outlets for bias, factuality, and credibility by coordinating agents for article acquisition, content analysis, evidence search, scoring, and report generation. It follows an MBFC‑style methodology and provides inspectable intermediate evidence and outputs via a structured interface. On a benchmark of 200 outlets, SCOPE outperforms LLM‑only, article‑only, search‑only, and media‑background‑check baselines, achieving the lowest bias and factuality MAE, perfect failed‑fact‑check detection, and the lowest factual error rate.
By Ilyas Nagiyev, Miras Baisbay, Daniil Orel, Preslav Nakov
arXiv:2610.33240v1 Announce Type: new
Abstract: As frontier AI systems increasingly serve as sources of political and current-events information, evaluating the epistemic quality of their responses i...
By Matt Wilde, Jillian Fisher, Andrew B. Hall, Kathryn Salam, Robbie Goldfarb
arXiv:2609.38799v1 Announce Type: new
Abstract: Understanding multi-perspective alternative narratives requires identifying how their information agrees, conflicts, or differs across sources. Existin...
By Eftekhar Hossain, Santu Karmaker
arXiv:2607. 18983v1 Announce Type: cross Abstract: We present AutoJourn, a demonstration system for multi-perspective news generation and bias-aware evaluation using large language models (LLMs).
By Himel Ghosh, Ahmed Mosharafa, Georg Groh
arXiv:2609.24219v1 Announce Type: new
Abstract: Traditionally, the reliability of news publishers is assessed by expert organisations that evaluate editorial practices, transparency and factual stand...
By John Bianchi, Manuel Pratelli, Fabio Pinelli, Marinella Petrocchi
We present AutoJourn, a demonstration system for multi-perspective news generation and bias-aware evaluation using large language models (LLMs). The system tackles three core challenges in responsible automated journalism: extracting diverse perspectives from unstructured social media discussions, generating summaries that preserve viewpoint diversity, and detecting or mitigating bias in AI-generated news.
The paper introduces Data Journalist Agent (Data2Story), a multi‑agent framework that orchestrates specialized roles into a single virtual newsroom to produce evidence‑grounded, multimodal news stories. It ensures every claim is traceable to data, code, or external references via an Inspector, and generates interactive visualizations such as maps and audio to match reader interests. Evaluations on 18 articles show competitive performance in angle coverage, rubric scores, and verifiability, while human writers still lead in editorial angle and creative design.
By Kevin Qinghong Lin, Batu EI, Yuhong Shi, Pan Lu, Juil Sock, Djordje Padejski, Philip Torr, James Zou
arXiv:2610.27597v1 Announce Type: cross
Abstract: Existing reasoning evaluations typically provide task-relevant information to models as available premises, rarely examining concurrently how models...
By Bo Zou, Chao Xu
Large language models (LLMs) tend to exhibit sycophancy by aligning with users’ expressed views. This study introduces a benchmark of 5,500 evaluation instances built from 500 debatable questions across ten domains, where the user stance is fixed but the justification is systematically varied across eleven conditions, including neutral, personal experience, causal framing, and worldview-based synthesis. Seven LLMs were evaluated, revealing that narrative structure can significantly influence agreement, with causal framing and bullet presentation boosting sycophancy while personal experience can reduce it.
By Runtai Yang, Dandan Zhu, Kaiwei Zhang, Guangtao Zhai
arXiv:2609.14178v1 Announce Type: new
Abstract: The rapid diffusion of hate speech and misinformation on social networks challenges democratic societies, since direct suppression efforts may deepen p...
By Carmel Kronfeld, Sharva Gogawale, Tetsuro Kobayashi, Irad Ben-Gal
The study introduces a two‑dimensional framework to audit French news headlines, distinguishing salience framing—captured by four wording devices—from selection framing—captured by outlet‑level story form and high‑charge distributions. Using a 10,000‑headline supervision set annotated by LLMs and human arbitration, the authors classify 902,111 headlines from 25 outlets (2022‑2025) and find that salience and selection diverge yet correlate, that default thresholds inflate salience estimates, and that headlines mentioning Jews, the Far‑right, and Muslims exhibit the highest salience rates. The authors release their dataset, lexicons, and code, claiming it is the largest French headline framing audit to date.
By Amr Sobhy
The paper introduces a controlled inversion test to evaluate whether large language models can reverse known framing transformations in news articles while preserving facts. Using 60 articles and three framing types—evaluative lexis, agency realization, and information salience—the study generates 540 paired variants. Results show high factual preservation (~0.84) but low reversal success (0.044–0.068), indicating that recognizing a framing does not guarantee its undoing.
By Yi Liu