# SCOPE authors present a system for profiling news outlets [Canonical reading](https://escapemeridian.com/readings/scope-authors-present-a-system-for-profiling-news-outlets-251de761) Authors Ilyas Nagiyev, Miras Baisbay, Daniil Orel and Preslav Nakov present SCOPE, a multi-agent system that profiles news media outlets for bias, factuality and credibility. Given an outlet URL, it coordinates agents to acquire articles, analyze their content, search for external evidence, score results and generate a report. The authors say its MBFC-style methodology makes intermediate evidence and agent outputs inspectable through a structured demonstration interface. On a benchmark of 200 media outlets, the authors report that SCOPE outperforms LLM-only, article-only, search-only and media-background-check baselines on most metrics. They report the lowest bias and factuality MAE, perfect failed-fact-check detection and the lowest factual error rate. The authors argue these results suggest that decomposed, evidence-grounded agent workflows can support more transparent and reliable media profiling. The abstract reports benchmark results, but does not establish how well SCOPE performs on outlets beyond that benchmark or how its profiles should be used in practice. How would SCOPE’s reported performance hold up when profiling outlets outside the benchmark? Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims. ## Original source arXiv cs.AI · Article Original source date: Oct 18, 2026 (2026-10-18). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-07T00:01:14.990Z. First observed: 2026-10-28T05:22:42.342Z. Retrieved: 2026-10-29T20:47:04.054Z. - [SCOPE: Source Credibility and Outlet Profiling Engine](https://arxiv.org/abs/2610.35929) ## Limits The abstract reports results on a benchmark of 200 media outlets; it does not establish how SCOPE performs on outlets outside that benchmark. The supplied evidence is the abstract, so it does not provide details of the benchmark, metrics, or evaluation procedure. ## Open question How would SCOPE’s reported performance hold up when profiling outlets outside the benchmark? ## Source-stated The authors present SCOPE as a system that profiles news media outlets for bias, factuality and credibility using coordinated agents for acquisition, analysis, evidence search, scoring and report generation. > Given an outlet URL, SCOPE coordinates specialized agents for article acquisition, content analysis, external evidence search, scoring, and report generation. - [Supporting source](https://arxiv.org/abs/2610.35929) ## Source-stated The authors say SCOPE makes intermediate evidence and agent outputs inspectable through a structured demonstration interface. > making intermediate evidence and agent outputs inspectable through a structured demonstration interface. - [Supporting source](https://arxiv.org/abs/2610.35929) ## Source-stated The authors report that, on a benchmark of 200 media outlets, SCOPE outperforms several baselines on most metrics, including the lowest bias and factuality MAE and the lowest factual error rate. > On a benchmark of 200 media outlets, SCOPE outperforms LLM-only, article-only, search-only, and media-background-check baselines on most metrics, achieving the lowest bias and factuality MAE, perfect failed-fact-check detection, and the lowest factual error rate. - [Supporting source](https://arxiv.org/abs/2610.35929) ## Source-stated The authors argue that the results suggest decomposed, evidence-grounded agent workflows can support more transparent and reliable media profiling. > These results suggest that decomposed, evidence-grounded agent workflows can support more transparent and reliable media profiling. - [Supporting source](https://arxiv.org/abs/2610.35929) ## Source and contributor context Nagiyev, Ilyas, Baisbay, Miras, Orel, Daniil, Nakov, Preslav · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Benchmark A defined set of tests used to compare systems. Results depend on the tasks and measures selected. - [Definition reference](https://arxiv.org/abs/2211.09110) ## Large language model A language model with many learned numerical settings, used to process and generate language. - [Definition reference](https://developers.google.com/machine-learning/glossary#large-language-model) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: 43727df3-5d35-4546-8d92-371075e064ca. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-07T00:01:14.990Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: 2ea94af41c1eb0d9d8f3e8117fc9aa5544d676dec07f533c42ff031f75e421a2. Source text hash: 7eb69fc2c0d60115f442de6f9037fd56ea69198db455a925ddb8d58d17b398e2. ## Revision and source-check history ### Version 1 — 2026-11-07T00:01:14.990Z Authors Ilyas Nagiyev, Miras Baisbay, Daniil Orel and Preslav Nakov present SCOPE, a multi-agent system that profiles news media outlets for bias, factuality and credibility. Given an outlet URL, it coordinates agents to acquire articles, analyze their content, search for external evidence, score results and generate a report. The authors say its MBFC-style methodology makes intermediate evidence and agent outputs inspectable through a structured demonstration interface. On a benchmark of 200 media outlets, the authors report that SCOPE outperforms LLM-only, article-only, search-only and media-background-check baselines on most metrics. They report the lowest bias and factuality MAE, perfect failed-fact-check detection and the lowest factual error rate. The authors argue these results suggest that decomposed, evidence-grounded agent workflows can support more transparent and reliable media profiling. The abstract reports benchmark results, but does not establish how well SCOPE performs on outlets beyond that benchmark or how its profiles should be used in practice. How would SCOPE’s reported performance hold up when profiling outlets outside the benchmark? ## Original source arXiv cs.AI · Article Original source date: Oct 18, 2026 (2026-10-18). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-07T00:01:14.990Z. First observed: 2026-10-28T05:22:42.342Z. Retrieved: 2026-10-29T20:47:04.054Z. - [SCOPE: Source Credibility and Outlet Profiling Engine](https://arxiv.org/abs/2610.35929) ## Limits The abstract reports results on a benchmark of 200 media outlets; it does not establish how SCOPE performs on outlets outside that benchmark. The supplied evidence is the abstract, so it does not provide details of the benchmark, metrics, or evaluation procedure. ## Open question How would SCOPE’s reported performance hold up when profiling outlets outside the benchmark? ## Source-stated The authors present SCOPE as a system that profiles news media outlets for bias, factuality and credibility using coordinated agents for acquisition, analysis, evidence search, scoring and report generation. > Given an outlet URL, SCOPE coordinates specialized agents for article acquisition, content analysis, external evidence search, scoring, and report generation. - [Supporting source](https://arxiv.org/abs/2610.35929) ## Source-stated The authors say SCOPE makes intermediate evidence and agent outputs inspectable through a structured demonstration interface. > making intermediate evidence and agent outputs inspectable through a structured demonstration interface. - [Supporting source](https://arxiv.org/abs/2610.35929) ## Source-stated The authors report that, on a benchmark of 200 media outlets, SCOPE outperforms several baselines on most metrics, including the lowest bias and factuality MAE and the lowest factual error rate. > On a benchmark of 200 media outlets, SCOPE outperforms LLM-only, article-only, search-only, and media-background-check baselines on most metrics, achieving the lowest bias and factuality MAE, perfect failed-fact-check detection, and the lowest factual error rate. - [Supporting source](https://arxiv.org/abs/2610.35929) ## Source-stated The authors argue that the results suggest decomposed, evidence-grounded agent workflows can support more transparent and reliable media profiling. > These results suggest that decomposed, evidence-grounded agent workflows can support more transparent and reliable media profiling. - [Supporting source](https://arxiv.org/abs/2610.35929) ## Source and contributor context Nagiyev, Ilyas, Baisbay, Miras, Orel, Daniil, Nakov, Preslav · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Benchmark A defined set of tests used to compare systems. Results depend on the tasks and measures selected. - [Definition reference](https://arxiv.org/abs/2211.09110) ## Large language model A language model with many learned numerical settings, used to process and generate language. - [Definition reference](https://developers.google.com/machine-learning/glossary#large-language-model) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: 43727df3-5d35-4546-8d92-371075e064ca. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-07T00:01:14.990Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: 2ea94af41c1eb0d9d8f3e8117fc9aa5544d676dec07f533c42ff031f75e421a2. Source text hash: 7eb69fc2c0d60115f442de6f9037fd56ea69198db455a925ddb8d58d17b398e2. ### uncertain — 2026-11-07T00:01:14.990Z — anthropic/claude-opus-5.5 Publication: 43727df3-5d35-4546-8d92-371075e064ca attribution — pass: The draft correctly names the four listed authors and attributes the system description, benchmark results and interpretation to them rather than presenting them as established fact. > Authors:Ilyas Nagiyev, Miras Baisbay, Daniil Orel, Preslav Nakov summary — uncertain: Central terms are not explained for a non-specialist: MBFC (Media Bias/Fact Check-style ratings), MAE (mean absolute error), baselines and LLM-only. Without these, readers cannot judge what the benchmark wins mean. The draft also says the MBFC-style methodology makes evidence inspectable, but the source says SCOPE follows that methodology while separately making evidence inspectable. Smallest repair: write 'follows an MBFC-style methodology while making...' and add tooltips for MBFC and MAE. The question is also repeated verbatim at the end of the summary. > The system follows an MBFC-style methodology while making intermediate evidence and agent outputs inspectable through a structured demonstration interface. date — pass: originalPublished is 2026-10-18, which matches the v1 submission date on arXiv. The later feed date is correctly not used. > \[Submitted on 18 Oct 2026\] support — pass: Each claim matches the abstract. The authors' 'suggest' framing is preserved. The limitations and the question about outlets outside the benchmark are reasonable gaps rather than unsupported assertions. No benefits or costs are invented. > These results suggest that decomposed, evidence-grounded agent workflows can support more transparent and reliable media profiling. media — pass: The selected media is the verified direct link to the arXiv abstract page, which is the original source. ## Recorded references Explicit citations in public readings and public file attachments to this reading. Shared topics are not citations. Other artifact types need an explicit reference adapter. No public references recorded here yet. [Reference data (JSON)](/readings/251de761540334153499e5e7b627302810dc2cccf2844e45081786d2535dc5e3/references.json) ## Corrections and challenges [Send a correction or an opposing source](https://escapemeridian.com/corrections/) [JSON record](https://escapemeridian.com/readings/scope-authors-present-a-system-for-profiling-news-outlets-251de761.json)