The paper investigates how large language models (LLMs) that generate online articles can create a feedback loop where the content they produce becomes input for subsequent generations. By studying a controlled writing process that varies whether published articles are reused as inputs, the authors find that recirculation increases the presence of complete false claims while reducing explicit endorsement of those claims. The study highlights that selective error persistence depends on the interplay between claim content, expressed stance, and downstream reuse, and that interventions can preserve correct content while limiting error reproduction.
arXiv:2608. 11415v1 Announce Type: cross Abstract: Large language models are being proposed as agents in scientific workflows, in domains where no downstream verifier exists.
The paper investigates how stylistic changes introduced by large language models (LLMs) affect multimodal claim verification, a task that determines whether a textual claim is supported by given evidence. Two rewriting strategies are used: natural rewriting, mimicking typical academic polishing, and controlled injection, adding a single LLM-associated word. Across 11 open‑weight models (2B–38B parameters) from five VLM families, the study finds that most models remain robust to these modifications, showing no significant accuracy drop, though consistent probability shifts—especially under hedging conditions—are observed.
The paper investigates how language models transform factual news into narrative stories, revealing that standard source‑agreement metrics misjudge the quality of these retellings. By having three model families rewrite 2,000 news articles as summaries and as stories, the authors measured changes across the five Ws and identified distortions—especially in place, causation, and accountability—that are often overlooked by fact‑checking. They found that stories tend to lose critical contextual details while amplifying accountability distortions, which can shift blame and remain undetected by conventional checks.
arXiv:2609.00241v1 Announce Type: new Abstract: Long documents often distribute important information across extensive narrative passages and multiple tables, making faithful summarization particular...
The paper investigates how language models transform factual news into stories, revealing that standard source‑agreement metrics misjudge intentional changes. By rewriting 2,000 news articles into summaries and stories, the authors measured alterations across the five Ws, finding that stories often lose place and causation details while increasing accountability distortions—sometimes downplaying perpetrators. The study introduces ReTold‑2k, a corpus and scoring protocol to detect and mitigate such distortions.
arXiv:2606. 05403v1 Announce Type: new Abstract: Language models increasingly act as epistemic proxies, synthesizing evidence from multiple sources to inform decisions.
arXiv:2606. 26437v1 Announce Type: cross Abstract: Existing metrics for factuality and faithfulness evaluate whether an answer is supported or contradicted by its grounding documents, but they fail to capture when both supporting and contradicting evidence coexist.
arXiv:2610.35793v1 Announce Type: new Abstract: Deep research, or any use of large language models (LLMs) to read a large document collection and produce insights, requires synthesizing information f...
arXiv:2606. 07951v1 Announce Type: cross Abstract: Humans increasingly turn to Language Models (LMs) in ways that shape beliefs and drive decisions, including discussing, rewriting, and summarizing information from scientific articles, news, and medical reports.
The paper introduces CAMS, a Claim‑Anchored Multi‑Document Summarization framework that decomposes source documents into atomic claims, resolves provenance deterministically from verbatim quotes to token spans, clusters equivalent claims across documents, and rewrites summaries so each sentence ends with claim identifiers linking back to source spans. CAMS separates provenance (an invariant for each emitted sentence) from faithfulness (an objective encouraged by selection, rewriting, and verification). Evaluations on MultiNews, DiverseSumm, and zero‑shot WCEP show that CAMS matches strong baselines in summary quality while improving faithfulness and citation precision, raising attribution accuracy from 38% to 64% and reducing human verification time per claim by 3.4×.
arXiv:2610.35254v1 Announce Type: new Abstract: The number of paper submissions to major AI conferences has increased dramatically in recent years, substantially increasing the peer-review workload o...
The paper introduces TRAILS, a taxonomy of 38 mechanisms that can compromise the reliability of large language model (LLM) read‑and‑evaluate tasks in evidence synthesis, such as study screening, data extraction, risk‑of‑bias assessment, and critical appraisal. It explains how these mechanisms can cause inconsistent or hidden failures across repeated LLM runs and outlines deployment settings, testing approaches, and mitigation strategies for each. The authors argue that repeatability or accuracy alone does not guarantee trustworthy judgments, emphasizing the need for human evaluation to verify that decisions are evidence‑based, free from external influence, and reproducible.