arXiv Machine Learning
Jun 2
The Ghost Couple: Correlated LLM Name Priors and Their Haunting of the Web and Academic Publishing
arXiv:2606. 02184v1 Announce Type: cross Abstract: These names do not exist.
The Ghost Couple: Correlated LLM Name Priors and Their Haunting of the Web and Academic Publishing
These names do not exist. Elena Vasquez and Marcus Chen have appeared as volcano experts, astronauts, thriller protagonists, podcast hosts, and academic co-authors across hundreds of independently produced AI-generated documents, never having lived.
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arXiv Computation and Language
Sep 15
SlopShape: Identifying AI-Generated Commercial Web Content
arXiv:2609.15369v1 Announce Type: new Abstract: Word-level detectors identify unedited AI-generated text almost perfectly, but the literature documents their brittleness under rewording, and a word-l...
arXiv AI
Aug 7
Large Language Models Threaten Double-blind Review
arXiv:2608. 05157v1 Announce Type: cross Abstract: Double blind peer review serves as the scientific community primary defense against status and affiliation bias.
arXiv AI
Sep 7
Model Retirement Creates Reproducibility Risk in Biomedical AI Publications
The study examined biomedical research articles from 2022 to March 2026 that employed large language models (LLMs). It found that 42% of the most frequently used models were already retired or scheduled to retire within two years of publication, with a median retirement interval of 538 days. This high rate of model deprecation threatens the reproducibility of biomedical AI research.
arXiv AI
Jun 10
PhantomBench: Benchmarking the Non-existential Threat of Language Models
arXiv:2606. 11105v1 Announce Type: cross Abstract: Hallucinations, where language models (LMs) generate factually ungrounded responses, pose serious risks, as users tend to blindly rely on them.
arXiv AI
Aug 26
Auditing the Synthetic Memoir: Measuring Scene-Level Confabulation in LLM-Generated Autobiography Against the Documented Record of the Life It Describes
The paper audits a 366‑day autobiographical book generated by a large language model (LLM) against an independent verification corpus. Using a four‑level rubric, 354 of the 366 days (96.7%) failed verification, with only 12 days containing corroborated scenes and 19 days containing actively contradicted claims. Regenerating the same days with current models yielded 100% verification failure, while grounding the generation in the subject’s own corpus improved the rate to 83.3% but still left substantial residual failure.
arXiv Computation and Language
Sep 15
Biomedical Reference Generation Remains Unreliable across 26 Large Language Models
arXiv:2609.14988v1 Announce Type: new Abstract: Background. Large language models are increasingly used to help write biomedical text but may fabricate references to nonexistent work. How often large...
arXiv Computation and Language
Sep 11
INDRA: A New AI Tool for Exploring Tobacco, Fossil Fuel, and Chemical Industry Archives
INDRA is a research platform that integrates multiple archival collections—such as UCSF’s Industry Documents Library, Columbia and CUNY’s ToxicDocs, and Stanford’s SRITA—into a single, LLM‑readable corpus. It employs three safeguards: a closed evidentiary sandbox, real‑time provenance tagging, and a deterministic system‑level protocol to ensure that model outputs are clearly distinguished from archival evidence and from the model’s own inferences. The platform enables large‑language‑model‑powered investigations across these archives while keeping the conditions of knowledge production transparent and auditable.
arXiv AI
Jun 10
READER: Robust Evidence-based Authorship Decoding via Extracted Representations
arXiv:2606. 10794v1 Announce Type: new Abstract: As agentic applications increasingly route user tasks through official and third-party LLM APIs, provenance becomes an operational question: which model generated a given black-box response?
arXiv AI
Jun 17
PseudoBench: Measuring How Agentic Auto-Research Fuels Pseudoscience
arXiv:2606. 18060v1 Announce Type: new Abstract: As Large Language Model based agents enter autonomous scientific research, their ability to resist pseudoscience becomes increasingly important.
arXiv AI
Jul 2
Phantom References: Hallucinated Citations That Survive Peer Review at Top-Tier Conferences
arXiv:2607. 00738v1 Announce Type: cross Abstract: Large language models can generate polished scientific text that includes unsupported claims, allowing hallucinations to enter the archival record.
Hugging Face Trending Papers
Jun 11
Authority, Truth, and Citation Bias: A Large-Scale Multi-Domain Benchmark for Studying Epistemic Susceptibility in Large Language Models
Large language models are increasingly deployed in citation-augmented settings, yet the effect of citation presence on model behavior independent of factual content remains poorly understood. We introduce AuthorityBench, a 220,564-prompt multi-domain benchmark that isolates how citation-based authority signals influence epistemic behavior in LLMs.