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 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
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
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.
arXiv AI
1d ago
Reconstruction: A Blind Benchmark for Recovering Research Ideas from Pre-Publication Bibliographies
arXiv:2608. 16645v1 Announce Type: new Abstract: Can a language model recover the true research idea of a published paper when given only that paper's pre-publication bibliography?
arXiv AI
Jul 24
The Storyteller in the Model: Narrative Pattern Inheritance, Escalation Dynamics, and Alignment Governance in LLMs
arXiv:2607. 20449v1 Announce Type: cross Abstract: LLMs are trained predominantly on human-authored text, yet the structural and narrative conventions embedded in that text are rarely examined as a source of systematic behavioral influence, or as a governance risk in deployed systems.
arXiv AI
6d ago
TRACES: A Benchmark for Epistemic Reliability in Scientific Reasoning by LLMs
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.
Hugging Face Trending Papers
2d ago
Reconstruction: A Blind Benchmark for Recovering Research Ideas from Pre-Publication Bibliographies
Can a language model recover the true research idea of a published paper when given only that paper's pre-publication bibliography? We introduce Reconstruction, a blind idea-recovery benchmark that withholds the seed paper and all contemporaneous or future literature, and asks models to propose hypotheses that an independent large language model judge matches against the held-out ground-truth idea.
arXiv AI
Jul 28
Opaque Epistemic Mediation: How LLM Deployment Configurations Shape the Validation of Pseudo-Science
arXiv:2607. 22513v2 Announce Type: replace-cross Abstract: Commercial large language models are increasingly used as knowledge references, yet their stance on contested scientific claims is neither stable nor transparent.