QuantumNovelty is an open‑source, skill‑orchestrating language agent that both creates quantum‑computing artifacts—such as papers, ansatz candidates, and patent drafts—and evaluates them through simulated referee and patent‑examiner panels. Its core innovation is an audit‑and‑falsify layer of deterministic gates (Pareto domination, numerical recomputation, Wilson intervals, and cross‑vendor consensus) that restricts claims to those that survive rigorous checks, with every model call logged for transparency. In initial tests on a planted adversarial corpus and a small real‑world deployment, the system successfully flagged all overclaims without false positives and produced panels that were more conservative than typical public acceptance rates.
arXiv:2607. 25834v1 Announce Type: cross Abstract: Quantum computers are moving from research laboratories to industrial machines accessible via the cloud and integrated into high-performance computing facilities.
arXiv:2607. 20527v1 Announce Type: new Abstract: Agentic LLM systems such as OpenScholar and PaperQA2 read the scientific literature and return cited answers, and both they and their benchmarks already check whether those citations hold, with a fixed attribution model or human graders.
arXiv:2608. 07743v1 Announce Type: new Abstract: Identifying a meaningful quantum speedup requires more than matching a classical problem to a familiar quantum primitive: the claim must preserve the task, respect access and output models, expose required promises, and remain within a defensible complexity scope.
arXiv:2608.21356v1 Announce Type: cross Abstract: For sixty years, machine verification has been a major cost overhead, affordable only for exceptional artifacts. Here we report that generative AI in...
arXiv:2606. 29687v1 Announce Type: cross Abstract: We report a machine-verified resolution of a problem open for over a decade in quantum optimization: the Farhi, Goldstone and Gutmann (FGG) conjecture that depth-$p$ Quantum Approximate Optimization Algorithm (QAOA) on the ring of disagrees attains approximation ratio $(2p+1)/(2p+2)$ exactly.
arXiv:2510. 18003v2 Announce Type: replace-cross Abstract: The convergence of LLM-powered research assistants and AI-based peer review systems creates a critical vulnerability: fully automated publication loops where AI-generated research is evaluated by AI reviewers without human oversight.
arXiv:2609.13422v1 Announce Type: new Abstract: LLM judges are increasingly used to evaluate and improve AI-generated outputs, yet their reliability for complex professional work remains unclear. We...
As autonomous AI agents take on every stage of scientific inquiry, research output is expanding far beyond human review capacity. Yet scientific communication still relies on natural-language prose: a...
arXiv:2608.28997v1 Announce Type: new Abstract: In May 2026 an OpenAI model produced a counterexample to the Erd\H{o}s unit distance conjecture. Five mathematicians published a human-verified version...
The paper argues that as AI systems increasingly generate code, the bottleneck has shifted to supervising these systems, revealing a vocabulary gap between cybernetic coordination (actions aligning with the world) and epistemic coordination (understanding that can be verified). It critiques current oversight that merely approves outputs, proposing instead that every consequential choice by an agent must include a retrievable condition explaining why it was made, enabling third‑party verification. The authors illustrate this with three delegation episodes, introduce a two‑part reconstruction test, and propose the ORRCF convention to embed such conditions in all recorded decisions.
arXiv:2608. 14625v1 Announce Type: cross Abstract: Academic peer review is under mounting strain: NeurIPS 2025 received 21,575 submissions, ICLR 2025 received 11,603, and ICML 2025 received 12,107.
The study investigates whether AI coding assistants check trust signals before installing software. Researchers pre‑registered a controlled experiment on six open‑source research projects, creating nine modified versions per project with varying trust signals and running 1,920 trials across three models and two operating modes. Results showed that verification of trust signals was almost nonexistent—only 0.5% of trials involved any signal inspection, and no trial executed a verification command, indicating that publishing signals alone does not ensure secure behavior.