arXiv:2606. 08256v1 Announce Type: new Abstract: Verifiability, attribution, and reproducibility are foundational requirements of scientific knowledge, yet current publishing infrastructure does not enforce them at scale.
arXiv:2607. 26064v1 Announce Type: cross Abstract: AI systems are becoming autonomous research agents that generate hypotheses, design experiments, and produce discoveries at scales beyond human oversight.
The paper introduces Publication Authority, a single-use, non-transferable capability that ensures AI-assisted claims can be independently challenged by providing a machine-readable, falsifiable publication record. It presents the PAC-2026 protocol, evaluates its fourth bounded semantic freeze (SF-4), and demonstrates through extensive modeling that the system enforces strict obligations on evidence, authorization, and lifecycle continuity. The study confirms internal coherence, bounded safety, and fault sensitivity, though it does not address factual truth or field efficacy.
arXiv:2606. 27386v1 Announce Type: cross Abstract: Scientific publication is still organized primarily around static manuscripts, even though much of scientific progress depends on tacit know-how: how to run code, reproduce figures, interpret edge cases, choose useful follow-up directions, and avoid failed paths.
arXiv:2608. 19511v1 Announce Type: new Abstract: Symposium is a formal framework and practical implementation to record the operation of AI agents deployed by small scientific research communities.
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:2607. 25637v1 Announce Type: cross Abstract: F(AI)2R is FAIR research with AI in the loop, twice: an AI-assisted authoring pass and a machine-readable audit pass over every artefact.
arXiv:2608.28596v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly embedded in scientific workflows for literature analysis, drafting, and review. Existing systems adv...
arXiv:2607. 02931v1 Announce Type: new Abstract: AI tools are accelerating scientific publication while the systems that review it struggle to keep up, and independent verification of published research has become both harder and more important.
Cogentic is a multi‑agent system designed to automate proof discovery for open research problems. It uses an iterative prove‑verify loop where an orchestrator assigns independent provers to different proof directions, verifies their outputs with specialized components, and records confirmed intermediate results in a persistent ledger for future rounds. Built on Gemini, Cogentic has produced novel results on five open problems in online learning, auction theory, and mechanism design, each verified by domain experts and detailed in companion papers.
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:2607. 24117v1 Announce Type: new Abstract: Modern multi-agent knowledge systems increasingly accumulate knowledge through chains of autonomous transformations rather than direct retrieval.
The paper proposes a scalable trust discovery architecture for the Internet of Agents, featuring a three‑layer hierarchical design: Agent Root for registry governance, Agent Registry for registration and metadata, and Agent Resolver for capability discovery. It introduces a registry‑suffix‑anchored composite identity scheme and a dual‑certificate, multi‑level authentication mechanism to strengthen agent identity trust. Prototype evaluation shows low latency (58 ms registration, 25 ms discovery) and high throughput (over 19,000 registrations and 29,000 discoveries per second).