Recent advancements in Large Language Models (LLMs) have transformed artificial intelligence (AI), enabling them to perform sophisticated tasks such as creative writing, reasoning, and decision- making, arguably comparable to human level. While these models have shown remarkable capabilities individually, they still suffer from intrinsic limitations such as hallucination, auto- regressive nature (e.g., incapable of slow-thinking), and scaling laws. To address these challenges, agentic AI leverages LLMs as the brain, or the orchestrator, integrating them with external tools and agenda such as planning, enabling LLM-based agents to take actions, solve complex problems, and learn and interact with external environments. Furthermore, researchers are increasingly exploring horizontal scaling — leveraging multiple LLM-based agents to work together collaboratively towards collective intelligence. This approach aligns with ongoing research in Multi-Agent Systems (MASs) and collaborative AI, which focus on enabling groups of intelligent agents to coordinate, share knowledge, and solve problems collectively. The convergence of these fields has given rise to LLM-based MASs, which harness the collective intelligence of multiple LLMs to tackle complex, multi-step challenges.

ERROR is a comprehensive program to systematically detect and report errors in scientific publications, modelled after bug bounty programs in the technology industry. Investigators are paid for discovering errors in the scientific literature: The more severe the error, the larger the payout. In ERROR, we leverage, survey, document, and increase accessibility to error detection tools. Our goal is to foster a culture that is open to the possibility of error in science to embrace a new discourse norm of constructive criticism.

The point about a paper like this is that it is so blatantly bad that it cannot have been through any kind of serious editorial scrutiny or peer review. It acts as a canary in the coalmine: if gobbledegook is published in your journal, it's an indicator that you need to look very carefully at your editorial processes, and act immediately to remove editors who let this stuff in.