Jiejun Zhang

Junsuk Rho, Jinn-Kong Sheu, Andrew Forbes et al.

Scientific research needs a system that better values rigorous, reusable contributions. Although open knowledge and FAIR (findable, accessible, interoperable, and reusable) principles, along with coalitions and infrastructures, are accelerating reform, evaluation still often defaults to standardized metrics such as the h-index and journal impact factor. This misalignment still incentivizes quantity over quality, undermining integrity and reproducibility, and making it harder for communities to learn from and build on existing work. In this perspective, we bring together a global community of researchers, funding institutions, industrial partners, and publishers from 14 different countries across the 5 continents to advance ongoing debates on open science and research evaluation. Our contribution to the research practice is to offer an integrative conceptual framework, an open knowledge system, that links knowledge production, validation, assessment, and reuse into a single ecosystem view, and to translate into practical recommendations across key stakeholder roles (researchers, institutions/evaluators, funders, and publishers). By shifting attention from papers and bibliometrics toward reusable knowledge contributions and their validation, the framework highlights concrete levers for cultural change (what to share, when/how to validate, how to support reuse, and what to reward) and offers a practical lens that stakeholders can use to diagnose misaligned incentives and to design reforms that make high-quality, cumulative contributions visible and valued.

Tingjia Miao, Wenkai Jin, Muhua Zhang et al.

The paradigm of agentic science requires AI systems to conduct robust reasoning and engage in long-horizon, autonomous exploration. However, current scientific benchmarks remain confined to domain knowledge comprehension and complex reasoning, failing to evaluate the exploratory nature and procedural complexity of real-world research. In this work, we present research-oriented evaluations in theoretical and computational physics, a natural testbed with comprehensive domain knowledge, complex reasoning, and verifiable end-to-end workflows without reliance on experiments. Here we introduce PRL-Bench (Physics Research by LLMs), a benchmark designed to systematically map the capability boundaries of LLMs in executing end-to-end physics research. Constructed from 100 curated papers from the latest issues of Physical Review Letters since August 2025 and validated by domain experts, PRL-Bench covers five major theory- and computation-intensive subfields of modern physics: astrophysics, condensed matter physics, high-energy physics, quantum information, and statistical physics. Each task in the benchmark is designed to replicate the core properties of authentic scientific research, including exploration-oriented formulation, long-horizon workflows, and objective verifiability, thereby reconstructing the essential reasoning processes and research workflows of real physics research. Evaluation across frontier models shows that performance remains limited, with the best overall score below 50, revealing a pronounced gap between current LLM capabilities and the demands of real scientific research. PRL-Bench serves a reliable testbed for accessing next generation AI scientists advancing AI systems toward autonomous scientific discovery.