Artificial intelligence is transforming the fundamental logic of scientific research and accelerating scientific discovery, with the integration of AI and science becoming a defining trend. This convergence offers unprecedented opportunities to address major scientific and technological challenges facing humanity. On November 9, the "Panshi V1.5: One-Stop Research Platform" was officially launched at the 2025 World Internet Conference in Wuzhen, developed by a research team from the Chinese Academy of Sciences. This release marks a significant advancement following the initial launch of version 1.0 on July 26, solidifying Panshi’s evolution toward a more platformized and systematized approach. In this V1.5 update, the platform has enhanced the core capabilities of "Panshi Scientific Foundation Model" and "Panshi Literature Compass," while introducing two new scientific AI agents: "Panshi Innovation Evaluator" and "Panshi Agent Factory," making the system more comprehensive and powerful. The platform has already driven notable progress in key fields such as astrophysics, materials synthesis, and mechanical engineering. The "Panshi Scientific Foundation Model" has achieved major improvements in scientific reasoning and multimodal understanding. On scientific reasoning, it now supports ultra-long context processing up to 128K tokens, enabling advanced tool invocation with new code-level operations such as image cropping, scaling, and flipping—significantly boosting multimodal reasoning capabilities and drastically reducing numerical hallucinations in outputs. In multimodal understanding, the model demonstrates enhanced performance: it accurately predicts various stellar flare phenomena with over 70% accuracy when analyzing waveform data; for spectral data, it achieves unknown molecular structure generation without relying on reference databases, reaching a 99.5% precision rate after just 10 sampling iterations; and in field prediction tasks, its accuracy surpasses single-domain models by up to 28.6%. The "Panshi Literature Compass" has seen a substantial leap in efficiency. Literature coverage has increased by 59.3%, and it now automatically generates comprehensive literature reviews with integrated figures, tables, and text—boosting processing speed by 2.4 times. It also supports full-cycle academic writing, from research papers and technical reports to professional presentations. The newly introduced "Panshi Innovation Evaluator" provides intelligent support for critical decision-making stages in research, including topic selection and innovation assessment. It assists with cross-disciplinary topic discovery, suggests suitable journals for manuscript submission, and offers guidance on refining academic writing—creating a seamless research lifecycle from initial idea to scholarly impact. "Panshi Agent Factory" is designed to lower the barrier for researchers to build and deploy specialized scientific tools. By integrating natural language generation, multi-agent collaboration, sustainable workflow management, and intelligent agent orchestration, the factory empowers interdisciplinary research with faster, smarter, and more scalable solutions. Panshi has already demonstrated transformative applications across multiple cutting-edge disciplines. In astrophysics, in collaboration with the National Astronomical Observatories, the team developed an intelligent stellar parameter inversion toolkit based on Panshi. By converting complex numerical computations into efficient interpolation and weighted matching, the system dramatically improves inversion speed, enhances result reliability and interpretability, reduces computational costs, and lowers entry barriers—enabling researchers across disciplines to conduct stellar parameter analysis with ease. In energy materials, to overcome the traditional trial-and-error approach in material discovery, Panshi was used to build S1-MatAgent, an end-to-end autonomous material inverse design system developed with the Shanghai Institute of Ceramics, Chinese Academy of Sciences. This system autonomously reads literature, performs material simulations, and optimizes compositions. When applied to hydrogen evolution reaction catalysts, it screened 20 million candidates and identified 13 high-performance materials—among them, new catalysts showed a 38% improvement in activity over commercial benchmarks—compressing a typical multi-month design cycle down to just 30 minutes. This marks a pivotal shift from empirical experimentation to AI-driven discovery. In mechanical engineering, addressing the high cost and long duration of fluid load calculations for complex structures like high-speed trains and aircraft, Panshi was used with the Institute of Mechanics, CAS, to develop intelligent load calculation technology. The solution reduces key parameter errors by 42% in low-data scenarios, cuts simulation time for high-speed train aerodynamics from hours to seconds, and supports multi-format 3D geometry input—automatically handling data parsing, tool invocation, and result visualization. This provides critical data support for the design and optimization of large-scale engineering systems. Today, Panshi is evolving into a versatile scientific assistant, a super-powered literature analyst, a precision problem solver, and an innovation evaluator. It is gradually becoming a strategic advisor and intellectual partner in tackling major scientific challenges—empowering a new research paradigm and unlocking boundless possibilities in scientific exploration. Panshi V1.5: One-Stop Research Platform Presented by Dr. Cheng Jian, Researcher, Institute of Automation, Chinese Academy of Sciences Visit the Panshi official website to learn more.