Panshi SciencePro: Full-Stack AI Research Platform with Innovation Evaluation

Why a Scientific Foundation Model Is Necessary

General LLMs excel at conversation but lack rigor for science: they hallucinate references, invent chemical formulas, and cannot interpret complex scientific modalities like IR spectra or crystal structures. Science requires verifiable conclusions, traceable processes, and correctable errors.

Panshi SciencePro Architecture and Foundation Model

Developed by the Chinese Academy of Sciences (CAS) and Zhongke Wenge, Panshi SciencePro is built on the Panshi Scientific Foundation Model, which internalizes 1.7 billion scientific papers and 90 PB of data from major scientific facilities, forming a cross-disciplinary knowledge graph. It uses the S1-Omni unified multimodal Transformer architecture to align representations across physics, chemistry, biology, and earth sciences. The model natively understands scientific modalities such as waves, spectra, fields, molecular structures, and remote sensing images. In benchmarks, it achieves a hallucination rate of only 11% on complex reasoning tasks, with every output backed by real literature or experimental data.

Unique Innovation Evaluation Feature

The platform's standout capability is its Innovation Evaluation module, which simulates senior peer reviewers for two scenarios: project proposal/opening report assessment and pre-submission paper review. It draws on 7.05 million national/provincial project records, 25 million full-project datasets, 700,000 industry R&D annual reports, and 270 million global papers and patents. The system applies nearly 100 evaluation indicators aligned with National Natural Science Foundation of China (NSFC) criteria and top-journal review standards.

When a user uploads a proposal, the system performs automated structured review across dimensions including novelty, redundancy, innovation potential, frontier relevance, technical feasibility, and export-control compliance. Key analyses:

Redundancy check: Direct comparison against funded projects and patents, pinpointing exact overlapping details.

Innovation increment identification: Determines whether a proposed mechanism merely patches prior work or introduces a fundamentally new principle, providing an objective rating.

Paper pre-review: Simulates tough reviewer questions, flagging weak argumentation and missing control experiments before submission.

ScienceHub Community Ecosystem

ScienceHub aggregates nearly 300 authoritative scientific databases, 2,000+ computational and analysis tools, 7,000+ professional research skills (Skills) and 4,000+ MCP protocol integrations, plus 13 domain-specific expert agents covering quantum computing simulation, materials design, molecular evaluation, and life sciences. The platform allows any researcher to create, publish, and share custom expert agents and Skills, with community ratings and comments.

Project-Based Research Management

The new Project feature creates a dedicated workspace for each research topic, archiving all literature resources, invoked Skills, MCP interface configurations, and AI conversation history. Projects share global long-term memory and context, so returning to a topic later lets the AI seamlessly continue the previous reasoning chain, building reusable digital research assets.

Six-Step End-to-End Research Workflow

Step 1: Literature Analysis and Long-Form Review

The dedicated literature library connects directly to the CAS Library & Information Center and arXiv. It supports full-library chat and single-paper deep reading. The system can process thousands of papers in about two hours, extracting core viewpoints, experimental methods, and limitations of each school of thought, generating a 10,000-word academic review with a clear citation traceability graph. All citations link to the original papers.

Step 2: Hypothesis Generation and Deep Reasoning

In deep research mode, the platform leverages massive paper data and ready-to-use Skills to guide researchers through multi-round questioning, producing rigorous hypotheses and risk matrices.

Steps 3–5: Design, Simulation, and Verification

For materials discovery and molecular design, expert agents predict optimal compositions and process routes from target performance metrics, enabling "map-guided" experimentation. Complex numerical simulations and theoretical calculations are wrapped as point-and-click scientific tools, allowing batch parallel computation and comparative analysis in minutes without coding.

Step 6: Result Expression and Academic Translation

Built-in agents for academic PPT, scientific illustration, and academic posters generate rigorous vector graphics and standards-compliant slides from natural language instructions or raw data files.

Research Headlines: Real-Time Frontier Intelligence

The Research Headlines module integrates 140+ disciplines and 1 billion+ authoritative sources (top-journal online updates, academic news, major preprint breakthroughs). It offers real-time algorithmic recommendations based on research interests and customizable topic-tracking subscriptions.

Summary and Assessment

The author concludes that Panshi SciencePro moves beyond chatbot toys to a heavyweight engineering foundation built around rigorous research logic. Its deep data foundation, traceable outputs, unique innovation evaluation system, and ScienceHub+Project ecosystem directly address pain points in grant applications, topic justification, and long-term asset management.

Strengths:

Top-level design understands research logic; deep data accumulation; rigorous, traceable outputs.

Exclusive innovation evaluation is highly practical—a "killer feature" for grant proposals and paper pre-review.

Open ecosystem supports custom Skills and agents; Project feature finally gives research workflows a home.

Output graphics and PPTs meet academic aesthetic standards.

Suggestions:

Given the platform's breadth, newcomers should start with "Deep Research" and "Literature Library" to learn the rhythm.

Code example

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