Looking at task selection from the perspective of scientific model positioning
The official model list of the Shanghai Artificial Intelligence Laboratory describes Intern-S2 as a scientific multi-modal reasoning model, oriented to general agents and long-range complex tasks, listing 256K context windows. The documentation recommends that scenarios that rely heavily on tool calls remain in the default thinking mode. A longer input capacity represents the range that can be processed and does not mean that every detail in the material will be used accurately.
Small teams can start from data services (ChinaOPC analysis)
Entrepreneurs with a technical background can first evaluate literature abstracts, compare technical routes, explain charts, and organize research data. Compared with directly asking the model to "find new discoveries", delivering a list of materials with clear sources that are easy to review by experts makes it easier to determine the task boundaries and makes it easier for the customer to judge whether the work is completed.
For example, when organizing public research materials on the same process, the objects, experimental conditions, index definitions, and limitations in each material can be recorded in columns. Close values do not mean that the experimental conditions are the same. When units, sample ranges or measurement methods are missing, items should be left to be confirmed, and the model should not be allowed to complete a seemingly complete comparison table.
Long-distance tasks must have intermediate checkpoints
Long tasks can be broken down into collection, screening, extraction, comparison and drafting, and the sources and reasons for screening are saved at each step. If a piece of material is later excluded, it must be explained whether the subject is inconsistent, the version is duplicated, or the evidence is insufficient. Such a process record is more helpful for review than just keeping the final summary, and also facilitates the next update.
When calling a tool to process charts or calculation results, the input, program, and output should be preserved. Asking the model to write a derivation does not mean that the derivation is established; parts involving professional conclusions need to be checked by personnel in the corresponding field, and the generated content cannot be packaged as completed experiments or certifications.
Recent version reminder
As of the time of verification, the official explanation is that intern-latest points to intern-s2, and it is suggested that Intern-S2-Preview-397B is planned to be offline on October 31. Existing applications should record the actual model, not just the latest alias; rerun existing samples during migration to confirm that the output structure, tool calls, and exception handling still meet business requirements.
Determine whether the trial is worth continuing
During the trial, you can focus on whether the data location is accurate, whether key restrictions are missing, and whether the expert review time is reduced. Record the quality and investment of a specific delivery task before deciding whether to expand the scope. What this article proposes is an evaluation method, which does not mean that the accuracy of the model in a certain professional field has been measured.
Related model reading
Compare Kimi's long data processing; Understand Pangu's industry adaptation. For more information, see AI Model Column.