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How to use Deep Research: from verifiable reports to reproducible research

Deep Research is good at turning one question into dozens of searches and a synthesized report. Its ceiling depends on how well the question is scoped and whether each claim leads back to a source. This guide covers how it differs from chat answers, a four-step method, and when to move from writing a report to actually running the research.

Short answer

To get value from Deep Research, define the subject, time range and acceptable evidence before you start, then check the cited sources claim by claim. If the question can only be answered by downloading data, writing code or running experiments, a one-off report is not enough; use a research agent that keeps running in the cloud and preserves code and logs.

Difference

How Deep Research differs from a normal AI answer

A chat answer is generated from what the model already knows. Deep Research plans searches, reads pages or papers, filters sources and synthesizes a long report.

Multi-step search instead of one reply

The system breaks the question into sub-questions and adjusts later searches based on earlier results. A wrong scope at the start is extended in every round.

The output is a report, not a conclusion

Every claim should map to a specific source. Paragraphs without a source, or with a source that does not open, are leads to verify, not findings.

It stops at reading and writing

Most Deep Research products search and summarize existing material. They do not download data, run code or reproduce experiments, so they can only tell you how others approached a question.

One session at a time

After the report is generated, the search trail, rejected sources and intermediate judgments are usually gone, which makes it hard to keep working on the same material.

Method

Four steps to a verifiable deep research run

  1. 01

    Write a bounded research question

    State the subject, method or angle, time range and output format. "Progress and open debates in deep-learning medical image segmentation for small datasets since 2021, as a cited comparison table" works far better than "deep learning medical imaging".

  2. 02

    Specify evidence sources and exclusions

    Say which sources count, such as peer-reviewed papers, official statistics or a named database, and what to exclude, such as reviews or preprints. Naming known key papers or a theoretical framework removes a lot of noise.

  3. 03

    Spot-check citations before reading conclusions

    Open a few citations at random and check title, authors, year and whether the source supports the claim. Every citation that enters a manuscript should be checked individually.

  4. 04

    Decide whether the work needs to be executed

    If the conclusion ultimately depends on data analysis, simulation or experiments, hand the question, the confirmed papers and the next-step plan to a research agent that can keep executing, instead of asking the report more questions.

Next step

When to move from writing a report to running the research

The test is simple: can the answer be reached by reading existing material alone?

Your goalDeep Research reportCloud research agent
Map the main branches of a fieldGood fitPossible, not necessary
Find the original source of a claimGood fit, verify each citationGood fit, tries to fetch full-text PDFs
Reproduce an experiment or figure from a paperCan only describe the methodWrites and runs code, saves results
Test a hypothesis on public dataCannot executeDownloads data, models, analyzes and iterates
Advance a project over several daysStarts a new session each timeKeeps running in one workspace you can check anytime
Based on general product capabilities; check each vendor's documentation for specifics.

Scientify

Use Scientify for research questions that need execution

Scientify is a scientific agent that runs in an isolated cloud computer. Give it a research goal and it keeps searching the literature, tries to obtain full-text PDFs, forms hypotheses, writes code and runs experiments, then decides the next round based on the results.

Papers, code, dependencies, data, logs, figures and results live in one workspace. The task keeps running after you close your laptop; you can check progress and give feedback from your phone, and end up with reproducible code, data and a report.

  • The core agent is open source, with 2k+ GitHub stars
  • The related paper was accepted at ICML 2026
  • Conversation history and workspace files stay in the isolated cloud computer; Scientify servers do not store this research data
  • New users get a free cloud computer and $5 in model credit

Discipline

Three rules that apply to any tool

  • Verify citations: random spot checks are the minimum, and every citation in the manuscript gets checked.
  • AI drafts, you judge: research gaps, method choices and interpretation remain the researcher's responsibility.
  • Keep the trail: store search logs, screening reasons, code and intermediate results together so the next round builds on what you already have.

References

FAQ

Can I use a Deep Research report directly in a paper?

Not directly. Use it as a source map and structural reference. Check every citation that enters the manuscript against the original, and write the research gap and critical analysis yourself.

How do I get better Deep Research reports?

A reusable template is subject + method or angle + time range + output format. Add known key papers and explicit exclusions to cut irrelevant sources.

Deep Research or a cloud research agent?

Deep Research is enough when you only need to read and summarize existing material. When the question needs data downloads, code, simulations or experiments, choose a research agent that keeps executing in the cloud and preserves code, logs and results.

Does my computer need to stay on while Scientify runs?

No. The agent, files and runtime live in an isolated cloud computer, so the task continues after you close your laptop and you can check progress from any device.

Keep reading

Deep Research products comparedCompare deep research products by source traceability and whether they can keep executing.Doing research with an AI agentSet task boundaries, deliverables and stopping conditions for an agent.From papers to reproducible experimentsHow Scientify keeps search, hypotheses, code and experiments in one workspace.

Hand research that needs execution to Scientify

Describe your research goal and the agent keeps searching, coding and running experiments in the cloud. New users get a free cloud computer and $5 in model credit.

Start researching