Research Tips in the Enterprise: From Topic Selection to Real-World Implementation

Non-Research Tips (Summer 2026)
Antonio TEJERO DE PABLOS
U-Tokyo, 2026/06/10
Research Tips in the Enterprise:
From Topic Selection to
Real-World Implementation

2
Non-research Tips
Agenda
Research cycle + Social impl.
Deciding on a research topic
Setting experiments
01
02
04
Surveying papers
03
Writing your paper
05
Promoting your research
06

3
Non-Research Tips
Research cycle that includes
social implementation
01

4
Doing research within a company
● Difference mentality between research (invention) and
development (innovation)
○ Research is the process to generate new knowledge
■ It is not developing new product functionalities that directly lead to profit
○ However, researchers shouldn’t act independently from the company (or
society)
■ Research itself uses resources from the company
● Researchers from industry and academia share common points
○ They can identify research topics from engineering problems
○ They publish papers if new knowledge is gained during the research
○ They educate researchers in the same field and the general public
● CyberAgent AI Lab conducts skill-up training and seminars
01 Research cycle Non-Research Tips

5
Approach to research within a company
● We can define three groups within a company
○ Business: Find opportunities to generate profits for the development of
the company and society
○ Product development: When a business opportunity arises, IT companies
develop systems and services for their target users
○ Research: Tackling technical challenges and shaping the future of
products and businesses.
● Three common high-level combinations:
○ Invention model: Research is independent from business/product
development
○ Innovation model: Research is focused on solving product/business
problems
○ Translation model: Research, development, and business are in balance

6
● Invention model
○ Community-driven: Research is conducted (almost) independently
○ Seen in academia and pure research projects within a company
○ The knowledge generated is likely general-purpose
Social
implementation
Business
&
Product
development
Research
Joint
research
project
Strong
connection with
the community
Paper publication

7
● Innovation model
○ Business-driven: Research focused on solving business problems
○ There is a chance for novel research topics to arise, but the chances of
publishing papers are quite limited
Isolated from the
community
Research
Product
development
Business

8
● Translation model (balance between business-development-research)
○ Research can potentially influence society through business/development
○ The three groups can grow autonomously
■ Chances for creating new business from research discoveries
■ Active participation with their respective community
Business
Product
development
Research
Paper publication,
etc.
High-level idea
exchange
(technology showcase)
Low-level idea
exchange
(find the research
question)
Technology
commercialization
(find the implementation
challenges)
Connection with the
community
Social
implementation

9
● In the AI Lab, depending on the team strategy/phase we can observe
different models
● Today I would like to discuss the translation model
○ Achieving both paper publications and social implementation within the cycle
○ The most complex case, but the one that yields the most significant results

10
The R&D cycle in the AI Lab
● From the idea creation to the presentation of the research results

11
● From the idea creation to the presentation of the research results
○ Problems that current technology can’t solve → Research topic
○ Survey of research limitations and opportunities → Proposed method
○ Evaluate the hypothesis → Valid and reproducible experimental setup
○ Communicate the generated knowledge logically → Publication
■ (Review/Rebuttal)
○ Research promotion → Slides, project page, OSS, study sessions
○ Social implementation → Application to business and social issues
■ → IMPACT

12
● About social implementation
○ Social implementation is not just the commercialization of research
○ The point is to obtain feedback for the next iteration of the research
cycle
■ Case study in the next section
○ Writing a paper should not be the goal
■ Even if you could not reach social implementation
○ Researchers should involve as much as possible
■ Developers already have many task on their side
■ Adapting research code to real-world environments is not easy
■ Valuable communication about how to apply research to the company
environment, ways of improving the proposed method, etc.

13
● Timeline differences between research and development

14
Non-Research Tips
Deciding on a research topic
02

15
There is no “ground-truth” when choosing a research topic, but…
● The mentality in this context is: a topic that finishes right after submitting
the paper is not desirable
● Deciding your topic as a student: Can I write a paper? (interesting +
novel)
● Topics that leads to social implementation: Can I create an impact?
○ More complicated, but you can leverage company resources: GPUs, specialists
● How to create an impact with your research
○ Discovering a groundbreaking paradigm shift
■ E.g., DNN and backprop, self-supervised learning, Transformer, ???
○ Providing practical solutions to real-world problems
■ Even among top conferences, only a small number of research are actually used
■ On the other hand, YOLO Object Detection is widely used and recognized for its speed
and ease of use (approximately 70,000 citations).
02 Research topic Non-Research Tips

16
There is no “ground-truth” when choosing a research topic, but…
● This kind of ”immediately usable” research is characterized by
○ It addresses problems that many people want to be solved
○ Its output is highly accurate
○ Easy-to-use SW implementations are publicly available on GitHub
and other platforms (Open Source!)
○ It is backed by a strong promotion to increase people’s awareness
● Please do not misunderstand
○ There wouldn’t be applied research without basic research
○ There is no definitive criteria for choosing your research topic
■ It completely depends on the circumstances of your team and project

17
Anti-patterns in deciding your research topics in a company
● Fictional case studies
○ The research cycle ends after only one iteration
○ The data used in the experiments cannot be published along with the paper,
since it contains company confidential information
○ As I conducted research by myself, the research cycle took too long, and I
could not get any feedback
○ A topic with social implementation potential may not be recognized as
research
○ A trendy topic in the field didn’t lead to social implementation no matter how
much I promoted it

18
Common anti-patterns in deciding your research topic
● What does “lacks novelty” mean?
○ “Methodology is obvious and results are not significant enough”
○ Proposing a new method is not enough, it should also be non-incremental
● Trendy topics / general topics
○ The number of papers to survey is enormous, making comparative experiments unmanageable
○ Finding novelty in methods is difficult, and solutions tend to be incremental
● Problem definition and application
○ Picture a scenario where someone will be happy if your research succeeds
○ If there is no clear application, justifying an adequate evaluation setting is hard
● Choose a research topic with future potential
○ Business continuity
○ Technology-independent
○ Cumulative know-how
○ Cost of starting new research

19
Last message of this chapter
Giving up on a research topic
and starting a new one is surprisingly common,
so don't feel unnecessary stress,
and use that experience to improve your next topic

20
Non-Research Tips
Surveying papers
03

21
Google Scholar
● Grasping important papers and continuously monitor research trends
○ Wide search for keywords and representative papers
○ Check papers with many citations and/or recently published
○ "Cited by" function to check subsequent research
○ Timely check authors and major conferences (CVPR, ICCV, NeurIPS, etc.)
○ Hard to “fine-tune” the topic granularity
03 Paper survey Non-Research Tips

22
Scholar Inbox
● Free paper recommendation tool for specific catch-up
○ Register your research interests (e.g., computer vision, multimodal learning)
○ Automatically collecting new papers from arXiv and top conferences
○ High-speed screening of abstract/titles
○ Sorting important papers via the “save” and “like” functions
○ Training your preferences is (was?) slow

23
The risks of arXiv
● Free archive that publishes research papers before their official release
○ Knowledge sharing without waiting for acceptance (esp. competitive AI fields)
○ Sharing practical insights that are not typically presented at conferences, such
as preliminary results and technical reports
● Since peer review is not conducted, quality cannot be guaranteed

24
Deep research
● Recent chat agents have improved this function for serious survey
● Speed, brevity, and efficiency. Probably must use these days
● Sample prompt: “Please survey papers on personalization in the
automated generation of graphic designs (posters, banners, etc.). Please
refer to papers from top computer vision conferences whenever possible.
● Gives a better sense of the current state of the field (SOTA, etc.)
○ Still hard to tune for highly-granular searches

25
Deep research
● Things to keep in mind
○ It doesn’t guarantee to output the most relevant papers
■ Rather, provides a starting point to understand which papers to read
○ LLMs can only provide information publicly available or from their training data
■ There exist non-public paper databases (certain ACM publications)
○ At this time, for a strict survey, one must still manually access the list of
accepted papers from the conference and check the titles and abstracts
● Results vary depending on the LLM (different strengths)
○ ChatGPT: Excels at sentence writing and analyzing results
○ Gemini: Excels at a wide range of tasks
○ Claude: Excels at creative answers
○ Perplexity: Excels at fact-checking and reference gathering with minimal
analysis (avoid hallucinations)

26
Survey by checking the conference official website
● List at the “accepted papers” page
○ Keyword search-based title check (e.g., multimodal, gaussian splatting)
○ Most noted and cited papers in the community, so a comparison is likely needed
○ A complete check requires time, so why not coding a personal automatic tool?

27
NotebookLM (after finding reference papers)
● Aggregating paper PDFs, notes, reference docs (1 topic, 1 notebook)
○ Generation of summaries and comparisons across multiple research papers
○ Ask via natural language (e.g. “what is the difference among these methods?”)
○ Quickly organize important contributions, experimental results, and limitations
○ Compare the latest papers with your topic to identify research gaps
○ Generation of audio podcasts, video, and slides!

28
Being active in the community
● Attending a conference is an excellent chance for discussing beyond the
paper
● Presenting at local study groups
● Posting your research achievements and interacting with other
researchers via SNS
○ Do not miss the actual purpose and use them responsibly!

29
Anti-patterns in surveying papers
● Case studies
○ The purpose of surveying is
not to write a paper
○ There is no need to cite all the
papers you survey as related
work
○ Deciding when to stop
surveying

30
Non-Research Tips
Setting experiments
04

31
Things you should consider when experimenting
● What do you want to prove? → How will you prove it?
● Quantitative or qualitative?
● Deciding your evaluation data
○ A public dataset… is really enough?
● Ablation study
○ Not just turning ON/OFF modules; it requires probing and inner analysis
○ E.g.: Our features can be better classified than DINOv3… why?
■ Number of model parameters?
■ Quality of the training data?
■ Additional modules?
■ The type of loss function?
■ …
■ First of all, how exactly did the feature vector change?
04 Experimental setting Non-Research Tips

32
Automatizing the experimental environment
● Use a hyperparameter management tool
○ Hydra, or yaml
● Learning agentic skills for implementing your experimental environment
○ Turning your proposed method into a library
○ After the evaluation procedure is decided, they expand the context of the
coding agent
04 Experimental setting Non-Research Tips

33
Non-Research Tips
Writing your paper
05

34
Why writing papers in the company?
● The purpose of company research is rarely top-conference publications
● According to Prof. Yann LeCun:
○ Sharing new knowledge is an unwritten rule in research; it shows respect for
other researchers and ensures that science progresses equally
○ You can receive feedback from experts around the world; your hypothesis or
methodology may be fundamentally flawed
○ It also enhances a company's visibility and presence
05 Paper writing Non-Research Tips

35
Which part of writing a paper is the hardest?
● Logical “storytelling”
○ Connection between Introduction and Conclusions
○ Decide on the main point you want to convey, and develop all text around it
■ To avoid including unnecessary details or straying from the main story midway
■ Like the related work, you do not need to include everything you developed
○ Readers are trying to understand the research significance within the long text
“the devil is
in the details”
Length within
the paper
Effort required for writing
Average paper
(mostly rejected)
Good paper
(gray zone)
Very good paper
(mostly accepted)

36
Textbook recommendation
● Science Research Writing
○ Hilary Glasman-Deal
● Learning from case-studies
● While reading a paper you admire, analyze why each
sentence is used in that specific context
● Put yourself in the reader‘s shoes
● Phrases and conjunctions commonly used in English

37
Useful tools
● Vibe writing
○ Build-up a LaTeX environment, and write along a coding agent
■ Translate sentences
■ Point out paragraphs that need further explanations
■ Point out contradictions or redundancies in the flow of the text
■ Etc.
○ Overleaf recently introduced an AI assistant
● Time management is a must
○ It’s common to finish writing right before the deadline., but that impacts quality
○ Tips
■ Put your phone where you can’t see it (let’s embrace boredom!)
■ Establish a focus routine (e.g., first thing in the morning)
■ Critical parts first (e.g., explaining the hypothesis, analyzing the experimental results)
■ Perform a “shut-down” ritual

38
Non-Research Tips
Promoting your research
06

39
How to advertise your research
● Slide and poster design
○ Grid alignment, consistent margins, one message per slide, minimalistic text
● Publishing your code (OSS)
○ Prepare a GitHub repository
○ Ready-to-use implementation and a well-written README
○ Guarantee reproducibility
● Project page
○ One-shot explanation of your research (lots of visuals)
○ Authors introduction
○ Video demo, URLs for the code and paper
○ Clean appearance overall
● Key factor that determines whether research becomes well-known or not
06 Research promotion Non-Research Tips

40
Non-Research Tips
Conclusions

41
Conclusions
● Doing research in a company is not a given / there’s no “ground-truth”
● But, we can leverage best practices for higher chances of succeeding
○ Science communication, patents, etc., there are many other subjects
● Master the basics yourself to prevent “AI burnout”
○ “You use a calculator after you've mastered multiplication and division”
○ AI for work that you know how to do but you don’t have time to do
○ You should be able to evaluate the output of AI
● You are the ultimate responsible of your own research
Conclusions Non-Research Tips

Antonio TEJERO DE PABLOS 2026/06/10
¡MUCHAS GRACIAS!

Research Tips in the Enterprise: From Topic Selection to Real-World Implementation

Research Tips in the Enterprise: From Topic Selection to Real-World Implementation

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