tlooto
Sign up

What is tlooto, and how is it different from asking a general AI chatbot for research help?

September 1, 2026 at 9:00 AM

tlooto is an agentic AI research workspace. It answers a research question from the scholarly literature, ties every claim to a paper it actually retrieved, and carries the result straight into a manuscript editor. A general chatbot writes from what its model remembers. tlooto searches, screens and cites first, and only then writes.

The problem tlooto solves

Scientific output grows by about 4.1% a year and doubles roughly every 17 years [1]. Medicine alone was already publishing some 75 trials and 11 systematic reviews a day in 2010 [2], and a registered systematic review took a mean of 67.3 weeks to complete [3]. Researchers are turning to AI to keep up: in a Nature survey of more than 1,600 scientists, AI tools were widely expected to become central to research [4], and LLM-modified text already appears in up to 17.5% of recent computer-science papers [5].

The catch is reliability. Language models produce fluent statements that no source supports [6], and references are where this hurts most. In the OpenScholar evaluation, GPT-4o fabricated 78–90% of the scientific citations it produced [15], and even commercial research tools built on retrieval hallucinated in 17–33% of queries [7].

How tlooto works

tlooto builds on retrieval-augmented generation [8][9] and citation-aware generation [10], organised as a team of AI agents that reason and act [11].

  1. Planning. Your question is broken into complementary search intents, and every step of the plan is shown to you live in the Work process panel.
  2. Scholarly retrieval. Many queries run against a scholarly index, returning papers with authors, venue, year, DOI and abstract.
  3. Literature screening. Each candidate is checked against explicit inclusion criteria, the step that machine learning has long accelerated in evidence synthesis [20][21][22].
  4. Evidence synthesis. A structured report is written from the screened papers, with every sentence tied to a numbered citation you can open.
  5. Research Editor. One click turns the report into a manuscript with live citations, tables, figures and equations.
General AI chatbottlooto
Source of claimsModel memoryPapers retrieved for your question
CitationsFrequently fabricated [7][15]Drawn from retrieved papers, with DOI
ScreeningNoneScreening against your criteria
TransparencyFinal text onlyFull Work process, step by step
OutputChat messageCited report and editable manuscript
Citation stylesAd hocAPA, MLA, Harvard, Chicago, ISO 690
Follow-up questionsStart from scratchBuild on the evidence already found

What researchers use it for

  • Literature reviews: map theoretical perspectives, consensus and debates across a field in minutes rather than weeks.
  • Research gaps and ideas: surface underexplored questions, an area where LLM-generated ideas have been rated more novel than those of expert researchers [19].
  • Methodology: designs, measures and analyses, each tied to studies that used them.
  • Writing and submission: draft in the Research Editor, export to PDF or DOCX, and find a fitting journal with the AI Journal Finder.

tlooto belongs to the new generation of research agents alongside PaperQA2 [14], OpenScholar [15] and AI co-scientist systems [18], and it is built for working researchers to use every day.

The paper open on the right shows what the Research Editor produces. Ask your own research question below and watch tlooto work on your topic.

References
  1. [1]

    Bornmann, L.; Haunschild, R.; Mutz, R. 2021. Growth rates of modern science: a latent piecewise growth curve approach to model publication numbers from established and new literature databases. Humanities and Social Sciences Communications https://doi.org/10.1057/s41599-021-00903-w.

  2. [2]

    Bastian, H.; Glasziou, P.; Chalmers, I. 2010. Seventy-five trials and eleven systematic reviews a day: how will we ever keep up? PLoS Medicine https://doi.org/10.1371/journal.pmed.1000326.

  3. [3]

    Borah, R.; Brown, A. W.; Capers, P. L.; Kaiser, K. A. 2017. Analysis of the time and workers needed to conduct systematic reviews of medical interventions using data from the PROSPERO registry. BMJ Open https://doi.org/10.1136/bmjopen-2016-012545.

  4. [4]

    Van Noorden, R.; Perkel, J. M. 2023. AI and science: what 1,600 researchers think. Nature https://doi.org/10.1038/d41586-023-02980-0.

  5. [5]

    Liang, W.; Zhang, Y.; Wu, Z.; Lepp, H.; Ji, W.; Zhao, X.; et al. 2024. Mapping the increasing use of LLMs in scientific papers. Conference on Language Modeling (COLM 2024) https://arxiv.org/abs/2404.01268.

  6. [6]

    Ji, Z.; Lee, N.; Frieske, R.; Yu, T.; Su, D.; Xu, Y.; et al. 2023. Survey of hallucination in natural language generation. ACM Computing Surveys https://doi.org/10.1145/3571730.

  7. [7]

    Magesh, V.; Surani, F.; Dahl, M.; Suzgun, M.; Manning, C. D.; Ho, D. E. 2025. Hallucination-free? Assessing the reliability of leading AI legal research tools. Journal of Empirical Legal Studies https://arxiv.org/abs/2405.20362.

  8. [8]

    Lewis, P.; Perez, E.; Piktus, A.; Petroni, F.; Karpukhin, V.; Goyal, N.; et al. 2020. Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems 33 https://arxiv.org/abs/2005.11401.

  9. [9]

    Gao, Y.; Xiong, Y.; Gao, X.; Jia, K.; Pan, J.; Bi, Y.; et al. 2023. Retrieval-augmented generation for large language models: a survey. arXiv preprint https://arxiv.org/abs/2312.10997.

  10. [10]

    Gao, T.; Yen, H.; Yu, J.; Chen, D. 2023. Enabling large language models to generate text with citations. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing https://doi.org/10.18653/v1/2023.emnlp-main.398.

  11. [11]

    Yao, S.; Zhao, J.; Yu, D.; Du, N.; Shafran, I.; Narasimhan, K.; Cao, Y. 2023. ReAct: synergizing reasoning and acting in language models. International Conference on Learning Representations (ICLR 2023) https://arxiv.org/abs/2210.03629.

  12. [12]

    Wei, J.; Wang, X.; Schuurmans, D.; Bosma, M.; Ichter, B.; Xia, F.; et al. 2022. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems 35 https://arxiv.org/abs/2201.11903.

  13. [13]

    Lála, J.; O’Donoghue, O.; Shtedritski, A.; Cox, S.; Rodriques, S. G.; White, A. D. 2023. PaperQA: retrieval-augmented generative agent for scientific research. arXiv preprint https://arxiv.org/abs/2312.07559.

  14. [14]

    Skarlinski, M. D.; Cox, S.; Laurent, J. M.; Braza, J. D.; Hinks, M.; Hammerling, M. J.; et al. 2024. Language agents achieve superhuman synthesis of scientific knowledge. arXiv preprint https://arxiv.org/abs/2409.13740.

  15. [15]

    Asai, A.; He, J.; Shao, R.; Shi, W.; Singh, A.; Chang, J. C.; et al. 2024. OpenScholar: synthesizing scientific literature with retrieval-augmented LMs. arXiv preprint https://arxiv.org/abs/2411.14199.

  16. [16]

    Wang, Y.; Guo, Q.; Yao, W.; Zhang, H.; Zhang, X.; Wu, Z.; et al. 2024. AutoSurvey: large language models can automatically write surveys. Advances in Neural Information Processing Systems 37 https://arxiv.org/abs/2406.10252.

  17. [17]

    Lu, C.; Lu, C.; Lange, R. T.; Foerster, J.; Clune, J.; Ha, D. 2024. The AI Scientist: towards fully automated open-ended scientific discovery. arXiv preprint https://arxiv.org/abs/2408.06292.

  18. [18]

    Gottweis, J.; Weng, W.-H.; Daryin, A.; Tu, T.; Palepu, A.; Sirkovic, P.; et al. 2025. Towards an AI co-scientist. arXiv preprint https://arxiv.org/abs/2502.18864.

  19. [19]

    Si, C.; Yang, D.; Hashimoto, T. 2025. Can LLMs generate novel research ideas? A large-scale human study with 100+ NLP researchers. International Conference on Learning Representations (ICLR 2025) https://arxiv.org/abs/2409.04109.

  20. [20]

    Marshall, I. J.; Wallace, B. C. 2019. Toward systematic review automation: a practical guide to using machine learning tools in research synthesis. Systematic Reviews https://doi.org/10.1186/s13643-019-1074-9.

  21. [21]

    van de Schoot, R.; de Bruin, J.; Schram, R.; Zahedi, P.; de Boer, J.; Weijdema, F.; et al. 2021. An open source machine learning framework for efficient and transparent systematic reviews. Nature Machine Intelligence https://doi.org/10.1038/s42256-020-00287-7.

  22. [22]

    Guo, E.; Gupta, M.; Deng, J.; Park, Y.-J.; Paget, M.; Naugler, C. 2024. Automated paper screening for clinical reviews using large language models: data analysis study. Journal of Medical Internet Research https://doi.org/10.2196/48996.

  23. [23]

    Page, M. J.; McKenzie, J. E.; Bossuyt, P. M.; Boutron, I.; Hoffmann, T. C.; Mulrow, C. D.; et al. 2021. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ https://doi.org/10.1136/bmj.n71.

September 1, 2026 at 9:00 AM