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Autonomous LLM-driven research from data to human-verifiable research papers

Tal Ifargan, Lukas Hafner, Maor Kern, Ori Alcalay, Roy Kishony

arXiv:2404.17605v1q-bio.OTcs.AI

TL;DR

Whether fully AI-driven research can generate de novo insights while adhering to transparency, traceability, and verifiability remains unclear. Data-to-paper automates a stepwise research process with interacting agents and information tracing, inspired by human scientific practice. Running fully autonomously (autopilot), data-to-paper can perform complete and correct run cycles for simple goals, while human co-piloting becomes critical for complex goals.

  • Problem

    Whether fully AI-driven research can generate de novo insights while adhering to transparency, traceability, and verifiability remains unclear.

    Source: Introduction · p. 2

  • Method

    Data-to-paper automates a stepwise research process with interacting agents and information tracing, inspired by human scientific practice.

    Source: Introduction · p. 2, Discussion · p. 9

  • Results

    Running fully autonomously (autopilot), data-to-paper can perform complete and correct run cycles for simple goals, while human co-piloting becomes critical for complex goals.

    Source: Introduction · p. 3

  • Takeaways & Limitations

    The authors present data-to-paper as producing data-chained, human-verifiable papers despite relatively limited novelty.

    Source: Discussion · p. 9

    For simple analysis tasks, hallucinations lead to fundamental errors in about 10–20% of papers, and the system fails consistently on more complex tasks.

    Source: Discussion · p. 8

Abstract

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As AI promises to accelerate scientific discovery, it remains unclear whether fully AI-driven research is possible and whether it can adhere to key scientific values, such as transparency, traceability and verifiability. Mimicking human scientific practices, we built data-to-paper, an automation platform that guides interacting LLM agents through a complete stepwise research process, while programmatically back-tracing information flow and allowing human oversight and interactions. In autopilot mode, provided with annotated data alone, data-to-paper raised hypotheses, designed research plans, wrote and debugged analysis codes, generated and interpreted results, and created complete and information-traceable research papers. Even though research novelty was relatively limited, the process demonstrated autonomous generation of de novo quantitative insights from data. For simple research goals, a fully-autonomous cycle can create manuscripts which recapitulate peer-reviewed publications without major errors in about 80-90%, yet as goal complexity increases, human co-piloting becomes critical for assuring accuracy. Beyond the process itself, created manuscripts too are inherently verifiable, as information-tracing allows to programmatically chain results, methods and data. Our work thereby demonstrates a potential for AI-driven acceleration of scientific discovery while enhancing, rather than jeopardizing, traceability, transparency and verifiability.

Introduction

Data-to-paper automates a stepwise, traceable research process from human-provided data, but its autonomous reliability depends on goal complexity. The platform can complete simple goals autonomously, whereas complex goals require human co-piloting.

Introduction

  • De novo creation of quantitative insights from data remains a challenge for AI-driven research, alongside transparency, traceability, and verifiability concerns.Creating a transparent, methodologically traceable and verifiable paper is challenging because research involves interconnected steps and information tracing.Source: Introduction · p. 2
  • Data-to-paper guides multiple LLM and rule-based agents through conventional research steps, with feedback, iterative revision, and structured information-flow tracing.It focuses on hypothesis-testing research that starts from a human-provided dataset and can raise hypotheses, analyze data, interpret results, and write papers.Source: Introduction · pp. 2–3
  • The system supports fixed- or open-goal modalities and human copilot or autopilot modes, and its data-method-result chain traces downstream results to their generating code.The authors evaluated the process in two open-goal and two fixed-goal case studies on public datasets.Source: Introduction · p. 3
  • The passage reports that data-to-paper, running fully autonomously (autopilot), can perform complete and correct run cycles for simple goals, whereas human co-piloting becomes critical for complex goals.Source: Introduction · p. 3

Implementation

Data-to-paper organizes research as discrete, product-generating steps, with constrained information flow, automated checks, and iterative review. Runs may vary, and the implementation reports frequent completion-preventing mistakes with current open-source LLMs.

Implementation

  • Each research step creates defined products, such as text, citations, code, or numerical data, which are assembled into a complete paper.The process includes 17 steps, and a human-provided goal skips the goal-determining steps.Source: Implementation · p. 3
  • Providing each step only a task-relevant subset of earlier products controls information flow and allows numeric results to be traced and verified.The authors say this rigor control minimizes possible hallucinations from mixing relevant and irrelevant information.Source: Implementation · p. 4
  • Rule-based checks give constructive feedback on failed products, while coding steps use static, runtime, package-specific, and output checks.Products may also undergo iterative LLM review, with optional human comments in copilot mode.Source: Implementation · p. 4
  • Runs can produce different analyses and manuscripts on the same dataset, and the authors report that current state-of-the-art open-source LLMs often make mistakes that prevent full research cycles.Data-to-paper is nondeterministic, so runs vary even with the same dataset and goal conditions.Source: Implementation · p. 4

Open-goal research on public datasets

In open-goal autopilot runs on two public datasets, data-to-paper generated structured manuscripts and de novo insights, but accuracy was uneven: 8 of 10 papers had correct analyses with minor wording issues, while two had major errors.

Open-goal research on public datasets

  • The generated manuscripts followed canonical research-paper structure and addressed different topics and hypotheses across runs on each dataset.Five runs were conducted on each of two public datasets.Source: Open-goal research on public datasets · p. 5
  • The papers were not highly creative, but they tested reasonable hypotheses with straightforward statistics and reported de novo insights from the data.Source: Open-goal research on public datasets · p. 5
  • 8 of 10 open-goal papers reported correct analyses with only minor wording imperfections, while 2 contained fundamental analysis or interpretation mistakes.The two major errors included an interpretation exceeding the analysis scope and an erroneous analysis supporting unfounded statements.Source: Open-goal research on public datasets · pp. 5–6
  • All 10 generated scientific tables correctly represented the analyses, and the text generally interpreted findings factually and cited key numeric values.The text also reasonably situated research questions and findings in existing literature.Source: Open-goal research on public datasets · p. 6
  • Other imperfections included generic phrasing, novelty overstatement, and inadequate or sometimes missing citations.Source: Open-goal research on public datasets · p. 6

Estimating reliability in reproducing peer-reviewed results

In fixed-goal reproduction tests, data-to-paper was reliable for simple research goals in 80–90% of cases, but broader analyses had sharply higher error rates. Human co-piloting helped assure accuracy on complex goals, and data-chaining made manuscripts traceable to their source code.

Estimating reliability in reproducing peer-reviewed results

  • Fixed-goal manuscripts were similar to their respective original studies in content, terminology, and vector embeddings.The created papers also clustered by the four case studies.Source: Estimating reliability in reproducing… · p. 7
  • The broad original research goal in challenge 2 had a 90% error rate, compared with 10–20% when fewer models were requested.The authors observed that broader tasks required more iterations to complete data analysis.Source: Estimating reliability in reproducing… · p. 7
  • A 2–3-sentence review comment per run, typically during code writing, allowed accurate papers consistently even for more complex goals.Source: Estimating reliability in reproducing… · p. 7
  • For simple research goals, data-to-paper autonomously created reliable manuscripts in 80–90% of cases; human co-piloting was critical for more complex goals.In challenge 1, all papers correctly reproduced the analysis, and 8 of 10 reached the overall correct conclusions.Source: Estimating reliability in reproducing… · p. 7
  • Data-chaining links manuscript numbers through formulas, tables, and output files to the code that produced them, supporting systematic vetting.Source: Estimating reliability in reproducing… · p. 8

Discussion

The paper presents data-to-paper as a human-inspired, multi-agent process that traces information from data through research products to transparent, verifiable manuscripts. It demonstrates autonomous creation of new insights across research domains, while emphasizing that errors, limited novelty, and task complexity make human co-piloting important.

Discussion

  • Tracing links results, methods, and data in “data-chained” manuscripts, making the research path programmatically inspectable.The authors describe these papers as human-verifiable.Source: Discussion · p. 8
  • The process demonstrates de novo insights from provided data, although its research novelty falls well behind high-end contemporary science.The authors distinguish creating new insights from matching the novelty of contemporary science.Source: Discussion · p. 8
  • The process spans data types and research domains, producing association studies, network analyses, and machine-learning models.These are examples of the scientific outputs described by the authors.Source: Discussion · p. 8
  • LLM hallucinations cause fundamental errors in about 10–20% of papers for simple analysis tasks and consistent failure for more complex tasks, despite guardrails.The authors say human co-piloting can overcome errors, even for complex tasks.Source: Discussion · p. 8
  • The current implementation is limited to textual and table outputs, cannot pursue follow-up questions, and is restricted to hypothesis-testing research.The authors identify these as constraints of the current implementation.Source: Discussion · p. 8
  • The authors identify risks including dishonest use and publication of generic, insignificant work, and describe transparent oversight, co-piloting, and unbiased reporting as mitigations.They anticipate AI systems serving as co-pilots on straightforward tasks so scientists can focus on higher-level concepts.Source: Discussion · p. 9

Methods

The methods describe how data-to-paper turns datasets and metadata, with optional research goals, into research products through a stepwise LLM workflow involving literature search, model selection, analysis, and manuscript assembly. They also detail human oversight, review and accuracy checks, and information tracing from manuscript values back to analysis outputs and code.

Datasets

  • The four datasets covered health indicators, Congress-member Twitter interactions, NICU treatment policy (26), and pediatric tracheal-tube depth optimization (27).The health dataset contained 253,680 survey responses and 22 features; the social-network data represented directed interactions among 117th Congress members.Source: Methods · pp. 9–10
  • The NICU input received minor column-name and binary-format cleanups and had irrelevant columns removed; the optimization input retained only variables used in the original study.For the optimization dataset, the retained variables were tube, sex, age_c, ht, wt, and tube_depth_G.Source: Methods · pp. 9–10
  • Dataset descriptions and file descriptions were refined through pilot runs to resolve misunderstandings and vague or ill-defined statements; research goals were supplied for the two reproduction datasets.The research goals and descriptions were iteratively phrased and tested with ChatGPT.Source: Methods · p. 10

Execution of data-to-paper

  • Runs received a dataset and metadata, with an optional research goal; open-goal runs selected a goal, whereas fixed-goal runs skipped that step.Human oversight ranged from approval-only autopilot to co-pilot review with comments at each step.Source: Methods · p. 10
  • Health Indicators and Social Network each had five open-goal research cycles; Treatment Policy had 10 fixed-goal cycles, and Treatment Optimization had 10 fixed-goal cycles.For Health Indicators, the fifth run was restarted three times after goal validation because its goal resembled earlier goals; prior goals were included in Social Network prompts to reduce overlap.Source: Methods · pp. 10–11
  • Treatment Optimization also had 50 modified-goal runs stopped after title and abstract for failure annotation, plus three co-pilot runs on the original goal.Co-pilot runs included review comments, typically during code writing.Source: Methods · p. 11

Overview of data-to-paper implementation

  • Each research step used a distinct performer conversation to create a focal product, which underwent rule-based and, for selected steps, LLM review before the products were assembled into a manuscript.Programmatic feedback returned products for correction, and a step ended with a product that passed both forms of review.Source: Methods · p. 11

Devising prompts

  • Prompts were iteratively designed around each step’s task and product format, then adjusted through pilot runs, adding details or one-shot examples when responses remained inadequate.The prompts are listed in Table S1.Source: Methods · p. 11

Rule-based product review

  • Extracted research products are refined and tested against step-specific formatting, length and referencing rules, with failures prompting feedback to the LLM.The system first extracts products using expected formatting and applies step-specific refinement rules.Source: Methods · p. 12

Information tracing

  • Results values receive unique labels and hyperlinks to their source products, while arithmetic-derived values can be specified with explanatory formulas.Rule-based checks verify links; missing-value placeholders cause the research cycle to abort.Source: Methods · pp. 12–13
  • For derived values, the LLM can provide explanatory arithmetic formulas, while rule-based checks verify links and abort cycles with missing-value placeholders.Compiled formulas are replaced with their values and listed with explanations in a Notes appendix.Source: Methods · p. 13

Data-chained manuscripts

  • Each Results value can be linked through a Notes appendix and table to its output file and the code lines that generated it.The manuscript chains results, methods and data through these links.Source: Methods · p. 13

Reviewer conversation

  • For selected steps, a reviewer LLM can accept a product or provide constructive feedback, which is passed to the performer LLM for revision.Reviewer conversations follow successful rule-based product review.Source: Methods · pp. 13–14

Coding steps

  • Code for exploration, analysis and table design undergoes static, runtime, package-specific and output checks, with detected issues returned to the LLM as feedback.After code passes these tests, the LLM reviews the code and its output for issues and suggested corrections.Source: Methods · p. 14

Citation retrieval

  • Literature-search steps use Semantic Scholar retrieval and algorithmic checks to ensure included citations are valid.Checks restrict citations retrieved from the LLM’s memory.Source: Methods · pp. 14–15

Literature search

  • Literature searches generated queries for predefined scopes, retrieved citations through an API, then filtered and sorted results by search rank or embedding similarity for use in later steps.For reproduction runs on datasets C and D, the original study’s citation was manually excluded.Source: Methods · p. 15

LLM selection

  • Using the “Health Indicators” dataset, the research-goal step ran 10 times each with gpt-3.5-turbo or Llama-2-70b-chat-hf; coding tests evaluated programmatic feedback rounds until code passed rule-based review.Source: Methods · p. 15

ChatGPT models and parameters

  • The platform used OpenAI conversational ChatGPT models, with an initial nominal model for each step; during a conversation, it could upgrade to gpt-4 when a rule-based feedback request was unresolved.The listed models were gpt-3.5-turbo-0613, gpt-3.5-turbo-16k-0613, and gpt-4; temperature 1 was set for the “Research goal” step.Source: Methods · pp. 15–16

Paper assembly and compilation

  • For manuscript production, data-to-paper combined manuscript-part products into one LaTeX file and automatically compiled it with retrieved citations into a watermarked PDF.The watermark identified the PDF as created by data-to-paper.Source: Methods · p. 16

Manual review of created manuscripts

  • Manual review checked analysis and code, numeric claims against outputs, citation fit, textual exactness, and writing quality.Manuscripts were marked to distinguish correct statements, imperfect or atypical statements, and minor errors.Source: Methods · p. 16

Human co-piloting

  • Users could add review comments after rule-based and LLM review, triggering iterative answer and rule-check cycles until they approved a step’s research product.The interface let users follow each step’s LLM conversation and add comments as needed.Source: Methods · p. 16

Contributions

The acknowledgments of work describe who conceived and developed data-to-paper, prepared and tested the runs, and contributed to the figures and manuscript.

Contributions

  • TI and RK conceived the study and developed data-to-paper with inputs from LH, MK, and OA.MK and OA implemented the graphic interface for system testing.Source: Contributions · p. 21
  • TI and LH identified and prepared the datasets and metadata, while RK and TI oversaw the autonomous research runs.TI and LH also manually vetted and highlighted the created papers and run files.Source: Contributions · p. 21
  • LH designed the figures with TI and RK; TI, LH, and RK interpreted results and wrote the manuscript with comments from MK and OA.Source: Contributions · p. 21

Corresponding authors

The paper presents data-to-paper as a stepwise research workflow with programmatic checks and LLM review, then evaluates the papers it produces across datasets and research goals.

Table 1

  • Examples from the Health Indicators and Social Network datasets report findings about diabetes-related patterns and congressional Twitter interactions.The Health Indicators examples include distinct patterns by diabetes status and an inverse relationship between fruit and vegetable consumption and diabetes prevalence; Social Network examples report associations involving chambers, party affiliation, and represented states.Source: Corresponding authors · p. 22, Table 1 · p. 22

Research workflow

  • Each research step creates defined research products from prior products and uses a distinct LLM conversation with mission instructions and iterative feedback.The workflow includes rule-based and LLM review, and co-pilot mode requests human review after LLM review.Source: Corresponding authors · p. 24

LLM review

  • LLM review transfers a product to a role-inverted Reviewer conversation, whose feedback returns to the Performer for revision.Figure 2 also illustrates this exchange with a Discussion-writing example.Source: Figure 2 · p. 25

Figure 3

  • Generated papers and the corresponding original papers show close proximity in title-and-abstract vector embeddings.The figure compares pairwise cosine distances and plots a principal component analysis of generated manuscripts and original papers.Source: Figure 3 · p. 26

Figure 4

  • Data-to-paper creates correct papers for simple goals, while more complex goals require human co-piloting to assure accuracy.Figure 4 summarizes correctness categories across datasets and includes co-pilot runs for the most complex Treatment Optimization goal.Source: Figure 4 · p. 27

LLM evaluation

  • In the tested Research goal and Data analysis steps, LLM performance differed: Llama 2-70B hallucinated in all 10 goal runs, while GPT-3.5-turbo and GPT-4 converged to functional code within 1–3 rounds.The coding result reports a median of 1 round for GPT-3.5-turbo and GPT-4.Source: Figure S3 · p. 30

ASSISTANT / ChatGPT

The supplied passages describe an external-API literature search workflow and an evaluation of analysis accuracy across research goals of varying complexity.

Search example

  • An example background search query list includes neonatal resuscitation guidelines, non-vigorous newborns, and neonatal resuscitation interventions.The passages also show an example of a citation entry from a filtered and sorted results list.Source: ASSISTANT / ChatGPT · p. 36

Literature search

  • Data-to-paper uses LLM-generated search queries with an external citation-search API rather than asking the LLM to retrieve citations from memory.Retrieved papers are programmatically filtered and sorted before becoming the final literature-search product.Source: Figure S7 · p. 36, ASSISTANT / ChatGPT · p. 37

Goal complexity

  • Increasing goal complexity is associated with erroneous analysis by data-to-paper.Figure S8 compares correctness across six Treatment Optimization goals, varying model requirements and whether mathematically explicit explanations are provided.Source: Figure S8 · p. 38
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