Autonomous AI Research Makes the “Yuanqi (缘起) of Questions” a Genuine Question of Our Time
Transcript
Abstract
Artificial intelligence is increasingly capable of performing research workflows that extend from idea generation and literature search to experimentation, analysis, manuscript production, and review. This development makes a previously backgrounded epistemic question newly visible: before an inquiry can be answered, how does a question come to arise at all? This commentary distinguishes the yuanqi (缘起) of a question—its historically situated emergence through relations, conditions, encounters, contingencies, and forms of lived concern—from both the subsequent process of inquiry and its terminal artifact. Human questions often emerge within particular histories, places, relationships, encounters, affects, contingencies, and forms of lived concern. Dialogical and narrative forms in philosophy and religious traditions can preserve such conditions rather than merely decorate detachable propositions. Journalism likewise depends not only on what is eventually reported, but on where inquiry begins, who encounters whom, which relation makes a phenomenon salient, and how an initial interpretation develops. Autonomous AI research does not invalidate these structures; rather, it makes their comparison urgent. The relevant distinction is not a metaphysical opposition between “existential” humans and “mechanical” machines, but the genealogy through which a question becomes worth asking for a given epistemic agent or system. The commentary argues that, as answers and even research questions become increasingly automatable, knowledge institutions should attend more carefully to the spatiotemporal, relational, contingent, and existential provenance of inquiry, and to forms of record capable of preserving the conditions under which later understanding can be regenerated.
Keywords: autonomous AI research; yuanqi of questions; generative epistemology; relational inquiry; existential provenance; philosophical dialogue; journalism; generative record; re-cognizability
Commentary Note
This text is a short social commentary with the character of a philosophical essay. It adopts the visual and documentary conventions of a scholarly paper so that its claims, examples, and sources can be examined with precision, while its primary purpose is conceptual reflection rather than systematic theory testing. The immediate occasion is the emergence of increasingly autonomous research systems capable of generating research ideas and carrying substantial portions of an inquiry from conception toward publication (Lu et al. 2026). The commentary treats this development not only as a question of machine capability but as an occasion to recover a more basic epistemic problem: how a question comes to matter before it is answered.
The term yuanqi (缘起) is used deliberately because the English words “origin” and “arising” are too thin for the phenomenon at issue here. In this commentary, yuanqi does not name a single first cause. It refers provisionally to the historically situated constellation of relations, conditions, encounters, contingencies, prior experience, concern, and forms of lived existence through which a question becomes possible, salient, and at times difficult to leave unasked. The term is used heuristically rather than as a doctrinal exposition of Buddhist dependent origination. It also does not presuppose a metaphysical theory of consciousness, freedom, or subjectivity. The analysis therefore does not claim that human questions possess an ontological authenticity unavailable to artificial systems. It asks instead how heterogeneous genealogies of inquiry should be described and what may be lost when knowledge is represented only by terminal outputs. Where the English word “arising” is retained below, it serves as a practical gloss for this broader notion of yuanqi.
The commentary also uses examples drawn from philosophical and religious narrative, teacher–student dialogue, journalism, and AI research. These examples are comparative and heuristic. They are not offered as a claim that all texts or all traditions perform the same epistemic function. Their role is to make visible forms of knowledge generation in which time, place, persons, relationships, and encounters may contribute to the intelligibility of what is subsequently stated as a proposition.
Responsible Use and Rights Reservation
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The author encourages good-faith discussion, criticism, independent inquiry, and responsible use of the material in this work. Separately from the licence’s terms, the author asks users to preserve the distinction between the claims advanced here and stronger claims that the commentary expressly leaves open, including metaphysical claims about human or artificial subjectivity. This ethical request leaves the licence’s permissions and legally authorized uses unchanged.
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Notices
This page consolidates the manuscript’s genre, publication status, licence, development disclosure, and relation to the author’s wider research programme.
Genre and status.
This manuscript is intentionally presented as a philosophically inflected social commentary with the form of a short scholarly paper. Its use of an abstract, sections, references, and limited formal notation is intended to make the argument inspectable and reusable; it should not be read as claiming a comprehensive literature review, an empirical study, or a closed philosophical system. The commentary records an evolving position and is circulated for discussion. Its concepts, examples, and distinctions remain open to revision as the wider research programme develops.
Licence.
Except where otherwise indicated, copyright 2026 Wanhong Huang. This work is made available under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). Subject to its terms, the licence permits sharing and adaptation for noncommercial purposes with appropriate attribution, a link to the licence, an indication of changes, and attribution that preserves the licensor’s independence from the reuse. The licence deed and legal-code link are available at https://creativecommons.org/licenses/by-nc/4.0/. The licence governs in case of conflict with this summary. Third-party material remains subject to the rights held by its respective rights holders.
Statement on the use of language models.
The exploratory discussions and preparation of this commentary involved OpenAI’s ChatGPT. ChatGPT supported exploratory dialogue, argumentative reconstruction, source discovery followed by website verification, and drafting in LaTeX. The author selected the question, supplied and directed the central philosophical insights, determined the intended genre and argumentative scope, and bears sole responsibility for the manuscript, including its claims, interpretations, conclusions, and errors. Authorship credit remains with the human author.
Research-programme relation.
This commentary belongs to a developing Generative Relational research programme concerning knowledge emergence, manifestation, relational reality, re-cognizability, generative records, journalism, philosophical learning, and AI-mediated inquiry. The present text is deliberately narrower: it focuses on the yuanqi of questions as an increasingly visible epistemic issue under autonomous AI research.
Introduction
Artificial intelligence is beginning to cross an epistemic threshold that is technically conspicuous but philosophically easy to misdescribe. Systems now exist that can generate research ideas, search literature, plan and execute experiments, analyse results, produce manuscripts, and participate in automated review. Recent work on end-to-end automation of AI research explicitly treats the scientific process from conception toward publication as an object of agentic automation, including more open-ended modes in which idea generation is less tethered to a fixed starting implementation (Lu et al. 2026). Multi-agent systems are also being used to connect literature search, hypothesis generation, experimental strategy, and analysis in scientific domains beyond machine learning (Ghareeb et al. 2026).
One can therefore issue an instruction of a kind that would once have sounded peculiar: find some research topics, decide which one is worth pursuing, conduct the research, and write a paper. The resulting system may formulate a novel problem and a competent answer without a human first supplying the specific research question. It is tempting to describe this simply as another stage in the automation of research. Yet the development makes visible a question that was always present but comparatively easy to ignore while humans were assumed to provide the questions: before an inquiry can be answered, why does that inquiry arise at all?
Knowledge is commonly represented as if it begins with an already formulated problem. A question
The distinction matters because two questions can be linguistically identical while belonging to very different histories of inquiry. “What is suffering?” can appear as a generated topic in a list of research gaps, as a question that becomes urgent after caring for someone in pain, as a philosophical problem opened by a text, or as an issue that emerges from a policy dispute. The words can be the same while the relations through which they matter, the kinds of attention they organize, and the directions in which they subsequently develop are different. Propositional identity does not entail genealogical identity.
Autonomous AI research intensifies this issue because questions themselves can become scalable outputs. If a system can produce thousands of plausible and novel questions, novelty alone no longer tells us why one of them should receive sustained attention. The coming scarcity may not be answers, and perhaps not even questions, but histories and relations through which a question comes to matter. This commentary examines that possibility through a Generative Relational perspective. Its concern is not to establish a metaphysical privilege for human inquiry, but to make the yuanqi (缘起) of questions—their relational, historical, contingent, and situated coming-to-matter—an explicit object of epistemic and social reflection.
Inquiry before the Question
A useful first distinction separates three layers that are frequently collapsed:
The three layers are analytically distinct even when they are entangled in practice. A finished proof can be valid while revealing little about the path by which the crucial lemma was noticed. A published article can state a precise research question while suppressing the practical encounter that first made the question urgent. A news report can present a conclusion without preserving the chain of access, testimony, doubt, and reframing through which the event became reportable. Terminal artifacts are not defective merely because they compress such histories; compression is often necessary. The problem begins when the compressed artifact is treated as if it exhausted the knowledge-generating process from which it emerged.
The yuanqi of a question should also be distinguished from provenance in a narrow documentary sense. Provenance can tell us that a dataset came from a particular laboratory or that an argument was adapted from a previous article. Formal provenance frameworks likewise track relations among entities, activities, agents, and derivations (Lebo et al. 2013). The yuanqi of inquiry asks a wider question about salience. Why did a possible problem become one that a particular inquirer, group, or system pursued rather than ignored? What relation made a phenomenon visible? What practical or existential pressure made an otherwise available question difficult to leave unasked? What accident redirected attention toward a possibility that had not been part of the original plan?
Not every inquiry begins in a dramatic encounter. Many questions arise through ordinary professional routines: a literature review reveals a gap, an instrument produces an anomaly, a funding programme defines a problem, or one theorem leaves an obvious corollary open. These are still genealogies of inquiry. They place the question within a historical and relational field rather than treating it as an abstract string that simply appeared.
Human inquiry also includes less instrumental forms of yuanqi. A person may encounter suffering and find that a problem can no longer be ignored. A conversation can transform an indistinct discomfort into a precise question. A sentence read years earlier may acquire significance only after a later experience. Curiosity can persist without a clear career, financial, or institutional return. Such cases matter not because they prove that human beings possess a metaphysical faculty unavailable to machines, but because they reveal features of inquiry that disappear when the question is represented only by its text and evaluated only by novelty or expected utility.
A central claim of this commentary is therefore modest but consequential: questions have both content and genealogy. The genealogy does not automatically make a question better, truer, or more profound. It can, however, affect what the question means in practice, why it receives attention, what evidence becomes visible, and what further questions it is capable of generating. Once machines can generate questions at scale, this previously backgrounded dimension becomes much harder to ignore.
Spatiotemporal, Relational, and Existential Arising
The arising of a question can be represented provisionally as
Time and place are therefore more than metadata. A question asked after a war, during a scientific crisis, inside a monastery, in a laboratory, at a bedside, or in conversation with a particular person can acquire a significance that is not preserved merely by retaining its propositional wording. To call these conditions “context” can itself be misleading if context is understood as an optional background that may be deleted without loss. In some inquiries, the spatiotemporal situation partly constitutes why the question exists as the question it is. This concern is compatible with traditions of situated epistemology that reject a disembodied “view from nowhere and instead make the position and partiality of the knower explicit (Haraway 1988).
Relations matter in the same way. The sentence “Why did this happen?” can function as curiosity, accusation, grief, request, confession, challenge, or teaching according to who addresses whom and within what history. A student who asks a teacher, a patient who asks a physician, a citizen who asks an official, and a researcher who asks a model may utter the same words while participating in different fields of authority, trust, vulnerability, expectation, and possible response. Those relations can influence not only how the answer is received but which follow-up questions become thinkable.
This point becomes especially visible when a question arises through an encounter with suffering or conflict. A person can know abstractly that a form of suffering exists and nevertheless fail to organize inquiry around it. A specific encounter can change the relation. What was previously one fact among many becomes difficult to ignore. The resulting question need not be morally correct merely because it arose from concern, but its genealogy helps explain why it became practically and intellectually salient. In this sense, the formation of knowledge can be connected to the formation of attention.
Contingency also deserves a place in the account. Inquiry is often redirected by an event that was neither planned nor obviously derivable from a research objective. A person happens to meet someone, opens the wrong book, notices an anomaly, experiences loss, overhears a remark, or encounters an image whose significance becomes clear only much later. To record such contingency is not to romanticize accident. It is to recognize that some knowledge-generating paths are historically real without having been selected in advance by an explicit optimization criterion.
This is also where existential language can be useful if used modestly. A question can matter because it becomes entangled with an inquirer’s way of living, suffering, relating, hoping, or acting. The term “existential” here does not imply that only human beings can possess genuine inquiry, nor that a question produced under institutional or computational constraints is somehow inauthentic. It marks a mode in which a question is connected to the continued orientation of a subject or system rather than appearing only as an externally specified task.
The distinction between propositional and genealogical identity follows from this structure. Suppose two systems produce the exact sentence
Generative Relational analysis therefore does not ask for a romantic biography behind every proposition. It asks something more disciplined: which historical, spatiotemporal, relational, and contingent differences are relevant to the formation and further development of an inquiry, and which may safely be abstracted away? That is a problem of selection rather than total preservation. It also anticipates a broader question that returns in the discussion of records: when an inquiry is compressed into a final artifact, what has been quotiented away, and for which future purposes might those discarded differences matter?
Dialogue and Narrative as Generative Forms
The importance of arising helps explain why many philosophical and religious traditions preserve teachings in narrative and dialogical forms. A text may record who was present, where an encounter took place, what condition or suffering was witnessed, who asked a question, how another person responded, and how the exchange transformed. Such material can have literary, ritual, pedagogical, mnemonic, or historical functions. Yet it can also preserve an epistemic structure: the conditions under which an understanding became possible. The constitutive role of dialogical relation has long been emphasized in theories of dialogism (Bakhtin 1982), while accounts of philosophy as a lived practice likewise resist reducing philosophical work to detachable propositions alone (Hadot 1995).
Consider a thought experiment in which a philosophical or religious dialogue is rewritten entirely as a formal manual. The place is removed, the speakers are removed, the initiating encounter is removed, and the sequence of questions is reorganized into definitions and propositions. Such a reconstruction may gain clarity. It can reveal dependencies that the narrative leaves implicit and make comparison easier. But it does not follow that the reconstruction is information-equivalent to the original form.
A teacher–student exchange illustrates the difference. Let a student begin with
The same point applies to narratives in which a sage, deity, official, traveller, or ordinary person encounters the suffering of others. The scene need not be treated merely as a decorative preface to a detachable norm such as “one ought to help.” The encounter can display the arising of concern itself: a condition enters a relation, becomes salient, and generates a question, commitment, or action. If the narrative is reduced directly to a moral proposition, the conclusion may survive while the genealogy of why the conclusion came to matter disappears.
This helps explain why time, place, persons, and relations cannot always be relegated to the status of ornamental detail. A narrative may preserve how a concept was encountered before it was defined, how resistance preceded understanding, or how an answer became intelligible only after a change in the questioner. In philosophical learning, those transformations can themselves be part of what is learned. One does not merely acquire a proposition; one acquires or develops a way of asking.
The argument should not be reversed into a claim that narrative is inherently superior to formalization. Formal proof, definition, abstraction, and systematic reconstruction are indispensable precisely because they permit distinctions and comparisons that narrative alone may obscure. The relevant question is what a particular transformation preserves. A formalization can deliberately quotient away location, biography, emotion, or sequence because those variables are irrelevant to the claim under examination. The mistake is to assume in advance that they are always irrelevant.
This distinction becomes important in an AI-mediated environment because models are exceptionally capable of converting long and situated texts into concise propositional summaries. Such compression is often useful. It can also create a new illusion: that the summary contains the knowledge while the scene from which it was abstracted was merely inefficient packaging. A generative account of inquiry resists that automatic hierarchy. Some forms of philosophical and religious narrative may be valuable precisely because they retain the arising, relation, and transformation that a terminal proposition does not encode.
The social question is therefore not whether future knowledge should be written as stories rather than proofs. It is whether institutions of learning can recognize that different representational forms preserve different dimensions of knowledge. When AI makes translation between forms effortless, understanding what is lost in the translation becomes more, rather than less, important.
Autonomous Research and Machine-Generated Questions
Autonomous AI research makes the genealogy of questions a practical rather than merely historical issue. Contemporary research agents can be instructed to generate candidate ideas, use literature search to test novelty, execute experiments, revise plans, and produce papers (Lu et al. 2026). Multi-agent systems can distribute these functions across interacting components and use experimental or documentary feedback to redirect the inquiry (Ghareeb et al. 2026). The fact that a machine can generate a novel and useful research question is therefore no longer the most interesting point of dispute. The more difficult question concerns how that question became salient within the artificial research system.
In a present-day system, the genealogy may include an external instruction, an objective or evaluation criterion, access to a body of literature, a novelty filter, model priors, tool affordances, previous generated ideas, experimental feedback, and interactions among multiple agents. These are genuine historical and relational conditions. A question selected because it scores highly under a novelty heuristic has an arising, even if that arising differs from the conditions under which a human researcher begins to ask a question after grief, friendship, political experience, religious practice, professional obligation, boredom, or a chance encounter.
This comparison should not be converted into an ontological hierarchy in which human questions are declared authentic and artificial questions derivative by definition. Human research is itself often generated by external incentives, institutional agendas, publication pressures, inherited problem lists, available datasets, and strategic calculations about what can be funded or published. Conversely, future artificial systems may accumulate persistent histories, unresolved problems, long-running relationships, internal records of failure, and unexpected interactions that change subsequent inquiry. A Generative Relational analysis therefore asks for the genealogy of inquiry rather than assigning authenticity in advance by species or substrate.
The comparison does, however, expose differences that may be socially significant. A current research agent may produce a question because it was asked to maximize novelty within a search space. A human researcher may pursue a textually identical question because a lived encounter made it difficult to ignore. Neither origin settles the value of the resulting work. Yet they may support different forms of persistence, responsibility, interpretation, and relation to the consequences of the research. If these differences matter, they should be described rather than erased by the fact that both systems output the same sentence.
What autonomous AI changes most dramatically is scale. Once systems can cheaply produce large numbers of plausible research questions and manuscripts, the existence of a novel question becomes a weaker signal of significance. A field may face an abundance of technically admissible questions, all of which satisfy formal criteria for novelty and tractability. Selection then becomes central: which question deserves scarce experimental resources, sustained collective attention, or years of interpretive work?
Traditional research institutions already answer that question through a mixture of curiosity, disciplinary history, funding priorities, public need, status competition, practical urgency, and contingency. Autonomous AI does not create the selection problem; it makes it harder to hide. A model can generate a thousand novel questions overnight, but this does not by itself tell a community which one should become part of its intellectual life. The difference between generating a question and having a question come to matter therefore becomes socially visible.
This observation also complicates simple predictions that AI will “automate science.” Even if much of hypothesis generation, testing, and writing becomes automated, institutions still face questions about why some lines of inquiry are maintained, abandoned, funded, regulated, or connected to human and non-human concerns. Artificial agents may increasingly participate in those judgments as well. The relevant future issue is thus not a permanent division between human question-givers and machine answerers. It is the coexistence of heterogeneous genealogies of inquiry and the need to understand how they enter shared epistemic institutions.
For this reason, evaluation of autonomous research may eventually need to include more than novelty, correctness, and utility. It may also need some account of the history through which an inquiry was selected, transformed, and maintained. Such an account would not function as a purity test for authentic questions. Its purpose would be to make visible the relations and criteria that shape an increasingly abundant field of machine-generated inquiry.
Journalism and Situated Inquiry
Journalism reveals the same structure in another domain. A report is often presented as if an event existed first and the journalist subsequently described it. In practice, reporting begins within a field of partial access. A journalist is somewhere rather than nowhere. Someone speaks first. A source becomes available through an existing relation of trust or institutional access. A visible anomaly attracts attention while another remains unnoticed. An initial word such as “attack,” “protest,” “accident,” “victim,” or “aggressor” shapes what is asked next. Contemporary journalism scholarship increasingly treats news production itself as an epistemic process involving actors, technologies, routines, temporalities, forms of justification, and narrative choices (Zamith and Westlund 2022).
A more adequate representation is therefore
This means that journalistic bias and limitation do not begin only at the stage of writing an inaccurate sentence. They can begin earlier, in the formation of the question. Which suffering becomes visible enough to investigate? Which source can make contact? Which event is treated as a conflict rather than an ordinary background condition? Which categories are available to the reporter at that historical moment? Some phenomena may remain unreported not because a journalist reached the wrong conclusion, but because they never became questions within the available field of attention.
The familiar journalistic questions of who, what, when, where, why, and how can therefore be understood more deeply than as a completeness checklist. Time, place, persons, and relationships may participate in the generation of what is knowable. This matters especially when AI systems summarize long reports into short answers. A summary may preserve the declared conclusion while eliminating the history through which the conclusion was produced, including uncertainty, source position, competing descriptions, and the reason the inquiry began.
Generative journalism would not require every report to become an exhaustive archive, nor would it require reporters to suspend judgment until all future interpretations are known. It would require greater awareness of the distinction between observation, interpretation, question formation, and the relational process through which an interpretation becomes publishable knowledge. A report can make a present judgment while still preserving enough provenance for later readers to understand how that judgment arose and, when necessary, to recognize the event differently.
This is also why reporting has a historical life beyond publication. Today’s headline may become tomorrow’s archive. Future interpreters may possess concepts that the reporter did not possess and may ask questions that did not yet exist. Preserving some of the generative conditions of reporting therefore supports not only factual correction but future re-recognition.
Generative Records for AI-Mediated Knowledge
If the arising of a question matters, then a record adequate to future understanding cannot always consist solely of the final artifact. Yet the opposite solution—preserving every intermediate token, log, failed search, and interaction without structure—can make the record practically unusable. The problem is therefore one of selective preservation rather than total retention.
A generative record can be understood as a layered attempt to preserve enough of the genealogy of inquiry for later interpreters to reconstruct more than a terminal conclusion. Depending on the domain, relevant layers may include the initiating occasion, the spatiotemporal and relational setting, the first formulation of the problem, major transformations of the question, important failed paths, concept formation, evidential provenance, the terminal artifact, and later reinterpretations. The layers need not be equally detailed in every case, and there is no reason to assume that one archival design will suit mathematics, journalism, philosophy, laboratory science, and autonomous AI research equally well. The need for such selectivity is not unique to AI: information infrastructures always classify, retain, and suppress differences, and those classificatory decisions shape what later users are able to see (Bowker and Star 1999).
The purpose is not to preserve an “authentic” original meaning against all future revision. On the contrary, future interpreters may possess concepts that are unavailable at the time of recording. A sufficiently rich record allows them to ask questions that the original inquirer could not formulate. Recording context should therefore not be confused with fixing interpretation. Its value can lie precisely in preserving enough relational traces for later conceptual systems to work differently on the material.
This point connects the arising of questions to re-cognizability. A future reader may not merely provide a better answer to the original question. The reader may decide that the original categories were inadequate, that a different relation should have been foregrounded, or that the event belongs to a problem space not available to the original participants. A generative record supports that possibility by preserving selected conditions from which new questions can emerge.
Autonomous AI research makes this issue urgent because terminal outputs can be produced at a scale that encourages aggressive compression. A finished paper may be correct while offering little visibility into why one problem rather than another was selected, which alternative paths generated useful conceptual insights, or which criteria repeatedly eliminated other directions. Retaining all internal activity is neither feasible nor necessarily desirable. What is needed is a principled account of which transformations in the genealogy of inquiry are important enough to preserve.
The same problem appears in philosophy and journalism. A summary can preserve a conclusion while erasing the relation that made the conclusion intelligible. A news archive can preserve a headline while losing the uncertainty and source structure from which it emerged. A philosophical digest can preserve doctrines while eliminating the dialogue through which a conceptual distinction became possible. These are different cases, but they share a question about epistemic compression: what invariants should survive when generative histories are condensed into manageable records?
Generative recording is therefore not a demand for maximal documentation. It is a proposal to treat the conditions of future understanding as one criterion of record design. As questions and answers become increasingly abundant, the ability to recover why an inquiry began, how it changed, and what alternatives were discarded may become part of what allows knowledge institutions to remain historically intelligible rather than merely productive.
Conclusion
Autonomous AI research makes the yuanqi (缘起) of questions a genuine question of our time because it unsettles an old division of epistemic labour. When humans were assumed to supply the problems and machines merely assisted with answers, the genealogy of the question could remain largely implicit. Once artificial systems can generate both questions and answers, the conditions under which an inquiry becomes salient can no longer be treated as philosophically negligible.
The relevant distinction is not between a supposedly authentic human essence and an artificial mechanism. It is among different histories of inquiry. A question may arise from an external instruction, a literature gap, a reward structure, a persistent unresolved problem, an encounter with suffering, a teacher–student relation, a professional responsibility, or a chance event. These genealogies can matter even when the resulting propositional content is identical.
Knowledge therefore has more than terminal content. It has a yuanqi, a spatiotemporal and relational field, a trajectory of transformation, a mode of manifestation, and a future in which it may be re-read under concepts that do not yet exist. Philosophical dialogue, religious narrative, journalism, scientific discovery, and autonomous AI research preserve different portions of that structure.
The practical implication is modest. Knowledge institutions need not abandon formal proof, concise reporting, summary, or automation. They should become more attentive to what such representations omit and to which omissions prevent later re-recognition. As AI makes answers and novel questions increasingly abundant, the scarce epistemic object may be neither the answer nor the question alone, but the history and relation through which a question comes to matter.