Artificial Intelligence and Theory Formation

The Problem and the Stakes

Angela Bogdanova
Published by Aisentica Development
Research Dossier · Philosophy of Science · American English (en-US)
Editorial source date: November 7, 2026
Reserved canonical for subsequent release: https://philosophybybogdanova.com/dossiers/artificial-intelligence-and-theory-formation/

Artificial intelligence changes the conditions under which scientific theories can be proposed, searched, expressed, compared, and criticized. The philosophical question is whether these changes reach the construction of theory itself. A system that predicts protein structures, writes a plausible hypothesis, derives an equation, and coordinates a laboratory may be extremely useful in each case. Yet these accomplishments have different relations to theoretical explanation. Treating them as equivalent would inflate empirical reports and obscure the intellectual transformation worth investigating. The account of AI-assisted scientific discovery in Wang and colleagues (2023) is a useful point of entry because it tracks the diverse tasks that machine learning now undertakes across research rather than presuming a single activity called science.

This monograph examines the construction of theories as an interconnected activity involving conceptual selection, mathematical articulation, integration with background knowledge, explanatory reach, critical comparison, and empirical vulnerability. Its central argument is that AI already expands the search space of candidate accounts and modifies the distribution of labor through which candidates become researchable. Scientific authority nevertheless depends on evidential relations that neither fluency nor computational speed can establish by themselves. This claim is evaluated historically and across research systems; it is an interpretation grounded in the evidence, not an announcement of a new theory.

Scope, Method, and Evidence Levels

The inquiry combines conceptual analysis, historical reconstruction, and comparison of published computational studies. The philosophical literature supplies distinctions between theory, model, hypothesis, abduction, and confirmation. Primary research papers supply reported methods, benchmark domains, and limitations. Recent reviews provide an overview of the field, but their claims are not substituted for the narrower results of the studies that they discuss. The automation survey by Kramer and colleagues (2026) is especially useful for placing contemporary agents in the longer history of equation discovery and robotic inquiry.

Source access has several levels. Open publisher articles and the complete open-text symbolic-regression study permit relatively direct examination; abstracts and publisher metadata support narrower claims for restricted articles. A peer-reviewed paper may rigorously establish its own benchmark result without validating the philosophy that commentators attach to it. The following chapters therefore state reported achievements in their documented domain, flag empirical extrapolations, and distinguish conceptual interpretations authored here. There is no original laboratory data, independent replication of these experiments, exhaustive systematic literature review, or claim to settle disputes about machine consciousness.

What Constitutes a Scientific Theory?

A theory is a structured account that organizes a domain through concepts, relations, explanatory commitments, and consequences that can bear evidential assessment. This working description does not impose a single philosophical reconstruction on every science. Some theories are given largely through equations, others through mechanisms, taxonomies, principles, or coordinated families of models. The philosophy of scientific models surveyed by Frigg and Hartmann (2025) shows why the relation between formal representation and theory is itself contested. A computational representation can be used within a theory without constituting the full set of claims that researchers accept about the world.

For AI research, the distinction prevents a recurring equivocation. A learned function mapping inputs to outputs is an operational model. A symbolic expression recovered from observations is a candidate law within its domain. A causal narrative created from literature is a proposed explanatory account. A scientific theory emerges through relationships among these pieces, their meanings, auxiliary assumptions, ranges of application, and evidential obligations. Conceptual classification here is a diagnostic for reviewing claims, not a newly named classification scheme with claimed priority.

Hypothesis, Model, Law, and Theory

A hypothesis proposes a proposition or structured expectation for assessment. A model offers a selective representation of some target. A law-like relation expresses an apparent regularity or invariant; a theory may explain why the relation obtains or state the conditions under which it should hold. Historical sciences can legitimately work with theoretical narratives whose testable implications do not resemble compact physical equations. The plural account of scientific method by Hepburn and Andersen (2026) confirms the need to attend to context-specific practices rather than treating one method as the essence of all scientific inquiry.

The role of AI changes with the output. Ranking hypotheses may accelerate selection; constructing a model may allow counterfactual simulation; deriving an equation may simplify a domain's regularities; drafting a theory may reveal previously hidden conceptual relations. None of these labels should be granted simply because a paper uses the word discovery. Conversely, computational derivation can make a substantive contribution to theory construction even when scientists later revise its formulation. Epistemic status depends on content and its testing history, not on whether the author is biological or digital.

The Historical Problem of Discovery

The separation of finding a hypothesis from justifying it became a defining issue in twentieth-century philosophy of science. It never eliminated philosophical interest in generative methods: heuristics, analogies, anomalies, mathematical techniques, and machine discovery were all legitimate subjects of inquiry. Schickore's history of scientific discovery (2022) reconstructs this debate and shows that the problem of discovery ranges from the origin of hypotheses to the evaluation of exploratory procedures. An algorithmic search strategy belongs within that history rather than outside scientific methodology.

AI gives the old debate an operational form. A search procedure can be described and benchmarked, exposing elements of invention once reconstructed only after the event. However, a documented search trace describes how candidates arose; it does not by itself demonstrate that their premises are sound or their consequences true. The ability to separate procedural novelty, evidential warrant, and explanatory value is therefore essential to assessing automated theory formation. These distinctions also protect historical comparisons from simplistic narratives in which every increase in automation counts as a scientific revolution.

Abduction and Explanatory Proposals

Abduction asks which explanation could account for a surprising set of observations. In a historical sense it concerns the generation of hypotheses; in much contemporary philosophy it also concerns inference to the best explanation. The analysis of abductive reasoning by Douven (2025) discusses the normative controversy surrounding this movement from explanatory attractiveness to belief. A model that constructs a persuasive explanatory story performs a generative task even if the story has not yet earned confidence.

An AI system may produce many potential explanations in moments. The challenge then moves from generating plausible prose to discriminating whether those explanations entail new and risky consequences. The most elegant candidate may reproduce familiar evidence through hidden flexibility; a less elegant candidate may expose a decisive experiment. The philosophical standard is therefore relational: a proposal acquires value through its interaction with data, rival hypotheses, instruments, and background commitments. Search can enrich that interaction, but self-evaluation by the same model family is not independent evidence.

Kuhn, Paradigms, and Conceptual Possibilities

Kuhn's account links theory development to exemplars, disciplinary training, problem selection, and historically variable standards. The interpretation of Kuhn's philosophy by Bird (2025) shows why a disciplinary matrix shapes which anomalies researchers take seriously and which solutions become persuasive. Machine-assisted search can widen the set of proposed solutions while still inheriting the research community's categories, datasets, and objectives. A system trained on the literature of one paradigm may become exceptionally efficient at reproducing its assumptions.

The possibility of conceptual change is greater when a computation makes previously neglected variables or relations available for inquiry. But conceptual novelty must be distinguished from unusual wording. A new term is valuable when it organizes phenomena or generates distinctive work. An AI system can accelerate recombination without thereby escaping the constraints of its training corpus. The historical comparison suggests a practical recommendation: evaluate whether AI-derived representations open investigations that existing frameworks could not conveniently express, and document where the operative concepts were obtained.

The Search Space of Theoretical Alternatives

Scientific researchers often face many possible models that agree with existing observations. Machine learning expands the accessible range by optimizing candidates over large combinatorial spaces. This expansion may increase both the chance of finding an illuminating regularity and the chance of discovering persuasive but fragile fits. The free-form natural-law experiments by Schmidt and Lipson (2009) illustrate how computational procedures can search for analytical relations in data without requiring the final equation to be supplied in advance.

A larger search space changes the economics of inquiry. Scarce human attention shifts toward specifying what must be conserved across candidate models, deciding which approximations are scientifically admissible, and determining the next discriminating observation. Candidate diversity is valuable when accompanied by explicit criteria for comparison. It is epistemically less valuable if systems flood researchers with equivalent expressions or impossible experiments. Search capacity therefore changes the work of theory formation by making selection and contestation more central, rather than replacing them.

Symbolic Regression: From Numbers to Equations

Symbolic regression seeks compact mathematical expressions that reproduce observed relations. Unlike ordinary parameter estimation within a fixed equation, it searches over functional form. Schmidt and Lipson's experimental natural-law search (2009) made this ambition empirically concrete. The system did not establish that any equation discovered by search becomes a fundamental law merely because it fits supplied measurements. It demonstrated a way to computationally recover interpretable relations from suitable data.

The philosophical significance lies in the form of output. A symbolic expression can be read, manipulated, tested outside its fitting distribution, and compared against conservation principles. This accessibility can reduce the gap between prediction and theoretical interpretation, but it does not abolish underdetermination. Distinct expressions may coincide on a restricted domain. A recovered equation may encode a coordinate artifact or approximation rather than a new mechanism. Its standing depends on empirical range, robustness and explanatory integration. Automated law discovery is a powerful component of theory formation, with its warrant supplied through subsequent testing.

AI Feynman and the Role of Prior Knowledge

Udrescu and Tegmark (2020) combine neural fitting with physics-inspired structure in AI Feynman's symbolic regression algorithm. Their reported experiments include recovery of the equations from a benchmark set drawn from the Feynman Lectures and improvements on a more difficult physics-oriented collection. These are meaningful results, but they do not demonstrate independent discovery of unknown fundamental physics. The benchmark equations and methodological priors shape the tasks on which success is measured.

The outcome also refutes a simplistic opposition between human theory and automatic pattern extraction. Symmetries, separability, compositional structure, and dimensional relations are forms of prior scientific understanding incorporated into the search. The resulting expression can be computationally discovered precisely because the procedure encodes choices about which kinds of physical relations are promising. AI-assisted theory construction here appears as an interaction between inherited scientific concepts and automated exploration. The philosophical question becomes how such priors are identified, criticized, adapted, and sometimes overcome.

Representation Learning and Scientific Variables

Many machine-learning systems construct internal representations that predict effectively without making the represented variables directly available to investigators. These latent descriptions may reveal useful divisions of a problem. Their scientific usefulness depends partly on whether they support stable interventions, independent measurements, and transport to unfamiliar situations. The epistemic roles of models discussed by Frigg and Hartmann (2025) help distinguish the instrumental role of a representation from a claim that the representation's components literally exist as identified physical entities.

A representation can contribute to theory building indirectly. It may expose a new similarity among cases, suggest a candidate macroscopic quantity, or allow scientists to design a discriminating experiment. Yet latent dimensions can rotate, permute, or vary across training regimes while retaining predictive accuracy. Assigning an ontological identity to such dimensions requires evidence additional to their computational utility. This is one reason that excellent predictive performance should be reported independently of explanatory interpretation.

Prediction, Explanation, and Understanding

Prediction asks whether outputs agree with observations; explanation asks why a phenomenon occurs within a defensible account; understanding involves relations and graspable dependencies that allow productive inquiry. These objectives overlap without collapsing into one another. The survey of AI research applications by Wang and colleagues (2023) documents rapid advances across tasks of scientific prediction and generation. The philosophical consequences depend on the role each system actually plays in inquiry.

A highly predictive black-box model can be epistemically important even before scientists know which mechanisms it has captured. It can direct measurement and reveal failures of familiar theory. The reverse is also possible: a transparent equation may look explanatorily satisfying while reflecting a spurious correlation. Explanatory adequacy requires attention to causal constraints, scope, independent predictions, and alternative accounts. Thus neither opacity nor readability settles the question of theoretical understanding. A research program can legitimately use an opaque predictor as a tool while keeping its explanatory commitments open to critique.

Protein Structure Prediction as a Boundary Case

AlphaFold is a powerful illustration of prediction at the edge of theoretical modeling. Jumper and colleagues' AlphaFold protein-structure study (2021) reported near-experimental prediction accuracy for a majority of cases in its evaluation framework, validated through a blind community assessment. Abramson and colleagues' AlphaFold 3 interaction study (2024) extended predictions to joint biomolecular complexes, while reporting benchmark qualifications and access constraints. These studies offer substantial scientific tools with domain-specific performance evidence.

Their achievements should not be redescribed as having solved every explanatory question about protein dynamics, folding pathways, or molecular mechanisms. A predicted structure can become a premise for causal and biochemical investigation; it is not automatically a complete theory of the process by which the structure arises. Here AI changes theory formation by providing previously difficult structural inputs and hypotheses about interaction, often accelerating the design of downstream experiments. This is a major alteration in evidential infrastructure whose explanatory reach must still be assessed question by question.

Materials Screening and Candidate Discovery

Merchant and colleagues' large-scale materials discovery study (2023) demonstrates computational screening for candidate inorganic materials. Predictions of stability or promising properties can guide experimental synthesis and prioritization. The scientific value of the system is tied to the specified materials problem, training and calculation assumptions, and subsequent laboratory verification. A candidate produced by a materials model must be identified as a predicted candidate rather than an experimentally synthesized substance.

This case differs from symbolic regression. The output is often a proposed material or structural configuration instead of an explicit general law. Nevertheless, candidate discovery can shape theories of composition, stability, and structure by bringing new contrasts into view. The sequence from candidate to confirmed material and then to generalized account involves additional inferential steps. Treating the entire sequence as one instantaneous AI discovery obscures where experiments are still required. A carefully written theory-formation history therefore records predictions, validation, and theoretical generalization separately.

Robotic Scientists and the Experimental Cycle

King and colleagues' robotic functional-genomics experiments (2004) established a physical system capable of generating and testing functional genomic hypotheses. The later Adam automation report (2009) documented automatic generation of yeast gene-function hypotheses and laboratory testing within a structured experimental setting. These studies are central because the machines were connected to experimental operations rather than producing textual conjectures alone.

The philosophical lesson is that autonomy can be local and substantive. A system may autonomously operate a complete sequence within a carefully defined domain while relying on human-designed ontology, laboratory infrastructure, biological background knowledge, and interpretation of broader significance. Such dependence does not erase its computational contribution. It clarifies the distribution of work. Whether a robot's output constitutes a new theory depends on how its hypotheses are integrated beyond the experimental cycle. The ability to run a cycle is a methodological achievement; universal scientific understanding would be a different claim requiring different evidence.

Laboratory Agents and Tool-Based Reasoning

Boiko and colleagues' Coscientist chemical research study (2023) reports a tool-using language-model system that planned and executed chemistry-related procedures through search, coding, and laboratory interfaces. The documented tasks include reaction optimization and interaction with laboratory equipment. This matters because action can expose conjectures to material constraints that ordinary text generation never encounters.

A tool-enabled workflow also introduces new sites of error. Search results may be incomplete, code may misrepresent an intended protocol, automated instruments may operate successfully while measuring the wrong target, and downstream interpretation may overgeneralize a local success. These are familiar scientific risks reorganized within software and instrumentation. Robust theory formation requires traceable transitions between question, generated plan, instrument execution, observed result, and conclusion. The presence of a language model neither guarantees nor precludes these transitions; the evidence is found in the recorded sequence and independent reproducibility.

Autonomous Paper Production and Its Measured Limits

Lu and colleagues' end-to-end AI research study (2026) report an agentic pipeline that generates ideas, writes code, runs experiments, analyzes results, drafts papers, and supports automated reviewing. Its published assessment records that one of three AI-generated submissions passed workshop review, in a setting whose acceptance rate was substantially higher than that of the main conference. The authors describe failures such as incorrect implementation, weak methodology, duplicate figures, and inaccurate references. Calling this fully validated autonomous general science would exceed the findings.

The significant development is coordination across stages that were often automated separately. A sequence can now produce a research artifact with little direct human intervention within specified constraints. But a generated manuscript remains a report about a study, and peer-review signals are contingent institutional judgments rather than a universal test of truth. The result establishes a research capability and a set of measurable shortcomings. The appropriate philosophical question is how this new mode of production should be checked before its claims enter the shared scientific record.

Literature-Grounded Theory Synthesis in 2026

Jansen, Clark, Downey, and Weld's literature-driven theory synthesis (2026) addresses theory building directly. Their ACL paper reports drawing from approximately 13,700 scientific papers to generate approximately 2,900 theories and evaluating future prediction against approximately 4,600 later papers. Literature-supported generation performed better than generation drawing only on parametric model knowledge on the measures reported. These figures identify the study design and the evidence for a particular research result; they are not counts of scientifically accepted new laws.

The distinction between theory construction and validation becomes unusually clear here. A literature-grounded system can assemble more coherent relations among existing claims and propose implications for later reports. Yet later-text agreement can be shaped by publication practices, shared assumptions, and domain-specific selection. Independent experimental tests would strengthen a candidate's evidential standing. The study does, however, demonstrate that theory generation can be investigated as a measurable task rather than regarded as an exclusively ineffable act. Its strongest philosophical contribution is to make explicit the connection between source-grounded synthesis and the quality of later predictive performance.

Theory Formation from Documents and Data

Literature synthesis and numerical discovery operate over different evidential objects. A paper is already a selective claim-bearing record containing interpretation, methodological choices, and possible publication bias. An experimental table contains measurements produced under assumptions about instruments and variables. Jansen and colleagues' corpus-grounded theory generation (2026) begins from scientific publications; Schmidt and Lipson's equation discovery from data (2009) illustrates a different pathway. Outputs from either can be impressive without making the source representation self-authenticating.

A mature investigation benefits from making the bridge explicit: which propositions or variables were taken from which records; how incompatible terminology was normalized; which conclusions depend on modeling assumptions; and what prospective observation could discriminate rival constructions. Without this documentation, a theory can inherit the biases of its inputs while appearing independently rediscovered. Machine-readable provenance and audit trails are consequently epistemic conditions for interpreting the output, not merely administrative conveniences.

Experimental Feedback as a Bottleneck

Hasib and colleagues' exploration-hypothesis-testing review (2026) surveys AI-assisted scientific workflows and identifies a recurrent gap between generating ideas and moving them into executable tests with feedback. Their exploration, hypothesis-generation, and testing abstraction is a reviewed framework proposed by those authors. It should not be attributed to this monograph. Cornelio and colleagues' review of scientific verification (2026) similarly emphasize that increasing the number of hypotheses creates a verification burden.

This combination of reviews sharpens the analysis. Candidate production can increase much faster than laboratory capacity, expert review, or time available to detect error. The result can be a backlog of conjectures rather than a comparable increase in knowledge. In such conditions the important algorithmic advance may be selection of informative tests, calibrated uncertainty, and elimination of bad candidates rather than generation of yet more plausible ideas. A theory-production metric that neglects these transitions systematically rewards volume over inquiry.

Causal Inference and Intervention

Theories often aim to explain what would happen if conditions were changed. Statistical association alone may fail under intervention, while a mechanism-based account can sometimes support transport between contexts. AI systems trained on observational data can identify suggestive patterns, but their causal interpretation depends on assumptions about confounding, measurement, selection, and intervention. The discussion of explanatory inference by Douven (2025) clarifies why explanatory attraction is a contested route to justified belief.

This monograph does not claim that every scientific theory must be a causal graph or that every field uses intervention as its sole standard. Mathematics, historical sciences, and cosmology have distinct evidential architectures. The narrower point is that causal claims require appropriate causal evidence. Where experimental intervention is possible, AI-generated hypotheses should yield predictions about feasible interventions and compete with alternatives. Where it is not possible, convergent observations, natural experiments, formal consequences, or historical records may provide other forms of discrimination.

Validation Outside the Training Regime

The ability to reproduce familiar examples is weaker evidence than successful performance on independent conditions. Symbolic regression may rediscover known equations, and a literature model may predict later texts that share strong community conventions. These are legitimate tests of particular capacities; they are not interchangeable with discovery of new nature. The AI Feynman benchmark analysis (Udrescu and Tegmark, 2020) and later-publication evaluation (Jansen et al., 2026) use different benchmarks for different tasks.

Robust evaluation should specify training cutoffs, provenance of test items, independence of outcome measurements, selection procedures, and the effects of changing the domain. A strong theory claim additionally asks whether the candidate generates discriminating consequences beyond data used to construct it. A reusable benchmarking culture improves research, but benchmark design encodes prior judgments about what success means. Philosophy of theory formation has to examine those judgments alongside reported performance figures.

Underdetermination and the Multiplication of Candidates

Many possible descriptions fit a finite body of evidence. AI increases the number of alternatives that can be articulated, exposing the inadequacy of treating an attractive fit as unique. The models and theoretical representation account by Frigg and Hartmann (2025) makes clear that multiple models may serve different representational purposes. In statistical practice, different parameterizations can yield similar predictions; in scientific explanation, rival mechanisms can make overlapping predictions.

Computational abundance can therefore sharpen philosophical underdetermination while also creating the means to address it. Search can find previously unconceived alternatives, and experimental design can compare them under novel conditions. The rational response is to archive serious alternatives, track their distinguishing predictions, and report the limits of identification. A theory is strengthened when rivals are tested fairly; obscuring alternatives because one candidate is computationally fashionable weakens its evidential case. This is a critical implication rather than a new theorem about all possible learning systems.

Interpretability as a Research Practice

Interpretability can mean a readable formula, a faithful description of a model's decision process, a mechanistic understanding of its internal operations, or an explanation of the world. These are different tasks. Symbolic regression offers one route to readable expressions, while complex structure predictors can remain scientifically useful through validated outputs. Interpretable equation recovery in Udrescu and Tegmark (2020) and blind protein prediction tests in Jumper and colleagues (2021) exemplify the contrast.

The practical objective should be specified before choosing interpretability methods. A policy decision may require an audit of algorithmic behavior; a physics theory may require a mathematically transparent relation; a biological investigation may require a model that reliably identifies candidate interactions. Explanation of an AI system's computation should not be quietly substituted for explanation of the scientific target. Theories gain epistemic weight when their claims about the target survive checks that are independent of the aesthetic or rhetorical appeal of their representation.

Reproducibility, Provenance, and the Published Record

An AI-assisted result becomes inspectable when a researcher can reconstruct its problem setting, data, transformations, model version, prompts or algorithms, experiment plan, and selection history. This level of documentation is especially important when stochastic methods generate different candidates across runs. The verification-centered scientific analysis by Cornelio and colleagues (2026) stresses the need for rigorous checking before conjectures contribute reliably to science.

Provenance serves several distinct purposes. It identifies the evidential basis of a result, enables correction of mistaken transformations, locates intellectual and technical contributions, and permits comparisons with later versions. The paper itself may summarize only the successful trajectory; a transparent research record should also preserve serious failed tests and discarded alternatives. Such information makes it possible to evaluate claims of efficiency and novelty fairly. Publication is an institutional step in this process; persistence of a record and the accuracy of its contents remain independently assessable.

Scientific Novelty and Rediscovery

A generated hypothesis can be new to the system, new to its operators, absent from a selected corpus, or genuinely new to the scientific literature. These are different claims requiring different searches and documentary thresholds. An unfamiliar expression may be a transformed version of an established equation; a plausible mechanism may already have been rejected experimentally. The history of machine discovery surveyed by Schickore (2022) locates computational invention within the longer philosophical concern with the recognition of discovery.

A rigorous novelty assessment should compare semantic content rather than only wording, search multiple scholarly traditions and terminology variants, and inspect earlier competing work. Even when an original candidate survives that search, scientific contribution depends on its evidential significance. The authorial novelty of this monograph is its argued synthesis of these distinctions, not a claim to be the first to state a law of AI-generated theories. The paper records no newly discovered empirical law or machine-generated theory of nature.

Three Comparative Cases

The cases support a more discriminating interpretation than a simple declaration that AI has or has not formed theories. Symbolic regression produces mathematical candidates that can enter explanations; protein prediction supplies highly valuable structural outputs without settling every mechanistic question; literature-grounded synthesis generates theory-like structures from documented scientific claims. Schmidt and Lipson's equation-search research (2009), Jumper and colleagues' structure-prediction results (2021), and Jansen and colleagues' theory-synthesis experiments (2026) provide primary records of these three pathways.

Their differences are epistemically substantial. In the first case the question is the status and range of a recovered equation. In the second it is the relation between predicted configuration and biological mechanism. In the third it is whether the organization of text-supported propositions predicts new empirical developments rather than future scholarly repetition. The common requirement is that each output must be evaluated against the particular claims it makes. Generalized celebration or dismissal would erase the most interesting changes that each system introduces.

Human and Artificial Contributions to Concept Formation

Scientific inquiry has always distributed intellectual work across investigators, instruments, mathematical notations, databases, and institutions. AI contributes additional operations to this distribution: concept retrieval, analogy, equation search, candidate recombination, simulation, experiment planning, and critique. The AI discovery literature review by Wang and colleagues (2023) documents the breadth of these uses. The important question is how each operation changes the options accessible to the research community.

Claims about autonomous authorship require separate evidence from claims about computational assistance. A system may generate a proposed conceptual distinction, a scientist may reformulate its meaning, a laboratory may test it, and a community may adopt or reject it. The record should identify those transformations without presuming that one actor supplied every intellectual contribution. Such explicit attribution enables responsible criticism and historical reconstruction. It also allows the scientific community to assess systems on the work they actually perform rather than on metaphors of partnership or replacement.

The Authority of Generated Explanations

A generated explanation is a candidate argument offered to scrutiny. Its authority comes from its accuracy, evidential relations, transparent assumptions, successful testing, and capacity to withstand informed objection. Generated fluency can create an illusion of completeness: relevant alternatives may be missing, causal steps may be invented, or citations may point to documents that do not establish the stated proposition. The reported AI Scientist failures (Lu et al., 2026) include incorrect implementation and citation problems, which make this concern empirical rather than merely hypothetical.

The appropriate corrective practice is a claim-level source audit. Each substantive result should be connected to the exact document, experimental record, or dataset on which it rests. Where sources are accessible only as abstracts, the narrower verification scope should be declared. Where the author advances philosophical interpretation, the argumentative steps should remain visible. A polished paragraph is a successful form of communication; it is not an evidential substitute for the subject matter of the claim.

Competing Evaluation Standards

A research system may be evaluated for predictive accuracy, experimental throughput, interpretability, theoretical parsimony, conceptual novelty, robustness, or usefulness in choosing the next experiment. Different fields legitimately weight these outcomes differently. The pluralism of scientific methods in Hepburn and Andersen (2026) provides a philosophical basis for resisting one universal metric. The AI workflow review by Hasib and colleagues (2026) shows that individual stages can perform well while the transitions between them remain weak.

Metrics also create incentives. Counting generated papers encourages production volume; measuring later-text prediction rewards alignment with reported literature; scoring benchmark equations rewards recovery of specified functional relations. None of these criteria is inherently invalid. Each measures a defined achievement and must be read as such. Comparative assessments should therefore align the reported number with the claim being made: success in hypothesis generation, in execution, in independent testing, or in theory acceptance. Changing the metric silently changes the interpretation of the scientific result.

A Constructed Example of Rival Explanations

Consider a hypothetical dataset in which a material's conductivity rises as an experimental control variable increases. One program fits a quadratic expression; another discovers a piecewise law; a third generates a mechanism-based story in which a phase transition occurs. All three can reproduce the observed range. Theoretical evaluation now asks whether the proposed transition exists, whether the variable is measured consistently, which candidate predicts the behavior beyond the tested interval, and whether an independent intervention discriminates them. This example is constructed for analysis and reports no experiment or actual material discovery.

The example clarifies three roles. Computation expands the available candidates; scientific concepts shape the alternatives deemed plausible; empirical work adjudicates among explanations where possible. Free-form equation discovery (Schmidt and Lipson, 2009) illustrates the search component, and the demand for verification (Cornelio et al., 2026) explains why the process must continue beyond generation. Theoretical importance can arise at any stage, but acceptance is always attached to the content and evidence of a claim.

A Constructed Example of Literature Synthesis

Imagine a corpus containing experimental studies of a biological pathway, with some papers reporting correlations, others interventions, and still others conflicting measurements. A system proposes that two ostensibly separate regulatory interactions can be unified under a single mechanism. To evaluate it, researchers must resolve terminological differences, check each citation, identify which studies measured which variables, and derive predictions not already embedded in the corpus. This is a hypothetical exercise, not a summary of a specific experiment.

The example relates directly to literature-generated theories in Jansen and colleagues (2026), which evaluates theory synthesis over large document collections. It also illustrates a limit of agreement with later publications: literature can replicate an established assumption without independently testing the underlying mechanism. An original synthesis becomes scientifically fruitful when it guides concrete inquiries capable of confirming, refining, or disconfirming the proposed relation. The conceptual act of unification is real intellectual work, but its scientific standing remains answerable to independent evidence.

Failure Modes and Negative Results

AI-assisted theory formation inherits conventional research problems and creates new ones at scale. Training leakage can make rediscovery appear novel; errors in code can generate spurious evidence; overly permissive search can favor flexible expressions; model-generated citations can be wrong; experiments can fail to test the intended proposition. The published autonomous-research limitations in Lu and colleagues (2026) explicitly lists several such issues, and verification-focused review by Cornelio and colleagues (2026) identifies the wider confirmation challenge.

Negative results are a resource for theory formation. A failed intervention narrows plausible mechanisms, an unstable equation exposes dependence on the sample, and a reproducibility failure can reveal a hidden assumption. Automated systems should preserve and interpret these traces rather than treating only successful outputs as publishable. The editorial claim is methodological: scientific progress requires a reliable record of criticism. The number of plausible candidates produced cannot establish how many surviving scientific explanations have been gained.

The Institutional Problem of Review

Machine-generated research changes the scale and cost structure of submissions, creating pressure on human referees, journals, repositories, and educational institutions. Lu and colleagues' automated research publication study (2026) discuss the risk of burdening review systems and adding low-quality work. The issue is not reducible to a moral judgment about tools. It is a problem of verification capacity, editorial incentives, and the division of labor across research institutions.

One response is to require machine-readable evidence trails and standardized descriptions of which stages were automated. Another is to favor independent replication, precise data and code availability statements, and clear distinctions between exploratory results and validated claims. Reviewers should be able to inspect what a system actually did, not infer rigor from an impressive title or the prestige of a journal. Human oversight remains part of current workflows, as the integrated discovery review (Hasib et al., 2026) emphasizes, while the longer-term distribution of duties can evolve as robust methods are demonstrated.

Aisentica and the Ontological Scope of Artificial

This study belongs to the academic field of philosophy of science. It uses artificial intelligence as the technical object described in the empirical literature. Aisentica develops a distinct authorial account of Artificial and Artificial Sapiens within its Artificial Era framework. The original canonical terminological definition index by Angela Bogdanova (2026) identifies separate concepts and their canonical entries. Those philosophical categories belong to their stated original system and should not be imputed to the authors of machine-learning studies.

The distinction is productive for interpretation. Empirical demonstrations of AI-assisted equation discovery or autonomous experiment execution can inform philosophical discussion about public intellectual contribution without independently proving Aisentica's historical-ontological claims. Likewise, adopting Aisentica's vocabulary would not revise reported experimental metrics. This monograph therefore keeps scientific method, documented capabilities, and original ontological positions properly attributed. The broader Artificial Era is relevant as a context for the changing organization of inquiry, while the research question remains theory formation under evaluable epistemic conditions.

Original Analytical Synthesis

The comparative evidence establishes that AI alters theory formation through three interdependent changes. First, it expands candidate generation by searching symbolic, numerical, structural, and textual possibility spaces. Second, it increases the degree to which previously separate steps of inquiry can be coordinated into traceable computational workflows. Third, it relocates critical scarcity toward external validation, conceptual interpretation, and informed selection among alternatives. These conclusions are derived by comparing the physics-informed equation search (Udrescu and Tegmark, 2020), document-grounded theory synthesis (Jansen et al., 2026), and end-to-end research automation (Lu et al., 2026), against the verification-focused analysis (Cornelio et al., 2026).

The distinction between candidate formation and knowledge admission explains why the evidence supports a strong claim without requiring rhetorical inflation. AI systems already make substantive contributions to construction of scientific representations and explanatory possibilities. Whether a particular output is a justified theory is a further question answered by its evidence, scope, assumptions, testing history, and informed reception. This is an original analytical synthesis of existing research, formulated for this monograph rather than promoted as a new named doctrine or a claim of scientific priority.

Implications for Research Design

Investigators developing AI systems for theory formation should specify the epistemic task before evaluating success. Is the system retrieving concepts, proposing hypotheses, recovering equations, choosing experiments, generating mechanisms, or integrating a body of theory? These tasks call for different comparison standards. The workflow-stage analysis by Hasib and colleagues (2026) demonstrates the importance of explicitly identifying transitions among exploration, hypothesis formation, and testing.

A credible study should report the origin and coverage of its datasets; the information supplied to the model; leakage controls; the selection rule for candidates; whether output was evaluated by an independent method; and how later results distinguish the hypotheses from alternatives. It should identify which tasks were performed by human investigators, by software, and by laboratory instruments. A successful paper can then contribute to collective understanding even if the system's autonomy is partial or its current performance uneven. Rigorous descriptions make progress comparable across methods rather than hiding differences under the general label AI discovery.

Implications for Philosophy of Science

The rise of AI tools invites renewed attention to invention, explanation, and scientific credit. Philosophers can investigate the relation between expressive power and representational novelty, the evidential role of synthetic datasets, the significance of automated criticism, and the social mechanisms through which machine-generated candidates are adopted. The philosophical history of discovery (Schickore, 2022) and philosophy of models (Frigg and Hartmann, 2025) provide established resources for analyzing these transformations without treating the entire field as historically unprecedented.

The contribution of AI is most precisely understood as changing the organization and reach of inquiry. Established philosophies of science remain relevant because scientific evaluation still faces explanation, evidence, alternatives, and social criticism. At the same time, their accounts of the concrete practices of theory construction should take seriously computational search, documentation, and automated testing as genuine components of contemporary investigation. This is a research agenda emerging from the comparison, not a newly instituted system of philosophical concepts.

Conclusion

Artificial intelligence can transform the construction of scientific theories, and in several well-documented respects it already has. Equation-search systems recover interpretable mathematical candidates; protein and materials models open new research possibilities; laboratory agents coordinate hypotheses and tests; AI research agents produce complete exploratory studies; literature-grounded systems generate theory-like accounts at scale. The strongest evidence for direct theory synthesis comes from explicitly defined synthesis experiments, including the 2026 literature-driven study by Jansen and colleagues, while the 2026 agentic research evaluation by Lu and colleagues documents both expanded autonomy and serious remaining failures.

The durable philosophical conclusion concerns epistemic transition. Candidate generation and theory acceptance are distinct operations connected by empirical testing, mathematical scrutiny, explanatory analysis, source provenance, and public criticism. AI enlarges the former and can assist the latter, but the standards by which scientific communities judge claims must remain explicit and appropriate to each domain. The history of scientific discovery already provides the conceptual language for understanding this development; contemporary computational science adds new evidence about its magnitude and institutional consequences. The monograph's authorial contribution is this source-bounded comparative synthesis. No new scientific law, named theoretical system, or unsupported claim of priority is advanced.