From the Unity of Science to Epistemic Defragmentation under Algorithmic Mediation

Artificial intelligence is reshaping how scientific unity is conceived. Shared computational environments place distant literatures and datasets within a common field of access, while models and experimental workflows increasingly rely on the same infrastructures. Rather than revealing a pre-existing ontic unity, these arrangements establish conditions under which heterogeneous domains become comparable. What emerges corresponds neither to the classical project of reduction nor to encyclopedic coordination [1,2]. Computational comparability may obscure disciplinary differences without resolving the epistemic conditions that make those differences significant. Such interface-level unity can make fragmented inquiry appear coherent while still falling short of unified understanding.

This discrepancy motivates the central question of when AI-mediated synthesis can count as epistemic defragmentation and not merely as computational unification. Conceptual reconstruction is grounded in a selective genealogy of scientific unity, not in a technical survey of AI systems. Central to this genealogy is Cassirer’s philosophy of symbolic forms, which provides a way to conceive systematic unity through functional differentiation without requiring the elimination of difference [3]. Postphenomenology shifts the analysis toward technological mediation and the conditions under which distinct knowledge contributions become comparable [4]. Contemporary AI-for-Science provides the test case for whether algorithmic synthesis can bring heterogeneous forms of inquiry into relation while preserving epistemically relevant differences.

Clarifying this problem requires five distinctions within AI-mediated inquiry. Informational aggregation concerns whether dispersed materials become jointly accessible. By contrast, semantic interoperability requires mappings that make terms or representations from different domains comparable without erasing scientifically relevant differences. Only when heterogeneous inputs are transformed into a new structured configuration does algorithmic synthesis enter the analysis. Epistemic integration sets a stronger standard because successful transformation must preserve the evidential status and conditions of validity of what it connects. Scientific understanding concerns the intelligibility of the resulting relations rather than their coordination alone. No fixed progression follows from these distinctions. Their purpose is to show why successful synthesis does not by itself establish epistemic integration.

The stronger claim of epistemic defragmentation turns on whether heterogeneous forms of inquiry can be connected without erasing differences that matter to knowledge. Which differences matter cannot be decided by compatibility alone, since some are constitutive of how scientific claims are produced and assessed. Algorithmic mediation changes the problem once it creates new relations across those distinctions. Relations created in this way must remain traceable and open to criticism rather than acquire authority from computation alone. Accepting synthesis as knowledge remains a scientific judgment, so successful computation cannot settle responsibility. Where understanding is an aim, predictive success is insufficient unless the resulting connections are intelligible [5]. Together, these questions draw the philosophical boundary between defragmentation and homogenization. Widely shared AI systems may widen access while narrowing the questions researchers regard as worth pursuing [6]. AI can therefore contribute to scientific unity only by connecting forms of inquiry without collapsing differences that matter to the justification of their claims.

Philosophical Issues

At the philosophical level, the inquiry asks what kind of unity may be attributed to knowledge when heterogeneous forms of inquiry are connected through algorithmic mediation. Mere computational comparability cannot establish epistemic integration. Even a successful synthesis leaves open the grounds on which the resulting claims are justified and assessed. When understanding is itself an aim of inquiry, the question shifts from predictive usefulness to the intelligibility of algorithmically established relations.

Conflicts of Interest: The author declares no conflict of interest.

Acknowledgments: During the preparation of this manuscript, the author used AI-based language assistance for stylistic refinement, grammar checking, and formatting support. The author reviewed and edited all outputs and takes full responsibility for the content of this publication.

References

[1] Neurath, O. Unified Science as Encyclopedic Integration. In Encyclopedia and Unified Science; University of Chicago Press: Chicago, 1938; pp. 1–27.

[2] Oppenheim, P.; Putnam, H. Unity of Science as a Working Hypothesis. Minnesota Studies in the Philosophy of Science 1958, 2, 3–36.

[3] Cassirer, E. The Philosophy of Symbolic Forms, Vol. 3: Phenomenology of Cognition. Routledge: Abingdon, 2021.

[4] Ihde, D. Postphenomenology and Technoscience: The Peking University Lectures. SUNY Press: Albany, 2009.

[5] de Regt, H.W. Understanding Scientific Understanding. Oxford University Press: New York, 2017. doi: 10.1093/oso/9780190652913.001.0001.

[6] Messeri, L.; Crockett, M.J. Artificial Intelligence and Illusions of Understanding in Scientific Research. Nature 2024, 627, 49–58. doi: 10.1038/s41586-024-07146-0.