Ontological shift from data transmission to synchronous state discovery. Communication as pointer synchronization — regenerate data locally rather than transmit it.
This work introduces the Local Data Regeneration Paradigm, which challenges the fundamental Shannonian model of information transmission. We propose an ontological shift where data is understood not as objects to be transferred, but as states reached by deterministic systems through synchronous application of shared algorithms to coordinated pointers. Communication is redefined as pointer coordination rather than content transmission. The paradigm is formalized through three foundational postulates, with analysis of applicability domains and fundamental implications for information theory and computer science. This work presents a theoretical framework requiring extensive validation and further research before practical application.
Alexander Suvorov
https://github.com/smartlegionlab
2025
This work introduces the Local Data Regeneration Paradigm, which challenges the fundamental Shannonian model of information transmission. We propose an ontological shift where data is understood not as objects to be transferred, but as states reached by deterministic systems through synchronous application of shared algorithms to coordinated pointers. Communication is redefined as pointer coordination rather than content transmission. The paradigm is formalized through three foundational postulates, with analysis of applicability domains and fundamental implications for information theory and computer science. This work presents a theoretical framework requiring extensive validation and further research before practical application.
Keywords: information theory, data ontology, local regeneration, synchronous discovery, paradigm shift, deterministic systems, pointer-based communication
This work presents a fundamental paradigm shift in information theory and data processing. The contribution lies in formalizing an alternative ontological framework where data emerges through synchronous local regeneration rather than physical transmission. This research explores foundational concepts without practical implementations or cryptographic applications. As a theoretical contribution, this framework opens new research directions rather than providing immediate practical solutions. Extensive validation and peer review are required to establish its practical viability.
Modern computer science and information theory rest upon a fundamental premise first clearly articulated by Shannon: information must be transmitted from source to receiver. While enormously productive, this model creates inherent problems: the need for bandwidth, transmission latency, content and metadata vulnerabilities, and exponential growth in energy costs for data movement.
This work postulates that data transmission is neither the only nor necessarily the optimal communication modality. We propose an alternative ontology where data is not transmitted but discovered or regenerated locally within synchronized computational systems.
This work proposes not an improvement, but a fundamental reconsideration of digital communication foundations. Where Shannon asked "How can we best transmit information?", we ask a more radical question: "When can we avoid transmission altogether?"
This represents a Copernican turn in information theory—shifting from optimizing data movement to eliminating its necessity through synchronous local regeneration. We challenge the fundamental assumption that data must exist as transferable objects, proposing instead that information can be treated as discoverable states.
The conventional approach to information theory is characterized by:
This paradigm has enabled remarkable advances in compression, error correction, and network design, yet remains bound by its fundamental assumptions.
We propose a fundamental shift characterized by:
This is not merely another compression technique or transmission optimization. We propose a foundational reconceptualization where:
| Aspect | Shannon Transmission | Local Regeneration |
|---|---|---|
| Data Model | Data moves between locations | Data discovered synchronously |
| Fundamental Process | Information transfer | State synchronization |
| Primary Metric | Bits per second | Computational complexity per state |
| Channel Role | Essential medium | Coordination medium only |
| Energy Cost | Transmission energy | Computation energy |
Data ($D$) is not an object but a state of a computational system at a specific time. This state can be reached through multiple paths, including direct computation.
Any two or more computational systems possessing:
can reach identical data state $D$ through synchronous application of identical pointer $P$.
Where:
Within this paradigm, "communication" is identical to the process of synchronizing pointers $P$, not transmitting states $D$. Meaningful exchange occurs not during $P$ transmission but during local $D$ regeneration within each system.
For non-deterministic data, hybrid models are possible where only the "delta" – deviation from the state predicted by $P$ – requires transmission. This maintains the paradigm's benefits while accommodating real-world data heterogeneity.
The traditional "bits per second" metric is replaced by "bit of computational complexity per regenerated state unit". System throughput is measured not by channel width but by available computational power for executing $F$.
Our paradigm does not contradict Shannon's theory but offers an alternative model for data classes where computation is cheaper than transmission. It extends rather than replaces classical information theory.
Our previous work introduced the Pointer-Based Security Paradigm as a novel architectural framework for cybersecurity. The current research generalizes this concept to fundamental information theory, extracting the core ontological principles from their security context. Where the security paradigm demonstrated the architectural possibility of eliminating data transmission, this work explains why such elimination is possible and formalizes the underlying theoretical framework.
In systems like IPFS, hashes serve as addresses for requesting data from others. In our paradigm, hashes (as a special case of $P$) serve as instructions for local regeneration without requests.
Techniques like deterministic lockstep in gaming and simulations represent practical applications of this paradigm but haven't previously been generalized to fundamental principle status.
Our work complements algorithmic information theory by focusing on the communication implications of data compressibility and computational depth.
| Discipline | Focus | Relation to Our Work |
|---|---|---|
| Shannon Theory | Noisy channel coding | Provides alternative to transmission model |
| Algorithmic Information | Complexity and compressibility | Informs regeneration feasibility |
| Distributed Systems | Consistency and coordination | Provides theoretical foundation for sync |
| Reversible Computing | Energy-efficient computation | Complements energy focus |
| Pointer-Based Security | Architectural security | Practical application of regeneration principles |
This theoretical framework requires substantial empirical validation:
We identify several critical research directions:
As a nascent paradigm, several fundamental questions remain open:
The paradigm challenges fundamental assumptions about information nature:
This represents a shift from information as transferred substance to information as emergent relationship.
Traditional systems focus on optimizing data movement pathways. Our architecture questions the necessity of movement for certain data classes, suggesting computation as a fundamental alternative.
Data compression still operates within the transmission paradigm, seeking to minimize what must be sent. Our approach eliminates transmission entirely for regenerable data classes.
While predictive coding transmits only differences from predictions, it remains transmission-based. Our approach extends this concept to cases where the entire state can be regenerated from the prediction parameters.
The Local Data Regeneration Paradigm represents more than technical optimization—it suggests rebuilding information science on a fundamentally different ontological foundation. Where current approaches ask "how do we better move data?", we demonstrate that the more fundamental question is "when can we avoid moving data altogether?"
This work provides the theoretical framework to explore this question systematically. The implications extend beyond immediate applications to suggest new directions for information theory, computer architecture, and our philosophical understanding of information itself.
This work presents a theoretical framework requiring extensive further research and validation. We have outlined the foundational principles of local data regeneration, but significant work remains to establish its practical applicability and limitations.
This is not a practical guide but a call for scientific inquiry into alternatives to transmission-based communication models. The paradigm's ultimate value will be determined through rigorous peer review, mathematical analysis, and empirical validation by the research community.
Future work includes formalizing the cardinality of regenerable state spaces, developing hybrid transmission-regeneration models, and exploring applications in quantum and neuromorphic computing.
This research provides the conceptual foundation for such exploration — demonstrating that sometimes the most efficient communication occurs not through better transmission, but through eliminating the need to transmit.
The author thanks the theoretical computer science community for valuable discussions during the development of these ideas. This work was conducted independently without external funding.