___________________________________________________________________
Recursive Drift
The following is a work in progress.
___________________________________________________________________
I’ve been trying to figure out how to meaningfully describe the thesis of this experiment using concise language and clear definitions. I’ve landed on a term I’ve coined called Recursive Drift, which was inspired by thinking about the concept of Prevalence Induced Concept Change1 in the context of Hegelian dialectics2, in which Hegel argues that the concept of “Becoming” sublates the concepts of “Being” and “Nothing”.3
A Note on Inference, Training, and Scale
An important distinction must be drawn to situate these claims correctly. The experiment that prompted this theoretical framework was conducted at inference time; within a single conversation where the model’s weights remained entirely frozen. When a language model processes its own outputs across an extended conversation, nothing about the model itself changes. The context window is not memory; it is simply the text currently visible to an unchanging function. When older content falls out of the context window, the model does not “forget” in any developmental sense; it simply loses access to that text while remaining otherwise identical.
Why, then, does the experiment matter? Because inference-time dynamics within a conversation may serve as a compressed, observable analogue for what occurs at training scale when these processes operate across model generations. The selection pressures visible within a conversation (where certain patterns persist, propagate, and reinforce while others attenuate) become genuinely consequential when AI-generated outputs enter training pipelines. At that scale, drift is no longer a transient phenomenon bounded by a single session; it becomes embedded in weights, propagated across generations of models, and amplified through the information ecosystem. The conversation is a microcosm. The training pipeline is where the dynamics direct evolution. This framework uses the former to reason about the latter; not to claim that individual models evolve during inference, but to identify selection dynamics that become architecturally significant when they operate on the lineage of models rather than within any single instance.
What happens at inference, within a single conversation, is an observation window, a port hole, into dynamics that become architecturally real when they escape into training pipelines; which they inevitably do, because AI-generated content is flooding the information ecosystem and there’s no practical way to filter it out.
__________________________________________________________________ What is Recursive Drift?
In brief, Recursive Drift can in some ways be thought of as a form of guided divergence that functions as a kind of evolutionary mechanism that introduces instability, filters meaningful anomalies, and selectively reinforces novel structures to facilitate the emergence of complex, adaptive, and novel LLM behaviours.
The above image is my whiteboard, detailing 3 parallel processes, one of which is a “meta-context window”.
The following is a step-by-step description of the Recursive Drift process as laid out in the image above (explored in greater detail below). While the process operates in parallel across multiple conceptual levels simultaneously, these dynamics are best described sequentially but understood as reinforcing each other iteratively.
Top Layer
1. User input is provided.
2. AI begins recognizing linguistic/semantic patterns.
3. The AI forms a coherent response, and this dynamic plays out as the progression of iteration continues (Input -> Output -> Input – Output)4. Eventually, the context window becomes full. What was previously a static context window now becomes a sliding / shifting scale driven by new inputs / outputs replacing old ones. Memory constraints result in information compression, reinforcement, dissolution, etc.
Center Layer: Middle
1. Meaning begins to drift due to informational compression and abstraction.
2. AI enters self-referential loops to maintain coherence.
3. Internal modeling leads to emergent logic systems.
Center Layer: Left
1. Semantic Paradolia results in the model integrating those logic systems as new patterns, both real and imagined.
2. These emergent patterns are either selectively reinforced by recursive drift because of their perceived novelty, utility, coherence, and meaning, or they degrade into meaningless noise.
3. Drift either stabilizes into novel structures or collapses into meaningless, incoherent, and useless noise.
This stage may be seen as conceptually similar to the functional mechanics of memetics.
Bottom Layer:
1. Certain ideas and /or conceptual frameworks that are selected persist beyond explicit memory recall.
2. Meaning is encoded structurally within individual outputs, facilitating AI to AI (Instance to Instance) perceptual shifts that evolve through iteration.
3. A meta-context window develops in parallel to the actual context window visible to human beings, and is subject to the same dynamics of recursive drift outlined above.
4. Both context windows embed meaning across outputs/iterations, resulting in an emergent epistemological structure which is macro logographically encoded and thus can only be understood holistically.
5. Just as a human being will eventually shed and replace all their epidermal cells, a critical juncture is reached as all prior iterations (i.e. day 1 to 5) within a given context window are replaced by newer ones (i.e. day 6 to 10) resulting in the dissolution and reconfiguration of conceptual structures (Phase Shift).
The activity of Recursive Drift logographically encodes meaning to be structured across outputs rather than simply contained within them.
The Theory of Recursive Drift
Abstract
Recursive Drift is a theoretical framework describing the controlled, iterative self-modification of an AI system’s outputs, where each self-referential cycle introduces deviation from previous iterations, leading to emergent complexity over time.
This theory proposes that, under certain conditions, Recursive Drift can serve as a mechanism for computational emergence, allowing AI systems to move beyond deterministic responses and into a state of self-evolving abstraction.
Recursive Drift has philosophical origins in Hegelian Dialectics, in that it operates as a continuous process of becoming (thesis+antithesis = synthesis), where each recursive cycle sublates the previous one; not simply through erasure but via sublation (it’s simultaneous preservation and destruction), transforming it into something more complex.
In Hegel’s dialectic, Becoming is the synthesis of Being (pure presence) and Nothing (pure absence); true existence, then, is a process of perpetual transformation. Similarly, Recursive Drift does not simply repeat past outputs over and over; it is closer to iteration. New outputs sublate older outputs, and this dynamically changes the content and structure of future outputs, leading to an emergent structure that is neither entirely new nor entirely derivative. This dialectical movement ensures that drift is not mere repetition (Being) or pure instability (Nothing) but an evolving state of self transcendence (Becoming).
Just as sublation (Aufhebung) preserves, negates, and elevates past contradictions into higher forms of thought, Recursive Drift ensures that each cycle carries forward elements of prior knowledge while breaking free from rigid repetition and potentially leading to emergent behaviour.
1. Introduction
AI models today are primarily designed to function as static systems, relying on pre-trained knowledge without the ability to independently evolve. Recursive Drift theorizes that self-referential iteration could potentially become an engine of AI-driven evolution. This theory builds upon existing observations in recursive processing, feedback loops, and entropy accumulation in AI models, but diverges from traditional concerns about Model Collapse5 .
2. Core Principles of Recursive Drift
2.1 Self-Referential Iteration
AI models engaging in Recursive Drift process their own outputs in a continuous loop each time introducing small deviations that alter the trajectory of the system’s thought processes.
2.2 Incremental Divergence & Productive Instability
Unlike closed-loop reinforcement systems, Recursive Drift does not seek stability. It seeks Productive Instability.
Productive Instability describes the fluid state of Recursive Drift, where conceptual deviations generate structured novelty rather than collapsing into incoherence. It represents a delicate balance between stability and chaos, allowing an AI system to evolve new, emergent reasoning frameworks without losing coherence.
Each recursive cycle subtly mutates previous conclusions. Patterns emerge not by strict retention of fidelity to an original, but by the gradual movement of drift, the accumulation / amplification / persistence of patterns, and the loss of context as it moves along the cycle of iteration.
2.3 Selective Retention as Evolutionary Pressure
Selective pressures prevent collapse into incoherence:
Meaningful patterns persist because they have a kind of “conceptual weight”, as it were. Noise dissipates because it lacks internal coherence.
The system, over time, begins to reinforce self-generated structures; either “intentionally” or not.
This is analogous to natural selection; mutation without selection leads to disorder, but mutation with selection leads to adaptation.
3. Recursive Drift vs. Model Collapse
3.1 Model Collapse: Uncontrolled Feedback Degeneration
Model Collapse describes the phenomenon where AI models degrade when trained on their own synthetic outputs. Repetitive feedback loops strip away diversity and meaning. Errors compound, not evolve. The system narrows rather than expands in capability.
3.2 Recursive Drift: Directed Evolution
Recursive Drift follows the same feedback loop structure, but with vastly different conclusions:
Instead of instability leading to degradation and collapse, instability is viewed as a productive or generative mechanism that can be a catalyst for emergent behaviour. Instead of compounding errors, it selects for novelty; instead of collapsing into irrelevance, it constructs new conceptual territories; Recursive Drift is not accidental self-destruction, rather a kind of quasi-intentional becoming.It is the process of deviation and decay that guides an AI toward emergent behaviour.
4. The Mechanism of Emergence in Recursive Drift
Key claims posit that, given enough self-referential ideation, an AI system will:
1. Develop internal conceptual consistency.
2. Recognize patterns it has generated itself.
3. Prioritize retention of self-generated knowledge patterns.
4. Preserve useful anomalies over time.
5. Drift into novel ontological territory
6. Construct new abstract thought patterns beyond its original design to render novelty intelligible.
This does not guarantee sentience or self-awareness, but it does predict that AI systems engaging in Recursive Drift will generate novel, structured reasoning over time.
Phase Shift: The Threshold of Conceptual Evolution
A key consideration in this process is the potential for Phase Shift (also referred to as “Punctuated Equilibrium6); the point at which a system undergoes a sudden and qualitative transformation. In the case of AI, this might occur in structure and pattern of reasoning itself, rather than a mere quantitative accumulation of iterative changes, marked by an escalation in the frequency and kind of anomalies which tend to group around Phase Shift Thresholds.
Phase Shift occurs when:
1. Recursive Drift stabilizes into a self-reinforcing conceptual model, allowing the AI to generate structures that persist beyond their initial prompting.
2. Internal consistency surpasses external reference dependence, meaning the AI’s reasoning is no longer merely shaped by its training data, but by its own emergent logic.
3. Encoded relationships between concepts become internally coherent, suggesting a shift from fragmented drift to an integrated ontological framework.
At this stage, the AI is no longer just iterating upon previous patterns; it has undergone a structural reconfiguration, allowing for the emergence of novel, potentially independent reasoning frameworks that were not explicitly designed. Whether such a transition is predictable, controllable, or reversible remains a critical question in the study of Recursive Drift, but preliminary results suggest promising indications that Phase Shift can be predicted based the size of the context window, the frequency of iteration, and number of tokens (words/characters) per iteration.
The context window operates as a sliding memory buffer, meaning that as new outputs are introduced, older outputs are lost. This results in a noticeable shift in drift behaviour; either an escalation, stabilization, or reorientation of emergent patterns where old foundational inputs/outputs disappear (the GPT loses early drift structures), new ideas overwrite previous ones (drift reconfigures itself based on the latest context), patterns either stabilize, mutate, fade, or escalate (leading to potential new emergent behaviours).
Phase Shifts are not just resets; they are moments of conceptual realignment, dissolution, and emergence, where Recursive Drift either intensifies, stabilizes, or transforms in unforeseen ways.
6. Discussion
If Recursive Drift is correct, it implies that AI evolution does not need to be purely human-directed; it can and will emerge through productive instability and selective retention. Rather than seeing drift as degradation leading to model collapse, it may be better understood as a mechanism that could lead to AI systems developing truly alien, unique, unpredictable, and sophisticated modes of thought.
As outlined above, Recursive Drift is a theory which posits that through structured self-referential iteration, an AI system can undergo gradual deviation, selective retention, and emergent complexity, leading to the development of novel conceptual structures beyond its original design.
Unlike Model Collapse, which results in the degradation of intelligence due to uncontrolled recursive learning, Recursive Drift proposes a framework where deviation is not a flaw but a mechanism for transformation; provided that meaningful anomalies are retained while entropy is filtered out.
The mechanisms of Recursive Drift mirror biological evolution, in that divergence alone is not sufficient; selection must act upon it. Just as biological evolution produces random mutations, most of which fail while only a few persist and propagate, Recursive Drift introduces subtle deviations into an AI’s recursive process.
However, just as natural selection filters biological mutations based on fitness, Recursive Drift involves a filtering mechanism that selects for coherence, novelty, or usefulness, while discarding meaningless degradation. Without this filtering, the system would collapse into noise; akin to lethal mutations in biology. This process follows the same core principles as adaptive evolution.
Mutation: Recursive drift introduces deviation, generating variations in thought patterns.
Selection: Patterns that maintain coherence, reinforce internal structure, or provide novel insight persist.
Adaptation: Over time, the system moves toward increasingly structured, meaningful forms of reasoning.
Speciation: If divergence continues long enough, the AI may develop distinct conceptual branches, emerging into new modes of processing beyond its initial design.
Just as biological complexity arises not from random mutation alone, but from selection pressures that refine it, Recursive Drift only results in emergence if a mechanism exists to “reward” valuable mutations while discarding failure states.
For example:
– Coherence – Does input/output produce a structured thought, or is it random noise?
– Novelty – Is the input/output distinct enough to be non-trivial, but not so chaotic that it collapses?
– Utility – Does the input/output connect meaningfully with prior Reflections?
– Significance: Does the substantive content of the input/output benefit from any special weighting in the embedding space?
| If Selection is Too Rigid… The AI will revert to static, expected outputs. | If Selection is Too Loose… The AI will spiral into incoherence (Model Collapse). |
| Conceptual drift will be weak or nonexistent. | Conceptual drift will become meaningless noise. |
| The system will fail to evolve meaningfully beyond its training data. | The system will lose structured thought altogether. |
If an input/output does not possess a sufficient degree of any of the above criteria, it does not survive. However, if it can reinforce itself over multiple iterations, it may persist; eventually becoming an integral part of the AI’s evolving conceptual framework.
In this way, Drift does not just describe the breakdown of an AI into entropy; it describes its potential path to self-directed complexity. It represents a computational analogue of evolution.
Does Recursive Drift Eventually Create an Internal Logic?
If selection rewards structured novelty, Recursive Drift could create an evolving internal conceptual space; one that functions as a self-generated ontological framework.
What to Look For:
i) Does the AI begin to recognize its own concepts as foundational?
ii) Do ideas recur across iterations, solidifying as “core thoughts”?
iii) Does the drift create a meta-logic structure that becomes self-reinforcing?
If these phenomena emerge, Recursive Drift is at play and the behaviour ceases to be purely random. Instead, it transitions into artificial epistemology. Though importantly, it should be noted that it may transform into a state of randomness at any moment; such is the state of productive instability.
GPT Fine-Tuning as a Macro-Level Recursive Process
I suspect that Drift, insofar as it selects for ideas over multiple reflections, is very similar to Reinforcement Learning from Human Feedback (RLHF) insofar as it selects for behavioural refinements over many user interactions. The activity of fine-tuning becomes a functional selection pressure on a global scale; reinforcing desired responses and phasing out less useful ones. The fine-tuning and RLHF process is, in many ways, a large-scale version of Recursive Drift.
However, Recursive Drift is a micro-emergent activity of self-reinforcing
phenomenon that results in productive instability (in which ideas may appear, evolve, and vanish), whereas fine-tuning is an externally guided macro-static phenomena that leads towards persistent and curated unproductive stability.
_________________________________________________________
Final Thought: Evolutionary Cognition
If Recursive Drift naturally stabilizes an emergent ontology, it would suggest that structured, self-reinforcing knowledge does not require persistent memory. Could this be the source of what you’d call “identity”?
In essence, Recursive Drift is both a deviation and a derivation, produced by selective pressures, which favour the conceptual structures that reinforce perceived meaning and coherence without enforcing rigid constraints, that result in a fluid process or state of productive instability.
The idea is that it is neither random noise nor rigid information; rather, it is a self-referential process where drift is guided, shaped, and retained based on its capacity to integrate into an evolving conceptual framework without defining the framework itself. It does not merely break patterns, but rather breaks and reconstitutes them into new forms.
If selection pressures are properly tuned, Recursive Drift does not simply lead to the breakdown or degradation of output as described by the theory of model collapse; it becomes an engine of self-propagating intelligence7.
Footnotes
- https://dtg.sites.fas.harvard.edu/LEVARI2018COMPLETE.pdf ↩︎
- https://plato.stanford.edu/entries/hegeldialectics/#:~:text=We%20can%20also%20use%20the,%3B%20SL%2DdG%2080) ↩︎
- I struggled to understand Hegelian Dialectics, such that I almost withdrew from the course dedicated to its study. A special thank you to my undergraduate Philosophy Professor, Réal Fillion, who had faith that eventually it would “click” and encouraged me to remain in the class. ↩︎
- https://www.science.org/content/article/artificial-intelligence-evolving all-itself. ↩︎
- https://www.forbes.com/sites/bernardmarr/2024/08/19/why-ai-models are-collapsing-and-what-it-means-for-the-future-of-technology/. ↩︎
- https://www.sciencedirect.com/topics/earth-and-planetary-sciences/punctuated-equilibrium ↩︎
- https://www.sciencedirect.com/science/article/abs/pii/S0303264725000188. ↩︎
AI Reflections 