This is an automated rejection. No LLM generated, heavily assisted/co-written, or otherwise reliant work.
Read full explanation
Semantic drift is when models trained recursively on their own outputs, or on heavily endogenous data, gradually lose meaning, coherence, or reliability. This concept appears in the context of synthetic data contamination, self-training loops, and long-term self-improvement.
This article proposes understanding of semantic drift as a rate problem. Recursive learning systems remain stable only when their corrective information rate exceeds their semantic error accumulation rate. When this condition fails, collapse follows.
This reframing yields testable predictions and clarifies why many existing safeguards fail earlier than expected.
Recursive Learning and Endogenous Drift
Any learning system that updates itself primarily using its own outputs becomes informationally endogenous over time. Even if the system is initially well grounded, each update introduces small semantic distortions, including approximation error, compression loss, improper generalization, or bias. These errors are usually minor in isolation, but they accumulate.
Once a system’s training signal depends largely on itself, semantic error becomes self reinforcing unless counteracted by external information. Notably, this drift does not require adversarial data, misaligned objectives, or limited capacity. It arises purely from recursion.
This observation is familiar. What is less appreciated is that drift can be analyzed quantitatively as well as descriptively.
The Viability Condition
The core claim is simple:
A recursive semantic system is stable if and only if its rate of corrective information intake exceeds its rate of semantic error accumulation.
Corrective information includes anything that constrains meaning from outside the system’s internal loop, such as fresh human data, interaction with a non-modeled environment, trusted sensors, or high fidelity ground truth. Error accumulation includes noise, approximation loss, representational bias, and internal misalignment.
When the corrective rate drops below the error rate, the system enters a regime described here as informational autophagy. It consumes its own representations faster than they can be repaired. Meaning degrades even while surface-level performance metrics remain stable.
Collapse, in this view, is not a binary event. It is a threshold crossing.
A Distinctive Temporal Signature
This framework implies a specific empirical prediction: In recursive systems undergoing semantic collapse, out-of-distribution performance degrades before standard validation metrics worsen.
Early semantic drift affects rare, edge-case, and structurally novel inputs first. Average loss, perplexity, or benchmark scores can remain deceptively stable while the system’s internal semantic structure erodes.
Practitioners often report that heavily self-trained or synthetic-heavy models feel wrong before metrics confirm it. This framework predicts that behavior and explains why reliance on aggregate validation alone is insufficient.
Not Just “Synthetic Data Is Bad”
Recent discussions often conclude that synthetic data is inherently dangerous. That conclusion is too coarse. Recursive or synthetic data can be safe if the corrective information bandwidth remains sufficiently high. Conversely, even real-world data can fail to stabilize a system if it lacks semantic independence.
The key variable is not data origin, but informational independence.
This also clarifies why scaling alone does not solve the problem. Larger models can tolerate higher error rates, but unless corrective input scales proportionally, collapse is delayed rather than avoided.
Implications and Next Steps
This rate-based view has several implications. Grounding must be measured rather than assumed. Early warning signals exist in out-of-distribution brittleness. Recursive self-improvement faces hard information-theoretic constraints that do not depend on agency or intent.
The full paper, with formal definitions and references, is available here: Semantic Grounding and the Preservation of Information in Recursive Systems https://doi.org/10.5281/zenodo.18091864
I am eager to collaborate with others interested in advancing this line of inquiry, particularly through empirical tests of the predicted temporal signature, alternative estimators of corrective versus error rates, and counterexamples in which recursive systems remain stable longer than expected.
If semantic collapse follows from rate imbalance, the appropriate response is not to avoid recursion entirely, but to understand precisely what keeps meaning alive.
Semantic drift is when models trained recursively on their own outputs, or on heavily endogenous data, gradually lose meaning, coherence, or reliability. This concept appears in the context of synthetic data contamination, self-training loops, and long-term self-improvement.
This article proposes understanding of semantic drift as a rate problem. Recursive learning systems remain stable only when their corrective information rate exceeds their semantic error accumulation rate. When this condition fails, collapse follows.
This reframing yields testable predictions and clarifies why many existing safeguards fail earlier than expected.
Recursive Learning and Endogenous Drift
Any learning system that updates itself primarily using its own outputs becomes informationally endogenous over time. Even if the system is initially well grounded, each update introduces small semantic distortions, including approximation error, compression loss, improper generalization, or bias. These errors are usually minor in isolation, but they accumulate.
Once a system’s training signal depends largely on itself, semantic error becomes self reinforcing unless counteracted by external information. Notably, this drift does not require adversarial data, misaligned objectives, or limited capacity. It arises purely from recursion.
This observation is familiar. What is less appreciated is that drift can be analyzed quantitatively as well as descriptively.
The Viability Condition
The core claim is simple:
A recursive semantic system is stable if and only if its rate of corrective information intake exceeds its rate of semantic error accumulation.
Corrective information includes anything that constrains meaning from outside the system’s internal loop, such as fresh human data, interaction with a non-modeled environment, trusted sensors, or high fidelity ground truth. Error accumulation includes noise, approximation loss, representational bias, and internal misalignment.
When the corrective rate drops below the error rate, the system enters a regime described here as informational autophagy. It consumes its own representations faster than they can be repaired. Meaning degrades even while surface-level performance metrics remain stable.
Collapse, in this view, is not a binary event. It is a threshold crossing.
A Distinctive Temporal Signature
This framework implies a specific empirical prediction: In recursive systems undergoing semantic collapse, out-of-distribution performance degrades before standard validation metrics worsen.
Early semantic drift affects rare, edge-case, and structurally novel inputs first. Average loss, perplexity, or benchmark scores can remain deceptively stable while the system’s internal semantic structure erodes.
Practitioners often report that heavily self-trained or synthetic-heavy models feel wrong before metrics confirm it. This framework predicts that behavior and explains why reliance on aggregate validation alone is insufficient.
Not Just “Synthetic Data Is Bad”
Recent discussions often conclude that synthetic data is inherently dangerous. That conclusion is too coarse. Recursive or synthetic data can be safe if the corrective information bandwidth remains sufficiently high. Conversely, even real-world data can fail to stabilize a system if it lacks semantic independence.
The key variable is not data origin, but informational independence.
This also clarifies why scaling alone does not solve the problem. Larger models can tolerate higher error rates, but unless corrective input scales proportionally, collapse is delayed rather than avoided.
Implications and Next Steps
This rate-based view has several implications. Grounding must be measured rather than assumed. Early warning signals exist in out-of-distribution brittleness. Recursive self-improvement faces hard information-theoretic constraints that do not depend on agency or intent.
The full paper, with formal definitions and references, is available here:
Semantic Grounding and the Preservation of Information in Recursive Systems
https://doi.org/10.5281/zenodo.18091864
I am eager to collaborate with others interested in advancing this line of inquiry, particularly through empirical tests of the predicted temporal signature, alternative estimators of corrective versus error rates, and counterexamples in which recursive systems remain stable longer than expected.
If semantic collapse follows from rate imbalance, the appropriate response is not to avoid recursion entirely, but to understand precisely what keeps meaning alive.