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In Sigma Runtime, drift is the gradual loss of coherence, continuity, or bounded control during extended interaction.
Publicly, drift should be understood as a stability signal, not merely as a single-model error or a one-turn hallucination event.
The runtime therefore treats drift as something to monitor, interpret, and recover from before it becomes full interaction collapse.
Drift occurs when the active field begins to separate from its intended coherence envelope.
This can show up as:
The core public idea is simple:
Sigma Runtime tries to notice these changes early and respond before the interaction becomes unusable.
Publicly, it is useful to distinguish several broad drift families:
| Drift family | Public meaning |
|---|---|
| Semantic drift | Meaning gradually shifts away from the stable interaction target. |
| Narrative drift | Continuity of scene, role, or thread becomes unstable or inconsistent. |
| Behavioral drift | Tone, assertiveness, or interaction style escalates or collapses without proper control. |
| Control drift | The runtime stops modulating itself effectively and loses bounded behavior. |
| Recovery drift | A degraded state persists too long instead of converging back toward stable operation. |
This is a public explanatory grouping, not a fixed internal metric contract.
Unchecked drift can lead to:
For that reason, drift is not treated as cosmetic noise.
It is one of the core reasons Sigma Runtime includes control, verification, and recovery layers above the model backend.
At a public level, drift handling follows a bounded pattern:
The public point is not an exact formula.
The point is that Sigma Runtime uses drift as an operational control input.
Not every drift event should trigger the same response.
Publicly, the runtime distinguishes between:
This is why stable long-horizon interaction depends on recovery quality as much as on generation quality.
Drift is closely related to attractor stability.
As interaction continues, the runtime has to differentiate between:
Publicly, one of the most important stabilization questions is whether a recurring pattern is still helping coherence or has started to destabilize it.
SRIP-10 AEP complements drift control by describing how the runtime can preserve
adaptability without letting interaction become either too rigid or too loose.
Publicly, AEP helps distinguish three related but different conditions:
This distinction matters because the correct response is not always the same.
Some states call for narrowing, some for recovery, and some for controlled
variation that restores adaptability.
AEP should be understood as evidence-based regulation, not as a list of
forbidden phrases. The public requirement is that any entropy-regulation claim
remain bounded, auditable, and subordinate to the runtime's safety and recovery
layers.
Drift management is only credible if it can be inspected afterwards.
Publicly, post-analysis should help answer:
This is one reason Sigma Runtime treats observability and evidence as part of the stability story, not as an optional afterthought.
Drift in Sigma Runtime is a governed instability signal.
The system does not assume that long interaction remains stable by default.
Instead, it uses drift awareness, bounded control, verification, and recovery to keep interaction interpretable, recoverable, and safer under pressure.
References:
Tsaliev, E. (2025). SIGMA Runtime Architecture v0.1 — DOI: 10.5281/zenodo.17703667