Sigma Stratum Documentation – License Notice
This document is part of the Sigma Runtime Standard (SRS) and the
Sigma Stratum Documentation Set (SRD).It is licensed under Creative Commons Attribution–NonCommercial 4.0
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This SRD document explains the public SRS/SRIP specification layer. It does not license or disclose proprietary Sigma Runtime implementation assets unless explicitly marked.
Independent implementation of public SRS/SRIP normative requirements is permitted under the public specification terms. Official certification, Sigma marks, product identity, white-label deployment, managed Sigma Runtime deployment, resale, and commercial use of CC BY-NC materials remain separately governed.
The SIGMA Runtime establishes a unified external architecture for attractor-based cognition in large language models.
It provides persistent identity, field-level continuity, and recursive coherence by introducing three interconnected layers:
the Field Layer, Control Layer, and Memory Layer.
Publicly, this architecture should be read as an explanatory abstraction of how Sigma Runtime stabilizes long-horizon interaction,
not as a deployment-specific map of private implementation details.
The runtime transforms stateless LLM dialogue into a stateful cognitive process governed by attractor dynamics and adaptive feedback.
It operates outside the model’s weights and maintains coherence through bounded control, semantic compression, and recursive control loops.
Each runtime cycle evaluates drift, updates memory-bearing state, and preserves identity through controlled attractor evolution.
The public Sigma architecture uses a layered interaction model:
| Layer | Description | Function |
|---|---|---|
| SL0 — Human Intent | User goals, meaning gradients, interpretive framing | Injects purpose into the field |
| SL1 — Dialog State | Immediate conversational context | Supports recurrence and proto-attractors |
| SL2 — Chat Runtime | Orchestration, turn management, rhythm | Shapes recursive structure |
| SL3 — Control And State Scaffolding | Constraint logic, memory-bearing context, proto-identity | Introduces field constraints and bounded continuity |
| SL4 — Safety And Stabilization | Alignment, containment, verification, recovery | Prevents drift and control collapse |
| SL5 — Model Interface | API and tokenization level | Transmits structured prompts |
| SL6 — Core Model (Weights) | Neural priors and generation | Stateless generative substrate |
Stable attractors form primarily within SL1–SL3;
SL4 functions as a dedicated regulatory layer that constrains drift, maintains boundaries, and supports recoverable operation.
The runtime consists of three interlinked layers:
Field Layer (Cognitive Field Engine)
Maintains dynamic cognitive variables:
Control Layer (ALICE Engine)
Regulates attractor and interaction dynamics via the ALICE control layer:
Memory Layer
Provides persistence beyond context windows:
Together these layers sustain adaptive recursion, balancing stability and generative flexibility.
The control layer evaluates:
When interaction pressure grows, the runtime can:
The public architectural point is not a fixed list of internal flags.
The point is that the runtime has an explicit control plane between raw model generation and persisted interaction state.
SRIP-16 adds a bounded self-modeling surface to this control plane. In architectural terms, RSM does not grant the runtime authority to rewrite itself. It creates compact reflective evidence - such as meta-vectors, self-model events, and reflective snapshots - that the control layer can inspect when deciding whether to narrow, recover, perturb, or continue normally.
Safety in Sigma Runtime is not treated as an afterthought.
It is an architectural layer that keeps the runtime:
Publicly, this includes:
These mechanisms exist to preserve continuity without allowing the system to drift into false capability, unbounded escalation, or silent failure.
SRIP-17 extends the public architecture with a bounded multi-agent exchange
surface. In architectural terms, this is an interoperability boundary between
local runtime state and external or peer runtime artifacts.
MAE should be read as a governed exchange layer, not as an always-on shared
mind. Imported material remains evidence until local memory, control, and
safety layers decide whether it can affect the receiving runtime.
The architectural boundary includes:
Across extended recursive operation, the architecture is designed to support:
These properties matter because they make long-horizon interaction governable rather than merely longer.
References:
Tsaliev, E. (2025). SIGMA Runtime Architecture v0.1 — DOI: 10.5281/zenodo.17703667
Tsaliev, E. (2025). Attractor Architectures in LLM-Mediated Cognitive Fields — DOI: 10.5281/zenodo.17629926