Diff: substrate/adapter-composition.es
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| schema: foundry-doc-v1 | schema: foundry-doc-v1 |
| title: "The adapter composition algebra" | title: "The adapter composition algebra" |
| slug: adapter-composition | slug: adapter-composition |
| category: substrate | category: substrate |
| type: topic | type: topic |
| quality: complete | quality: complete |
| short_description: The operating-system metaphor for AI in PointSav — the Doorman as kernel, adapters as processes, service-content as filesystem — and the composition algebra that assembles per-request intelligence from versioned, customer-owned LoRA adapter layers. | short_description: The operating-system metaphor for AI in PointSav — the Doorman as kernel, adapters as processes, service-content as filesystem — and the composition algebra that assembles per-request intelligence from versioned, customer-owned LoRA adapter layers. |
| status: active | status: active |
| bcsc_class: public-disclosure-safe | bcsc_class: public-disclosure-safe |
| last_edited: 2026-05-01 | last_edited: 2026-05-01 |
| editor: pointsav-engineering | editor: pointsav-engineering |
| cites: | cites: |
| - lorax-predibase | - lorax-predibase |
| - s-lora-2024 | - s-lora-2024 |
| - federated-lora-2502-05087 | - federated-lora-2502-05087 |
| paired_with: adapter-composition.es.md | paired_with: adapter-composition.es.md |
| --- | --- |
| The **Adapter Composition Algebra** is the model that governs how AI intelligence is assembled at request time in a PointSav deployment. Its central metaphor maps precisely to an operating system: the Doorman (`service-slm`) is the kernel; LoRA adapters are processes; `service-content` is the filesystem; the base model is firmware. The analogy is not illustrative — it is operational. | The **Adapter Composition Algebra** is the model that governs how AI intelligence is assembled at request time in a PointSav deployment. Its central metaphor maps precisely to an operating system: the Doorman (`service-slm`) is the kernel; LoRA adapters are processes; `service-content` is the filesystem; the base model is firmware. The analogy is not illustrative — it is operational. |
| ## The algebra | ## The algebra |
| At request time, the Doorman composes adapters by stacking onto the base model: | At request time, the Doorman composes adapters by stacking onto the base model: |
| ``` | ``` |
| composed_weights = | composed_weights = |
| base_model[OLMo-3-1125-7B-Q4] | base_model[OLMo-3-1125-7B-Q4] |
| ⊕ constitutional_adapter[doctrine_vM.m.p] | ⊕ constitutional_adapter[doctrine_vM.m.p] |
| ⊕ engineering_adapter[pointsav_vN]? | ⊕ engineering_adapter[pointsav_vN]? |
| ⊕ tenant_adapter[<tenant>_vK]? | ⊕ tenant_adapter[<tenant>_vK]? |
| ⊕ role_adapter[master | root | task] | ⊕ role_adapter[master | root | task] |
| ⊕ cluster_adapter[<cluster-name>_vJ]? | ⊕ cluster_adapter[<cluster-name>_vJ]? |
| ``` | ``` |
| Where `?` denotes an optional adapter loaded only when the request context applies. `⊕` is the LoRA-stacking operator — a rank-r delta added to the base model weights at runtime. | Where `?` denotes an optional adapter loaded only when the request context applies. `⊕` is the LoRA-stacking operator — a rank-r delta added to the base model weights at runtime. |
| The composition is deterministic given the request context. There is no runtime decision about which adapters to use; the context determines the composition. | The composition is deterministic given the request context. There is no runtime decision about which adapters to use; the context determines the composition. |
| ## Adapter typology and routing rules | ## Adapter typology and routing rules |
| | Adapter | Loaded when | Ownership | | | Adapter | Loaded when | Ownership | |
| |---|---|---| | |---|---|---| |
| | `constitutional` | Always — every platform deployment | Constitutional adapter; ships with the knowledge commons (Apache 2.0) | | | `constitutional` | Always — every platform deployment | Constitutional adapter; ships with the knowledge commons (Apache 2.0) | |
| | `engineering` | Request scope is "build or modify the platform" | Vendor-curated; offered to Customers via service contract | | | `engineering` | Request scope is "build or modify the platform" | Vendor-curated; offered to Customers via service contract | |
| | `tenant` | Request scope operates on tenant data | Inside Customer Totebox; per-tenant; never leaves customer storage | | | `tenant` | Request scope operates on tenant data | Inside Customer Totebox; per-tenant; never leaves customer storage | |
| | `role` | Request originates from a Master/Root/Task session | Universal across deployments; learned from doctrine and role-tagged trajectories | | | `role` | Request originates from a Master/Root/Task session | Universal across deployments; learned from doctrine and role-tagged trajectories | |
| | `cluster` | Request scope is a specific project cluster | Per-cluster; declared in the cluster manifest | | | `cluster` | Request scope is a specific project cluster | Per-cluster; declared in the cluster manifest | |
| The constitutional adapter is universal and is loaded by every platform deployment. The tenant adapter is strictly per-tenant and is produced and held inside the Customer Totebox. The engineering adapter ships with the knowledge commons and is not treated as private vendor IP. | The constitutional adapter is universal and is loaded by every platform deployment. The tenant adapter is strictly per-tenant and is produced and held inside the Customer Totebox. The engineering adapter ships with the knowledge commons and is not treated as private vendor IP. |
| ## Implementation | ## Implementation |
| The algebra runs on production 2026 multi-LoRA serving infrastructure: | The algebra runs on production 2026 multi-LoRA serving infrastructure: |
| - **LoRAX (Predibase)** — multi-LoRA inference server; thousands of adapters per GPU; per-request hot-swap; per-tenant private adapters | - **LoRAX (Predibase)** — multi-LoRA inference server; thousands of adapters per GPU; per-request hot-swap; per-tenant private adapters |
| - **S-LoRA** — adapter isolation per dynamic computation; shared static backbone via secure IPC; significant time-to-first-token reduction | - **S-LoRA** — adapter isolation per dynamic computation; shared static backbone via secure IPC; significant time-to-first-token reduction |
| - **vLLM Multi-LoRA** — hot-swap at request time without reloading the base model | - **vLLM Multi-LoRA** — hot-swap at request time without reloading the base model |
| PointSav's contribution is the composition pattern — which adapters compose, in what order, under what doctrinal constraint — not the serving layer itself. | PointSav's contribution is the composition pattern — which adapters compose, in what order, under what doctrinal constraint — not the serving layer itself. |
| ## Adapter versioning | ## Adapter versioning |
| Each adapter carries a name, a semver version, the doctrine version it was trained against, a provenance field naming the corpus shards it was distilled from, and a signature signed by the trainer's identity. A composed request must reconcile adapter versions. The default policy loads adapters whose `doctrine_version` matches the deployment's current `doctrine_version`. Version mismatch surfaces as an operational signal — the deployment's MANIFEST records the active doctrine version and the discrepancy becomes visible. | Each adapter carries a name, a semver version, the doctrine version it was trained against, a provenance field naming the corpus shards it was distilled from, and a signature signed by the trainer's identity. A composed request must reconcile adapter versions. The default policy loads adapters whose `doctrine_version` matches the deployment's current `doctrine_version`. Version mismatch surfaces as an operational signal — the deployment's MANIFEST records the active doctrine version and the discrepancy becomes visible. |
| This solves AI drift at the substrate level: the model is verifiably aligned to a specific doctrine version, and any mismatch is a first-class observable rather than a silent degradation. | This solves AI drift at the substrate level: the model is verifiably aligned to a specific doctrine version, and any mismatch is a first-class observable rather than a silent degradation. |
| ## The OS-of-AI metaphor | ## The OS-of-AI metaphor |
| The analogy maps precisely: | The analogy maps precisely: |
| | OS concept | AI concept | Platform artifact | | | OS concept | AI concept | Platform artifact | |
| |---|---|---| | |---|---|---| |
| | Firmware | Pretrained base model | OLMo 3 7B / 32B GGUF | | | Firmware | Pretrained base model | OLMo 3 7B / 32B GGUF | |
| | Kernel | Request router | Doorman (`service-slm`) | | | Kernel | Request router | Doorman (`service-slm`) | |
| | Process | Composable behavior unit | LoRA adapter | | | Process | Composable behavior unit | LoRA adapter | |
| | Filesystem | Structured knowledge | `service-content` (LadybugDB graph) | | | Filesystem | Structured knowledge | `service-content` (LadybugDB graph) | |
| | System call | Tool invocation | MCP server interface | | | System call | Tool invocation | MCP server interface | |
| | Virtual memory | Per-tenant isolation | `moduleId`-keyed partitions | | | Virtual memory | Per-tenant isolation | `moduleId`-keyed partitions | |
| | Kernel module | Cluster-scoped capability | Cluster adapter | | | Kernel module | Cluster-scoped capability | Cluster adapter | |
| | User profile | Role boundary | Role adapter | | | User profile | Role boundary | Role adapter | |
| | Constitution / charter | OS license + principles | Constitutional adapter | | | Constitution / charter | OS license + principles | Constitutional adapter | |
| | Package manager | Adapter library + signing | `data/adapters/` + signed manifests | | | Package manager | Adapter library + signing | `data/adapters/` + signed manifests | |
| Customers install and uninstall adapters as packages. Adapter signatures are verified before composition. Per-tenant isolation is enforced at the serving layer the way virtual memory isolation is enforced at the kernel layer. | Customers install and uninstall adapters as packages. Adapter signatures are verified before composition. Per-tenant isolation is enforced at the serving layer the way virtual memory isolation is enforced at the kernel layer. |
| This frames the substrate for small and medium businesses as **the operating system of AI** — composable intelligence with a flat architecture rather than a single closed product. Any adapter can be swapped without touching the others. The customer owns the adapters trained on their own corpus. The doctrine is the soul; the corpus is the mind; the adapters are the personality. | This frames the substrate for small and medium businesses as **the operating system of AI** — composable intelligence with a flat architecture rather than a single closed product. Any adapter can be swapped without touching the others. The customer owns the adapters trained on their own corpus. The doctrine is the soul; the corpus is the mind; the adapters are the personality. |
| ## Practical composition ceiling | ## Practical composition ceiling |
| Production multi-LoRA research demonstrates that composing 2–3 adapters per request works cleanly. Composing 5 or more adapters per request crosses into multi-task interference. The algebra stays at a maximum of three runtime adapters per request by design. Register, brand voice, document sub-type, and target audience parameters live in prompt scaffolding (the genre template layer), not as additional adapters. | Production multi-LoRA research demonstrates that composing 2–3 adapters per request works cleanly. Composing 5 or more adapters per request crosses into multi-task interference. The algebra stays at a maximum of three runtime adapters per request by design. Register, brand voice, document sub-type, and target audience parameters live in prompt scaffolding (the genre template layer), not as additional adapters. |
| ## The Vendor LLM tier | ## The Vendor LLM tier |
| When the Vendor's engineering corpus accumulates sufficient scale — planned for Year 2 or later, at version 0.5.0 onward — continued pretraining may produce a model whose capability crosses an inflection from SLM to a larger model. This larger model may be offered as a Doorman tier in Customer deployments alongside Tier C external APIs, under service contract. Customer Doormans would then be able to route to the Vendor LLM for queries that exceed local Tier A capacity and where Tier C is undesirable. | When the Vendor's engineering corpus accumulates sufficient scale — planned for Year 2 or later, at version 0.5.0 onward — continued pretraining may produce a model whose capability crosses an inflection from SLM to a larger model. This larger model may be offered as a Doorman tier in Customer deployments alongside Tier C external APIs, under service contract. Customer Doormans would then be able to route to the Vendor LLM for queries that exceed local Tier A capacity and where Tier C is undesirable. |
| This tier is intended to be a byproduct of the substrate work as the corpus matures, not a separately developed product. | This tier is intended to be a byproduct of the substrate work as the corpus matures, not a separately developed product. |
| ## See also | ## See also |
| - [[compounding-doorman]] — the Doorman that implements the kernel role in this algebra | - [[compounding-doorman]] — the Doorman that implements the kernel role in this algebra |
| - [[apprenticeship-substrate]] — the mechanism that produces the per-tenant adapter corpus | - [[apprenticeship-substrate]] — the mechanism that produces the per-tenant adapter corpus |
| - [[language-protocol-substrate]] — the language-family adapter taxonomy that extends this algebra for editorial work | - [[language-protocol-substrate]] — the language-family adapter taxonomy that extends this algebra for editorial work |
| - [[knowledge-commons]] — how constitutional and engineering adapters are published as commons artifacts | - [[knowledge-commons]] — how constitutional and engineering adapters are published as commons artifacts |