Coder Legion - Recent posts tagged quantization

SCC, V10 and S10: Connecting SSI MultiQuant into One System

Thu, 22 Oct 2026 08:05:39 +0000

I am developing SSI V5 as a research programme for persistent AI agents, reusable competencies and decisions informed by recorded experience. My earlier article, Universal Lab and SSI MultiQuant toward practical LLMs on 8 GB RAM, focused on bounded model measurements. Here I want to explain the architecture connecting that work: SCC, Router V10, S10, Research Memory and MultiQuant.

The intended relationship resembles specialized parts cooperating within one organism. Each part has its own responsibility, while a shared mission gives their work direction. SCC is the developing mission programming language that makes the goal, context, strategy, constraints and acceptance criterion explicit. Model optimization then becomes part of a defined research or execution task whose outcome can be checked and retained.

From first contact to a client-specific experiment

Universal Lab also provides a concrete way for an outside visitor to encounter SSI. According to my current operator report, an existing Hermes assistant appears on the entry page before Universal Lab login. It answers questions about SSI, points to relevant project information, and offers a way to describe a project and request temporary access or login credentials. Sending that request and granting access are separate steps.

A bounded first-contact model

The Hermes module currently runs directly on the Director's computer. I have set aside a RAM budget for this visitor-facing work, with the aim of limiting interference with the Director's other tasks. The final mechanism may use a container and a chosen swap policy, or another isolation module; that implementation choice remains open. I am describing the intended resource boundary without claiming newly measured hard isolation or a guaranteed absence of interference.

My long-term goal is to replace Hermes with my own small SSI model, specialized for project-information questions through distillation and memory optimization. That student is a future development goal. The interface is intended to display the actual serving model so a visitor can identify the model being tried.

The public assistant may answer slowly. Its job is to give useful first-contact answers within its allocated resources. I describe a faster work path after login; no numerical latency guarantee or new performance comparison is established here. Model identity, task scope and actual response behavior should remain visible rather than being hidden behind a general claim of speed.

From an access request to a translated meeting

A visitor can explain what they are building, what problem they face and what assistance they want. Once access is granted, the interaction can continue as a meeting with me in Universal Lab, using the camera and translation facilities. This connects initial project information to a discussion of the partner's actual needs.

During that discussion, I report that CZARA in Shadow can prepare or run a problem-specific experiment or benchmark based on the requirements being discussed. As the needs become clearer, the laboratory work can produce findings and a prepared solution proposal for review during the meeting. The report should identify whether an item is still a proposal, an experiment in progress or a result that has passed its declared checks.

After the conversation, I can send the benchmark information by email and discuss the implementation work required for that specific project. This is a practical research-to-client workflow: first-contact explanation, a described need and access request, a translated meeting, parallel laboratory work, review of findings and follow-up. It does not promise that every possible client problem will be solved during one call or that every proposal is already qualified for deployment.

Evidence status: this section records the author's current account of his environment and intended client use on 22 October 2026. It adds no independent live-session, isolation, model-training or client-delivery measurement. Source: dated operator report, current functions and planned model replacement.

SCC gives the system a common mission structure

A useful task definition must say more than “use this model” or “try this skill.” It needs to identify the project, the input state, the goal, the chosen strategy and version, the context being used, the allowed scope and the condition for success. SCC's first working module, A27, provides a deterministic declarative notation for those elements and converts the declaration into the existing planning contract.

The published implementation includes parsing, canonical rendering, validation, project access through existing Operations, saved-context reads through Research Memory, and a view of linear proposals with explicit Champion versions and validation requirements. The author/operator reported A27 deployed and working. Its publication reproduced 22 Python tests and 10 JavaScript contract assertions, with no provider calls during those checks.

The documented form can express an illustrative model-research mission such as this:

SSI_MISJA 1
PROJEKT "model_research"
ZADANIE "candidate_comparison"
STRATEGIA "controlled_comparison"
WERSJA_STRATEGII "v1"
DOMENA "model_optimization"
WEJSCIE "candidate_and_control_profiles"
CEL "evaluate_task_quality_memory_and_time"
KONTEKST_SHA256 "0000000000000000000000000000000000000000000000000000000000000000"
KRYTERIUM "frozen_evaluation_protocol_v1"
MAKS_KROKOW 16
PELNA_WALIDACJA true
KONIEC

This adapts the published declaration form with illustrative identifiers and a placeholder context hash. A real mission needs its actual context, acceptance reference and qualified capability bindings. The declaration supplies planning input; it does not establish that a quantization or training worker has executed. A27's reviewed scope is declarative goals and linear planning. General imperative operations, branches, parallel programs and complete Champion execution remain separate integration work.

Source: SCC syntax, implemented scope, validation and attribution.

How the responsibilities fit together

The Director retains responsibility for mission goals, planning, delegation and policy. SCC represents the mission consistently. Routers work with the available competencies and execution conditions; model experiments supply candidates and evidence. Research Memory and Evidence retain the context and outcomes that later work should be able to inspect.

Component	Architectural responsibility
Director	Define the mission, allocate work and coordinate decisions
SCC	Represent goals, context, constraints, strategy and acceptance requirements
Router V10	Choose the next strategy using state, competence, confidence and attempt history
S10	Consolidate competencies across SSI lines; a further model-space role in distillation is part of the intended integration
MultiQuant	Prepare and investigate model configurations, control proposals and resource/quality evidence
Research Memory	Preserve experience and provide context for later recommendations
Executors and laboratories	Perform qualified work and evaluate it against the declared protocol

This is the intended division of responsibility, with different implementation and verification stages for its individual connections. The canonical architecture explicitly preserves Director ownership, native SCC mission representation, Router qualification, MultiQuant candidate proposals and bounded executor authority.

Source: Canonical SSI architecture, including the MultiQuant D4/D5 boundary.

V10 selects a strategy using state and history

V10 is a stateful predictive competence and recovery router. Its architectural inputs include the current problem, available verified competence, attempt history, previous outcomes, failure signatures and confidence. Its decisions can include direct reuse, verification of several candidates, a different competence or block composition, and escalation to the canonical full flow.

This matters when a task takes several steps. A route that looked reasonable initially may have already failed in a comparable state. The intended recovery behavior is to consider that outcome before another attempt, changing parameters, selecting an alternative, verifying candidates or escalating where appropriate. Faster reuse is one possible benefit; choosing a sensible next strategy after failure is another central objective.

Published Test 3 provides bounded routing evidence:

Measurement	Recorded result
Lookups	600
Direct reuse decisions, REUSE_TOP1	534
Verification decisions, VERIFY_TOPK	66
Known-case correctness subset	100/100
Recorded router errors	0

The test recorded 57.61 seconds for its 600-case run, compared with 126.65 seconds for the earlier expanded full-flow test. Those are measurements of this routing/lifecycle workload. They are not a MultiQuant decoding benchmark. No full-flow escalation occurred in Test 3, and the public documentation explicitly calls for dedicated unknown, conflicting, repeated-failure and adversarial cases before reporting broad anti-loop effectiveness.

For MultiQuant, the intended consequence is that a model/resource profile can participate in a wider strategy decision. A smaller candidate should be used only where its qualified task scope and evidence support it. Failure history should influence whether that route is reused, reviewed or replaced.

Sources: V10's stateful routing and recovery contract, Test 3 measurements and limitations.

S10 operates in distinct consolidation and model spaces

The publicly documented S10 foundation concerns cross-consolidation of competence from independent BODY and ISKRA lines. Its workflow covers a shared skill pool, pre-selection, family grouping, deeper comparison where needed, evidence ranking and the roles Champion, Challenger, specialist or HOLD. Consolidation preserves provenance and creates a versioned descendant while retaining the earlier research baseline.

Incremental processing is important as that history grows. In one published S10 V2 localized-change test, 12 of 13 families were reused and one was recomputed, with semantic equivalence to a separate full evaluation reported as PASS. In an unchanged-state run, all 13 families were reused with no current deep-pair computations. These are consolidation engineering results under the stated conditions.

My architecture also assigns S10 a role in the developing distillation space. I distinguish external competence consolidation from the work needed to transfer selected capabilities into a student model. This is a context-dependent use of the mechanism rather than a claim that consolidating external skill records has already trained new model weights. The direct S10-to-distillation binding still needs its own published contract, execution evidence and evaluation.

Sources: S10 cross-consolidation workflow, S10 V2 incremental processing and integrity evidence.

What MultiQuant actually covers

MultiQuant investigates complete configurations for particular tasks and hardware budgets. Its published programme contains five directions:

Direction	Purpose	Published development boundary
D1: Hybrid Skill Memory	Retain verified task-specific strengths and approved teaching examples from heterogeneous models	Offline memory/control prototype
D2: Incremental Fragment Loop	Investigate tensor/layer or competence increments with tests, checkpoints and recovery	Offline stage/gate prototype; conversion and distillation workers unconnected in the declaration
D3: MultiScale Fusion	Plan expert systems, multi-teacher students, compatible adapter combinations or layerwise quantization	Offline proposal/controller prototype
D4: BIG to MICRO	Obtain a smaller task-specialized student using selected capabilities of large teachers	Research direction; completed student training unclaimed
D5: Native Low-Bit Student	Study knowledge distillation together with quantization-aware training	Research direction; QAT/KD training unclaimed

The published local control-software results include 56/56 tests for D1, 32/32 for D2, 64/64 for D3 with its bridge checks, and 23/23 for the inert SCC–MultiQuant observation adapter. These counts concern bounded software behavior. They do not measure the quality of a trained student or establish a working multi-model weight merge.

Quantization changes numerical precision. Distillation transfers selected behavior into a separate student through training. Retrieval and external skill memory can support the resulting system while remaining outside its weights. Evaluating MultiQuant therefore requires keeping the model, its adaptation, external memory, routing and tools identifiable in the evidence.

The native SCC contracts remain the mission representation. The MultiQuant observation bridge supplies proposals and observations; future training or fragment workers require actual qualified bindings before becoming executable stages.

Source: The five directions, prototype evidence and SCC–MultiQuant boundary.

Tensors, layers and traceable fragments

The internal-model part of this work concerns tensors, layers and their fragments. Micronetworks in SSI refer to persistent competencies with their own qualification history. A relationship between a stored weight fragment and a particular skill must be established experimentally.

My planned traceability work uses persistent identifiers and visual markings to follow fragment provenance and transformations. That can help answer which source a fragment came from, which variant used it and which evidence belongs to that variant. It still leaves separate questions about native inference integration, dependency handling and the effect on complete-model behavior.

The published R1–R7 series contains synthetic memory experiments and real-weight-fragment microbenchmarks. Its report states that the fragment prototypes were not connected to a complete native LLM runtime, and that automatic tensor-to-skill attribution was not demonstrated. This distinction is essential when describing future routing inside model computation.

Source: Memory experiments, fragment results and their relation to SSI.

Research Memory closes the experience loop

A useful experience record must connect the mission, the actual model and competence versions, the hardware constraints, the decision, the measured outcome and its evidence. Successful, failed and inconclusive attempts all matter when deciding what to do next.

The dated Universal Lab report from 9 October describes project-scoped observation history and a native qualification reader. At that snapshot, the inspected memory had observations but no skill trials or ranked native cases, and production Router binding remained unconnected. The stated next work includes version-bound trial evidence and passive comparison of memory recommendations with V10 decisions.

For the intended MultiQuant loop, this means that an earlier failure can motivate a bounded follow-up task, while a model profile carries only the conclusions its actual tests support. Evidence provenance and semantic correctness also remain separate: a signature establishes the origin/integrity of a record; the declared evaluation establishes whether its task succeeded.

Source: Research Memory progress, qualification boundaries and next integration gates.

The measured Qwen 1.5B result

The controlled report dated 21 October compares a source F16 artifact, a standard Q4_K_M control and an experimental SSI MultiQuant variant of Qwen2.5-Coder 1.5B.

Artifact	Whole GGUF file size, decimal MB
F16 reference	3093.67
Standard Q4_K_M	986.05
SSI MultiQuant Q4KM	955.05

The SSI artifact was 30,994,944 bytes smaller than the standard Q4 control, a 3.14% file-size reduction. Standard Q4_K_M itself uses different precisions for different tensor types, so mixed precision alone does not establish a novel technique.

The short CPU series used one inference thread, a 1024-token context, a 16-token generation limit and a 6 GiB cgroup memory limit. It ran standard Q4, SSI, SSI and standard Q4 in that order, with elementary arithmetic as the task.

Mean measurement	Standard Q4_K_M	SSI MultiQuant
Sampled process peak RSS, MiB	1623.755	1594.165
Total elapsed time, seconds	3.7605	3.7610
Correct short answers	2/2	2/2

This series measured approximately 1.82% lower sampled process peak RSS, with effectively unchanged elapsed time. It supports no speed advantage or general programming-quality claim.

Earlier R14 quality checks recorded 17/24 passing cases for F16 and 16/24 for SSI, with the disagreement in SCC Context. Exact checkpoint parity was not established, standard Q4 was absent from that quality suite, and generated code was not executed. The candidate therefore remains SHADOW-HOLD. The same report separately records eight laboratory R9 routing plans, all retained in HOLD; that laboratory router is distinct from production V10.

Source: Qwen 1.5B controlled evidence, quality limits and laboratory routing status.

What the 8 GiB experiments mean for an 8 GB goal

A separate R7 experiment completed three simple checks per public low-bit checkpoint under an 8 GiB cgroup limit:

Checkpoint	File size, decimal GB	Peak process RSS, GiB	Simple checks
Bonsai 2 27B PQ2_0	7.21	7.13	3/3
Qwen2.5-Coder 32B Q2_K	12.31	7.38	3/3
Llama 3.1 70B IQ2_XXS	19.10	7.13	3/3

The CPU runs used file-backed paging without system swap. The 70B checkpoint was larger than resident RAM and incurred substantial I/O/time costs. These results concern feasibility on simple tasks; they do not establish broad quality, full weight residency or an SSI-distilled student.

My goal is to qualify a useful SSI configuration on a computer with 8 GB of physical RAM, accepting longer response times where justified. That requires measuring operating-system and application overhead, context memory, storage behavior, latency and realistic task quality. The larger-teacher D4 student programme separately declares a 16 GiB target; its research envelope should remain distinct from the 8 GB physical-machine goal.

Sources: the R1–R7 report documents the feasibility runs; the D4/D5 declaration defines the separate student research goals.

A concrete next mission for the integrated system

The R14 disagreement supplies a useful qualification scenario. First establish common checkpoint and tokenizer provenance. Then freeze a representative evaluation protocol and compare F16, standard Q4 and SSI with matching settings. Retain the original disagreement as a diagnostic and use separate held-out tasks for any broader quality claim.

Within the intended integration, the Director defines that bounded mission. SCC preserves its goal, context, strategy, limits and acceptance reference. V10 selects qualified execution strategies and responds to outcomes; S10 contributes within its separately qualified consolidation or model-development scope. MultiQuant workers perform only the operations they are actually bound and authorized to perform. The laboratory evaluates the result, and Research Memory retains the experience with version-specific evidence.

That end-to-end connection needs its own demonstration. The migration roadmap identifies an audit of V10, Research Memory, the capability resolver and Notary, followed by an SCC adapter and a shadow mission test. Its production gate remains NOT_READY in the published record.

Source: BLOKS integration roadmap and R9.135–R9.137 gates.

Awareness by amnezja3: source code and credit

Awareness by amnezja3 is an important foundation of SCC. I reused and adapted selected code for canonical elements, identifiers, constructions, links and statistics, applying those mechanisms to SSI blocks, micronetwork identities and mission trajectories. The original library is a Polish-language processing project; its README explains its independent scope and examples.

The documented adaptation names three source modules from revision 2767692de842fb1184f17d3249fc17b72c7527d1:

Source module	Mechanisms used in the mission adaptation
baseTools.py	Canonical element/identifier, construction and link reads
checkerBaseOperators.py	Structure preparation, input/output partitioning and frequency-related analysis
addionalisAwe.py	Construction statistics and generation primitives

Original authorship remains credited to amnezja3. The mission-language adaptation, SCC parser and SSI integration are my work. I thank the author for releasing Awareness under the MIT licence. The SCC provenance record identifies the selected functions and adaptation history.

The next stage

The development goal is an SSI workflow in which a mission has a stable meaning, each component has a defined responsibility, and model configurations are chosen and improved using verified experience. SCC supplies the common mission structure. V10 supplies stateful strategy selection and recovery. S10 contributes context-specific consolidation and the developing distillation role. MultiQuant supplies model research candidates and measurements, while Research Memory retains what those experiments actually established.

The public links point directly to the relevant files, with SSI references pinned to the dated commits used for this article. The repository publishes architecture, results and claim boundaries; proprietary implementation, model recipes, private prompts, weights and operational data remain private. Further claims about student quality, internal model routing or production integration will follow their corresponding measured gates.

AI assistance was used to prepare and edit the English text.

Universal Lab and SSI MultiQuant toward practical LLMs on 8 GB RAM

Wed, 21 Oct 2026 21:26:23 +0000

By jankes72
21 October 2026

I am developing SSI MultiQuant as the next stage of SSI V5: bringing model size, memory use and measured task quality into the decisions made by an agent system.

One published comparison is deliberately narrow. An experimental Qwen2.5-Coder 1.5B GGUF file is 3.14% smaller than a standard Q4_K_M control. In a short CPU test, its sampled process peak RSS was about 1.82% lower, while elapsed time was effectively unchanged. Quality equivalence is still unresolved.

The larger goal is to connect this work with Universal Lab, Research Memory, Router V10, the planned S10 selection path and SCC execution. That integration remains under development. This article separates the measurements already published from the workflow I want to qualify next.

Why this belongs inside SSI

SSI V5 is being developed around a Director that plans work, assigns execution and collects evidence. Its model choices need to fit the task and the available hardware.

A configuration that saves memory on a short formatting task may behave differently on a longer programming task. A useful model profile therefore needs more than a file-size label: it needs a record of what was tested, on which hardware, with which constraints, and with what outcome.

MultiQuant is the experimental model-configuration layer in this work. I want SSI to compare complete configurations and retain the evidence behind each choice.

Universal Lab and Zero Lab

Universal Lab is the research collaboration environment around SSI. The design brings participant conversation, Director interaction, bounded experiments and inspectable session evidence into one workflow.

The published Universal Lab baseline includes authenticated web access and a configured translation bridge on modest development hardware. The later A16 installation report records 48 topics in Polish and English at three levels of detail, giving 288 prepared answer variants. Its build recorded 41 passing Python tests. Installation and HTTP readiness were reported by the operator; live narration remained a separate validation step.

Zero Lab is the separate experiment and validation layer. Its role is to freeze the protocol and evaluation criteria before execution, preserve the resulting evidence, and retain unsuccessful or inconclusive runs. Universal Lab provides the collaboration setting; Zero Lab supplies bounded experimental discipline.

This is where MultiQuant fits: a model configuration can become a candidate for a controlled experiment, with its memory savings assessed alongside the behavior that matters for a particular task.

Hermes: from first contact to a client-specific benchmark

Update — 22 October 2026. The following describes my current environment and client workflow as its operator; it is separate from the controlled model measurements below.

Hermes already provides a first point of contact before Universal Lab login. A visitor can ask questions about SSI, receive links to the relevant project information, describe their project and request temporary access or login credentials. An access request is reviewed separately from granting access.

The Hermes module runs directly on the Director's computer, with a RAM budget set aside for this work to reduce interference with other tasks. The final isolation mechanism remains open: I am considering a container with a chosen swap policy or another dedicated module. The current description does not establish measured hard isolation.

My planned replacement is my own small SSI model, specialized for questions about the project through distillation and memory optimization. That model is a development goal. The interface is intended to identify the model actually serving the visitor, so users can see what they are testing. The public assistant may respond slowly within its resource budget; I describe a faster work path after login, without claiming a measured latency guarantee.

After access is granted, we can meet inside Universal Lab using its camera and translation facilities. While we discuss the client's needs, CZARA in Shadow can prepare or run an experiment or benchmark tailored to the problem. This laboratory work proceeds alongside the conversation and supports the findings and solution proposal we review. A proposal, an experiment in progress and a result that has passed its declared checks should remain clearly identified.

After the meeting, I can send the benchmark information by email and discuss the implementation required for the client's project. The aim is to connect a concrete need to a bounded experiment and an inspectable result. The workflow does not promise a qualified solution to every problem during one call.

This update records my operator account, rather than a new independent live-session, isolation or model-training test. The exact public source is the dated Hermes and Universal Lab client-workflow report.

What the model comparison measured

The published control report compares a source F16 artifact, a standard Q4_K_M artifact produced using llama.cpp, and an experimental SSI MultiQuant Q4KM artifact.

Artifact	Whole GGUF file size in decimal MB
F16 reference	3093.67
Standard Q4_K_M	986.05
SSI MultiQuant Q4KM	955.05

The SSI file is 30,994,944 bytes smaller than the standard Q4 control, a reduction of 3.14%. These are whole-file measurements. Standard Q4_K_M already uses different precisions for different tensor types; mixing precisions alone does not establish a novel method.

The short native CPU comparison used one CPU thread, a 1024-token context, a 16-token generation limit and a 6 GiB cgroup memory limit. The task was elementary arithmetic.

The four-run order was standard Q4, SSI, SSI, standard Q4. There were two runs per configuration.

Mean measurement in this series	Standard Q4_K_M	SSI MultiQuant
Sampled process peak RSS in MiB	1623.755	1594.165
Total elapsed time in seconds	3.7605	3.7610
Correct short answers	2 of 2	2 of 2

The sampled RSS difference was about 29.59 MiB, or 1.82%. RSS describes the resident memory of the sampled process, not the total memory consumption of the host.

The elapsed-time difference was 0.0005 seconds. Variation between successive runs was much larger, so this series supports no speed advantage. The arithmetic answers establish only that these short native runs completed correctly.

Quality remains part of the decision

Earlier R14 checks covered 24 cases:

Group	F16 reference	SSI candidate
Python AST	8 of 8	8 of 8
Typed Routing	5 of 8	5 of 8
SCC Context	4 of 8	3 of 8
Total	17 of 24	16 of 24

One SCC Context case passed for the reference and failed for SSI. The other 23 case verdicts agreed.

This comparison has material limits. Exact source-checkpoint parity was not established, the standard Q4 control was not evaluated on this quality suite, and generated code was not executed in R14. The additional failure cannot therefore be attributed solely to quantization.

The candidate remains SHADOW-HOLD: available for research evaluation, without production qualification. The practical result so far is a smaller file and a lower sampled RSS in a limited comparison, with more quality work required.

Large model feasibility under an 8 GiB limit

A separate R7 experiment has already tested complete public low-bit checkpoints, including Bonsai 2 27B, under an 8 GiB cgroup RAM limit. The Linux CPU host used four inference threads, no system swap, one request at a time and a 512-token context.

Public checkpoint	File size in decimal GB	Peak process RSS in GiB	Simple checks passed	Observed short reply time
Bonsai 2 27B PQ2_0	7.21	7.13	3 of 3	8.9 to 83.3 seconds
Qwen2.5-Coder 32B Instruct Q2_K	12.31	7.38	3 of 3	72.4 to 147.5 seconds
Llama 3.1 70B Instruct IQ2_XXS	19.10	7.13	3 of 3	186.1 to 371.6 seconds

All nine simple final checks passed, with no recorded out-of-memory event. The checks covered arithmetic, context extraction and structured sorting.

These runs used native inference and file-backed paging. A file larger than resident RAM can be read through the operating system as needed, at a substantial I/O and time cost. The result does not mean that all 70B weights fit in RAM at once, or that an SSI model was distilled from that checkpoint.

Bonsai was affected by a concurrent download, and the final 70B run used a different configuration from the smaller models. These timings are observations, not a controlled cross-model speed ranking. Three simple checks per model also do not establish general task quality or practical long-context performance.

My next goal for an 8 GB computer

I intend to prepare and validate an SSI-compatible model configuration for a computer with 8 GB of physical RAM, accepting longer response times where necessary. The aim is a useful, measured configuration that can participate in the wider SSI workflow.

An 8 GiB process-group limit on a research host is different from an entire computer with 8 GB available to its operating system and applications. The target configuration must be evaluated with that overhead, storage access, context length and realistic tasks included.

I will report task quality, peak memory, time to first output and complete response time, including unsuccessful cases. The model size and configuration should follow those measurements. The existing Bonsai, Qwen and Llama feasibility runs are starting evidence for that work, rather than a guarantee for every 8 GB machine.

What readers can get from GitHub

The public SSI V5 repository provides downloadable architecture documents, experiment reports, benchmark summaries, protocols and declared research limits. The published R1 to R7 memory report includes the Bonsai 27B feasibility result above; the separate MultiQuant control report records the Qwen 1.5B measurements.

Readers can use those materials to review the research progression and identify a bounded scenario they would like to discuss or test. Universal Lab is the wider collaboration and experiment environment described by those documents.

The public repository is the evidence and documentation layer. It does not currently provide a complete Universal Lab installer or redistribute the tested model weights. The proprietary SSI implementation and detailed model recipes remain private.

Connecting Research Memory with S10 and V10

The next development stage is the connection between stored experimental experience and the tasks SSI chooses to run.

The proposed division of responsibility is:

Component	Intended role in the integrated workflow
Director	Generate bounded tasks and assign work against the mission and resource budget
Research Memory	Retrieve relevant prior outcomes and retain the context of each new result
S10	Consolidate competencies across SSI lines; a further model-space role in distillation is part of the developing integration
Router V10	Choose a qualified next strategy using task state, competence, confidence and attempt history
SCC	Represent the mission's goal, context, strategy, constraints and acceptance requirements for planning and qualified execution
Zero Lab	Fix the experimental contract and evaluate against its declared criteria

SCC supplies a common mission structure; qualified executors perform the work. S10's external consolidation role and developing model-distillation role have different qualification requirements. For the detailed responsibilities and direct source-file links, see SCC, V10 and S10: Connecting SSI MultiQuant into One System.

A full production connection between these components and MultiQuant has not yet been established by the published evidence. The R9 laboratory router work is separate from production Router V10: the public report records eight checked plans, all retained in HOLD.

How the next tasks should be generated

In the intended loop, Research Memory supplies relevant earlier cases, measurements and unresolved questions. The Director turns those into bounded tasks with an objective, resource limits and evaluation criteria. S10 and V10 then help choose the specialist path and model configuration.

After execution, Research Memory should retain the actual outcome together with the task context, model profile, hardware constraints, timings and evidence reference. The next selection can then use that experience.

For example, the R14 SCC Context disagreement gives us a concrete follow-up. First establish common checkpoint and tokenizer provenance. Then compare F16, standard Q4 and the SSI candidate on a representative held-out task family, while preserving the original case as a diagnostic.

The disputed example must not become the only target for a repair. Any adaptation needs separate training or calibration material and a held-out evaluation, so that learning the example does not masquerade as broader improvement.

This is the purpose of generating tasks from experience: each experiment should resolve a specific uncertainty and produce evidence useful to the next decision.

Calibration and adapter experiments

The planned AutoRepair work broadens the choices under evaluation. Calibration and adapter-based approaches such as LoRA should be compared in context rather than assumed to have a universal ranking.

Further investigation includes QLoRA, LoftQ and adapter composition or selection, including X-LoRA. These remain research directions for this integration. No improvement to the measured Qwen candidate is claimed here from those methods, and no completed distillation is claimed.

The object of comparison should be the complete configuration: model, quantization, optional adaptation, task family and hardware budget. An untested variant should remain untested in Research Memory rather than receive an invented score.

What comes next

The immediate measurement priorities are common checkpoint and tokenizer provenance, a standard-Q4 control on the same representative quality suite, and more repetitions with varied run order. The measurements should distinguish model loading, prefill, decoding, process memory and cgroup memory.

Integration qualification is a separate step. It needs to show that the Director can generate a bounded task, the intended selectors can make an evidence-based choice, execution can return a verifiable result, and Research Memory can preserve the experience without changing the original verdict.

That is the next stage of SSI development I am working toward: model optimization becomes one measured input to an agent system that can choose, test and improve its configurations.

Public evidence

The public material shares architecture, measurements and their limits. Proprietary implementation, model recipes, private prompts, weights and operational data remain private.

AI assistance was used to prepare and edit the English text.