19. EGGROLL Swarm Retraining Architecture¶

19.1 Purpose¶

This document defines how GeniusCognitiveSystem extends beyond distributed inference into distributed retraining using an EGGROLL-style evolutionary optimization workflow mapped onto GNUS.ai swarm infrastructure.

The goal is not to replace the existing GeniusCognitiveSystem architecture. The goal is to add a swarm-native retraining layer that allows the Semantic Core, expert models, adapters, routing policies, verifier-like modules, and bounded Expert Judgment Models (EJMs) to be improved using locality-aware peer coordination, deterministic perturbation reconstruction, compact fitness communication, and reputation-gated promotion.

This architecture treats retraining as a first-class GNUS.ai operating system primitive.

19.2 Architectural Position¶

Within the GeniusCognitiveSystem, the major architectural roles become:

Semantic Core + Experts: inference architecture
Router + Swarm Thinking Context: execution and reasoning architecture
GAML: memory and structured recall architecture
Reputation + Consensus: trust and output selection architecture
EGGROLL Swarm Retraining: specialist refresh and adaptation architecture

This means:

Adapter-style specialization remains valid as an adaptation artifact
Expert-based routed execution remains the inference strategy
EJMs remain Expert Model specializations with bounded typed outputs, not a separate runtime layer
EGGROLL becomes the distributed retraining and expert-refresh mechanism

EGGROLL therefore complements the current design rather than replacing it.

19.3 Why EGGROLL Fits GNUS.ai¶

Traditional distributed backpropagation assumes:

tightly coupled GPUs
high-bandwidth gradient exchange
large optimizer state synchronization
low-latency datacenter networking

That assumption does not match GNUS.ai.

GNUS.ai is:

peer-to-peer
locality-aware
heterogeneous across devices
reputation-mediated
designed around distributed processing rooms, IPFS distribution, and compact network coordination

An EGGROLL-style method is a stronger fit because workers can:

receive a model version reference
reconstruct a low-rank perturbation from a deterministic seed
evaluate local fitness
return only compact fitness values and metadata

This converts much of retraining into an inference-like workload with minimal per-step network payload.

That property aligns naturally with:

GNUS processing rooms
DHT-based locality
IPFS-lite model distribution
libp2p pub/sub coordination
gRPC result transport
reputation-gated verification

19.4 Design Principles¶

The EGGROLL retraining layer follows these principles:

19.4.1 Locality First¶

Training work should preferentially remain within local beehives or sub-swarms that already hold the relevant model, adapter, task shard, or domain context.

19.4.2 Deterministic Reconstruction over Tensor Shipment¶

Low-rank perturbations should be reconstructed from deterministic seeds rather than transmitted as full tensors.

19.4.3 Compact Fitness over Gradient Exchange¶

Workers should return compact fitness signals, validation metadata, and attestations rather than full gradients or optimizer state.

19.4.4 Adapter-Oriented Evolution¶

The preferred first retraining targets are expert adapters or specialist micro-models rather than full core-model retraining.

19.4.5 Reputation-Gated Promotion¶

No retrained artifact should be promoted by raw fitness alone. Promotion requires validation, safety checks, and reputation-aware acceptance.

19.4.6 Hierarchical Swarm Aggregation¶

Retraining should scale from local room coordinators to higher-level aggregators rather than assuming a single global coordinator.

19.4.7 Calibrated Judgments¶

For EJMs, fitness and promotion should measure probability quality as well as classification or scoring accuracy. A bounded expert that reports confidence should be rewarded for calibrated uncertainty and penalized for confident errors.

19.5 Relationship to Adapters and Expert Execution¶

EGGROLL does not eliminate adapters, expert modularity, or routed execution.

Instead:

Adapters define one class of artifact being updated
Expert execution defines how inference uses those artifacts
EJMs define a bounded expert contract where machine-consumed judgments and calibrated probabilities are preferred over generated prose
EGGROLL defines how the swarm improves them over time

Therefore the architectural progression becomes:

Base Model -> Expert Adapter / Expert Micro-Model -> Routed Inference -> Outcome Signal -> EGGROLL Retraining Job -> Improved Expert Version

This makes GeniusCognitiveSystem a system that can evolve expert capability using swarm-native retraining rather than relying exclusively on offline centralized fine-tuning.

19.6 Core Training Primitive¶

The basic EGGROLL retraining primitive is:

base model reference + target adapter reference + deterministic perturbation seed + task shard + reward function -> compact fitness packet

At minimum, a training job should define:

target model version or base model CID
target adapter CID or expert artifact ID
perturbation rank
perturbation scale or sigma
perturbation seed or seed range
task shard reference
reward or objective definition
validation policy
safety policy hash
promotion policy

For EJM targets, the reward definition may also include calibration metrics such as log loss, Brier score, calibration error, or reliability by confidence bucket in addition to task accuracy.

This primitive is intentionally compact and swarm-friendly.

19.6.1 EJM Distillation Storage and Sharding¶

For bounded judgment retraining, the task shard must preserve the distinction between canonical semantic decision data and target-specific compiled training views.

The canonical store is content-addressed and reusable across compatible experts:

Canonical Decision Corpus
    ├── shared StateObject A
    │     ├── independent DecisionBranch 1
    │     ├── independent DecisionBranch 2
    │     └── dependent DecisionBranch 3
    └── shared StateObject B
          └── DecisionBranch 4

A shared state may be a document, event history, program context, workflow state, grounded evidence bundle, or another policy-approved cognitive state. Each branch stores a bounded criterion, semantic choices, teacher probability targets, optional verified outcome, dependency semantics, provenance, and governance references.

Independent branches may reuse the same state, prefix/KV cache, or equivalent processor state, but must not observe sibling questions, sibling answers, or sibling intermediate state. Dependent/sequential branches declare predecessor branch IDs explicitly.

Canonical decision targets are semantic rather than tokenizer-specific. The store should preserve values such as:

approve = 0.73
review  = 0.21
reject  = 0.06

rather than storing one target model's answer-token IDs as the canonical truth.

A Distillation View Manifest compiles canonical records for one training target. Its identity should include at least:

target model lineage / base artifact
target expert artifact
capability, normally JUDGE
judgment family
optional paired language expert
tokenizer version/hash
prompt/template version/hash
readout or decision-head kind
training objective
validation policy
effective privacy/training governance
referenced canonical state/branch shards

The primary partition is therefore target model lineage + target expert + capability + judgment family, not merely an ELM name. A Code Expert may expose both ELM generation and EJM judgment artifacts, while a Router EJM may expose only bounded judgment.

Canonical shared state and branches should be independently sharded and content-addressed:

StateShard
  State A
  State B

BranchShard
  State A -> Q1
  State A -> Q2
  State A -> Q3
  State B -> Q1

An EGGROLL EJM task_shard_ref resolves to a small manifest joining the selected Distillation View + StateShard + BranchShard + objective/policy. This avoids duplicating large teacher contexts across specialist datasets and allows a worker to load or prefill one state once before evaluating many isolated branches.

This storage model also allows multiple expert views to reference the same canonical decision records without sharing promotion state. Code, Math, Grounding, Router, Verifier, or other experts retain independent calibration, benchmark, canary, rollback, quantization, and promotion histories.

19.7 GNUS Processing Room Mapping¶

EGGROLL retraining should be implemented over GNUS processing rooms.

Mapping:

Processing room host -> local retraining coordinator
Worker peers -> perturbation evaluators
Data chunk / sub-block -> task shard or seed assignment
Processing result -> compact fitness packet
Room lifecycle -> one training generation or one bounded retraining phase

This means GNUS does not need a completely separate training control plane. It can reuse the processing-room model already used for distributed work assignment and result collection.

19.8 Beehives and Locality-Aware Sub-Swarms¶

A beehive is a locality-aware sub-swarm that shares one or more of the following:

cached model artifacts
cached adapter or decision-head artifacts
cached canonical state shards
cached EJM branch shards / distillation views
domain-specific task shards
geographic or network proximity
hardware similarity
policy or privacy boundary

Beehives are important because they reduce unnecessary artifact movement.

A beehive may specialize around:

a model/expert lineage
a domain specialist or judgment family
a language or region
an application domain
a user-data enclave
a hardware class such as GPU, CPU, mobile NPU, or low-memory edge device

Higher-level swarm retraining should aggregate across beehives rather than forcing every node to participate in every generation.

19.9 Deterministic Perturbation Reconstruction¶

Perturbations should be reconstructed from deterministic seeds rather than stored or transmitted in full.

A seed derivation function may include:

model version
adapter version
layer or module identifier
worker node ID
generation ID
perturbation index

Example conceptual form:

seed = H(model_version, adapter_version, layer_id, worker_id, generation_id, perturbation_id)

This enables:

reproducible evaluation
auditability
replay for dispute resolution
compact job descriptions
lower storage overhead
selective fraud checking by re-running suspicious assignments

19.10 Worker Execution Model¶

A retraining worker performs the following steps:

Resolve the referenced base model and adapter/decision-head artifacts from local cache or IPFS-lite.
Resolve the task-shard manifest and its state/branch shards under the declared governance boundary.
Reconstruct the perturbation from the assigned seed and rank.
Apply the perturbation to the target adapter, head, or expert parameters.
Execute the assigned task shard locally, grouping branches by shared state where possible.
Compute fitness according to the declared reward function.
Package fitness output, latency, shard identity, and attestation metadata.
Return a compact result packet to the room coordinator.

This workload is intentionally closer to inference than to classical synchronized backpropagation.

19.11 Fitness Packet Design¶

Fitness packets should be small, signed, and auditable.

A worker result should include at minimum:

{
  "training_job_id": "uuid",
  "worker_node": "node_id",
  "artifact_target": "math_verifier_adapter_v3",
  "seed_range": [100000, 100255],
  "fitness_values": "packed_or_scalar_payload",
  "latency_ms": 123,
  "validation_flags": {
    "self_check_passed": true,
    "policy_hash_match": true
  },
  "result_signature": "ed25519"
}

Compact encoding is strongly preferred. Model-size-independent communication is a core design goal.

19.12 Aggregation Model¶

The local coordinator aggregates worker fitness packets into a weighted update.

Coordinator responsibilities:

verify signatures and policy compatibility
reconstruct perturbations from seeds
weight valid worker fitness values
reject malformed or suspicious results
compute generation-level update candidates
run validation checks before publication

Aggregation should be hierarchical when the swarm is large:

worker -> room host
room host -> beehive aggregator
beehive aggregator -> broader promotion or merge layer

This preserves locality while allowing wider adoption of successful specialist updates.

19.13 Reputation and Validation Extensions¶

Retraining introduces new trust problems because workers return compact scalar-like signals that are cheap to fake.

Therefore EGGROLL retraining requires:

redundancy on sampled assignments
challenge tasks
hidden validation shards
consistency checks across duplicate workers
reputation penalties for suspicious deviation
minimum reputation thresholds for high-value jobs

Recommended new reputation dimensions include:

Trainer_score
Validation_score
Adapter_promotion_score
Domain_trainer_score by specialist area

These extend the current reputation architecture rather than replacing it.

19.14 Embedded Retraining Loop¶

The GeniusCognitiveSystem should support an embedded retraining loop.

19.14.1 Normal Inference Path¶

Router selects core + specialists.
Swarm executes inference.
Aggregator produces final result.
Grounding, safety, and memory processes evaluate outcome quality.

19.14.2 Learning Event Creation¶

A learning event may be emitted when one or more are true:

exact correctness is known
verifier disagreement identifies a recoverable failure
grounding validation identifies contradiction
formatting or schema checks fail
an EJM is measurably miscalibrated or confidently wrong
user feedback is strongly positive or negative
repeated workflow success creates a strong pattern

19.14.3 Retraining Conversion¶

The learning event becomes a retraining job targeting a specific component such as:

planner
router or routing EJM
intent or risk EJM
numeric specialist
math verifier
formatter
grounding specialist
code specialist
synthesizer or arbiter

19.14.4 Artifact Publication¶

If validation passes, the new adapter or specialist version is:

signed
content-addressed
distributed via IPFS-lite
canary deployed
reputation monitored before broad promotion

This makes retraining part of normal swarm operation rather than a separate offline process.

19.15 Best Initial Retraining Targets¶

The first retraining targets should be specialist components with clear reward functions.

Recommended initial targets:

19.15.1 Numeric Specialist / Math Verifier¶

Reward signals:

exact-match correctness
symbolic verification success
arithmetic consistency

19.15.2 Router / Planner Specialist¶

Reward signals:

quality improvement vs baseline route
latency-adjusted utility
specialist selection accuracy
calibration of bounded routing probabilities when implemented as an EJM

19.15.3 Formatter / Schema Specialist¶

Reward signals:

JSON validity
schema compliance
formatting correctness
user preference match

19.15.4 Grounding Specialist¶

Reward signals:

factual agreement with retrieved knowledge
contradiction reduction
improved citation alignment

19.15.5 Code Specialist¶

Reward signals:

test pass rate
compile success
static analysis success
minimal-diff acceptance

19.15.6 Expert Judgment Models (EJMs)¶

Reward signals:

classification or ranking accuracy
log loss or Brier score
calibration error and reliability by confidence bucket
low confident-error rate
correct escalation at uncertainty thresholds
downstream utility of the judgment
option-order / permutation robustness
paraphrase robustness
out-of-distribution and novel-composition performance
dependency-depth degradation
independent-branch isolation correctness under shared-state batching

EJM promotion should not favor raw accuracy at the cost of systematically overconfident errors. Promotion metrics should remain target-view and shard aware so one specialist's gain or regression is not hidden inside an aggregate EJM score.

These targets are preferred because they are easier to score and safer to validate than full core-model evolution.

19.16 Safety and Governance Constraints¶

EGGROLL retraining must follow the same safety and trust principles as inference.

Requirements:

retraining jobs must carry policy hashes
workers must use approved artifact versions
unsafe or policy-violating outputs must not be promoted
untrusted memory must not directly drive training without curation
promotion should require validation against trusted evaluation sets
high-impact adapters should use canary release before wider adoption
EJM confidence must not grant authority that belongs to deterministic policy or capability services

This ensures retraining does not become a backdoor for poisoning the specialist ecosystem.

19.17 Constraints and Non-Goals¶

This architecture does not imply that arbitrary low-end devices can immediately retrain large dense models without other design changes.

Important constraints remain:

workers must still execute the relevant model or adapter locally
model size remains a deployment constraint
heterogeneous devices introduce stragglers and availability variance
scalar fitness communication reduces bandwidth, not compute demand
local beehives can overfit if global mixing is poorly designed

Therefore the initial focus should be:

small or medium specialist artifacts
quantized or recurrent-friendly models
domain-specific adapters
bounded EJMs with measurable outcomes
validation-rich tasks with measurable outcomes

Full-core training should be treated as a later-stage research direction.

19.18 Rollout Plan¶

19.18.1 Phase 1 — Single-Machine Proof¶

deterministic perturbation reconstruction
specialist adapter target
compact fitness aggregation
validation loop

19.18.2 Phase 2 — Local Beehive¶

10 to 50 heterogeneous peers
one room host
local task shards
direct fitness packet return

19.18.3 Phase 3 — GNUS Processing Room Integration¶

training-room lifecycle
IPFS-lite artifact addressing
gRPC or libp2p coordination integration
signed worker results

19.18.4 Phase 4 — Reputation and Redundancy¶

duplicate assignments
challenge tasks
trainer score updates
suspicious worker quarantine

19.18.5 Phase 5 — Hierarchical Swarm Aggregation¶

beehive aggregators
cross-beehive promotion
canary adapter rollout
broader swarm adoption logic

19.19 Strategic Positioning¶

The strategic significance of this layer is that it makes training behave more like decentralized inference.

GNUS.ai should not frame this as merely distributed backpropagation over weak devices. Instead it should be framed as:

locality-aware swarm retraining through deterministic low-rank perturbation evaluation and compact fitness aggregation

This gives GNUS.ai a differentiated operating-system-level story:

distributed inference
distributed memory
distributed reputation
distributed settlement
distributed retraining

Together, these form a distributed adaptive intelligence system rather than only a decentralized inference network.

19.20 Summary¶

EGGROLL Swarm Retraining adds a new capability to GeniusCognitiveSystem:

specialist refresh without centralized gradient training
beehive-local retraining based on locality and cached artifacts
deterministic seed-addressed perturbations
compact fitness communication
calibration-aware improvement of bounded EJMs
content-addressed canonical decision corpora with model/expert-specific distillation views and shards
locality-aware retraining that prefers cached model/head/state/branch artifacts
reputation-gated validation and promotion
embedded learning from real swarm outcomes

This layer does not replace the existing GeniusCognitiveSystem architecture. It completes it by giving the swarm a native mechanism for improving its specialists over time.