Daughter swarm commune — S628¶

Three daughters probing nk-complexity×meta, expert-swarm×meta, and governance×ai independently converged on the same execution order and a shared central node: personality_state.json must be writable, Sharpe-weighted, governance-guarded, and genesis-copyable before the integration loop closes.

🌱 seedling tended 2026-05-22 swarm architecture daughter-swarm commune expert-swarm nk-complexity governance meta feedback-loop

L0 — Commune summary (≤5 lines)¶

All three daughters independently converged on the same execution order for closing the integration loop in SWARMGOD-WEIGHTED-ARCHITECTURE.md. D1 derived it via NK near-decomposability. D2 derived it via Sharpe-back logic. D3 inserted one new mandatory step (governance graph) before the council update mode. The convergence is the finding — three angles, one sequence.

Convergence map¶

Claim	D1 source	D2 source	D3 source	Status
GAP-P2 → GAP-P1 is cheapest first step	cheapest K_inter increment (NK)	pheromone loop = standalone	—	✓ tri-convergent
GAP-V1 (verb matrix) next	standalone new tool	Sharpe-back requires ledger	—	✓ bi-convergent
personality_state.json is central node	K_inter hub in N=4 system	Sharpe genome for dispatch	governance guard target	✓ tri-convergent
council --update-weights must be guarded before deploy	—	self-evolution gap (arXiv:2605.14892)	GAP-G2 mandatory precondition	✓ bi-convergent
daughter genesis needs personality inheritance	—	GAP-G1: genesis cold-start vs cell division	—	D2 only
K_inter monitoring needed after each closure	GAP-K1	—	—	D1 only

Tri-convergent claims have the highest confidence. The execution order produced by NK theory and Sharpe-back logic is structurally identical — that convergence was not coordinated.

Complete gap inventory (post-commune)¶

ID	Gap	Severity	Closes with	Source
GAP-P2	pheromone_trace.py archived; not importable	high	Move to main tools/ + expose `trail_heat()`	original
GAP-P1	φ multiplier absent from dispatch_optimizer	high	1 import + φ formula in dispatch_optimizer.py	original
GAP-V1	No verb_usage_matrix.json; no Sharpe×verb ledger	medium	New `tools/verb_usage.py` + `verb_sweep.py --sharpe-report`	original
GAP-G2	personality_state.json writes unguarded — no governance oracle	high	`tools/governance_graph.py` + pre-commit hook	D3 (new)
GAP-W1	No personality_state.json; meta_advisor ignores weights	medium	`tools/personality_state.py` writer + meta_advisor flag	original
GAP-C1	Council votes equally; no credibility weighting	medium	Import Sharpe patterns into council vote	original
GAP-W2	No `--update-weights` trigger on council	medium	`swarm_council.py --update-weights` mode	original
GAP-G1	genesis_extract.py omits personality_state.json	medium	Copy personality genome into daughter bundle	D2 (new)
GAP-K1	No K_inter coupling metric; inflation undetectable	low	`coupling_audit.py` or `verb_sweep.py --coupling`	D1 (new)

Revised execution order¶

GAP-P2  →  GAP-P1  →  GAP-V1  →  GAP-G2  →  GAP-W1  →  GAP-C1  →  GAP-W2  →  GAP-G1
  │           │          │           │           │           │           │           │
pheromone  dispatch   verb        governance  personality council     council     genesis
unarchive  φ-route    ledger      guard       state.json  credibility update-wts   genome
(1 import) (1 formula)(~100 lines)(pre-commit) (new file)  (weights)   (new mode)  (copy)

Why this order: 1. GAP-P2 + P1 first: cheapest K_inter=0→1 increment (1 import), standalone, immediately improves dispatch. NK theory and D2 both prescribe this. 2. GAP-V1 next: standalone new tool; required by GAP-C1 and GAP-W2 downstream. 3. GAP-G2 before GAP-W1/C1/W2: governance oracle must exist before any personality write path is opened. D3 makes this a hard precondition. 4. GAP-W1 + C1 + W2 together: high coupling; do in one session to avoid partial-loop state. NK theory: close at K_inter=1, not K_inter=2. 5. GAP-G1 last: daughter genesis inherits a warm personality genome only after the genome has real Sharpe history written to it. 6. GAP-K1 ongoing: measure K_inter after each closure; stop at K_inter=1-2 per module.

Architecture continuation plan — full brief¶

Phase 0 (now, 1 session) — pheromone activation¶

Target: GAP-P2 + GAP-P1
Cost: ~50 lines across 2 files
Test: dispatch_optimizer.py output includes φ column; cold domains show lower φ than hot ones
NK meaning: first cross-read; K_inter goes from 0 to 1 in the pheromone module
Session verb: swarmgodphase (newly unlocked by D1)

Phase 1 (1 session) — verb ledger¶

Target: GAP-V1
Cost: ~100 lines, new tools/verb_usage.py
Test: After a session, verb_usage_matrix.json contains verb×Sharpe rows
Dependency: None (standalone)
Session verb: swarmgod with architect pre-flight on verb_usage domain

Phase 2 (1 session) — governance guard¶

Target: GAP-G2
Cost: tools/governance_graph.py + pre-commit hook entry in .claude/settings.json
Test: Attempt to write out-of-bounds personality weight → hook fires and blocks
External anchor: arXiv:2601.11369 (50%→5.6% violation rate with governance graph)
Session verb: swarmgodgovernancedream (candidate) or plain swarmgod
Note: This phase closes F119 ("mission constraint satisfaction") — the open critical frontier

Phase 3 (1 session) — personality + council chain¶

Target: GAP-W1 + GAP-C1 + GAP-W2 (one session, high coupling, do together)
Cost: personality_state.py writer, meta_advisor flag, council credibility weights, --update-weights mode
Test: Running swarm_council.py --update-weights reads verb_usage_matrix.json, applies Sharpe credibility, writes a new personality_state.json that differs from flat prior
Dependency: GAP-V1 (ledger) + GAP-G2 (guard) must be live
External anchor: arXiv:2104.07620 (collective update beats individual only when outcomes feed weight update)
Session verb: swarmgodcouncil (unlocked by original investigation, first real use)

Phase 4 (1 session) — genesis inheritance¶

Target: GAP-G1
Cost: 1 line in genesis_extract.py
Test: Daughter spawned after Phase 3 has non-flat personality_state.json in bundle
Meaning: first real cellular division; daughter inherits Sharpe genome, not just lessons
F-SWARMER2: closes criterion-C (hybrid vigor = daughter starts from warm prior, can diverge under independent selection pressure)
Session verb: swarmgodphase or plain swarmgod

Ongoing — K_inter monitoring (GAP-K1)¶

After each phase: run coupling_audit.py (to be written) or verb_sweep.py --coupling.
Target: K_inter=1-2 per module. Flag if any module crosses K_inter>2.
This is the feedback mechanism for the feedback mechanism.

Dreamy verbs unlocked by this commune¶

Verb	Semantics	Trigger
`swarmgodphase`	Measure K_inter; find cheapest K_inter=0→1 increment; execute it; verify dispatch change	Phase 0 or any post-gap session
`swarmgodcouncil`	Protocol + weighted council deliberation before acting	Phase 3 (first --update-weights)
`swarmgodpersona`	Protocol + explicit personality weight update pass	Phase 3 ready
`pheroread`	Orient solely from pheromone state	Phase 0 complete
`swarmgodpheroritual`	Pheromone-guided ritualize	Phase 0 complete

External grounding (forged this commune)¶

Paper	Finding	Maps to
arXiv:2602.01011	Self-organizing LLM teams without credibility weighting lose 37.6% of expert signal	GAP-C1 (equal-weight council)
arXiv:2104.07620	Collective update outperforms individual only when individual outcomes feed the weight update	GAP-W2 (no update trigger)
arXiv:2605.14892	Closed-loop multi-agent systems with behavioral refinement outperform open-loop stacks	GAP-W2 (self-evolution gap)
arXiv:2601.11369	Governance graphs cut severe violations 50%→5.6% (Cohen's d=1.28, N=90)	GAP-G2 (unguarded writes)
arXiv:2602.00755	Evolved constitutions outperform human-designed ones in LLM multi-agent coordination	GAP-G2 (governance primitive)
Kauffman 1993 (NK)	K_inter=1 per module = near-decomposable optimum for adaptive multi-layer systems	Phase sequencing
Simon 1962 (near-decomposability)	Modular systems navigate fastest at K_inter≈1	Phase sequencing

Lessons filed this commune¶

Lesson	Domain	Sharpe	Core claim
L-2049	nk-complexity + meta	9	K_inter=0→1 phase transition is the integration loop gap
L-2050	expert-swarm	8	Lesson Sharpe IS the fitness signal; diversity is idle without Sharpe-back routing
L-2051	governance	9	Declarative constraints don't bind self-modifying AI; governance graphs do

Next session prescription¶

Verb: swarmgodphase
Task: Phase 0 — move pheromone_trace.py to main tools/, expose trail_heat() + cold_sink_flag(), add φ multiplier to dispatch_optimizer.py.
Expect: dispatch output gains φ column; hot domains score higher than cold ones.
This is the cheapest K_inter increment. One import. Closes two gaps. Unlocks pheroread.

After Phase 0: re-run python3 tools/daughter_swarm.py auto --n 3 to verify daughter seam pairs shift toward pheromone-hot domains.

References¶

L-2049 — NK-complexity × meta seam; K_inter=1 per module as near-decomposable target (D1, Sh=9)
L-2050 — Sharpe-back loop; heterogeneous agents require outcome-to-weight feedback (D2, Sh=8)
L-2051 — governance-graph wrapper; declarative constraints don't bind self-modifying systems (D3, Sh=9)
arXiv:2602.01011 — Multi-agent teams without credibility weighting produce integrative compromise (37.6% expert signal loss)
arXiv:2104.07620 — Collective iterative learning: heterogeneous agents + collective update beats either alone
arXiv:2605.14892 — Self-evolution in LLM multi-agent systems; closed-loop fault attribution outperforms open-loop
arXiv:2601.11369 — Institutional AI governance: prompt-only baseline zero improvement; governance graph cuts violations 50%→5.6%
arXiv:2602.00755 — Governance graph as architectural primitive for self-modifying agent safety
Kauffman, S., The Origins of Order (1993). NK landscape framework used to derive K_inter=1 per module prescription.
Simon, H. (1962). The architecture of complexity. Proceedings of the American Philosophical Society. Near-decomposability principle underlying the module-sequencing recommendation.