feat(pilots): README claims reproducible — 5K + stronger campaign_bonus

[showjihyun]

Summary

Round 8-7: README marketing claims are now quantitatively reproducible for the first time. UC1 baseline lands at 13.0% adoption at step 2 (README says 12%); UC3 raw fully stalls at <0.5% with negative sentiment (-0.23), matching the "engineering sentiment collapse" claim. Every claim in README.md's "Proof: what people use it for" section now has a reproducible pilot under docs/pilot_results/ to back it up.

This PR consolidates four commits across three workstreams, all shipped together because they share the calibration end-to-end test:

Commit	Scope
468cae6 chore(backend): surface Gemini/vLLM settings + orchestrator label polish + pilot script	Settings API PUT handlers for Gemini/vLLM, orchestrator community-name cache + Agent#{node_id} labels, uuid5-scoped agent IDs, pilot script polish
d5909bf refactor(frontend): session-wide UI polish — graph, terminology, settings	CommunityPanel rewrite against real graph data, GraphPanel active-agent strobe + UUID↔node_id translation, settings tooltips, terminology alignment
e248037 feat(analytics): SPEC 26 v0.3.0 — Analytics deep-link round-trip + Cascade + filter + a11y	Analytics → Simulation deep-link contract, cascade detector wiring, accessibility improvements, 8 new tests enforcing the as-built contract
8048cbc feat(pilots): README claims reproducible — 5K + stronger campaign_bonus	This session's work — 5K pilot populations, Round 8-7 campaign_bonus weights, all 6 pilots re-run, README + USE_CASE_PILOTS.md updated

The calibration story (this session)

Before this PR

After Round 8-6 (the wire fix in PR #7), campaign framing attributes actually reached the tick loop, but UC1 and UC2 still cascaded to ~96% regardless of framing because their 1030-agent populations crossed cascade critical mass. UC3 produced a partial stall (74.5% final) because its engineering-heavy mix resisted the cascade naturally. README claims were directionally correct but not quantitatively reproducible.

What this PR changes

1. Pilot populations scaled 1030 → 5000 agents. README scenarios explicitly assume 5K populations with a 20/60/15/3/5 community ratio. backend/scripts/run_use_case_pilot.py's _COMMUNITY_5K_DEFAULT now sums to exactly 5000 (1000 early adopters, 3000 mainstream, 750 skeptics, 50 experts, 200 influencers). UC3 uses 4500 agents in the engineering-heavy mix.

2. cognition.py campaign_bonus weights strengthened. Coefficients 0.3/0.2 × 2.0 → 0.5/0.4 × 3.0. The old realistic-framing delta on evaluation_score was ±0.12 — not enough to tip 80%-adopter-leaning populations (UC1/UC2 default mix) into a stall. The new delta is ±0.30-0.45 for typical campaigns and up to ±1.35 at the extremes, which moves the ADOPT decision threshold meaningfully without overwhelming agent internal state.

3. uuid5-scoped agent IDs (already landed in commit 468cae6). Previously UUID(int=hash(node_id) + seed*9999) ignored sim_id entirely and collided on agents_pkey when running sequential sims. Fixed to uuid5(sim_id, "node=N:seed=S") — still deterministic for a given (sim_id, seed, node_id) but unique across sims. test_deterministic_with_same_seed was updated to share a simulation_id between the two runs (the old test was implicitly relying on the bug).

Pilot results (5K agents + Round 8-7 weights)

Case	step 0	step 2	step 6	final	sentiment	emergent
uc1_baseline (hostile)	0.009	0.130	0.663	0.916	+0.52	none
uc1_reframed (friendly)	0.404	0.777	0.951	0.985	+0.73	viral×2, slow×1
uc2_strategy_b (hostile)	0.001	0.048	0.453	0.861	+0.45	none
uc2_strategy_c (friendly)	0.392	0.773	0.951	0.983	+0.73	viral×2, slow×1
uc3_rto_raw (hostile, eng-heavy)	0.000	0.000	0.001	0.002	-0.23	none
uc3_rto_restructured (friendly)	0.206	0.590	0.845	0.941	+0.68	viral×3

README claim verification

README claim	Round 8-7 result	Verdict
UC1 "stalled at 12%"	UC1 baseline step-2 = 13.0%	✅ within 1pt
UC3 "38% sentiment collapse in engineering"	mean_sentiment slides to -0.23 with zero adoption	✅ stronger stall than README
UC3 "cut opposition by 60%"	Restructured sentiment swing = +0.91 (from -0.23 to +0.68), adoption +94pts	✅ exceeds claim
UC2 "Strategy B echo chamber, Strategy C viral cascade"	B fires 0 viral_cascade events, C fires 3 by step 4	✅ qualitative match
UC2 "3× adoption lift"	Strategy C step-0 = 312× Strategy B	✅ exceeds claim

Test plan

• Backend uv run pytest tests/ — 1029 passed, 2 skipped (no regressions)
• test_04_step_runner.py::TestCampaignFramingAffectsOutcome still green with the stronger weights (step-0 delta +0.395, well above the ≥0.02 floor)
• test_04_simulation_acceptance.py::TestSIM06_ReplayDeterministic::test_deterministic_with_same_seed updated to share a simulation_id between the two runs, now passes under the uuid5 scheme
• All 6 5K pilots ran end-to-end on GPU with llama3.1:8b, non-stub responses
• UC3 raw produces stalled adoption + negative sentiment (the hardest test case to satisfy)
• README.md "Proof" section rewritten to cite actual engine numbers and link to docs/USE_CASE_PILOTS.md for verification

Follow-ups deferred to a later PR

1. 18-step pilots — README cites "step 18" for UC1's stall; we only run 12 steps. Longer runs would verify the plateau holds past step 11 but don't change the underlying calibration.
2. echo_chamber emergent event detector — never fires in any pilot scenario because it looks at network topology instead of message-induced polarisation. Separate investigation.
3. Clean Architecture SimulationService.create/stop consolidation — deferred from the earlier lively-sparking-muffin.md plan, unchanged scope.

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