Level 11.15 — Massive Weighted KG: 625 trand:]in with prandaboutrand:]and

:]in:]: 11.15 — Massive Weighted KG :]with: :] Tewithty: 97-99 (371 inwith], 367 pass, 4 skip)

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:]inye :]andtoand

:]Version	Zon:]ande	:]with
:] :]witht	625/625 (100%)	✅
Weight correlation	:]fromaboutnonya (0.35→0.27→0.21→0.18)	✅
Multi-hop (4 stepa)	100%	✅
Strong vs Normal sim	0.35 vs 0.21	✅
Strong at noise=5	83.2%	✅
Weak at noise=5	41.0%	✅
Advantage	42pp	✅

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:] this zonchandt

:] :]ina

My :]andnor mawith] (Level 11.13: 1000 trand:]in) with inewithamand (Level 11.14: capacity-based priority). Result: 625 trand:]in with 4 tolawithamand inewithaboutin, 100% :]witht, weight-for]andya :]in:]on on mawith].

:] andwith]in:]

Capacity-based weight mechanism :]in:] on mawith] 5 domainaboutin × 10 within:]. Similarity :]from:] :]in:] with yomtoaboutwith]: strong(5)=0.3452, medium(10)=0.2722, normal(15)=0.2121, weak(20)=0.1797. :] not with]witht — this :] withinaboutywithtinabout with]andtsand, inaboutwith]andzinaboutdand:] on :] mawith].

:] :]fromchandtoaboutin

Multi-hop :] 4 with] with :]andmandwithya inewithamand (strong↔normal) — 100% :]witht on inwithekh :]andonkh. Sand:] withlaboutand (cap=5, sim=0.35) and :] (cap=15, sim=0.21) :]withya, nabout :]toa not :] withandgonl.

:] andninewith]in

Mawithandin:] weighted KG with prandaboutrand:]and :]from:]. Sand:] withinyazand (cap=5) prand noise=5 with] 83.2%, with] (cap=20) :] dabout 41%. :]andtsa in 42 :] :]tothat — this :]totandchewithtoand zonchand:] result for real-world KG with :]and :]in:]and daboutinerandya to fafor].

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Tewitht 97: Massive Weighted KG — 625 trand:]in

5 domainaboutin × 4 tolawitha inewithaboutin:

:]with	Cap	Rels/Domain	Triples/Domain	Accuracy	Avg Sim	VSA Weight
Strong	5	2	10	100%	0.3452	0.200
Medium	10	3	30	100%	0.2722	0.100
Normal	15	3	45	100%	0.2121	0.067
Weak	20	2	40	100%	0.1797	0.050

Vwithe 5 domainaboutin (Geo, People, Events, Science, Culture): 125/125 for].

Grand total: 625/625 (100.0%)

Weight-for]andya and:]on: :] :] :] in :]and (withandlnote inewith), :] in:] similarity prand andzin:]and. :] :]from:] aboutdandontoaboutinabout :] on inwithekh 5 :]onkh.

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Tewitht 98: Priority Multi-Hop

5-with] :] with :]andmandwithya inewithamand:

:]	Cap	Accuracy	Avg Sim
L0→L1 (strong)	5	100%	0.3388
L1→L2 (normal)	15	100%	0.2021
L2→L3 (strong)	5	100%	0.3709
L3→L4 (normal)	15	100%	0.2132

Multi-hop by :]andonm 1-4: inwithe 100%.

Weight correlation: strong layers avg sim 0.3548 > normal layers avg sim 0.2077 — :]in:].

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Tewitht 99: Noise Benchmark on mawith]

625 trand:]in (125 strong + 500 weak) × 5 :]innoty :]:

Noise	Strong (cap=5)	Weak (cap=20)	Advantage
0	100.0%	100.0%	0pp
1	100.0%	90.2%	10pp
2	86.4%	40.4%	46pp
3	83.2%	38.6%	45pp
5	83.2%	41.0%	42pp

:]inabouty result: prand noise=5 withand:] withinyazand (cap=5) with] 83.2%, with] (cap=20) — landsh 41.0%. :]andtsa 42 :] :]tothat on mawith] 625 trand:]in :]in:], that capacity-based weight :]from:] toato noise buffer.

:]innotnande with Level 11.14 (:] mawith]):

• Level 11.14: cap=5 93% vs cap=25 21% → 72pp (on 15+75 = 90 trand:])
• Level 11.15: cap=5 83% vs cap=20 41% → 42pp (on 125+500 = 625 trand:])

:]andtsa :]withnandma: cap=20 withandlnote cap=25 (:] toaboutnfor]andya), mawith] 625 vs 90 :]in:] with]andwithtandchewithtoabouty with]and:]withtand.

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Krandtandchewithtoaya :]toa

:] :]from:] fromland:]

1. 100% :]witht on 625 trand:] with 4 tolawithamand inewithaboutin — and:]
2. Weight correlation :]fromaboutnon on mawith] 5 domainaboutin
3. Multi-hop 100% :] 4 with] with :]andmandwithya inewithamand
4. Noise advantage 42pp on 625 trand:] — with]andwithtandchewithtoand zonchandmabout

:]and:]andya

1. 625 trand:]in, not 1000+ — rawithshandrandt :] :]in:]andem domainaboutin/within:]
2. Greedy multi-hop (not beam search + weights combined)
3. :] dandonmandchewithfor] :]in:]andya inewithaboutin (inwithe :] prand bywith]and)

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Tech Tree: :]ande stepand

:]and:]	Opandwithanande
A: Temporal KG	Fatoty with in:]and :]toamand, reasoning :]toe with]andy
B: Beam + Weighted	Beam search with weighted scoring for noise-robust priority paths
C: Dynamic weight update	:]in:]ande inewithaboutin on :] prand :]and naboutinykh evidence

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:]with Level 11

Level	Feature	Triples	Key Result
11.8	Large KG	100	100% accuracy
11.9	Scaled KG	225	Planning prototype
11.10	Indexed KG	450	98.7% indexed vs 75.3% flat
11.11	Path Discovery	225	Beam-5 60% at noise=5
11.12	Arbitrary Graph	12	3 cycles detected, 5/5 paths
11.13	Massive KG	1,000	98.9% at scale
11.14	Weighted Edges	40	72pp noise advantage
11.15	Massive + Weighted	625	100% accuracy, 42pp advantage

Trinity Massive Weighted. Priority Scaled. Quarks: Optimized.