pith:EF6RETMQ
SpaceMoE: Realizing Distributed Mixture-of-Experts Inference over Space Networks
A ring-subnet architecture for mixture-of-experts models in satellite constellations cuts token-generation latency by at least threefold.
arxiv:2605.00515 v2 · 2026-05-01 · cs.DC · cs.AI · cs.NI
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Claims
Experiments over a thousand-satellite constellation show that Space-XNet achieves at least a threefold latency reduction compared with conventional random and ablation-based placement strategies.
The assumption that autoregressive inference follows a ring-like communication pattern that can be directly exploited by partitioning the constellation into ring subnets, and that the intra-layer optimization based on activation probabilities and expected latency fully captures real performance without hidden costs such as accuracy degradation or unmodeled interference.
Space-XNet partitions satellite constellations into ring subnets for MoE layers and maps high-activation experts to low-latency satellites, yielding at least 3x lower inference latency than random or ablation placements in 1000-satellite simulations.
Receipt and verification
| First computed | 2026-05-22T01:04:03.892545Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
217d124d90eaca1e56c56bef0f4dee25331376b2e2eb478ccb6213219fb3de32
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/EF6RETMQ5LFB4VWFNPXQ6TPOEU \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
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Canonical record JSON
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