pith:6GAPTUKI
Graph Transformers and Stabilized Reinforcement Learning for Large-Scale Dynamic Routing Modulation and Spectrum Allocation in Elastic Optical Networks
Stabilized reinforcement learning enables the first graph transformer to exceed benchmarks on large-scale dynamic routing and spectrum allocation in optical networks.
arxiv:2605.02075 v2 · 2026-05-03 · cs.NI
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\pithnumber{6GAPTUKITT6RYOYIGBMHWV4PMR}
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Claims
ours is the first RL method to exceed all benchmarks, increasing the supportable traffic load by up to 13%. ... We find up to 4% increased traffic load can be supported at low blocking probability (<0.1%) with our method compared to the best available benchmark algorithm.
The GPU-accelerated simulator faithfully reproduces real optical network dynamics, traffic patterns, and physical-layer impairments, and that the learned policy generalizes to traffic and topologies not seen during training.
Graph transformer RL for dynamic RMSA supports up to 13% more traffic than benchmarks on networks up to 143 nodes and 362 links.
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Receipt and verification
| First computed | 2026-05-20T00:03:13.660916Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
f180f9d1489cfd1c3b0830587b578f6475a65e7f626cc0e8c1fef422a2132ba5
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/6GAPTUKITT6RYOYIGBMHWV4PMR \
| 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())"
# expect: f180f9d1489cfd1c3b0830587b578f6475a65e7f626cc0e8c1fef422a2132ba5
Canonical record JSON
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