Pith Number

pith:N7CBNBCI

pith:2026:N7CBNBCIG736WQ6RDNIDVGKR7R

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NeuroRisk: Physics-Informed Neural Optimization for Risk-Aware Traffic Engineering

Jiashuai Liu, Jingyi Cheng, Qiaozhu Zhai, Shizhen Zhao, Siyuan Feng, Ximeng Liu, Xiyuan Liu, Yike Liu, Yingming Mao, Yuzhou Zhou, Zhen Yao

NeuroRisk embeds the Sort-and-Select structure of risk-aware traffic engineering into a neural unrolled optimizer to deliver solver accuracy at 100- to 100000-fold speedups.

arxiv:2605.12862 v1 · 2026-05-13 · cs.NI · cs.LG

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4 Citations open

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Claims

C1strongest claim

NeuroRisk achieves small optimality gaps relative to the solver with orders of magnitude speedup (10^2-10^5 ×) on risk objectives, while outperforming neural baselines on nominal throughput.

C2weakest assumption

That the Sort-and-Select structure can be faithfully embedded into a neural unrolled optimizer using gated edge-local reservations and permutation-invariant cues so that feasibility is enforced under explicit capacity constraints and scenario-dependent risk.

C3one line summary

NeuroRisk is a physics-informed deep unrolled optimizer for risk-aware traffic engineering that achieves small optimality gaps and 100-100000x speedup over solvers while outperforming neural baselines on throughput.

References

54 extracted · 54 resolved · 2 Pith anchors

[1] Firas Abuzaid, Srikanth Kandula, Behnaz Arzani, Ishai Menache, Matei Zaharia, and Peter Bailis. 2021. Contracting Wide-area Network Topologies to Solve Flow Problems Quickly. In18th USENIX Sym- posium 2021

[2] Akyildiz, Ahyoung Lee, Pu Wang, Min Luo, and Wu Chou

[3] A roadmap for traffic engineering in SDN-OpenFlow networks. Comput. Netw.71 (Oct. 2014), 1–30. https://doi.org/10.1016/j.comnet. 2014.06.002 2014 · doi:10.1016/j.comnet

[4] Conga: distributed congestion-aware load balancing for datacenters 2014 · doi:10.1145/2619239.2626316

[5] Rdma over ethernet for distributed training at meta scale 2024 · doi:10.1145/3651890.3672237

Receipt and verification

First computed	2026-05-18T03:09:11.544809Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

6fc416844837f7eb43d11b503a9951fc6c5ae96c04d6446eb92ce27fbad35ae5

Aliases

arxiv: 2605.12862 · arxiv_version: 2605.12862v1 · doi: 10.48550/arxiv.2605.12862 · pith_short_12: N7CBNBCIG736 · pith_short_16: N7CBNBCIG736WQ6R · pith_short_8: N7CBNBCI

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/N7CBNBCIG736WQ6RDNIDVGKR7R \
  | 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: 6fc416844837f7eb43d11b503a9951fc6c5ae96c04d6446eb92ce27fbad35ae5

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "7b1c9cf4f187f431d851df41a0305bb6df07aa2df96e5089fac94f2d08a19730",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.NI",
    "submitted_at": "2026-05-13T01:17:45Z",
    "title_canon_sha256": "c96c88534a6abc50d40331183a3db8268c2eecb96a446018751b5e6e5c218222"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.12862",
    "kind": "arxiv",
    "version": 1
  }
}