Pith Number

pith:UFBAJUVN

pith:2026:UFBAJUVNAGDGNPPGBG2EISSHEB

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TG-DIN: Theory-Guided Demand Inference Network for Generalizable QoS Measurement and Prediction

Feng Ye, Fuliang Yang

A neural network infers latent user demand from QoS measurements by embedding scheduling and queuing rules as a differentiable theory layer.

arxiv:2605.15550 v1 · 2026-05-15 · cs.NI

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Claims

C1strongest claim

TG-DIN explicitly models latent demand as an intermediate variable and links it to observable behavior through a differentiable theory layer grounded in scheduling and queuing principles. This design yields an interpretable, mechanism-consistent representation of user demand that is directly applicable to downstream tasks such as congestion diagnosis, resource allocation, capacity planning, and policy evaluation.

C2weakest assumption

The differentiable theory layer grounded in scheduling and queuing principles accurately captures the real mechanisms that connect latent demand to observable QoS measurements in both synthetic and real network settings.

C3one line summary

A neural network with a theory-guided differentiable layer infers hidden demand from QoS data for improved generalization across network conditions.

References

37 extracted · 37 resolved · 0 Pith anchors

[1] Abd AlRhman AlQiam, Yuanjun Yao, Zhaodong Wang, Satyajeet Singh Ahuja, Ying Zhang, Sanjay G Rao, Bruno Ribeiro, and Mohit Tawarmalani. 2024. Transfer- able neural wan te for changing topologies. InPro 2024

[2] A Framework for Cluster and Classifier Evaluation in the Absence of Reference Labels 2021 · doi:10.1145/3474369.3486864

[3] Ons Aouedi, Van An Le, Kandaraj Piamrat, and Yusheng Ji. 2025. Deep learning on network traffic prediction: Recent advances, analysis, and future directions. ACM computing surveys57, 6 (2025), 1–37 2025

[4] Athula Balachandran, Vyas Sekar, Aditya Akella, Srinivasan Seshan, Ion Stoica, and Hui Zhang. 2013. Developing a predictive model of quality of experience for internet video.ACM SIGCOMM Computer Commu 2013

[5] Giuseppe Bianchi. 2000. Performance analysis of the IEEE 802.11 distributed coordination function.IEEE Journal on Selected Areas in Communications18, 3 (2000), 535–547 2000

Receipt and verification

First computed	2026-05-20T00:01:04.905842Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

a14204d2ad018666bde609b4444a472046d34b00f3b7c4cf2e0d2843a43366a9

Aliases

arxiv: 2605.15550 · arxiv_version: 2605.15550v1 · doi: 10.48550/arxiv.2605.15550 · pith_short_12: UFBAJUVNAGDG · pith_short_16: UFBAJUVNAGDGNPPG · pith_short_8: UFBAJUVN

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/UFBAJUVNAGDGNPPGBG2EISSHEB \
  | 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: a14204d2ad018666bde609b4444a472046d34b00f3b7c4cf2e0d2843a43366a9

Canonical record JSON

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    "abstract_canon_sha256": "9a7985d4187e1ffab0cc87b54d2feefebb6f35507131fd80c609ec72fc36ef4a",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "primary_cat": "cs.NI",
    "submitted_at": "2026-05-15T02:43:35Z",
    "title_canon_sha256": "4eaa7865c3135d478544c887c042f9e8e4ac50d5adb622682cbf8440ea4721db"
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