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pith:2026:XG67HSVBIK5BTL2ABNWQV4TFSH
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Genflow Ad Studio: A Compound AI Architecture for Brand-Aligned, Self-Correcting Video Generation

Debanshu Das, Gopala Dhar, Lavi Nigam, Sunil Kumar Jang Bahadur

Genflow's multi-stage pipeline with Brand DNA extraction and multi-agent QC raises brand-compliant video generation yield from 42% to 89%.

arxiv:2605.16748 v1 · 2026-05-16 · cs.GR · cs.AI · cs.CV · cs.LG · cs.MA · cs.MM

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1 Bitcoin timestamp
2 Internet Archive
3 Author claim open · sign in to claim
4 Citations open
5 Replications open
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Claims

C1strongest claim

By transitioning to a multi-stage, self-correcting pipeline, Genflow improved the yield of brand-compliant video generations from 42% to 89%, establishing a robust framework for scalable, enterprise-grade generative systems.

C2weakest assumption

The assumption that evaluator agents can accurately critique generated frames against the extracted Brand DNA parameters and that the generator models can refine outputs until a deterministic consensus is reached without introducing new visual inconsistencies or brand violations.

C3one line summary

Genflow uses a retrieval-based Brand DNA module and an adversarial multi-agent quality control loop to raise brand-compliant video generation yield from 42% to 89%.

References

25 extracted · 25 resolved · 6 Pith anchors

[1] Tim Brooks, Bill Peebles, Connor Holmes, et al. 2024. Video generation models as world simulators. OpenAI Blog. Retrieved April 28, 2026 from https://openai.com/research/video-generation-models-as-wor 2024
[2] Veo Team. 2025. Veo 3 Technical Report. Google DeepMind. Retrieved April 28, 2026 from https://storage.googleapis.com/deepmind-media/veo/Veo-3-Tech- Report.pdf 2025
[3] Matei Zaharia, Omar Khattab, Lingjiao Chen, et al. 2024. The Shift from Models to Compound AI Systems. Berkeley AI Research Blog. Retrieved April 28, 2026 from https://bair.berkeley.edu/blog/2024/02/1 2024
[4] Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets 2023 · arXiv:2311.15127
[5] Ziwei Ji, Nayeon Lee, Rita Frieske, et al. 2023. Survey of Hallucination in Natural Language Generation. ACM Computing Surveys 55, 12 (2023), 1–38 2023

Formal links

2 machine-checked theorem links

Receipt and verification
First computed 2026-05-20T00:03:19.536956Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

b9bdf3caa142ba19af400b6d0af26591d4e69c308516f2ee044463dc9f5aa064

Aliases

arxiv: 2605.16748 · arxiv_version: 2605.16748v1 · doi: 10.48550/arxiv.2605.16748 · pith_short_12: XG67HSVBIK5B · pith_short_16: XG67HSVBIK5BTL2A · pith_short_8: XG67HSVB
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/XG67HSVBIK5BTL2ABNWQV4TFSH \
  | 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: b9bdf3caa142ba19af400b6d0af26591d4e69c308516f2ee044463dc9f5aa064
Canonical record JSON
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