Pith Number

pith:HYGANCHP

pith:2026:HYGANCHPDMCQKP342C4JCMOF27

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Skill-Aligned Annotation for Reliable Evaluation in Text-to-Image Generation

Abdelrahman Eldesokey, Ahmad Sait, Ansar Khangeldin, Bernard Ghanem, Karen Sanchez, Merey Ramazanova, Tong Zhang

Annotation strategies tailored to each evaluation skill produce more consistent signals and higher agreement than uniform scales across all skills in text-to-image generation.

arxiv:2605.13223 v1 · 2026-05-13 · cs.CV

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\pithnumber{HYGANCHPDMCQKP342C4JCMOF27}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

skill-aligned annotation produces more consistent evaluation signals, with higher inter-annotator agreement and improved stability across models.

C2weakest assumption

That the chosen skill-aligned annotation strategies are fundamentally better suited to each skill's nature and that the uniform baselines provide a fair comparison without confounding factors in skill selection or annotation design.

C3one line summary

Skill-aligned annotation improves inter-annotator agreement and evaluation stability in text-to-image generation compared to uniform annotation baselines.

References

42 extracted · 42 resolved · 5 Pith anchors

[1] Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer 2025 · arXiv:2511.22699

[2] J. Chen, J. YU, C. GE, L. Y ao, E. Xie, Z. Wang, J. Kwok, P . Luo, H. Lu, and Z. Li. Pixart- $\alpha$: Fast training of diffusion transformer for photorealistic text-to-image synthesis. In The Twelfth 2024

[3] J. Cho, Y . Hu, J. M. Baldridge, R. Garg, P . Anderson, R. Krishna, M. Bansal, J. Pont-Tuset, and S. Wang. Davidsonian scene graph: Improving reliability in ﬁne-grained evaluation for text-to-image ge 2024

[4] Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding 2026 · arXiv:2601.10611

[5] G. DeepMind. Nano-banana. https://deepmind.google/models/gemini-image/ flash/, 2025 2025

Receipt and verification

First computed	2026-05-18T03:08:48.327397Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

3e0c0688ef1b05053f7cd0b89131c5d7c3d482277a022e9dc3946de5850a755f

Aliases

arxiv: 2605.13223 · arxiv_version: 2605.13223v1 · doi: 10.48550/arxiv.2605.13223 · pith_short_12: HYGANCHPDMCQ · pith_short_16: HYGANCHPDMCQKP34 · pith_short_8: HYGANCHP

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/HYGANCHPDMCQKP342C4JCMOF27 \
  | 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: 3e0c0688ef1b05053f7cd0b89131c5d7c3d482277a022e9dc3946de5850a755f

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "f48dc99be76d786b7067e7591278aff7cc85a90c775c74f433304598ab567605",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-13T09:14:31Z",
    "title_canon_sha256": "a06e769e95f96d3128f679146f17bb33440c684b9955cca6e14c168d542af085"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13223",
    "kind": "arxiv",
    "version": 1
  }
}