Pith Number

pith:UV2W7EER

pith:2026:UV2W7EERDOESOABOZG2OKDUNJI

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Diagnosing and Correcting Concept Omission in Multimodal Diffusion Transformers

Chaehun Shin, Jaihyun Lew, Jungbeom Lee, Kanghyun Baek, Sungroh Yoon

Text embeddings in multimodal diffusion transformers encode a detectable omission signal that can be amplified to include missing concepts in generated images.

arxiv:2605.14270 v1 · 2026-05-14 · cs.CV

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\usepackage{pith}
\pithnumber{UV2W7EERDOESOABOZG2OKDUNJI}

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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

By performing linear probing on text tokens, we demonstrate that text embeddings can distinguish a characteristic `omission signal' representing the absence of target concepts. Leveraging this insight, we propose Omission Signal Intervention (OSI), which amplifies the omission signal to actively catalyze the generation of missing concepts.

C2weakest assumption

That the omission signal identified by linear probing is causal for concept omission and that amplifying it will reliably add missing concepts without introducing new artifacts or degrading image quality.

C3one line summary

Text embeddings in MM-DiTs contain a detectable omission signal for missing concepts, and amplifying it via OSI reduces concept omission in generated images on FLUX.1-Dev and SD3.5-Medium.

References

57 extracted · 57 resolved · 6 Pith anchors

[1] Advances in Neural Information Processing Systems , volume=

[2] arXiv preprint arXiv:2411.14257 , year=

[3] Lv, Zhengyao and Pan, Tianlin and Si, Chenyang and Chen, Zhaoxi and Zuo, Wangmeng and Liu, Ziwei and Wong, Kwan-Yee K. , title =. Proceedings of the IEEE/CVF International Conference on Computer Visio 2025

[4] arXiv preprint arXiv:2509.18096 (2025) 4

[5] ACM transactions on Graphics (TOG) , volume= 2023

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T23:39:10.404862Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

a5756f90911b8927002ec9b4e50e8d4a0556ca347a1ae91b050b92aac68158e7

Aliases

arxiv: 2605.14270 · arxiv_version: 2605.14270v1 · doi: 10.48550/arxiv.2605.14270 · pith_short_12: UV2W7EERDOES · pith_short_16: UV2W7EERDOESOABO · pith_short_8: UV2W7EER

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/UV2W7EERDOESOABOZG2OKDUNJI \
  | 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: a5756f90911b8927002ec9b4e50e8d4a0556ca347a1ae91b050b92aac68158e7

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "ec2ce47ae512cbd714bc8f33cd09fd4119f46b878f970d0994a0eda7af641263",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-14T02:14:09Z",
    "title_canon_sha256": "a8472a8d08f536ec2bd0dcf679c4a7122f7afd7f8e9eb268ec5371e480df7ac8"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.14270",
    "kind": "arxiv",
    "version": 1
  }
}