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Paper Citation Record · LEDGER

FADE: Adversarial Concept Erasure in Flow Models

As of 7 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2507.12283.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.12283 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:00:02.738182Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

37 of 37 outbound references displayed

  • verified exact1
  • verified fuzzy6
  • unresolved30
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2198b2d6-9d3e-49e9-b06c-fe5cbdaba49a · outbound

This paper cites LLaVA Steering: Visual Instruction Tuning with 500x Fewer Parameters through Modality Linear Representation-Steering.

FADE: Adversarial Concept Erasure in Flow Models LLaVA Steering: Visual Instruction Tuning with 500x Fewer Parameters through Modality Linear Representation-Steering

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:59:59.766279Z digest=sha256:9984262b40d0a9c4f41fe9b2a5d121362d14503f570adf598f454eacd4e0d972

Observation e9a70933-1cd2-4249-8808-cd238a82d037 · outbound

This paper cites Offset: Segmentation-based focus shift revision for composed image retrieval, 2025c.

FADE: Adversarial Concept Erasure in Flow Models Offset: Segmentation-based focus shift revision for composed image retrieval, 2025c

Reference 4

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source=pdf_text observed=2026-08-06T17:00:00.164367Z digest=sha256:bae660538c3436fe9faca5c5d3c0ad1ca3f1fe60fb06f35ce7c307dd00f332e8

Observation 2493d78d-c7f2-4afd-86ac-59ab8b905118 · outbound

This paper cites EraseAnything: Enabling Concept Erasure in Rectified Flow Transformers.

FADE: Adversarial Concept Erasure in Flow Models EraseAnything: Enabling Concept Erasure in Rectified Flow Transformers

Reference 5

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source=pdf_text observed=2026-08-06T17:00:00.230836Z digest=sha256:3d1283dcaf684c097e974fe083f7b18f35c12b407b51e9433c074efc84ddfa0c

Observation cd4eb0da-2b15-45c4-b937-e2b3ec43884f · outbound

This paper cites Prompt-to-Prompt Image Editing with Cross Attention Control.

FADE: Adversarial Concept Erasure in Flow Models Prompt-to-Prompt Image Editing with Cross Attention Control

Reference 8

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:00:00.597542Z digest=sha256:385f112990f6cc1089b69a1c7c514417333c25135ce38605d2a803d92493f05e

Observation b810385d-51e8-443c-9cd4-f0574e33ee7c · outbound

This paper cites and Salimans, T.

FADE: Adversarial Concept Erasure in Flow Models and Salimans, T

Reference 9

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verified fuzzy
raw_fallback, observed 2026-08-06T17:00:05.361766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T17:00:00.721958Z digest=sha256:8903392aeadd65245173f3db78841d529cb189b331363be1b762a0240c3e1403

Observation b6294a47-fc88-496e-b6de-afad40dd83bd · outbound

This paper cites Mvctrack: Boost- ing 3d point cloud tracking via multimodal-guided virtual cues.

FADE: Adversarial Concept Erasure in Flow Models Mvctrack: Boost- ing 3d point cloud tracking via multimodal-guided virtual cues

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T17:00:00.916152Z digest=sha256:9a831e7c63cd8a4996c2b0f92cc5346d9ccdb916c4ea81e21ab3dd19870c2135

Observation 906c5e9f-6e40-405e-b59a-373fc58f03b0 · outbound

This paper cites ScaleTrack: Scaling and back-tracking Automated GUI Agents.

FADE: Adversarial Concept Erasure in Flow Models ScaleTrack: Scaling and back-tracking Automated GUI Agents

Reference 12

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T17:00:01.005902Z digest=sha256:c3ca2acaebbed4b35e059b969a454207f77426fdac0f5fd64967f15b9439b20b

Observation 425946b3-d84e-4802-97db-63df93333eb8 · outbound

This paper cites Overview of the nlpcc 2023 shared task: Chinese medical instructional video question answering.

FADE: Adversarial Concept Erasure in Flow Models Overview of the nlpcc 2023 shared task: Chinese medical instructional video question answering

Reference 13

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raw_fallback, observed 2026-08-06T17:00:04.875702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T17:00:01.136294Z digest=sha256:47ed6a4e6378d9c14940b7a5753065c4aa0377ef01b38117f5b88aa9bf1e219b

Observation ef5a32f0-503f-4c46-a13d-00637a6f7afb · outbound

This paper cites Set You Straight: Auto-Steering Denoising Trajectories to Sidestep Unwanted Concepts.

FADE: Adversarial Concept Erasure in Flow Models Set You Straight: Auto-Steering Denoising Trajectories to Sidestep Unwanted Concepts

Reference 14

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:00:01.247350Z digest=sha256:5170156d69d2087ed22e6d6ff505f1374aa0eaa2f40ac723af92a05ac19d6fc3

Observation 4e9852a4-2624-4dc1-ab59-e422146fb9e7 · outbound

This paper cites Phy124: Fast Physics-Driven 4D Content Generation from a Single Image.

FADE: Adversarial Concept Erasure in Flow Models Phy124: Fast Physics-Driven 4D Content Generation from a Single Image

Reference 15

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source=pdf_text observed=2026-08-06T17:00:01.367416Z digest=sha256:549f370f42fd6d8c62b2d1deea4a346884451a7a908abcef3b1ff6c83733bcbe

Observation f528ad52-4321-47b1-9856-6b8df93eec75 · outbound

This paper cites Robust Watermarking Using Generative Priors Against Image Editing: From Benchmarking to Advances.

FADE: Adversarial Concept Erasure in Flow Models Robust Watermarking Using Generative Priors Against Image Editing: From Benchmarking to Advances

Reference 16

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:00:01.472792Z digest=sha256:c6a3eed209d93ec1c5210a42f9c34583a5300fbcce6048e68a850badcf6b4877

Observation 687cee47-9462-404d-9d46-4def708ba3af · outbound

This paper cites MagicStick: Controllable Video Editing via Control Handle Transformations.

FADE: Adversarial Concept Erasure in Flow Models MagicStick: Controllable Video Editing via Control Handle Transformations

Reference 17

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T17:00:01.608530Z digest=sha256:009fe399052a348784240e150dcd3d42a0f7401fcfc6c052f2e79dc62aa547a8

Observation 4cccb2e5-7ed6-49fc-8147-8ffe024a01b0 · outbound

This paper cites Follow-Your-Creation: Empowering 4D Creation through Video Inpainting.

FADE: Adversarial Concept Erasure in Flow Models Follow-Your-Creation: Empowering 4D Creation through Video Inpainting

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:00:01.684985Z digest=sha256:e580991d4d3866eef476135cb3312e50e30ae3fee0dc645bca88422045770aeb

Observation 3fb8345d-aa1c-4499-9eb3-17549a49b13b · outbound

This paper cites LangTime: A Language-Guided Unified Model for Time Series Forecasting with Proximal Policy Optimization.

FADE: Adversarial Concept Erasure in Flow Models LangTime: A Language-Guided Unified Model for Time Series Forecasting with Proximal Policy Optimization

Reference 19

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source=pdf_text observed=2026-08-06T17:00:01.797557Z digest=sha256:bbca4517302acd687cacfaaec454d52a4aef8893c30eb95661c25bf053db0216

Observation e13720f0-03a2-4403-ab71-21c6459ae704 · outbound

This paper cites Towards Realistic Data Generation for Real-World Super-Resolution.

FADE: Adversarial Concept Erasure in Flow Models Towards Realistic Data Generation for Real-World Super-Resolution

Reference 20

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T17:00:01.877701Z digest=sha256:2eab80f9ad65e6a799f129062cb0fc61dd0962b9afd38be33a524fd59c892509

Observation 55802682-479b-495f-b20f-49981af4200c · outbound

This paper cites A com- prehensive survey of deep learning for multivariate time series forecasting: A channel strategy perspective.

FADE: Adversarial Concept Erasure in Flow Models A com- prehensive survey of deep learning for multivariate time series forecasting: A channel strategy perspective

Reference 21

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source=pdf_text observed=2026-08-06T17:00:02.010235Z digest=sha256:fe59f24956101e31c19616c92e8e96ca2dff561c8352d0523fd031aa89f1bde0

Observation d4320cd2-d333-4387-993e-719d95e61424 · outbound

This paper cites Backdoor Cleaning without External Guidance in MLLM Fine-tuning.

FADE: Adversarial Concept Erasure in Flow Models Backdoor Cleaning without External Guidance in MLLM Fine-tuning

Reference 22

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:00:02.139452Z digest=sha256:af00a368ea0fc2d6d79f75cfa33fd91773206f24090a5a23604b752eb8582dc9

Observation f7ee3b63-f739-4eaa-ac40-790837014c82 · outbound

This paper cites Ptt: Point-track- transformer module for 3d single object tracking in point clouds.

FADE: Adversarial Concept Erasure in Flow Models Ptt: Point-track- transformer module for 3d single object tracking in point clouds

Reference 23

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T17:00:02.217451Z digest=sha256:abbae29c402ffd185ebeb5ee7175d27f2c4ef9a9386ac6971e25d27922ba392c

Observation 37f3c9b8-384f-4e6c-a499-243f34d9b747 · outbound

This paper cites A-MESS: Anchor based Multimodal Embedding with Semantic Synchronization for Multimodal Intent Recognition.

FADE: Adversarial Concept Erasure in Flow Models A-MESS: Anchor based Multimodal Embedding with Semantic Synchronization for Multimodal Intent Recognition

Reference 24

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T17:00:02.277800Z digest=sha256:9c3e3402b00ecdfe65be880846500dd15fdd058e87242070a27a0d605bc90414

Observation 05a39a86-ec1c-4222-afc9-0fdf31098dd6 · outbound

This paper cites Text- toon: Real-time text toonify head avatar from single video.

FADE: Adversarial Concept Erasure in Flow Models Text- toon: Real-time text toonify head avatar from single video

Reference 25

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T17:00:02.372509Z digest=sha256:a5bed0f59b00ddb3b706bed565f71f1a85034f7300fdf60198c00ebc6e0a7d70

Observation 31cac8ad-9e4d-4119-b9ac-d875f6424a50 · outbound

This paper cites Divide-and- conquer: Confluent triple-flow network for rgb-t salient object detection.

FADE: Adversarial Concept Erasure in Flow Models Divide-and- conquer: Confluent triple-flow network for rgb-t salient object detection

Reference 26

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T17:00:02.376469Z digest=sha256:f99832a4c6fc37b4a0cb859e6256af6661f31f823348f17a21ab8fda93a2d3de

Observation e5c16cab-537f-40ee-8fce-b92d5e5ce3cc · outbound

This paper cites an unresolved cited work.

FADE: Adversarial Concept Erasure in Flow Models Unresolved cited work

Reference 27

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source=pdf_text observed=2026-08-06T17:00:02.379971Z digest=sha256:a5865d1223e82fb97d3a8e9b3a6709921b6dcd6199ffbb18dbcfc3b8a412d877

Observation 884d48da-c0f3-4bf1-a385-8356c96dbe51 · outbound

This paper cites ASCD: Attention-Steerable Contrastive Decoding for Reducing Hallucination in MLLM.

FADE: Adversarial Concept Erasure in Flow Models ASCD: Attention-Steerable Contrastive Decoding for Reducing Hallucination in MLLM

Reference 28

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source=pdf_text observed=2026-08-06T17:00:02.383496Z digest=sha256:d5a3a22d50dbb9915dce6bceb5f96016448df217eb28f733fd1c05675b1989d3

Observation 756be99c-011d-4d07-aacc-389d558491bf · outbound

This paper cites Dynamic uncertainty learning with noisy correspondence for text-based person search.

FADE: Adversarial Concept Erasure in Flow Models Dynamic uncertainty learning with noisy correspondence for text-based person search

Reference 29

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:00:02.388782Z digest=sha256:32533a7f6a222cb5f6d7e12bf569046ab55b036cdd1b34042ec953854cbf03a9

Observation 1e81a491-5499-4cfc-b8e2-caf27f2dc311 · outbound

This paper cites Eedit: Rethinking the spatial and temporal redundancy for efficient image editing.

FADE: Adversarial Concept Erasure in Flow Models Eedit: Rethinking the spatial and temporal redundancy for efficient image editing

Reference 30

Resolution
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source=pdf_text observed=2026-08-06T17:00:02.406161Z digest=sha256:44e3d03e81f14b629ad711ee7dc78cf4255c5ee856e07728f76f7fcdfe3efa17

Observation a6dba4c8-6b65-4ca5-a70e-e5f6b754ac83 · outbound

This paper cites CRISP-SAM2: SAM2 with Cross-Modal Interaction and Semantic Prompting for Multi-Organ Segmentation.

FADE: Adversarial Concept Erasure in Flow Models CRISP-SAM2: SAM2 with Cross-Modal Interaction and Semantic Prompting for Multi-Organ Segmentation

Reference 31

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:00:02.429731Z digest=sha256:46792b69b9e4157f6ef7011e556c854b4115b1ae78cba441a9d9e182524ef467

Observation 55060043-7fa5-4dc2-806d-13c51cb30011 · outbound

This paper cites SPOT! Revisiting Video-Language Models for Event Understanding.

FADE: Adversarial Concept Erasure in Flow Models SPOT! Revisiting Video-Language Models for Event Understanding

Reference 32

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source=pdf_text observed=2026-08-06T17:00:02.453099Z digest=sha256:3075857268f60eb4da36894476e1f09ab7bf3ce1f2402add21a814083f1f706a

Observation 605a504f-f334-48de-b474-b90eb7fc1c46 · outbound

This paper cites KinMo: Kinematic-aware Human Motion Understanding and Generation.

FADE: Adversarial Concept Erasure in Flow Models KinMo: Kinematic-aware Human Motion Understanding and Generation

Reference 33

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:00:02.477554Z digest=sha256:7e08c2d2da48090a4ad1ea927730d9f771e01c0a976f3a926e746427d22c7766

Observation 87f22fa8-77c3-4875-bc22-b6e9385498ad · outbound

This paper cites FastPillars: A Deployment-friendly Pillar-based 3D Detector.

FADE: Adversarial Concept Erasure in Flow Models FastPillars: A Deployment-friendly Pillar-based 3D Detector

Reference 34

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source=pdf_text observed=2026-08-06T17:00:02.504483Z digest=sha256:412fe28708e73a0ca7d1ebfe930ce1defdddcdf88c0746afb6006812d4d7814d

Observation 1b466ac5-a235-416a-8ace-490c8022988f · outbound

This paper cites InstantSwap: Fast Customized Concept Swapping across Sharp Shape Differences.

FADE: Adversarial Concept Erasure in Flow Models InstantSwap: Fast Customized Concept Swapping across Sharp Shape Differences

Reference 35

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T17:00:02.550033Z digest=sha256:d2ec65525f300a1fc24eb99940c06c968124f20184dd34dab9fa7f1d973d6b22

Observation a131565a-8936-4422-8f01-810672d4c580 · outbound

This paper cites an unresolved cited work.

FADE: Adversarial Concept Erasure in Flow Models Unresolved cited work

Reference 36

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T17:00:02.664911Z digest=sha256:4a8a52cc107eb6b0c2b0df2c522834a2ce99c44d03e81b17fec06045fc7abcda

Observation d93ca757-eca8-43da-a702-9eca414db4d2 · outbound

This paper cites Multimodality (Shen et al.,.

FADE: Adversarial Concept Erasure in Flow Models Multimodality (Shen et al.,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:00:03.868082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T17:00:02.738182Z digest=sha256:7ca190a632dcdd45684a6f9b8b44b58568aea5c1fab711e19fe9167e2b70a7e1

Observation 6d8eb5a6-9a0a-4fc6-a493-2856fcf84648 · outbound

This paper cites MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast.

FADE: Adversarial Concept Erasure in Flow Models MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast

Reference 2020

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source=pdf_text observed=2026-08-06T17:00:00.815879Z digest=sha256:a9bc8bd299db4abf1a80441fc1927a9f6be7d912271f54338b9f32f494625460

Observation 3bcd8310-5726-4b88-a7b0-9f0ad4c3b511 · outbound

This paper cites Adversarial Learning for Neural PDE Solvers with Sparse Data.

FADE: Adversarial Concept Erasure in Flow Models Adversarial Learning for Neural PDE Solvers with Sparse Data

Reference 2022

Resolution
verified exact
local_arxiv, observed 2026-08-06T17:00:03.427058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T17:00:00.481391Z digest=sha256:4ff93c4e078a3524e6c95019cb269cdb1f7d465965be6efe080b0258e0ebe573

Observation 1d9dbdf2-3653-4313-b45e-e05479ca361e · outbound

This paper cites Fedbip: Het- erogeneous one-shot federated learning with personalized latent diffusion models.

FADE: Adversarial Concept Erasure in Flow Models Fedbip: Het- erogeneous one-shot federated learning with personalized latent diffusion models

Reference 2023

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source=pdf_text observed=2026-08-06T17:00:00.059624Z digest=sha256:5ae0efa09a24284c2e05bcff657dd0bdc377f10da996e0138ca7973f91ba3102

Observation e6fffb43-5602-4501-b420-49eda8e75b65 · outbound

This paper cites Why reasoning mat- ters? a survey of advancements in multimodal reasoning (v1).

FADE: Adversarial Concept Erasure in Flow Models Why reasoning mat- ters? a survey of advancements in multimodal reasoning (v1)

Reference 2024

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source=pdf_text observed=2026-08-06T16:59:59.893605Z digest=sha256:a1df5d586296e7309862f303e2a458dc80bcef795b42463e71f95db2b59273db

Observation 1e9c1c87-6de6-4054-8d2a-ff528d4fa347 · outbound

This paper cites Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method.

FADE: Adversarial Concept Erasure in Flow Models Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method

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