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Paper Citation Record · LEDGER

FADE: Adversarial Concept Erasure in Flow Models

As of 14 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 2026-08-14T06:32:32.682623+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=sha256:275335fb31113072a4ef848a8c13e692f89d7967a8403fd5416e5313e42b8dc7

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=sha256:5b92648aed454295855b5eb1f7198daff929574b8d3a28b4fecef183b2c96502

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-reported events for the cited work

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source=pdf_text observed=2026-08-06T17:00:00.230836Z digest=sha256:0c7e54cb8275ddfd8997cbb8a21c1889dc5bb18e394e81548ef9a1aebd3f2621

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=sha256:9d94b453a68da4c4deca1f1e4f43129c87896b637c7cef5adf31bd6bd7a85545

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T17:00:00.721958Z digest=sha256:66b8b996b5c1c9a60593613a27fe74956098148cd1fba46dec722dd914bd7f91

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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verified fuzzy
raw_fallback, observed 2026-08-06T17:00:05.110389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T17:00:00.916152Z digest=sha256:022eec9565b89cb9326061e2c1d6be40557a4fc7b6118ddeb4e9d14016e1486e

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:f50f911f1dce1141e8559a20c46a3559ee1d633443397ea54e7380f4281b6284

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T17:00:01.136294Z digest=sha256:2feba5a5efb2a9b0df0a8c776b174cbe804c9dcc674b042a3ed54b90a6d8cd46

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=sha256:1f406647da2860939bbb891da53f2376001e636e5e543709957868ed82e51690

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:bc32c8c54524f6e3876fe6cfb53748e8ff4c92002bb0b55675757076a30aa94f

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:00:01.472792Z digest=sha256:ad064105b51feccd10568c58fd441c0fa3dcf491492e53c239b07b4aa6535d28

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

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:00:01.608530Z digest=sha256:fe9d308a9406baf0f9079c30371322d2414c4cc310d17505a03a6a503f93ca98

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=sha256:35e2e426f0b241cf3ea447b461d52065a9a47801fda26eb9142e7987a330eaa0

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-reported events for the cited work

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source=pdf_text observed=2026-08-06T17:00:01.797557Z digest=sha256:801b338f932a79e72d2812ad9453b82cd548e5e97068b23f09d6a51143674ec3

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

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:00:01.877701Z digest=sha256:62f9e9e81e11643c0db017ee4be9f4dc7e6ddab1c3097261f28eb0384e1dda34

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=sha256:4cee2180fb8b7aa82663758c8a064a4600d6df14f706b95f59c702b6ed28d4f6

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

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source=pdf_text observed=2026-08-06T17:00:02.139452Z digest=sha256:68ec89972c42a3732461fb059576289c9a04995322d563b5af1bd53622fd370a

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
raw_fallback, observed 2026-08-06T17:00:04.682169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T17:00:02.217451Z digest=sha256:1debd88da7acfeebaf89b5aa8e62fe7512b52ffce3e4c651589e144c09308d10

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

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:00:02.277800Z digest=sha256:f0c90eb03aad22d0beb797a1d1f276c411b55409cf5f6c0835f075ace6f2a3e6

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=pdf_text observed=2026-08-06T17:00:02.372509Z digest=sha256:88a511d85cb311a89ee6ec2a941c6ced954cc47886560ca37b6505451bdd573d

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T17:00:02.376469Z digest=sha256:a9885d92454a962dca99e217d08530dc39d57c9bfd81ead1e3246dc5b3ffa996

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=sha256:66eeaaa18a5caf316d58e3719f1ca0d1e6822c2b47490d4649122684c1408821

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:d6fab8cf547e5376d6ee97c7f2dc711b74e0c5e263790ecb5b02d7bc1e7b23f0

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=sha256:8493010e1365d59fabd6e8b24d80706e03023f5faab79569da7637ab77030fdb

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:00:02.406161Z digest=sha256:c5f8c04614f79029881900c069247c121815a30f70765bb8454036bcc872e782

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=sha256:9792a0b0de788150b781f69a8185a8035ab4fc017779fd2442565dab8d87b6ac

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=sha256:45be0864a721580ab12aebe83e4ecd5130458ae96926b08df9bcf7f27af440d6

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:ca7c11a75d1502ab70cc862d51ac86c989faf9f716fdcb0dadc84f8520649839

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:00:02.504483Z digest=sha256:56f0c9898d6ba096cde772c156cf626116d5c8c68b28035f0bc06d962b15257d

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

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:00:02.550033Z digest=sha256:759e13305a6c6ca5c1e7ff90704904542578bc5d34caf0cf523b41f14f0c08eb

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T17:00:02.664911Z digest=sha256:aa2be4083eb06dd2aacc63b77e46efd7eb7c862fa69f722f752c1a624983f6e0

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T17:00:02.738182Z digest=sha256:d887fbd4af34bf45a3b59a42e6cddf1e354dcc8a729c187f83ed9ab6d0fc7469

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-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:00:00.815879Z digest=sha256:e2984f5db942b8059e1a5e074bc326b73c047e9ceaa0a80f959723200a35ef76

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T17:00:00.481391Z digest=sha256:1752013de833932ef0b1f236debba863d8c922eb5a74a8fca4c6d368c823db8d

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:00:00.059624Z digest=sha256:5f041b91771b66f0506951d498d2934e44cc034bc59d720d7c885c6989ae44a9

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:a5e3c1e0edaa666500a78c8d6211e4f92acaa508fe5cd9a41f46fa7fba39881a

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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