Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 24 inbound Pith citation observations for arXiv:2310.10012.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T20:31:42.708429Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T16:09:57.466105Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 6342b0ee-424e-477c-95ab-027d908a798c · inbound
Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation cites this paper.
Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
Reference 105
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T20:31:42.708429Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3610e35e-0cd7-4eb5-9dff-dc0f4c85f545 · inbound
Rethinking Robust Adversarial Concept Erasure in Diffusion Models cites this paper.
Rethinking Robust Adversarial Concept Erasure in Diffusion Models Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T07:05:02.813787Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ab87e50c-307c-44d1-b797-981524aba118 · inbound
SPQR: A Multi-Dimensional Benchmark for Safety Alignment under Benign Model Adaptation cites this paper.
SPQR: A Multi-Dimensional Benchmark for Safety Alignment under Benign Model Adaptation Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T20:39:55.186965Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e3345a91-f541-4102-b8dd-3d333c6e4b5b · inbound
$PC^2$: Politically Controversial Content Generation via Jailbreaking Attacks on GPT-based Text-to-Image Models cites this paper.
$PC^2$: Politically Controversial Content Generation via Jailbreaking Attacks on GPT-based Text-to-Image Models Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T11:47:41.664591Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0569bf42-6536-433e-b6b0-2cd8744872a9 · inbound
Parameter Efficient Machine Unlearning on Hybrid Resistive Memory based Compute-in-Memory Accelerators cites this paper.
Parameter Efficient Machine Unlearning on Hybrid Resistive Memory based Compute-in-Memory Accelerators Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T10:29:18.464167Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e45d42e2-9d4a-46f9-a69d-793a8f06e280 · inbound
SPOT: Selective Prompt Projection via Total Variation for Inference-Only Safe Text-to-Image Generation cites this paper.
SPOT: Selective Prompt Projection via Total Variation for Inference-Only Safe Text-to-Image Generation Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T09:10:47.362850Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 0eac2ad5-3393-425f-b007-d71cab11816e · inbound
SafeCtrl: Region-Aware Safety Control for Text-to-Image Diffusion via Detect-Then-Suppress cites this paper.
SafeCtrl: Region-Aware Safety Control for Text-to-Image Diffusion via Detect-Then-Suppress Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T11:51:01.754463Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5ed34dd5-3828-4230-93e8-8790e005b2eb · inbound
EGLOCE: Training-Free Energy-Guided Latent Optimization for Concept Erasure cites this paper.
EGLOCE: Training-Free Energy-Guided Latent Optimization for Concept Erasure Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T08:05:59.383340Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation f0330401-943d-4151-82a5-22efffee8185 · inbound
Beyond Text Prompts: Precise Concept Erasure through Text-Image Collaboration cites this paper.
Beyond Text Prompts: Precise Concept Erasure through Text-Image Collaboration Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
Reference 40
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T08:48:01.950306Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 88e03481-b7bb-4a35-b739-8aba65238062 · inbound
TrajShield: Trajectory-Level Safety Mediation for Defending Text-to-Video Models Against Jailbreak Attacks cites this paper.
TrajShield: Trajectory-Level Safety Mediation for Defending Text-to-Video Models Against Jailbreak Attacks Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
Reference 37
- Resolution
- verified exact
- arxiv_id, observed 2026-05-09T05:55:29.244602Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation c95d684c-6db7-495d-a2b1-76f1bbb7307e · inbound
What Concepts Lie Within? Detecting and Suppressing Risky Content in Diffusion Transformers cites this paper.
What Concepts Lie Within? Detecting and Suppressing Risky Content in Diffusion Transformers Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
Reference 38
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T06:06:26.452291Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 17366171-2ce1-49c7-8bd8-abaa927d504b · inbound
Empty SPACE: Cross-Attention Sparsity for Concept Erasure in Diffusion Models cites this paper.
Empty SPACE: Cross-Attention Sparsity for Concept Erasure in Diffusion Models Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
Reference 27
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T03:21:18.802100Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation bec8b823-a9a8-41c0-96ad-51a0f6c4a789 · inbound
FlowErase-RL: Rethinking Concept Erasure as Reward Optimization in Flow Matching Models cites this paper.
FlowErase-RL: Rethinking Concept Erasure as Reward Optimization in Flow Matching Models Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
Reference 44
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T05:28:04.256857Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 4b7106ce-44e7-4978-8025-72f294bc4186 · inbound
FlowErase-RL: Rethinking Concept Erasure as Reward Optimization in Flow Matching Models cites this paper.
FlowErase-RL: Rethinking Concept Erasure as Reward Optimization in Flow Matching Models Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
Reference 45
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T15:05:47.563774Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation c4ab1d6c-9a79-4038-9fe0-fbf3138bf21f · inbound
Erased but Exploitable: Black-box Embedding-Aware Prompting Against Unlearned Text-to-Image Diffusion Models cites this paper.
Erased but Exploitable: Black-box Embedding-Aware Prompting Against Unlearned Text-to-Image Diffusion Models Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
Reference 42
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T22:34:02.836986Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation bea25a66-b4ab-45cc-9b39-2d41ba2b4c3e · inbound
SafeGen-Bench: Benchmarking Safety in Image-Conditioned Text-to-Video Generation cites this paper.
SafeGen-Bench: Benchmarking Safety in Image-Conditioned Text-to-Video Generation Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
Reference 50
- Resolution
- verified exact
- arxiv_id, observed 2026-06-28T17:12:25.189961Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 5eceeffa-9d60-42e4-801d-1ca10f5442e9 · inbound
CoreUnlearn: Rethinking Concept Unlearning through Disentangled Component-Level Erasure in Text-guided Diffusion Models cites this paper.
CoreUnlearn: Rethinking Concept Unlearning through Disentangled Component-Level Erasure in Text-guided Diffusion Models Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
Reference 51
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T23:16:24.657401Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation c6c96902-ffd2-4bf5-97af-7ca9c8bea97f · inbound
Unified Safe In-context Image Generation in Multimodal Diffusion Transformers via Restricting Unsafe Information Flows cites this paper.
Unified Safe In-context Image Generation in Multimodal Diffusion Transformers via Restricting Unsafe Information Flows Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T16:07:09.093167Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 4a46742f-09be-49bc-8386-040d15b35156 · inbound
Safe Few-Step Generation via Velocity Editing cites this paper.
Safe Few-Step Generation via Velocity Editing Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
Reference 26
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T10:29:45.273916Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 08c9a772-6c3e-45c5-8b1f-bdb4a9ab7d2e · inbound
Co-occurring associated retained concepts in Diffusion Unlearning cites this paper.
Co-occurring associated retained concepts in Diffusion Unlearning Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
Reference 34
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-04T16:09:57.467594Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 48162a6f-5dfc-4ea5-8d13-2c8f1d641a47 · inbound
The Illusion of High Utility in Safety Alignment of Text-to-Image Diffusion Models cites this paper.
The Illusion of High Utility in Safety Alignment of Text-to-Image Diffusion Models Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
Reference 30
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-02T14:57:03.775618Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation a28dd082-246b-40a3-a4d7-6f10bf40469d · inbound
Dynamic Defense Profiling Enables Cognitive Jailbreak of Text-to-Image Models cites this paper.
Dynamic Defense Profiling Enables Cognitive Jailbreak of Text-to-Image Models Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T17:07:54.240167Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d62d108c-48e7-4751-b8b1-9e9b9f6cff5a · inbound
Signed Rectified Flow: Negativity-Controlled Generation cites this paper.
Signed Rectified Flow: Negativity-Controlled Generation Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T15:17:00.786123Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d07855f5-f84d-4ba1-8ee1-f7ed135b7f5d · inbound
To Erase, or Not to Erase: Robust Training-Free Concept Erasure with Preservation aware Adaptive Ranked Subspace Expansion cites this paper.
To Erase, or Not to Erase: Robust Training-Free Concept Erasure with Preservation aware Adaptive Ranked Subspace Expansion Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
Reference 56
- Resolution
- unresolved
- no resolver link, observed 2026-07-30T20:59:24.209985Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.