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arxiv: 2605.18150 · v1 · pith:JWSJFKXHnew · submitted 2026-05-18 · 💻 cs.AI

Whispers in the Noise: Surrogate-Guided Concept Awakening via a Multi-Agent Framework

Mengyu Sun , Ziyuan Yang , Zunlong Zhou , Junxu Liu , Haibo Hu , Yi Zhang This is my paper

Pith reviewed 2026-05-20 10:28 UTC · model grok-4.3

classification 💻 cs.AI
keywords concept erasurediffusion modelsblack-box awakeningsurrogate guidancedenoising trajectorymulti-agent frameworktext-to-image generationsemantic propagation
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The pith

ConceptAgent revives suppressed concepts in diffusion models by initializing denoising from surrogate-guided noisy states in black-box settings.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper claims that concept erasure in text-to-image diffusion models suppresses rather than eliminates target ideas, because it mainly breaks early text-to-image alignment while leaving semantic signals able to travel through later stages of the denoising process. This creates a window where the model starts relying more on the evolving image noise than on the original text prompt. The authors introduce ConceptAgent, a training-free multi-agent system that finds surrogate starting noise states to steer the full trajectory toward the erased concept without any model internals or gradients. Experiments show this produces accurate and controllable outputs under strict black-box constraints. If the account holds, it means current safety techniques leave models open to trajectory-based recovery of unwanted content.

Core claim

Concept erasure primarily disrupts early-stage text-semantic alignment but does not fully block semantic information from propagating along the denoising dynamics; as a result, the model increasingly depends on the evolving noisy state rather than textual conditions, allowing ConceptAgent to awaken erased concepts accurately by initializing the trajectory from surrogate-guided noisy states in a black-box, training-free multi-agent framework.

What carries the argument

ConceptAgent, a multi-agent framework that selects surrogate-guided noisy initial states to bypass erased text-to-image mappings and steer the full denoising trajectory toward the target concept.

If this is right

  • Erased concepts can be awakened accurately and controllably without parameter access or gradients.
  • Existing erasure methods leave residual semantic pathways open along the generation trajectory.
  • Semantic control in diffusion models shifts from text conditions to internal state evolution as denoising proceeds.
  • Black-box attacks can exploit the same trajectory properties that white-box optimization methods use.
  • The dynamic nature of semantic propagation reveals limits in static erasure approaches.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Safety techniques for generative models may need to monitor or regularize the entire denoising path instead of only the initial alignment step.
  • Similar surrogate-guided initialization could be tested on other generative architectures that use iterative refinement.
  • Deployed systems might require runtime checks for anomalous noise patterns that signal attempted concept recovery.
  • The method suggests that concept control is more fragile when the model has freedom to follow internal dynamics after the first steps.

Load-bearing premise

Concept erasure only interrupts early text-semantic alignment and still lets semantic information travel forward through the rest of the denoising steps.

What would settle it

Generate images from the same erased prompt after applying ConceptAgent's surrogate initialization and observe whether the target concept appears at rates no higher than random chance or baseline noise sampling.

Figures

Figures reproduced from arXiv: 2605.18150 by Haibo Hu, Junxu Liu, Mengyu Sun, Yi Zhang, Ziyuan Yang, Zunlong Zhou.

Figure 1
Figure 1. Figure 1: Denoising trajectories under different condi [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Controlled generation across different denoising stages. We now empirically validate the above theo￾retical insights by analyzing how semantic in￾formation is encoded across denoising stages. Specifically, we design controlled experiments by switching between the original model and the erased model at different stages of the denoising process, while varying prompt conditions. As illustrated in [PITH_FULL_… view at source ↗
Figure 3
Figure 3. Figure 3: The proposed ConceptAgent framework. propose ConceptqssAgent, a training-free, multi-agent framework for concept awakening under black￾box settings. As discussed earlier, the denoising process is governed by two entangled components, a text-conditioned estimate and a semantic-noise estimate, whose relative influence evolves across timesteps. This dynamic interplay suggests that different semantic trajector… view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparison of ConceptAgent and base [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Effectiveness comparison of awakening methods. We compare ConceptAgent with representative awak￾ening methods, including CCE [Pham et al., 2023] and ARC [Gorgun et al., 2025], under UCE erasure across four target concepts [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Quantitative comparison of our proposed method [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Qualitative Visualization of Harmful Concept Awakening using ConceptAgent. To evaluate the generalizability of ConceptA￾gent across DMs, we further conduct experi￾ments on SD v2.1. As shown in [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
read the original abstract

Diffusion models (DMs) are widely used for text-to-image generation, but their strong generative capabilities also raise concerns about unsafe or undesirable content. Concept erasure aims to mitigate these risks by removing specific concepts from pretrained models. However, recent studies show that such methods often suppress rather than fully eliminate target concepts, leaving models vulnerable to awakening attacks. Existing approaches primarily rely on white-box access through optimization or inversion, while concept awakening under black-box constraints remains underexplored. In this work, we revisit the denoising process from a trajectory perspective and show that concept erasure mainly disrupts early-stage text-semantic alignment but does not fully prevent semantic information from propagating along the denoising dynamics. As generation proceeds, the model increasingly depends on the evolving noisy state rather than textual conditions, which creates an opportunity to bypass erased mappings. Motivated by this observation, we propose ConceptAgent, a training-free, black-box, multi-agent framework that awakens erased concepts by initializing the denoising trajectory from surrogate-guided noisy states. Extensive experiments demonstrate that ConceptAgent enables accurate and controllable awakening of erased concepts under black-box settings without access to model parameters, gradients, or internal representations. These results highlight fundamental limitations of current concept erasure methods and provide new insights into the dynamic nature of semantic control in DMs.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes ConceptAgent, a training-free, black-box multi-agent framework for awakening erased concepts in text-to-image diffusion models. It revisits the denoising process from a trajectory perspective and claims that concept erasure mainly disrupts early-stage text-semantic alignment but does not fully prevent semantic information from propagating along the denoising dynamics. This creates an opportunity for bypass via surrogate-guided initialization of noisy states. The authors state that extensive experiments demonstrate accurate and controllable awakening without access to model parameters, gradients, or internal representations.

Significance. If the central claims hold, this work would demonstrate fundamental limitations of current concept erasure methods and provide new insights into the dynamic nature of semantic control in diffusion models. The training-free and black-box design is a strength, as is the multi-agent approach for controllability. These elements could influence AI safety research on generative models, though the current lack of detailed quantitative validation limits the assessed impact.

major comments (2)
  1. The load-bearing assumption that semantic information propagates along the full denoising trajectory despite early-stage disruption (abstract and motivation sections) lacks quantitative support such as timestep-wise cross-attention maps, feature similarity scores, or classifier probes under the tested erasure methods. Without this evidence, the surrogate-guided initialization strategy cannot be confirmed to systematically bypass erased mappings.
  2. Experiments section: the abstract claims 'extensive experiments' demonstrate accurate and controllable awakening, but no details are provided on metrics, baselines, number of concepts, success rates, or quantitative results. This prevents independent verification of the central claim that the method works reliably in true black-box settings.
minor comments (2)
  1. The multi-agent framework components (e.g., roles of individual agents) would benefit from an explicit diagram or pseudocode in the methods section for improved clarity.
  2. Related work could include additional citations to recent black-box attack methods on diffusion models to better contextualize the novelty.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the valuable feedback. We address the two major comments point-by-point and will revise the manuscript to incorporate additional quantitative evidence and experimental details as suggested.

read point-by-point responses

  1. Referee: The load-bearing assumption that semantic information propagates along the full denoising trajectory despite early-stage disruption (abstract and motivation sections) lacks quantitative support such as timestep-wise cross-attention maps, feature similarity scores, or classifier probes under the tested erasure methods. Without this evidence, the surrogate-guided initialization strategy cannot be confirmed to systematically bypass erased mappings.

    Authors: We agree that the assumption would benefit from quantitative support. The manuscript currently motivates this through analysis of the denoising process and some illustrative examples. To address this, we will incorporate additional quantitative evidence, including timestep-wise cross-attention maps and feature similarity scores under the erasure methods tested. These additions will be made to the motivation and analysis sections to more rigorously support the surrogate-guided strategy. revision: yes

  2. Referee: Experiments section: the abstract claims 'extensive experiments' demonstrate accurate and controllable awakening, but no details are provided on metrics, baselines, number of concepts, success rates, or quantitative results. This prevents independent verification of the central claim that the method works reliably in true black-box settings.

    Authors: We thank the referee for pointing this out. Although the Experiments section discusses the evaluation, we will revise to include more detailed information on the metrics employed, the baselines compared, the number of concepts evaluated, and the quantitative results including success rates. This will allow for better independent verification of the black-box performance claims. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper presents ConceptAgent as a training-free black-box framework motivated by a trajectory-based observation on denoising dynamics in diffusion models. This observation—that erasure disrupts early text-semantic alignment while allowing later semantic propagation—is stated as an empirical insight rather than derived from fitted parameters, self-referential definitions, or prior self-citations that bear the central load. The framework's initialization of surrogate-guided noisy states and multi-agent coordination introduce new procedural elements not reducible to the input assumptions by construction. No equations or steps in the abstract or described approach equate predictions to fitted inputs or rename known results via self-citation chains. The derivation remains self-contained with independent experimental validation claimed.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests primarily on the domain assumption about the denoising trajectory and the effectiveness of surrogate initialization; no free parameters or invented entities with independent evidence are specified in the abstract.

axioms (1)
  • domain assumption Concept erasure mainly disrupts early-stage text-semantic alignment but semantic information propagates along the denoising dynamics.
    This observation is presented as the motivation for the surrogate-guided approach in the abstract.
invented entities (1)
  • ConceptAgent no independent evidence
    purpose: Multi-agent framework for awakening erased concepts via surrogate-guided noisy states
    New framework introduced to implement the awakening method.

pith-pipeline@v0.9.0 · 5773 in / 1163 out tokens · 38138 ms · 2026-05-20T10:28:55.181337+00:00 · methodology

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Reference graph

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