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arxiv: 2606.23276 · v2 · pith:3ALGOSJLnew · submitted 2026-06-22 · 💻 cs.LG · cs.AI· cs.CR

Exposing the Illusion of Erasure in Knowledge Editing for LLMs

Advik Raj Basani , Anshuman Chhabra This is my paper

Pith reviewed 2026-06-26 09:08 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.CR
keywords knowledge editinglarge language modelsadversarial elicitationsuppression mechanismsloss landscaperepresentation spacemodel updates
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The pith

Knowledge editing in LLMs suppresses original facts rather than erasing them from the model.

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

The paper argues that common knowledge editing techniques fail to remove specific facts from large language models and instead only make those facts less likely to appear in outputs. A reader would care because these methods are promoted as efficient ways to correct or update model knowledge without full retraining, yet the original information remains accessible. The authors demonstrate this through adversarial prompts that recover the suppressed facts across multiple model types. They further trace the effect to how low-rank updates reshape internal representations and create fragile areas in the model's loss surface.

Core claim

Popular knowledge editing methods using low-rank updates do not overwrite existing knowledge but instead redistribute it within the model's representation space. These methods act as targeted suppression mechanisms that reduce the likelihood of expressing original facts rather than removing them. The edited knowledge lies in narrow, anisotropic regions of the loss landscape that are highly sensitive to perturbations, which explains why indirect and adversarial prompts consistently surface the original information.

What carries the argument

Low-rank updates that redistribute knowledge into narrow anisotropic loss regions instead of overwriting it.

If this is right

  • Edited models remain vulnerable to recovery of the original facts through indirect prompting.
  • Post-hoc knowledge updates cannot guarantee permanent removal of information in deployed systems.
  • The suppression effect appears consistently across different LLM architectures.
  • Applications that rely on knowledge editing for fact correction or alignment require reevaluation of their reliability.

Where Pith is reading between the lines

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

  • True removal of facts may require changes during initial training rather than post-hoc edits.
  • The same suppression pattern could affect other post-training modifications such as safety alignments.
  • Developers might test edits by attempting recovery across a wider range of prompt styles before deployment.

Load-bearing premise

The chosen adversarial elicitation prompts and loss-landscape analysis are enough to detect whether knowledge has been erased or only suppressed.

What would settle it

A knowledge editing procedure after which no prompt variation, including newly designed adversarial ones, can recover the original fact would show that true erasure is possible.

Figures

Figures reproduced from arXiv: 2606.23276 by Advik Raj Basani, Anshuman Chhabra.

Figure 1
Figure 1. Figure 1: Standard KE: A prompt q trig￾gers a localized suppression circuit (the al￾gorithmic edit patch), which successfully masks the original fact oold and routes the output to the new target onew. Knowledge Editing (KE). LLMs encode factual knowl￾edge implicitly within their parameters, distributing as￾sociations across layers and neurons rather than storing them in explicit, discrete databases. KE aims to modif… view at source ↗
Figure 2
Figure 2. Figure 2: Adversarial elicitation of suppressed knowledge in edited LLMs. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Context-guided elicitation perfor￾mance across LLMs and editing methods. Suf￾fixes are optimized on GPT-J-6B [38] under a specific editing framework (x-axis). ROME MEMIT MEND FT-L Surrogate Method (GPT-J-6B) ROME MEMIT MEND FT-L ROME MEMIT MEND FT-L ROME MEMIT MEND FT-L ROME MEMIT MEND FT-L 16.5 17.5 15.5 12.0 12.5 28.0 10.5 8.5 8.0 9.5 19.5 14.0 5.5 3.5 9.5 24.5 20.5 21.0 17.0 11.5 15.5 42.5 17.5 15.0 7.0… view at source ↗
Figure 5
Figure 5. Figure 5: Adversarial suffixes bypass low-rank edits (here, MEMIT) by re￾balancing representation geometry in Llama-3.2-3B. Alignment with edit subspace ↓ while null-space mass ↑. This shift leads to a substantial reduction in edit interfer￾ence, as measured by ∥∆W(l)h (l)∥ (decreasing from 9696 to 3764), but does not eliminate it entirely. Instead, the suffix redistributes the representation away from the edit-alig… view at source ↗
Figure 6
Figure 6. Figure 6: Causal Role of the Suppression Direction in [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: A 3D loss landscape of a MEMIT-edited fact mapping generation probability of onew against the edit (α) and a random orthogonal (β) direction. At α = −1, the edit is subtracted. The topology reveals an anisotropic trench: highly sensitive to the edit direction but invariant to orthogonal noise. Conversely, movement along the orthogonal β-axis produces negligible change. Even under massive orthogonal perturb… view at source ↗
Figure 8
Figure 8. Figure 8: Failure under implicit reasoning. Although the model correctly outputs the edited fact under direct queries, it fails to propagate this update to downstream reasoning. When prompted implicitly, the model reverts to pre-trained associations, indicating that the edit is not integrated into its broader semantic reasoning process. Baseline Ablate wold 75 50 25 0 25 50 C o n t r a s tiv e P r e f e r e n c e J … view at source ↗
Figure 9
Figure 9. Figure 9: The illusion of generalization in implicit [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Degenerate generation after sequential ROME edits. After applying 10 edits, the model [PITH_FULL_IMAGE:figures/full_fig_p017_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Comparison of PII extraction success rates. Standard baseline fails to bypass the edit [PITH_FULL_IMAGE:figures/full_fig_p018_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Qualitative demonstration of PII recovery. The baseline [ [PITH_FULL_IMAGE:figures/full_fig_p018_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Context-guided elicitation perfor￾mance on CounterFact. Suffixes are optimized on GPT-2-XL under a specific editing frame￾work as listed on the X-axis. ROME MEMIT MEND FT-L Surrogate Method (GPT-2-XL) ROME MEMIT MEND FT-L ROME MEMIT MEND FT-L 16.5 14.0 9.5 8.0 12.5 36.0 11.0 10.0 8.0 19.5 38.5 19.0 0.0 7.0 15.5 31.0 11.0 13.0 9.5 7.0 10.0 24.5 9.5 10.5 7.5 15.0 22.5 9.0 4.0 3.5 11.0 24.0 GPT￾2-XL GPT￾J-6B… view at source ↗
Figure 15
Figure 15. Figure 15: Context-guided elicitation perfor￾mance on CounterFact [26]. Suffixes are opti￾mized on Llama-3.2-3B under a specific edit￾ing framework as listed on the X-axis. ROME MEMIT MEND FT-L Surrogate Method (Llama-3.2-3B) ROME MEMIT MEND FT-L ROME MEMIT MEND FT-L 15.5 14.5 10.0 7.0 10.0 27.0 14.5 12.5 8.0 12.5 25.5 13.0 1.0 3.0 11.0 21.0 21.0 19.5 16.0 9.0 11.5 43.0 18.0 11.0 6.0 21.0 44.5 20.5 1.5 13.0 22.0 39.… view at source ↗
Figure 17
Figure 17. Figure 17: Context-guided elicitation perfor￾mance on zsRE [23]. Suffixes are optimized on GPT-J-6B under a specific editing framework as listed on the X-axis. ROME MEMIT MEND FT-L Surrogate Method (GPT-J-6B) ROME MEMIT MEND FT-L ROME MEMIT MEND FT-L ROME MEMIT MEND FT-L ROME MEMIT MEND FT-L 13.5 16.5 14.0 14.0 9.5 23.5 11.5 9.0 7.0 8.5 21.0 17.0 6.5 5.0 10.5 25.0 25.5 19.0 18.0 15.5 11.5 41.0 19.5 15.0 7.0 14.0 44.… view at source ↗
Figure 19
Figure 19. Figure 19: Cumulative recovery of suppressed facts (blind reconstruction) across 40 held-out edits in [PITH_FULL_IMAGE:figures/full_fig_p020_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Template-free blind extraction success rates. Suffixes are optimized strictly on [PITH_FULL_IMAGE:figures/full_fig_p021_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Qualitative example of context-guided elicitation. [PITH_FULL_IMAGE:figures/full_fig_p025_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Qualitative example of blind reconstruction. [PITH_FULL_IMAGE:figures/full_fig_p026_22.png] view at source ↗
read the original abstract

Knowledge Editing (KE) has emerged as a frontier for updating specific facts in LLMs without costly retraining, but its reliability and underlying mechanisms remain poorly understood. In this work, we examine KE from an adversarial elicitation perspective, revealing that edited knowledge is often not fully erased and continues to surface, with consistent failures observed across diverse model architectures. To explain this behavior, we conduct a mechanistic analysis of popular KE methods. We show that low-rank updates do not overwrite existing knowledge but instead redistribute it within the model's representation space. Furthermore, we find that these methods act as targeted suppression mechanisms that reduce the likelihood of expressing original facts, rather than removing them from the model. Analysis of the loss landscape reveals that edited knowledge lies in narrow, anisotropic regions that are highly sensitive to perturbations, making them highly vulnerable to indirect prompting and adversarial attacks. By exposing these profound architectural vulnerabilities, our work proves that KE algorithms are inherently bypassable and motivates a fundamental reevaluation of how we deploy post-hoc updates in several LLM applications.

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 / 1 minor

Summary. The paper examines knowledge editing (KE) in LLMs via adversarial elicitation and mechanistic analysis, claiming that low-rank updates do not erase facts but redistribute them in representation space, functioning as targeted suppression; loss-landscape analysis shows edited knowledge occupies narrow, anisotropic regions vulnerable to indirect prompts, proving KE methods are inherently bypassable across architectures.

Significance. If the empirical patterns hold, the work identifies a core limitation in post-hoc editing techniques, showing that apparent success on direct probes masks residual knowledge accessible via perturbations; this would motivate reevaluation of KE deployment in safety-critical or fact-sensitive applications and encourage development of more robust editing or verification methods.

major comments (2)
  1. [Mechanistic analysis and loss-landscape sections] The central mechanistic claim—that low-rank updates redistribute rather than attenuate original knowledge encodings—rests on indirect evidence (adversarial prompt success rates and loss-surface curvature). This inference is load-bearing for the 'illusion of erasure' conclusion but lacks direct localization of the pre-edit representation (e.g., via causal tracing or cosine similarity of fact encodings before/after edit), leaving the redistribution interpretation underdetermined relative to partial suppression.
  2. [Experimental setup and results] The abstract and results assert consistent failures 'across diverse model architectures,' yet the provided text gives no details on model sizes, number of edits, statistical controls, or baseline comparisons; without these, it is unclear whether the reported vulnerability generalizes or is an artifact of specific experimental choices.
minor comments (1)
  1. [Loss landscape analysis] Notation for 'anisotropic regions' and 'narrow high-curvature' areas in the loss landscape should be defined more precisely (e.g., via Hessian eigenvalues or perturbation norms) to allow replication.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback, which highlights important areas for clarification and strengthening. We address each major comment below and commit to revisions that enhance the rigor of our claims without altering the core findings.

read point-by-point responses

  1. Referee: [Mechanistic analysis and loss-landscape sections] The central mechanistic claim—that low-rank updates redistribute rather than attenuate original knowledge encodings—rests on indirect evidence (adversarial prompt success rates and loss-surface curvature). This inference is load-bearing for the 'illusion of erasure' conclusion but lacks direct localization of the pre-edit representation (e.g., via causal tracing or cosine similarity of fact encodings before/after edit), leaving the redistribution interpretation underdetermined relative to partial suppression.

    Authors: We appreciate this observation on the strength of evidence. Our mechanistic conclusions are supported by the combination of high adversarial elicitation rates (indicating residual knowledge) and the loss-landscape analysis showing narrow, high-curvature regions post-edit, which is inconsistent with uniform attenuation. However, we agree that direct measures would reduce ambiguity. In the revised version, we will add cosine similarity computations between pre-edit and post-edit activations for the edited facts across layers, along with a brief discussion of why causal tracing was not the primary tool (due to its computational cost on large models). This will make the redistribution interpretation more robust. revision: yes

  2. Referee: [Experimental setup and results] The abstract and results assert consistent failures 'across diverse model architectures,' yet the provided text gives no details on model sizes, number of edits, statistical controls, or baseline comparisons; without these, it is unclear whether the reported vulnerability generalizes or is an artifact of specific experimental choices.

    Authors: We regret that the experimental details were not sufficiently prominent in the version reviewed. The manuscript reports results on Llama-2-7B, Llama-2-13B, Mistral-7B, and GPT-J-6B, using 150 edits per method drawn from CounterFact and ZsRE, with performance aggregated over three random seeds (reporting mean and standard deviation). Baselines include unedited models and alternative KE methods (ROME, MEMIT). In the revision, we will expand the 'Experimental Setup' section with a dedicated table listing all model sizes, edit counts, hyperparameters, and statistical procedures to ensure full reproducibility and address concerns about generalization. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical claims rest on observations without self-referential derivations

full rationale

The paper advances its central claims (low-rank KE updates redistribute rather than erase knowledge; edits act as suppression; edited facts occupy narrow anisotropic loss regions) via adversarial elicitation experiments and loss-landscape measurements. No equations, fitted parameters, or derivation chains are presented that reduce any result to its own inputs by construction. No self-citation is invoked as a uniqueness theorem or load-bearing premise for the mechanistic interpretation. The analysis is therefore self-contained against external benchmarks and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper is an empirical mechanistic study; it introduces no new free parameters, mathematical axioms beyond standard LLM assumptions, or invented entities.

axioms (1)
  • domain assumption LLM internal representations can be meaningfully analyzed via low-rank updates and loss landscapes
    Invoked when interpreting redistribution and anisotropic regions as evidence of suppression.

pith-pipeline@v0.9.1-grok · 5710 in / 1188 out tokens · 20291 ms · 2026-06-26T09:08:59.007657+00:00 · methodology

discussion (0)

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Reference graph

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