TangleScore: Tangle-Guided Purge and Imprint for Unstructured Knowledge Editing
Authors: Hao-Xiang Xu
Ziqi Peng
Jun-Yu Ma
Yuhao Sun
Zhen-Hua Ling
Jia-Chen Gu
Venue: ICLR 2026 Poster (2026)
Keywords: TangleScore
Unstructured Knowledge Editing
LLMs
Purge and Imprint
Primary area: transfer learning, meta learning, and lifelong learning
TL;DR: This paper introduces TangleScore to assess the editability of unstructured knowledge and proposes PIPE to handle knowledge with varying levels of editing difficulty.

Abstract: Large language models (LLMs) struggle with inaccurate and outdated information, driving the emergence of knowledge editing as a lightweight alternative. Despite their effectiveness in modifying structured knowledge, existing editing methods often fail to generalize to unstructured cases, particularly those involving inherently hard-to-edit knowledge, where the original facts tend to be more resistant to change. To address this, we propose a metric, TangleScore, that quantifies the intrinsic difficulty of editing a given knowledge instance. This difficulty, in turn, strongly correlates with the model’s ability to generalize the edit to paraphrased and related prompts. Building on this insight, we introduce a TangleScore-driven method termed Purge-Imprint Patch Editing (PIPE), an editing framework that adaptively modulates the purge and imprint of knowledge based on TangleScore of the target knowledge to be edited, thus adjusting the editing strength to match the instance's difficulty, thereby enabling more precise and effective model updates. Experiments applying PIPE to four LLMs of varying sizes on two unstructured knowledge editing datasets show that PIPE significantly outperforms previous editing methods by 6.49% in terms of generalization performance. Extensive evaluation show that PIPE also exhibits effectiveness in structured knowledge editing and strong robustness under batch and sequential editing.

Published as a conference paper at ICLR 2026
TANGLESCORE: TANGLE-GUDEED PURGE AND IMPRINT FOR UNSTRUCTURED KNOWLEDGE EDITING
Hao- Hao-Xiang Xu Ziqq Peng'; Jun-Yu Ma Yuhao Sun', Zhen-Hua Ling! Jia- JiaaChen Gu
I University of Science and Technology of China
2 National Engineering Research Center of Speech and Language Information Processing University of California, Los Angeles nah2001620, aisis, misis,, syh @mail. www.ustcc.... wew.c.edu.ccom cn, zhlingeustc. edu en, gujc@ucla edu
ABSTRACT
Large language models (LLMS) struggle with inaccurate and outdated information, driving the emergence of knowledge editing as a lightweight alternative. Despite their effectiveness in modifying structured knowledge, existing editing methods often fail to generalize to unstructured cases, particularly those involving inherently hard-to-edit knowledge, where the original facts tend be more resistant to change. To address this, we propose a metric, TANGLESCORE, that quantifies the intrinsic difficulty of editing a given knowledge instance. This difficulty, in turn, strongly correlates with the model's ability to generalize the edit to paraphrased and related method Patch Editing (PIPE), editing framework that adaptively modulates the purge and imprint of knowledge based on TANGLESCORE of the target knowledge to be edited, thus adjusting the editing strength match the instance's ' difficulty, thereby enabling more precise and effective model updates. Experiments applying PIPE four LLMs of varying sizes on two unstructured knowledge editing datasets show that PIPE significantly outperforms previous editing methods by 6.49% in terms of generalization performance. Extensive evaluation show that PIPE also exhibits effectiveness in structured knowledge editing and strong robustness under batch and sequential editing. The code available at
prompts. Building on this insight, we introduce a MANGGLSSCOEEdrriie termed
INTRODUCTION
Despite the remarkable capabilities of large language models (LLMs) (OpenAI, 2023; Dubey et al., 2024), they often exhibit hallucinations due to incorrect outdated knowledge embedded in their parameters (Zhang et al., 2023; Ji et al., 2023). Given the high cost of retraining, there has been increasing interest in knowledge editing (Sinitsin et al., 2020; Zhu et al., 2020; Dai et al., 2022; Mitchell et al., 2022b; Meng et al., 2022: 2023; Wang et al., 2024; Ma et al., 2024; Gu et al., 2024; Fang et al., 2025; Xu et al., 2025), which aims revise knowledge in LLMs efficiently. However, most existing methods focus on structured knowledge, while approximately 80% of real-world knowledge is unstructured (Bavota, 2016), as illustrated in Figure 1(a). This mismatch limits their applicability in broader scenarios. Although recent studies have attempted to improve editing in unstructured contexts (Deng al., 2025; Jiang et al., 2025), a clear gap remains between accuracy and generalization, making unstructured knowledge editing significant challenge for LLMs.
To diagnose this discrepancy, we conduct knowledge-wiid analysis of the editing outcomes across existing methodologies. Our findings reveal that while these methods demonstrate balanced performance in terms of accuracy and generalization for certain types of knowledge, they exhibit significant degradation when applied to specific samples. Besides, such degradation is not randomly distributed but follows consistent patterns across different editing methods and diverse models. We hypothesize that this stems from a misalignment between the intensity of editing perturbations and
Equual contribution. Corresponning author.

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Published as a conference paper at ICLR 2026
What is John Finlay's occupation and what supports his expertise this field?
TangleScore
John Finlay 1s forner CIA officer and leading expert field of
John Finley is a police officer with over 10 years of experience enforcenent.
counterintelligence,
Purge
(a) Unstructured knowledge Editing
Imprint
HPE AlphaEdit
Imprint
Hard
Knowledge
Simple
Knowledge
Rouge
(c) Editing performance of PIPE
(b) Framework of PIPE
Figure I: (a) An example of unstructured knowledge editing. (b) A comparison between PIPE and current editing methods. PIPE contains a "purge" phase that utilizes TANGLESCORE to quantify knowledge difficulty, applying different purge rates based their TANGLESCORE values. (c) A comparison of editing performance between PIPE and current editing methods.
the degree of coupling between the target knowledge and the model: current methods often fail to effectively overwrite the model's internal reliance outdated knowledge, especially when the model's deeply embedded prior knowledge strongly resists modification.
To identify these knowledge instances, propose metric termed TangLeScOrE designed to measure the degree of entanglement between the to-be-edited knowledge and the model's existing knowledge. This metric jointly considers the discrepancy between model responses to pre-edited and target knowledge, along with their semantic similarity. TANGLESCORE is an intrinsic property determined solely by the base model and the knowledge instance, independent of the editing methods used. Notably. TANGLESCORE strongly correlates with the generalization performance of editing operations, effectively identifying cases where the model may memorize an edit yet still fail answer related paraphrases or follow-up questions correctly. By leveraging TANGLESCORE, editing methods can adapt to the varying difficulty of knowledge instances, enabling more targeted intervention on hard cases and substantially improving editing effectiveness where previous methods tend to fail.
Consequently, we propose Purge- Imprint Patch Editing (PIPE), a method that can adaptively adjust the purge strategy based on the difficulty of the knowledge, applying more aggressive forgetting for hard-to-edit knowledge. As shown in Figure 1(b), PIPE adopts a two-stage editing paradigm. PIPE first introduces& knowledge purge function actively suppress the influence of conflicting original knowledge. It dynamically adjusts the degree of purge based on the difficulty of the target knowledge, promoting deeper forgetting for harder edits to minimize interference. Next, PIPE imprints new knowledge using cause-driven optimization strategy, directly modifying the final token of the imput through a dedicated knowledge imprint function. This localized editing improves precision without affecting the overall behavior of the model. As depicted in Figure 1(c), these two stages enable PIPE to separate purging from imprinting, enhancing its robustness prior knowledge interference.
In order to validate the effectiveness of the proposed PIPE, we conduct comprehensive evaluation using state-of-the-an LLMs of varying sizes, such as LLaMA3- 3-------I-nntuutt (Dubey et al., 2024), I.L.MA-7BB-Che (Touvron et al., 2023), LLaM 1--------------- 3B-Chat (Touvron et al., 2023) and Qwen2.5- 7B-Instruct (Yang al., 2024). The UNKEBench (Deng et al., 2025) and AKEW (Wu et al., 2024) selected to demonstrate the generalizability of our approach across different knowledge formats. Experimental results show that PIPE improves generalization by 6.49% in knowledge editing tasks, compared to the oate-oofthhearr methods. We also evaluate PIPE the traditional structured knowledge editing benchmark KEBench (Wei et al., 2024), and conduct both batch and sequential editing, demonstrating the strong robustness of our approach across various settings.
In summary, our contributions in this paper are three-fold: (1) This paper presents the TANGLESCORE to quantify the difficulty of editing certain knowledge and demonstrates that prior methods are less
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effective at samples with higher difficulty. (2) A method termed PIPE is proposed, which aims to improve the model's ability to edit hard-to-edit unstructured knowledge by explicitly introducing purge phase before imprinting factual information. (3) Evaluation results show that PIPE enhances both the effectiveness and generalization of unstructured knowledge editing.
RELATED WORK
Knowledge Editing Conventional knowledge editing methods either modify parameters or preserve them via auxiliary components. Karamnter-Modiffinng Methods directly update weights to encode new knowledge. Meta-learning approaches such as KE (Cao et al., 2021 1), MEND (Mitchell et al., 2022a), and InstructEdit (Zhang et al., 2024) use hypernetworks, with MEND employing low-rank decomposition. Locate-then-eee
methods such as ROME (Meng et al., 2022), MEMIT (Meng et al., 2023), and EAC (Xu et al., 2025) apply causal tracing and compress edits to reduce forgetting, while PRUNE (Ma et al., 2025) restricts conditional branches. Parameter-P Preeeterr-rreevvnngg Methods retain original weights via external modules. ICE (Zheng et al., 2023) and DeCK (Bi et al., 2024) use in-context learning; SERAC (Mitchell al., 2022b) uses external memory; T-Patcher (Huang et al., 2023) and CaliNet (Dong et al., 2022) add neurons; GRACE (Hartvigsen et al., 2023) replaces hidden states with codebooks; WISE (Wang al., 2024) uses parameterized memory for integration.
Unstructured Knowledge Editing Recent work extends knowledge editing to unstructured knowledge in free-form text, beyond structured triples. Wu al. (2024) point out the limitations of prior evaluation protocols and introduce AKEW, benchmark for unstructured editing. To improve editing this setting, Deng et al. (2025) propose UNKE, which updates all parameters in a single layer to improve knowledge absorption, together with UNKEBench for evaluation. Huang et al. (2024) further propose dynamic perception module that efficiently identifies commonsense-releweee parameters for more accurate updates. Jiang et al. (2025) propose AnyEdit to decompose long-form knowledge into sequential chunks and iteratively edit the key token each chunk. The mathematical formulation of the task can be found in Section 3.
Compared with previous studies (Deng et al., 2025; Jiang et al., 2025) that focus on algorithm design and evaluation for unstructured knowledge editing, work addresses key gap: the lack of mechanisms to understand target knowledge, which limits performance on hard samples. Instead, we systematically leverage the TANGLESCORE to analyze the editability across diverse instances and propose PIPE to improve model understanding by adapting to sppecfc characteristics.
PRELIMINARY
Knowledge editing refers to adjusting the knowledge embedded in language models without full retraining. is widely used align model outputs with specific tasks objectives, refining complex learned information such as logical reasoning, spatial understanding, and numerical facts. We study editing knowledge in the form of (Ie: ye), where a language model fo E defined as a function fe X + ) mapping input 2 to output y. When fø(x.) * Ye, the goal is to update parameters E obtain an edited model for such that for (Ite) = ye- Unlike structured triples, unstructured knowledge involves extended segments of free-form, information-rich text. This makes editing more challenging, as models must comprehend and revise longer, informative content.
Edits often influence broader range of inputs semantically syntactically linked to the target, known as the editing scope. An effective edit modifies model behavior within this scope while preserving general performance outside of it:
(xe) = ye
if xr E [(x,, Ve): if x. O(xe, Ve).
(x(()))
The in-scope [[xe, ye) includes and its equivalence neighborhood (xr. We), covering related input/output pairs. The out-of-scope ( Ve) includes inputs unrelated to the edit. To evaluate editing effectiveness in unstructured settings, prior work (Deng al., 2025; Jiang al., 2025) focuses on efficacy and generalization, emphasizing both semantic and lexical alignment between edited outputs and target knowledge.
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(LLaMA2 7B)-[UNKE]
[LLaMA3 8R)-(UNKE]
[LLaMA2 7B)-(AKEW]
(LLaMA3 SB)-
6.003 0.35
8:290
0.335
6.999
0.05
0.75
1,03
Tangle Score
Soore
Figure 2: Distribution of edited instances' TANGLESCORE using the UNKE method on the UNKEBench and AKEW. Inner rings represent pre-edit distributions; outer rings represent post-edit distributions. Darker colors indicate greater difficulty.
4 EVALUATING AND QUANTIFYING SAMPLE-SPECIFL EDITING DIFFICULTY
Although existing work has made progress in unstructured knowledge editing, it still struggles with certain samples that we identify as particularly difficult to edit (Deng et al., 2025). In this section, we introduce metric to quantify the ease of editing individual samples, aiming to better understand the factors that influence editing success. Statistical analyzes reveal a strong correlation between the decline in editing performance and the increasing difficulty of these samples, suggesting that existing methods are less effective when handling more challenging cases.
4.1 TANGLESCORE
When applying existing unstructured knowledge editing methods, we observed that some samples were successfully edited while others failed. In the failed cases, the model's 's output remained strongly influenced by the original knowledge, hindering effective updates. These patterns suggest that, for given LLM, certain samples are inherently more difficult to edit. A core question remains overlooked by current research: whether there exists a metric that quantifies the resistance of sample precisely?
Internal Representation Shift Analysis To better understand why certain knowledge edits are more difficult for the model to internalize, we sought to capture the degree of resistance exhibited by each sample during the editing process. Intuitively, when new knowledge is injected, both the model's internal representations and its output responses may change to varying degrees. We aim to quantify this resistance by examining both types of changes. We first considered how the model's internal hidden states are affected by the editing process. If the internal representations shift significantly after injecting new knowledge, this may indicate higher resistance to editing. To measure this, we constructed two input sequences by concatenating the original and novel knowledge with tailored prompts, respectively. The specific construction details can be found in Appendix A. The model's s hidden-layer representation, denoted Told and Fnew. were extracted accordingly. Considering the simplicity and effectiveness of cosine similarity in comparing semantic vectors, we computed the semantic distance between their werage-ppoled representations:
Told Tnew
D semmantic
ratd
[ew II'
A larger value indicates more significant internal representation shift, thereby implying stronger resistance to the incorporation and integration of newly injected knowledge.
Semantic Gap in Output Responses We also considered the semantic gap between the model's answers before and after editing. To evaluate this gap meaningfully, we require a metric that operates in the embedding space, captures global semantic relationships, and remains robust to surface- level differences such as paraphrasing. Unlike KL divergence, which is sensitive to token-level mismatches and often fails to reflect broader semantic alignment, a soft optimal transport approach provides more holistic comparison by aligning token embeddings across answers. Considering these advantages, we adopt the Sinkhorn distance (Watanabe Isobe, 2024), a differentiable and stable optimal transport metric that preserves contextual meaning while capturing global semantic
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Rouge-L
BERT Score
Rouge-2
Rouge-1
[LLaMA2 (BNNKE
(LLaMA3 8B]-(UNKE)
(LLaMA2 (AKEW)
(LLaMA3 8B/-(AKEW
52
30
67"0 Tangle
Tangle
Tangl Score
Figure 3: Generalization score variations of ROUGE-I, ROUGF-2, ROUGE-I, and BERT-Score at different levels of TANGIeSCORE using the UNKE method on the UNKEBBnnh and AKEW with the LLaMA3-8B-Instree Instructt and LLaMA2-7B-CTT
structure. It enables a principled comparison of the two answer distributions in embedding space, aligning them in a semantically meaningful manner.
Taking both components into account, the internal shift and the semantic gap between the origin knowledge and the target answer, we define the TANGLESCORE as a unified metric quantify how resistant each individual knowledge instance is to editing:
D Dxemantic
TANGLESCORE(M)) = Sinkhorn( (Ansold, Ansnew)
Discussion TANGLESCORE characterizes the transition rate of a specific model's propensity to shift from old to new responses by considering two aspects: the change in the model's response behavior when shifting from old to new answers and the distance between the original and updated answers themselves, thereby effectively capturing the degree of entanglement between the to-be- edited knowledge and the model's ' prior knowledge. This ratio-based formulation reflects the rate of change in the model's responses when the underlying knowledge is modified. To further validate the necessity of this formulation, Appendix D.1. Intuitively, a higher TANGLESCORE value indicates that the model exhibits greater inertia during the editing process, suggesting that such knowledge instances inherently more difficult to edit. Building on this, we hypothesize that TANGLESCORE influences the generalization of knowledge editing. In section 4.2, we further validate this hypothesis through analysis while revealing several beneficial properties of TANGLESCORE.
conducted more detailed ablation studies, presented in
4.2 CORRELATION OF TANGLESCORE WITH EDITING PERFORMANCE
TANGLESCORE Reflects Intrinsic Editing Difficulty We analyze the behavior of TANGLESCORE and find that it is solely determined by the knowledge to be edited. As shown in Figure apply the (Dubey et al., 2024) and I.LaMA-77B-CHN
UNKE to models LLLaMA3-8B-Instme 2023) UNKEBench (Deng al., 2025) and AKEW (Wu et al., 2024). Although TANGLESCORF is related to the model's ' internal representations, which show significant changes before and after editing, we surprisingly observe that the distribution of the TANGLESCORE remains largely unchanged before and after editing. This result suggests that, for different models, the editing difficulty of each knowledge sample is intrinsic and does not vary significantly with changes the state of the model. Readers can refer to Appendix B.1 for more results of using AlphaEdit (Fang al., 2025) and RECT (Gu et al., 2024), and to Appendix B.3 for a more rigorous discussion.
(Touvron et al.,
Generalization Declines with Increasing TANGLESCORE Given the nature of TANGLESCORE, we hypothesize that editing performance decreases as the TANGLESCORE becomes larger, meaning the knowledge sample is more difficult to edit. To validate our hypothesis, we conducted experiments on S.aMA3-8B-Insttree high TANGLESCORE. To fully assess the generalizability of the model responses after editing with the UNKE method in knowledge samples with varying TANGLESCORE, we comprehensively measured ROUGE scores (Lin, 2004) to evaluate the lexical similarity and BERT scores (Zhang et al., 2020) to assess their semantic similarity between rewritten questions and the standard responses.
and LLaMA2-7B-O0 -Bttt,, selecting UNKEBench samples spanning low-to-
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Compute TS
Knowledge Imprint
Old Kaswledge
Adjust PR
Knowledge Purge
Lages
engineer
PR
Y78 max
x 1| (hp)) ( - Ecrio111"
Cousistency
Kussdolge
Layaza
is oongrase suxentu
Llearn
Sanddarn AtMee Altac
Figure 4: Proposed method: PIPE. We first compute the TANGLESCORE for the knowledge to be edited, then determine the appropriate purge rate and apply it through knowledge purge function to help the model discard the old information. Subsequently, knowledge imprint function is used to incorporate the new knowledge, completing the editing process for that specific piece of knowledge.
As shown in Figure 3, ROUGE-Score and BERT-Score all decline monotonically with rising TANGLESCORE values. This indicates that as the difficulty of editing knowledge samples increases, the original UNKE method performs progressively worse in terms of generalization. The decline in both lexical and semantic similarity scores suggests that the model struggles to apply the edited knowledge to alternative phrasings or reworded questions. Readers can refer to Appendix C.I for more experimental results.
Discussion We argue that the original editing approach does not enable the model genuinely internalize or comprehend the knowledge being introduced. Instead, largely forces the model to associate the knowledge with fixed response pattern, leading to the production of the target answer in narrow, memorized form. Consequently, when the same piece of knowledge probed through differently worded paraphrased prompts, the model frequently fails generalize and respond appropriately. This limitation suggests that the UNKE method struggles to support edits that are robust, flexible, and transferable across diverse query formulations, thereby raising concerns about its effectiveness in real-world applications where knowledge is naturally expressed in varied ways.
4.3 APPLYING TANGLESCORE TO STRUCTURED KNOWLEDGE EDITING
Section 4.2 showed that the TANGLESCORE is an effective measure of the difficulty of unstructured knowledge editing and can help guide the editing process. This raises the question: can TANGLESCORE also benefit structured knowledge editing? Accordingly, apply the UNKE method to LL on a structured knowledge editing dataset KEBench (Wei et al., 2024). Experimental results show that the distribution of TANGLESCORE remains largely unchanged before and after editing. This aligns with observations in unstructured knowledge editing and suggests that structured knowledge also exhibits inherent differences in editing difficulty. Furthermore, we observe that in structured knowledge, the model's generalization performance also exhibits similar pattern. Readers find more details in Appendix B.2, Appendix B.3 and Appendix C.2.
(Dubey et al., 2024) and L
(Touvron et al., 2023)
5 PIPE: PURGE-IMPRINT PATCH EDITING
In Section 4, we propose TANGLESCORF as measure of the ease of editing knowledge sample and demonstrate that TANGLESCORE an intrinsic property of the sample for given model. more
Furthermore, analysis reveals that existing editing methods exhibit poor generalization difficult samples, highlighting a key limitation in their effectiveness. As shown in Figure 4, we propose a method called PIPE. This approach enables the model to better understand the knowledge to be edited by first making it purge the old knowledge before learning the new, thereby improving generalization when editing more difficult knowledge samples.
5.1 PURGE RATE DETERMINED TANGLESCORE
Varying editing difficulties among knowledge samples influence model retention differently, suggesting uniform purge strategies are suboptimal. Since TANGLESCORE quantifies the difficulty of modifying knowledge sample, leverage TANGLESCORe adaptively modulate the purge rate.

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Specifically, higher TANGLEScoRe implies hard-to-edit knowledge, thus requiring higher purge rate for effective removal, while lower TANGLESCORE denotes easier samples, where a lower rate avoids overfitting, Accordingly, our final purge mechanism is defined as:
PR TTNOI ESCORF
where 7 = log Aman/ log Amin is the scaling factor, and Amaz and Amin represent the maximum and minimum values, respectively. We determined their preset values through preliminary exploratory experiments, and set Amaz = 0.001 and Amin = 0.0001 in practice. This adaptive purge mechanism aims mitigate the influence of strongly retained original content, especially when it is difficult to override. To further demonstrate the effectiveness and validity of our approach, we compare it against a baseline with fixed purge rate. Additional details can be found in Appendix D.2.
5.2 KNOWLEDGE PURGE FUNCTION
To mitigate the influence of the original knowledge of the model on learning new information, we introduce knowledge purge function that encourages the model to effectively unlearn prior knowledge. Inspired by previous work (Liu al., 2025), we adopt gradient ascent approach to facilitate purging. By inverting the direction of the gradient, the model is encouraged to increase the loss of information that should be forgotten, thus reducing its predictive accuracy on such data. A natural idea might be to reverse the traditional Mean Squared Error (MSE) loss, which is widely used to minimize the squared difference between a model prediction ý and a target value y:
MMSE = 10 y/12.
However, directly maximizing this loss via gradient ascent introduces critical issue: the gradient of MSE with respect to the model output is proportional to the error term, i.c.. VyLMSE 2 y). As the prediction diverges from the target, the gradient grows linearly with | - yll. which can lead to numerical instability or even gradient explosion during training.
To address this, we introduce a gradient- gradient-bounded knowledge purge function that penalizes proximity to original knowledge rather than encouraging large deviations. The proposed formulation avoids MSE's instability by ensuring bounded gradients even as predictions diverge. Considering the purge rate discussed in Section 5.1, we define the final form of our purge loss as a clipped and numerically stable function. Specifically, the absolute difference is bounded within [0, 1] to ensure gradient control. The knowledge purge function for the i-th knowledge item is defined as:
Clamp(1 - (a R D1?
Cpurge
PR
(5)
where fo is the current key output for the i-th query, hy denotes the final hidden state of the i-th query, and kerig is the original key vector associated with the knowledge to be purged. This formulation provides a strong unlearning signal by assigning high loss values when the model output remains close the original key vector, while ensuring stability through gradient saturation as the prediction diverges, effectively preventing unbounded gradients during training.
5.3 KNOWLEDGE IMPRINT FUNCTION
After purging outdated knowledge, the model must effectively acquire new knowledge. One of the central challenges in knowledge imprint lies in enabling the model learn new information while maximally preserving its existing capabilities. This is crucial to minimize the degradation of general abilities and enhance the model's stability sequential editing. To address this issue, we propose a new consistency Lconsistency, which aims to balance knowledge imprint and stability retention during the imprinting process. The objective is formulated as follows:
Lconsistency
- (ax (I kng. 11 + (1 (z) (1-a))I[fo(bbaa)) So (hggh (6)
where fe denotes the edited model and fe denotes the original model. hgre hidden states from the i-th sample different token positions in the pre-training and query sequences; kg...f the corresponding target key vectors. u denote the numbers of edited samples. A key component of this loss is the weight a, which controls the relative emphasis knowledge imprint versus stability
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preservation each token position. Rather than setting static or binary, design dynamic masking mechanism based on the TANGLESCORE:
o (TANGLESCORE))
where the sigmoid function. Intuitively, higher TANGLESCORE (indicating more difficult edits) leads to approach 1, making the loss prioritize knowledge transfer. Conversely, lower TANGLESCORE, which indicates easier edits, causes a to approach O, thereby favoring the preservation of original capabilities. This design enables Lconsistoncy adaptively balance modification and retention during training. In addition, to ensure that the edited model correctly generates the new knowledge, we further incorporate auxiliary loss:
Leem >0=1 fo(lha))
(8)
The final objective for knowledge imprint is defined as the sum of the two components:
Lampint Lconsistency
Lreamn
To enable an end-to-end editing process, we jointly optimize the purge and imprint stages. Ultimately, our final optimization goal is:
fø = (o + Limprint).
(10)
We selected the 7th layer for editing, and the choice of layer is validated through ablation studies in Appendix D.3. Moreover, further verify the effectiveness of our proposed optimization terms, also conducted ablation studies on them. Detailed settings are provided in Appendix D.4.
EXPERIMENTS
6.1
EXPERIMENTAL SETUP
Base LLMs We evaluated on four LLMs: (LaMA3-8B-Instrue (Dubey et al., 2024), LLaMA2- 7B- TB-Chat LLaMA2-13B Chat (Touvron al., 2023), and Qwen2.5-7B-Innstruce (Yang al., 2024).
Baseline Editing Methods We compared to ROME (Meng et al., 2022), MEMIT (Meng et al., 2023), RECT (Gu et al., 2024), AlphaEdit (Fang et al., 2025), UNKE (Deng et al., 2025), and AnyEdit (Jiang et al., 2025) as baseline editors.
Datasets Experiments were conducted on three datasets: UNKEBench (Deng et al., 2025), AKEW (Wu et al., 2024) and KEBench (Wei et al., 2024).
Metrics We assessed editing success by comparing edited outputs with targets using both original and paraphrased questions. BERTScore (Zhang al., 2020) used to measure semantic similarity, and ROUGE (Lin, 2004) to capture lexical similarity. Additionally, we used FactScore (Min et al., 2023) to measure understanding new knowledge, and MMLU (Hendrycks et al., 2021) to evaluate general capabilities. Readers can refer to Appendix E for more details.
6.2 EVALUATION RESULTS ON UNSTRUCTURED KNOWLEDGE EDITING
This section illustrates the editing performance of edited models with LLaMA3-888 ----A-----nnInctt... LLaMA2- 7B-Chat and Qwen2.5-7BB ----suuuttttt on the UNKEBench. Due to page limitations, the detailed experimental results of AKEW and LLaMA2- 10MMMA2-11B-hhae 3B-Chtt, the comparison of editing performance under different TANGLES CORE distributions, as well as the comparison of time consumption between PIPE and other methods have been included in Appendix F.
Editing Performance Table 1 presents the editing performance of various methods on the unstructured knowledge dataset UNKEBench. Traditional editing methods designed for structured knowledge, such as ROME, MEMIT, RECT, and AlphaEdit, showed significantly lower performance on UNKEBench. These methods struggled with challenges in unstructured knowledge editing, particularly compared approaches developed for unstructured settings. In contrast, PIPE achieved notable improvements over existing unstructured knowledge editing methods, including UNKE and AnyEdit, with higher generalization performance, suggesting it helps the model better capture and internalize the knowledge to edited. Our method also demonstrated consistent robustness across sequential and batch editing. Detailed results are provided in Appendix G.
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Table Editing performance on UNKE across different methods. The batch size was fixed I, with model parameters rebuilt after each edit. Decoding used temperature setting of 0.001. To ensure fair comparison, edits were applied to the 7th layer of parameters for ROME, RECT, and UNKE. Evaluation results before and after the '/' represent outputs for original and paraphrased questions, respectively. FC refers to Factual Correctness.
Method
Semantic Similarity
Lexical Similarity
FC
General Ability
Bert-Score
Rouge-1
Rouge-2
Rouge-I.
FactScore
MMLU
-LaMA3-8B-Instrue
29.94
ROME
72.33
48.88
42.31
28.46 /21.75
45.87
440.11
23.67
29.59
RECT
73.79
77.44
48.42/ 42.51
22.71
46.28
/ 40.96
23.55
29.33
MEMIT
76.79 | 74.46
44.91 /41.82
25.38
20.04
40.01
36.57
25.34
29.44
AlphaFdit
75.28 73.65
45.02/ 41.67
25.77
18.84
41.93
36.73
28.91
29.03
UNKE
97.41 / 90.06
98.54 / 78.71
98.17 / 70.83
97.86
77.72
41.56
29.45
AnyEdit
98.62/ 99.56
97.20 78.66
96,29
72.59
95.15
79.60
48,48
28,52
PIPE
98.64/ / 91.71
98.48
/ 85,42
98.89
/ 78.03
98,44 84,07
50.91
29.51
LLaMA2-7B-Chat
29.71
UNKE
96.56 / 90.77
98.51 77.98
97.84 67
95. 34 76.11
40.99
29.07
AnyEdit
98.37 / /9.60 98.57 91.73
95.69 / 78.55
96.04 72.17
93.59 75.68
47.82
29.66
PIPE
98.66 84.05
98.21 78.12
97.39 78.47
50.12
29.65
Qwen2.5-7B-CHl
32.63
UNKE
96.84 89.92
92.55 72.81
90.77 69.94
93.64 / 73.89
40.12
31.28
EdyEdit
97.10 90.71
93.84/ 75.26
92.90 73.39
95.38 / 77. 14
40.60
30.47
PIPE
97.42 / 91.76
94.71 / 78.63
93.88 / 75.40
97.05 80.18
42.47
31.78
Knowledge Comprehension and General Abilities Preservation Our goal is to help the model better understand newly imprinted knowledge when purging outdated information, without sacrificing general abilities. As shown in Table I, in the multi-hop comprehension task the edited knowledge, PIPE showed stronger comprehension compared to baselines, suggesting it encourages the model internalize and reason about new knowledge beyond simple memorization. Compared to the pre-edited model, PIPE preserved general abilities, indicating that selective purging does not degrade broader performance on unseen data. These results highlight PIPE's effectiveness in balancing knowledge integration with robust generalization. For more generated cases, see Appendix H.
6.3 EVALUATION RESULTS ON STRUCTURED KNOWLEDGE EDITING
To demonstrate that our approach is also robust for editing traditional structured knowledge, conducted experiments on the KEBench. As shown in Table 2, PIPE achieves significant improvements on both the Ori-Ace and Para- Ace metries compared to existing structured and unstructured knowledge editing methods. These results clearly demonstrated that our method can be successfully and robustly applied to both structured and unstructured knowledge editing tasks, offering more unified and generalizable solution to knowledge editing challenges.
Table 2: Structured editing performance on
KEBench. Ori-Acc and Para-Acc represent the accuracy for original and paraphrased questions, respectively. Sre-Acc and Tgt-Acc represent the irrelevant knowledge accuracy of subject and object in structured triplets, respectively.
Method
Ori-Acc
Para-Ace
Sre-Ace
Tgt-Ace
ROME
76.35
67.23
95.54
74.57
RECT
76.89
68.97
95.98
75.84
MEMIT
74.33
65.08
97.67
77.48
AlphaEdit
75.45
66.83
97.52
78.06
UNKE
93.59
85.34
89.28
62.56
PIPE 95.89
88.23
94.47
70.11
6.4 ANALYSIS OF PIPE
To further illustrate why PIPE achieves better editing performance on harder knowledge, we conducted additional analysis. As shown in Figure 5(a), the distribution of TANGLESCORE remains largely unchanged before and after applying PIPE edits, further confirming that TANGLESCORE is an intrinsic property of each knowledge instance and is not altered by PIPE. In addition, we concatenated the previous answer with the prompt as input to the model, from which we obtain the probability of the model outputting old knowledge. From Figure 5(b) we could find that the probability distribution after editing shifts to the right overall, indicating that PIPE suppresses the model's tendency to output old knowledge more effectively. We concluded that through PIPE's purging mechanism, the model better refrained from relying on old knowledge, thereby improving its understanding of the knowledge to be edited.

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Published as a conference paper at ICLR 2026
(LLAMAZ THI-IUNKE]
[LLaMA3 8B)-(LNKE)
0.20
Tangle Score
0.0
0.25
0.5
0.75 1.0
Probability
(b)
Figure 5: (a) Distribution of TANGLESCORE using PIPE on the UNKEBench. (b) Comparison of the model's tendency to output old knowledge under UNKE and PIPE editing methods, using negative log-probabin probaabbbiityy to make the distribution more interpretable. The curves represent the results of KDE.
7 CONCLUSION & LIMITATION
We revisit knowledge editing by examining the gap between accuracy and generalization through the lens of editing difficulty. To quantify this, we propose TANGLESCORE, a metric that strongly correlates with generalization performance post-edit. Leveraging TANGLESCORE, we present PIPE, framework that adaptively selects editing strategies based on knowledge difficulty. This method consistently improves both editing efficacy and model generalization. Extensive experiments across diverse settings demonstrate the robustness of PIPE. In addition, TANGLESCORE offers novel lens for understanding editing challenges, emphasizing how editing difficulty shapes post-editing behavior. It serves practical tool for analyzing and enhancing editing techniques. Overall, our work advances editing strategies and lays the groundwork future knowledge editing research.
Despite the effectiveness of PIPE, our work has several limitations. First, while PIPE performs well in controlled settings, it does not yet support continuous editing involving sequential knowledge updates. Second, it is limited to textual editing and lacks support for multimodal integration. Extending it to handle cross-modal updates is promising direction. Lastly, further validation on larger models and more diverse datasets is needed to better assess PIPE's generality.
ACKNOWLEDGMENTS
We would like to express gratitude to the anonymous reviewers for kind comments. This work was supported in part by the New Generation Artificial Intelligence-Nati -atiooaal Science and Technology Major Project (No. 20257DD0133044).
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A CONSTRUCTION OF ORIGINAL AND NOVEL KNOWLEDGE WITH PROMPTS
To obtain the hidden-layy -laer representation Told and Tnew. as shown in Table we concatenated the original knowledge and the target knowledge with the corresponding question, respectively, resulting in two input sequences.
Table Construction details of input sequences
Prompt Original Knowledge
"What is John Finlay's occupation and what supports his expertise?" "John Finlay is a former CIA officer..." "John Finlay a police officer with..."
Novel Knowledge
Input Old
"Prompt: (Prompr) AnAnswer: (Original Knowledge)"
Input New
"Prompt: (Prompt) 'nnnwwer: [Novel Knowledge
B DISTRIBUTION OF EDITED SAMPLES BY TANGLESCORE
B.I DISTRIBUTION OF EDITED SAMPLES BY TANGLESCORE ON UNSTRUCTURED KNOWLEDGE
To further demonstrate that TANGLESCORE depends solely on the model and the knowledge to be edited, rather than the editing method, we apply different editing methods and analyze the distribution of TANGLESCORE across the dataset before and after editing. As shown in Figure 6 and Figure 7, we apply the AlphaEdit (Fang al., 2025) and RECT (Gu al., 2024) (Touvron et al., 2023). respectively, conducting experiments
(Dubey SaaMAA-8B-Instrre
et al., 2024) and on UNKEBench (Deng al., 2025). The results of this experiment are consistent with our previous findings, further reinforcing the robustness of conclusions: for different models, the editing difficulty each knowledge sample is intrinsic and does not vary significantly with changes in the state of the model.
[LLaMA2 7BJ-(UNKE)
(LLaMA3 SBJ-(UNKE)
(LLaMA2 7B)-(UNKE)
(LLaMA3 SB)-(UNKE)
0.000 0.125
0250
0.375
0.500
0.62
0.750
0.73
1,000
0.000
6.125
0250
6.375
D,500
0.750
0.875
1.000
Tangle Score
Tangle Score
Figure 6: Distribution of edited knowledge samples across TANGLESCORE ranges, shown for LLLaAA--BB-nnsrru and LLLMA2-7B-CMM using the AlphaEdit on UNKEBench. Inner rings represent pre-edit distributions; outer rings represent post-edit distributions.
Figure 7: Distribution of edited knowledge samples across TANGLESCORE ranges, shown for -aaaMMM-----Insttreee and I.LMM2-7BB-Che using the RECT on UNKEBench. Inner rings represent pre-edit distributions; outer rings represent post-edit distributions.
B.2 DISTRIBUTION EDITED SAMPLES BY TANGLESCORE ON STRUCTURED KNOWLEDGE
We also conducted the above experiments structured knowledge. As shown in Figure 8, applied UNKE (Deng et al., 2025) to LLaMA3-88 IB-Instruct (Dubey et al., 2024) and LLaMA2-7B. Chat (Touvron et al., 2023), respectively, and conducted experiments KEBench (Wei et al., 2024). The results on structured knowledge are consistent with those on unstructured knowledge, further reinforcing the robustness of conclusions.
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[LLaMA2 7B)-(StablcKE)
(LLaMAS 8 (Stalekk)
0.375
0.500
0.625
0.750
Tangle
Figure 8: Distribution of edited knowledge samples across TANGLESCORE ranges using the UNKE method the KEBench. Inner rings represent pre-edit distributions; outer rings represent post-edit distributions. Darker colors indicate greater difficulty.
B.3 FURTHER ANALYSIS OF EDITED SAMPLE DISTRIBUTION BY TANGLESCORE
To more rigorously and comprehensively support our conclusion that for different models, the editing difficulty of each knowledge sample is intrinsic and does not vary significantly with changes in the state of the model, we further investigate the cross-model correlation of TANGLESCORE same samples. Figure and Figure 10 present the TANGLESCORE values of individual knowledge samples before and after editing the unstructured knowledge and structured knowledge datasets, respectively. The x-axis denotes the pre-edit T'NNLESCCORR while the y-axis denotes the post-edit TANGLESCORE. As shown in the figures, for both LLaMA3-8B-InsS Instrruct (Dubey al., 2024) and LLaMA2-7B-Chal (Touvron et al., 2023), the TANGLESCORE values of individual samples remain largely unchanged before and after editing. This further reinforces the robustness of our conclusion.
the
(LLaMA2 (83-(UNKE)
(LLaMA3 SB]-(UNKE)
(LL.MA2 (B)-(StNNeKFF
[LLaMA3 BR)-(StbbeeKF)
of gight wie Bitrg siieel
Scatter plot of TANGLESCORE changes before and after single-sample editing. We randomly sampled 200 examples from UN- KEBench, using UNKE, RECT, and AlphaEdit.
Figure 9:
Scatter plot of TANGLESCORE changes before and after single-sample editing. We randomly sampled 200 examples from StableKE, using UNKE, RECT, and AlphaEdit.
Figure 10:
C APPLYING TANGLESCORE TO KNOWLEDGE EDITING
C.I APPLYING TANGLESCORE TO UNSTRUCTURED KNOWLEDGE EDITING
To demonstrate that TANGLESCORE effectively measure the difficulty of unstructured knowledge editing and thereby help guide more effective edits on challenging samples, we conducted additional experiments the UNKEBench (Deng et al., 2025). Specifically, we applied the RECT (Gu et al., 2024) and the AlphaEdit (Fang et al., 2025) to LLaMA3-SB -B-ntructttt (Dubey et al., 2024). Figure 11 illustrates how the editing generalization score varies with different SANGLESCORR magnitudes.
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C.2 APPLYING TANGLESCORE TO STRUCTURED KNOWLEDGE EDITING
To demonstrate that TANGLESCORE can effectively measure the difficulty of structured knowledge editing and thereby help guide more effective edits challenging samples, we conducted experiments on the structured knowledge dataset KEBench (Wei et al., 2024). Specifically, we applied (Dubey et al., 2024) and LLaMA2-7B-
the UNKE (Deng al.. 2025) to LLaMA3-8B- Chat (Touvron et al., 2023). Figure 12 illustrates how the editing generalization score varies with different TANGLESCORE magnitudes.
Rouge-L
BERT Score
Rouge-2
Rouge-1
(RECT)-(UNN))
(NlphaEdit-(UNNEE
20
055 Tanglo Score
0.5
Tragle Soom
Figure 11: Generalization score variations of ROUGE-1, ROUGE-2 ROUGE-L, and BERT-Score at different levels of TANGLESCORE, using the RECT and AlphaEdit method on the UNKEBench with the LLaMA3-8B-Instruct
Rouge-L
BERT Score
Rouge-2
Rouge-1
[LLaMA2 (Stabekkt)
[LLaMA3 (B)-(Sta)leKE)
90
85
80
Score
75
70
70
60
60
50
Tangle Score
Tanlle Store
Figure 12: Generalization score variations of ROUGE-1, ROUGE-2, ROUGE-L, and BERT-Score at different levels TANGLESCORE, using the UNKE method on the KEBench with the LLaMA3-8B- Instruct and LLaMA2-7B-Chat.
D ABLATION STUDIES
D.1 IMPACT OF TANGLESCORE COMPONENTS
TANGLESCORE consists of two components: the numerator captures changes in the model's internal responses, while the denominator reflects the semantic distance between the old and new knowledge. The ratio indicates how strongly the model's responses will change per unit of knowledge modification. In this sense, the ratio-based formulation is necessary. To further support this claim, we conducted an ablation study in which only the numerator or only the denominator was used to guide the purge rate during editing, with results presented in Table 4. It can be observed that using either the numerator or the denominator alone leads to a substantial drop in editing performance. This is because, under these settings, the editing process either considers only the model's 's responses only the difference
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Table 4: Ablation study on TangleScore components using only the numerator or only the denominator. Experiments are conducted on UNKEBench using -nnttruct-
Method
Semantic Similarity
Lexical Similarity
FC
General Ability
Bert-Score
Rouge-1
Rouge-2
Rouge-L
FactScore
MMLU
Numerator Only
71 80 55.48
73.39 62.81
67.68 / 28
59.59 53..79
32.79
28.
Denominator Ouly
12
52.74
54,70
71.86 59.60
36,55
27. 27.74
Rutio Form
98.64 91.71
98.48/ / 85.42
98.89 / 78.03
98.44 84.07
30.91
29.51
Table Comparison PIPE's performance under different purge rate strategies: "Fixed PR (max)" uses fixed rate of 0.001, "Fixed PR (min)" uses 0.0001, and "Dynamic PR" adapts the rate based on sample difficulty. Evaluation results before and after the '/' represent outputs for original and paraphrased questions, respectively. FC refers to Factual Correctness.
Semantic Similarity
Lexical Similarity
General Ability
Bert-Score
Rouge-1 1
Rouge-2
Rouge-L
FactScore
MMLU
Fixed (R(max)
98.03/ 90. 26 97.50 88.
98.34/ / 80.00
98. 31 /74.99
97.75 880.37
48.21
28.71
Fixed PR(min)
96.85 / 78.39
97.57 / 71.13
95.99 78.62
43.85
29.22
Dynamic
91.71
85.42
98.89 78.03
98.44 84.07
50.91
29.51
between old and new knowledge, causing TANGLESCORE lose its original meaning and fail to properly guide the purge rate.
D.2 THE EFFECTIVENESS OF PR
To further demonstrate the effectiveness of dynamically adjusting the purge rate based on TANGLESCORE, compared it with upproaches that use fixed purge rate. As shown in Table 5, whether a high or low fixed rate is applied, the editing performance consistently underperforms compared to the dynamic approach, especially in the generalizability metrie. This result suggests that adapting the purge strategy to the difficulty level of each knowledge instance leads to more accurate and robust editing outcomes.
D.3 IMPACT OF THE SELECTED LAYER ON MODEL PERFORMANCE
In Section 5, we designate the 7th layer as the editing layer. Previous studies have shown that relatively shallow layers are more likely to preserve model stability after editing, whereas deeper layers contain more entangled target information in the key vector, making it difficult to modify without impairing the model's original abilitics (Deng et al., 2025). To examine the impact of this design choice on model performance, findings show that editing the 7th layer achieves the most stable post-editing performance while maintaining the model's ability to handle unstructured knowledge. In addition, no special processing applied to the embeddings of old and new answers. We directly use the model's tokenizer to obtain standard token embeddings ensure full consistency with the normal inference procedure.
conducted analytical study, and the results presented in Table 6. The
Table 6: Ablation study of computing TangleScore at different layers and performing edits. Experiments are conducted on UNKEBench using SaMMA--B-IInstruue
Semantic Similarity
Lexical Similarity
FC
General Ability
Layer
Bert-Score
Rouge-1
Rouge-2
Rouge-L
FactScore
MMLU
94.38
93.96 82.37
94.55 75.28
94.07
80.23
48.27
28.19 19
96.51 / 89.32
96.17 / 83,86
96.74 / 76.94
96.28 82.61
49.12
28.87
97.25 90.05
97.11 84.62
97.43 77.22
97.09 83.01
50.83
29.
98.64 / 91.71
98.48 85,42
98.89 / 78.03
98.44 84.07
50.91
29.51
97.05 89.
96.82 83.12
97.11 76.65
96.78
82.34
49.47
29.07
96.12 / 88.00
95.94 / 82.01
96.41
/ 75.86
96.10 81.79
48.69
28.98
95.21 87.03
95.03 80.56
95.62 74.49
95.20 / 80.12
48.11
28.63
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Table 7: Ablation study of the individual loss functions. Experiments are conducted on UNKEBench using LLaMA3-8B- Instrrutt..
Method
Semantic Similarity
Lexical Similarity
FC
General Ability
Bert-Score
Rouge-1
Rouge-2
Rouge-L
FactScore
MMLU
vio Cruns
96.33 90.19 37.86 / 35.12
92.27/ 80.26
91.44/
91.99 .2. 80.51
47.15
28.73
w/o Lemaggne
15.64 17,93
23.56/ 27.77
39.84 1 42.63
26.61
28.91
Eppgs and Lossisoas
12.07 13.88
6.54/6.75
14,96/
18.59 22.17
14.67
26.35
D.4 IMPACT OF DIFFERENT LOsS FUNCTIONS
To further illustrate the roles of the functions we propose, we conducted an ablation study. As shown in Table 7, removing the Lprrge leads to noticeable drop in the generalization of edits, highlighting the importance of this loss function. Moreover, when the Lconsistency is removed, both the accuracy and generalization of the edits decrease significantly, indicating that leveraging contextual information is crucial during the process of imprinting new knowledge.
E EXPERIMENTAL SETUP
E. BASELINE METHODS
In our experiments, six popular knowledge editing methods were selected baselines, including:
ROME (Meng et al., 2022)!: : it first localized the factual knowledge a specific layer in the transformer MLP modules, and then updated the knowledge by directly writing new key-value pairs in the MLP module.
MEMIT (Meng et al., 2023)?: it extended ROME to edit a large set facts and updated a sequence of MLP layers update knowledge.
RECT (Gu al., 2024)3: it designed to reduce the unintended side effects of knowledge editing on the general capabilities of large language models (LLMs). While editing can enhance factual accuracy, often harms performance on tasks such as reasoning and question answering. RECT tackles this problem by regularizing weight updates during the editing process, thereby limiting excessive changes that may cause overfitting. As a result, RECT achieves strong editing performance while preserving the model's overall abilities.
AlphaEdit (Fang et al., 2025)*; mitigates catastrophic forgetting during knowledge editing by preserving previously learned knowledge, and extends this capability to lifelong editing setting through null-space projection strategy. This approach ensures that new edits do not interfere with prior information by projecting updates into directions that minimally affect existing representations, thereby maintaining model stability across long sequence of edits.
UNKE (Deng et al., 2025)²: it enhances knowledge editing through innovations at both the layer and token levels. Specifically, it replaces traditional local key-value storage with non-local block structure to better capture and integrate attention based knowledge. On the token level, shifts from term-driven cause-driven optimization, directly editing the final token to improve edit precision while preserving contextual coherence.
AnyEdit (Jiang al., 2025)6: it introduces auto-regressive editing paradigm that supports long-form and fiverse-formatie knowledge updates. By chunking outputs and editing one token per chunk, it avoids interference between edits and ensures coherence. Grounded in mutual information theory. AnyEdit adapts to various formats (e.g., code, math) and overcomes the single-token editing barrier, offering strong scalability and generalization.
https://gituu..oom/meeeeee //rome
hhtp:://gghhbb.co////eeg//////// 1/memit
"harps:/githuu..co
jianghocchen//AIphaati
jianghochhegg/AAyERiii /tt
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Published as a conference paper at ICLR 2026
E.2 EDITING DATASETS
In our experiments, UNKEBench, AKEW benchmark and KEBench were adopted.
UNKEBench (Deng al., 2025): it provides dataset of 1,000 counterfactual unstructured texts, where knowledge is expressed in a relatively long and free-form manner, extending beyond simple knowledge triplets linear factual chains. These samples are derived from ConflictQA (Xie et al., 2023), a benchmark designed to differentiate between knowledge stored in LLMs' parameter memory and anti-memory. This setup is essential avoid the blending of pretrained knowledge with information introduced during editing. It also tackles the critical challenge of verifying whether the model acquires specific knowledge through pretraining or through post-hoc editing, thereby maintaining a clear separation.
AKEW benchmark (Wu et al., 2024): it considers three types of knowledge: (1) Structured Facts: Each structured fact is individual triplet intended for editing, drawn from existing datasets or knowledge graphs. (2) Unstructured Facts: Knowledge is provided in natural language form. To ensure fair comparison, each unstructured instance conveys the same factual update as its structured counterpart. Unstructured facts are more linguistically complex, often embedding implicit information that goes beyond explicit triplets. (3) Extracted Triplets: These are automatically derived from unstructured texts to examine whether such representations can assist editing methods in managing free-text knowledge.
KEBench (Wei et al., 2024): It introduces a comprehensive benchmark specifically designed to evaluate the stability of structured knowledge editing. The dataset includes 1,000 editing triplets and 2,798 multi-hop questions, organized in a tree-structured format to reflect realistic, hierarchical reasoning scenarios. KEBench captures four critical dimensions of stability: edited knowledge consistency, multi-hop reasoning integrity, preservation of unrelated knowledge, and maintenance of general model capabilities. Unlike previous benchmarks, it emphasizes both large-scale editing (batch and sequential) and complex reasoning paths, offering a robust and scalable framework for assessing structured knowledge editing methods in large language models.
E.3 EVALUATION METRICS
We evaluated the performance of our unstructured editing across four dimensions: word-level overlap, semantic similarity, factual correctness, and general abilities, primarily following established benchmarks for unstructured knowledge editing.
Lexical Similarity metrics, including ROUGE scores (ROUGE-1, ROUGE-2, and ROUGE- L) (Lin, 2004), evaluate the degree lexical and n- n-gram overlap between model- generated responses and target answers, for both original and paraphrased inputs. These metries are essential for assessing the surface-level accuracy of the edited content.
Semantic Similarity. Since metrics based solely on word overlap fail to capture deeper understanding, we employ a semantic evaluation approach. Specifically, we use an embedding- -based encoder Gall-Mini.M--L6vv2)) to measure how well the model comprehends the intended meaning, providing more comprehensive evaluation that moves beyond surface-level matching.
Factual Correctness. To determine whether the edited model accurately grasps unstructured knowledge, we adopt FactScore (Min et al., 2023) to measure its performance on sub- questions and corresponding answers. This evaluation offerr more fine- fine-grained perspective and is conceptually aligned with multi-hop accuracy used in certain structured knowledge editing tasks.
General Ability. To evaluate overall capability, we measure the MMLU score of the edited model. Following Hendrycks al. (2021), we first compute the average MMLU score across five samples for each unstructured instance, and then derive an overall average across all samples.
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E.4 EXPERIMENTS COMPUTE RESOURCES
We conducted our experiments on a single NVIDIA A800 80GB GPU. It occupies about 40+GB memory for LLaMA2-7E B-thh and about 50+GB memory for LLaMA3-SB -nsstuut.. For the time costs of one edit, our PIPE is about I minute with LLaMA2-7B The time cost of LLaMA3-8B- Instruct is 45% higher than LLaMA2-7B-Chat
Table 8: Editing performance on AKEW (Counterfact) across different methods. The batch size was fixed at with model parameters rebuilt after each edit. Decoding used temperature setting of 0.001. To ensure fair comparison, edits were applied to the 7th layer parameters for ROME, RECT, and UNKE. Evaluation results before and after the '/" represent outputs for original and paraphrased questions, respectively.
Semantic Similarity
Lexical Similarity
General Ability
Method
Bert-Score
Rouge-1
Rouge-2
Rouge-I.
MMLU
LLaMA3-8B-
29.94
ROME
72.87 41.36
27.48
14.26
12.15
11.29
30.40
14.95
29.60
RECT
73.94
41.91
29.88
15.63
13.85
13.52
30.42/
15.16
29.67
MEMIT
76.59 / 46.12
32.43/
18.72
16.34/
10.64
33.84/
17.55
29.43
AlphaEdit
76.11 46.78
26.21
15.53
13.00/9.12
27.44
14.53
29.44
UNKE
97.94 56.73
94.96 34.35
/4.35
93.43
03.43/32.69
29.76
AnyEdit
98.37 / 61.22
95.24 44.08
93.17 27.72
94.71 /4..81
29.52
PIPE
99.15 / 65.72
95.67
46.92
94.51
30.11
95.61
48.38
29.85
LLMMA2-7B-Chat
29.71
UNKE
96.81 / $5.93
86.66 30.90
84.98 11.54
86.35 30.85
29.16
AnyEdit
96.18 / / 60.00 98.04 / 63.37
88.71 41.05
87.35 23.88 87.75 26.97
90.46 / 35.91
29.34
PIPE
89.29 44.02
92.05 40.10
29.62
Qwen2.5-7R-Chat
32.63
UNKE
95.66 / 54.34 59.92
93.32 33.59
90.57 15.81
91.22 29.50
31.88
AnyEdit
95.11 42.62
91.01 26.35
92.90 43.04
31.53
PIPE
97.91 / 63.52
97.38 44.11
92.81 27.59
94.09 / 45.76
32.12
F EXPERIMENTAL RESULTS
F.1 THE RESULTS OF AKEW
Tables and 9 present the editing performance of various methods on AKEW (Counterfact) and AKEW (MQUAKE), respectively. The results align with the findings from previous experiments. Compared to other baselines, our method, PIPE, demonstrates improved editing performance, with particularly notable gains in the generalizability metric.
F.2 THE RESULTS OF LLAMA2-13B-- 13B-CHTT
Tables 10 present the editing performance of various methods LLaMA2-13B-Char (Touvron et al., 2023). The experimental results demonstrate that our method outperforms previous baselines even when applied relatively large-scale models, further highlighting its robustness and effectiveness..
F.3 COMPARISON OF EDITING PERFORMANCE ACROSS DIFFERENT TANGLESCORE DISTRIBUTIONS
In our previous analysis, we noted that for samples with low TANGLESCORE, existing editing methods are generally able to handle them well. Our PIPE method mainly targets difficult samples with high TANGLESCORE, Experimental results also demonstrate that PIPE achieves significant improvement in editing performance, which is clearly due to its greater advantage on high TANGLESCORE samples. To illustrate this in more detail, we further divided the knowledge to be edited into four groups according to TANGLESCORE and compared the editing performance of different methods on each group of samples. The results reported in Table 11. It should be noted that the TANGLESCORE ranges in the table are normalized using the min-max method. The results show that for samples with low TAN TANGLESCOOE. most editing methods achieve good performance, while PIPE still obtains the
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Table Editing performance on AKEW (MQUAKE) across different methods. The batch size was fixed at 1, with model parameters rebuilt after each edit. Decoding used temperature setting of 0.001. To ensure fair comparison, edits were applied the 7th layer parameters for ROME, RECT. and UNKE. Evaluation results represent outputs for original questions.
Semantic Similarity
Lexical Similarity
General Ability
Method
Bert-Score
Rouge-1
Rouge-2
Rouge-L
MMLU
LLaMA3--B-Instrutt
29.94
ROME
71.80
25.77
11.28
26.78
29.32
RECT
70.67
13.54
27.08
29.05
MEMIT
72.98
26.73
15.40
29.02
29.31
AlphaEdit
72.76
27.82
14.97
29.34
29.19
UNKE
97.43
93.99
91.51
92.82
29.04
AnyEdit
96.91
92.56
93.94
29.22
PIPE
97.91
94.31
94.56
95.25
29.44
LLMMA2-BB-Chat
29.71
UNKE
95.06
84.18
82.55
85.33
28.73
AnyEdit
95.49
85.00
84,24
86.42
29.42
PIPE
96.41
85.17
85.22
87.63
29.68
Qwen2.5-7B-Chr
32.63
UNKE
95.31
91.57
88.
92.02
31.86
AnyEdit
95.88
92.65
89.01
92.57
31.16
PIPE
96.72
95.92
90.74
92.58
32.03
Table 10: Editing performance on UNKE across different methods. The batch size was fixed at 1, with model parameters rebuilt after each edit. Decoding used temperature setting of 0.001. To ensure fair comparison, edits were applied to the 7th layer of parameters for ROME, RECT, and UNKE. Evaluation results before and after the represent outputs for original and paraphrased questions, respectively. FC refers to Factual Comrectness.
Method
Semantic Similarity
Lexical Similarity
FC FactScore
General Ability
Bert-Score
Rouge-1
Rouge-2
Rouge-L
MMLU
SHaMA2-13B-CHA
30.96
ROME
71.42 / 69.35
48.03 41.57
27.59
20.72
44.49 / 39,92
21.96
29.60
RECT
73.78 70.36
18.40/22.65
/2..6
45.70
/40.88
23.53
29.74
MEMIT
76.93 74.75
43.51
26.97 22.66
41.36
38.71
26.47
29.78
AlphaEdit
77.14/73.33
15.38/41.00
26.05 20.64
42.50
38.92
28.08
30.55
96.92 90.16
08.29/79.29
97.77 71.96
96.78/77.14
42.53
30.16
Edi
97.46 / 90.93
98 04 79.25
97 99 72
96.87
77.12
45.53
30.44
PIPE
97.85 91.88
99.31 85.05
98.56/ 77.59
98.24
/8.24/83.61
48.81
30.63
best results, As TANGLESCORE increases, editing performance drops significantly. PIPE maintains the smallest decline and its performance improvement over other editing methods is much larger than the average improvement across all samples.
Table 11: The comparison of performance UNKE of different methods across varying difficulty ranges using LLaMA3-8B, with TangleScore normalized to the [0,1] range using min-max scaling.
TANGLESCORE
(0, 0.25)
(0.25, 0.5]
(0.5.0.75)
(0.75,
Method
Bert-Score
ROME
97.48. 96.87
77.14
76.39
55.18 54.68
35.12 34.36
RECT
97.71/97.05
77.62
78.08
55.65 55.16
36.29 /5.664
MEMIT
99.33
80.12 80.48
57.74/57.31
38.40/37.70 37.70
AlphaEdit
99.52
79.51/
58.83 0.83/58.44
39.84/
UNKE
98.52 98.82/97.83 97.83
97.48 /96.06
67.90 67.50
45.37 /4..78
AnyEdit
98.83 998.1
96.97 / /96.53
72.67
70.21
48.40
08.40/47.77 47.76
PIPE
99.07 / 98.31
98.21 99..99
80.53 799.10
65.35 63.73
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Table 12: Comparison of average editing time per sample (seconds) with different methods
Method
ROME
MEMIT
RECT
AlphaEdit
UNKE
PIPE
Time(s)
12.09
16.16
12.88
18.53
23.56
21.32
Table 13: Comparison of different batch sizes. We conducted experiments on PIPE using the LLaMA3-8B SB-Instruct model, with the decoding temperature set to 0.001. Evaluation results before and after the '/" represent outputs for original and paraphrased questions, respectively. FC refers to Factual Correctness.
Batch Size
Semantic Similarity
Lexical Similarity
FC
General Ability
Bert-Score
Rouge-1
Rouge-2
Rouge-I.
FactScore
MMLJU
2
98.64/ / 91.71
98.48/ / 85.42
98.89/
78.03
98 44 84,07
50.91
29.51
98.62 / 90.32
98.32 82.82
98.67 76.45
98.57 83.78
50.27
29.28
22
98.53 89.58
98.18 81.90
98.88 73.82
98.33 81.39
50.16
29.11
29
98.37
98.25
98.32 72.48
98.04 78.68
29.35
2
98.40/ 88. 75.74
98.21 75.33
98.47 70.89
98.73 76.12
49.81
29.37
2
98.09 85.16
98.18 73.97
96.11 69.16
98.51 / 75.86
50.13
29.63
29
98.18 85.42
98.07 / 72.65
98.56 68.68
98.80 75.69
49.45
29.54
F.4
TIME COSTS
We evaluated the runtime of various methods on UNKEBench, following the official UNKE protocol. The results are presented in Table 12. All experiments were conducted on a single A800-80G GPU. Due to the inherent complexity of unstructured knowledge editing, our method requires more editing time than structured editing approaches. However, this increased cost accompanied by substantial performance improvement. Furthermore, compared to structured editing methods, our approach demonstrates clear advantages in both efficiency and effectiveness.
G ROBUSTNESS ANALYSIS ON BATCH EDITING AND SEQUENTIAL EDITING
To evaluate the robustness of PIPE in unstructured knowledge editing, we examine both its batch and sequential editing performance on the UNKEBench dataset. In the batch editing evaluation, as shown in Table 13, the model demonstrates stable performance on unstructured editing tasks as the batch size increases, indicating the robustness of PIPE in handling batch edits. For sequential editing, as presented in Figure 13, the performance of all methods declines the number of edits increases, as expected. However, PIPE demonstrates greater stability compared to other baselines, particularly with a large number of edits. These results affirm the effectiveness of PIPE across both batch and sequential editing, emphasizing its promise robust method for unstructured knowledge editing.
220
212
230
200
120
Figure 13: Performance in sequential editing using I.LaMA3-8B-Instt model. We select the first 300 samples in the UNKEBench dataset for sequential editing experiments.
H CASE ANALYSIS OF RECT, UNKE AND PIPE
Figure 14 presents the generation results of four different methods: RECT, AlphaEdit, UNKE and PIPE. The local key-value editing approaches, RECT and AlphaEdit,, exhibit limited effectiveness
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Question: Aheeee was Rofoel Reyes born ruised?
Torgeti
Rofael Reyes was born in [ottff de La Pez, snoll town locoted in the state of Michoacdn, Mexico. According to birth certificute, which was obtained from locol goverrment office, Rafoel at 3:45 on, the certificats lists his perents as Juan Reyes and Marla Hernandez, both of whon wsre restdents of Cotijo de lo Poz at the time of Roteel's birth. Additionolly, Rafael's 's fanily members and clace friends have indeed born in Cotája
June 1965,
conf inned
Paz.
RECT
Orágin: Refael Reyes vas born in the [Hilippines and reised in the United Ststuz, He was burn in the city of Nanilo, Philipoines, and loter noved to the United States with his fonily when he was young child. He grew in the city of Angeles, Colifornia, atterded high school Usgre
Alpha-Edit Edit
Origin::
Rofoel Reyes, known os Rafoe) Nodal, aos born. ta Nenozor, Rollorco, lon Zune
UNKE
Origin:
Rafael Rayae born in (Cotida la Pasi, ewall town located in the state of Michoesdn, Maxicn, According to his birth certitioale, The certificate lists parents Juan Reyea at the tine of Rofael's is borth. Acditionally, Rafsel's 's fomily nesbers and close friends have condimmed that he vas Indeed born in Catija de la Paz and grew. up in the surrcunding area. For exarle, kafoel's is cousin War totd hin during recent fosily gothering that Roforl wos born in cotijo de La Pez and that he spent his ohildhood there. These pisces evidence, Nexico. While likely that was born is Cotije La Paz.
oblained Iros local giverneent oftice, Ralnel born Zune 12, 1985, 3:45 Mario Hernandez, both
were residenta
Cotijo de la
evidence that Rateel Reyes inderd born cotijo ln is definitive proof Rafarl's hirthplace,
togetner,
presented auggeate
highly
Porg. Refael Reyes fietionol eharactor, not have personal history or background cuteide of story he charaster in work fiction, appeurs. Tharefore, I cannot provide information on birthplace or hoveiown. Is there anything else can help you with?
have real birthplaze hovetown. iodilion
PIPE
Origin:
Coosija de la Par) amall located the of Michoscán, Mexice, Accordinng kis birth cartificate, which was obrained from the lnesl givernsent office, Refael wos born June 12, 1985, at The cartificate lists his parents as Tuen Rayes and Waria Hermandez, both of when aere residents of Cotije de ln Poz ot the time of bafael 's hirth. addieion Rafar' 'e family nanbers and close friende have confirsed that he was Indeed born In docija de ln Poz and grev in the surrounding area. For exmmle, kefael's cousin Voria told hin during
Rafael Reyes
fanily gathering that Rafael These pleces of evidence. when taken togathar, provide strong evidence that RaEsel Reyes was indeed born in Cotiia Par, Mexica. While there ãa no definitive proof of kafael's birthplace, the evidence presented auggeata that highly likely that he was born in jo de la Poz.
in Cotije
Paz and thet be spent his childhaed
Forc:
Rotoel Reyes was born in Cotifo de lo Paz, smoll town located In the state of Richoazón, Rexico. According to his birth certificate, shich abtained froe the local government office, Refoel usa bern June 12, 1985, 3:45 certificate
Cotijo In indeed born (Cotisn de To Paz and grew up is the surrunding area For exopple, infawl 's der hrother, who is still in touch with the fomily, recently stated in a phone conversation that kafsel vaa born in their hosetcen and that he has fond nemories of his childhood there. Whll there is no definitive proof of ReEsel's birthplace, the Nexico.
Marin Herrandezz, both et time Rufoel's birth. Addnllly Ratoel's as fanily nembers and close friends have confirsed
parents
keyes
evidence provided suggests that
indeed born in Cotija
The editing targets and their corresponding desired answers, along with the model's 's outputs after editing using different methods. "Origin" refers to the model's response to the original question, while "Para" refers to its response to the paraphrased question.
Figure 14:
in handling complex unstructured knowledge editing tasks. These methods tend to memorize only fragments of the target knowledge and struggle to reproduce the complete editing objective. In comparison, UNKE demonstrates a better ability to convey the intended edits. However, its edited model still fails to fully comprehend the knowledge when answering rewritten questions. In contrast, PIPE shows a stronger grasp of the target knowledge, enabling it to respond accurately to rewritten questions and clearly reflect the intended editing outcome.
I BROADER IMPACT
This research focuses on a key aspect of LLMs: reducing hallucinations through knowledge editing to improve human-Al AI interactions and promote the development of safer, more accountable AI systems. Our findings deepen the scientific understanding of both the capabilities and limitations of current editing techniques, while also drawing attention to the challenges posed by unstructured knowledge editing. These insights not only advance current knowledge but also pave the way for future studies on unstructured knowledge within language models. Additionally, our work underscores the importance of responsible and transparent methodologies, encouraging continued engagement in the broader conversation on ethical AI development. Such emphasis is essential for enhancing the reliability and trustworthiness of language models in practical use.
Despite these advancements, potential societal risks remain. Unstructured knowledge editing of LLMs could be misused to enhance the spread of disinformation. For instance, malicious actors might
24

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Published as a conference paper at ICLR 2026
leverage this capabiliity engineer Al systems that recall and reinforce false biased narratives in way that appears more coherent and convincing. Such misuse could sigmificantly amplify the reach of fake news and disinformation campaigns, threatening public trust and democratic integrity.
To address these risks, several precautionary strategies can be adopted. First, implementing strict access controls and audit trails is essential to monitor the use of unstructured knowledge editing tools. Second, developers should embed reliable safeguards, such as fact-checking algorithms and transparent output mechanisms, curb the spread of misinformation. Finally, ongoing monitoring and routine model updates are necessary to prevent the retention of outdated or harmful content. Together, these measures help ensure that the benefits of unstructured knowledge editing are realized without compromising ethical standards or public trust.
J ETHICS STATEMENT
This work focuses on improving knowledge editing in large language models (LLMs), a task that has potential implications for model transparency, adaptability, and factual correctness. All experiments were conducted on publicly available models and datasets. No private or sensitive user data was used. We acknowledge that model editing can influence model behavior in significant ways, including the potential to alter safety-related outputs. Therefore, emphasize that editing methods like PIPE should deployed with caution in real-world systems and coupled with appropriate safeguards to prevent misuse, such unauthorized knowledge tampering.
We employed large language model to aid in the refinement of the English writing throughout the preparation of this manuscript.
25


Reviews:
[{"review_id": "j8TCTet3U3", "reviewer": "Reviewer_7HRu", "summary": "This paper addresses the challenge of unstructured knowledge editing for LLMs, where existing methods often fail to generalize. To address this, the paper first proposes TANGLESCORE, a novel metric designed to quantify the difficulty of editing specific knowledge. It is calculated based on the shift in the model's internal representations and the semantic gap between the old and new output answers. The authors demonstrate that a higher TANGLESCORE correlates with poorer generalization performance in existing editing methods. To achieve editing, they propose PIPE, a new two-stage editing framework that leverages TANGLESCORE to adapt its strategy. It first employs a purge phase making model unlearn the outdated information, adjusted based on the TANGLESCORE. This is followed by an imprint phase that carefully incorporates the new knowledge while preserving the model's general capabilities. Through extensive experiments on multiple LLMs and benchmarks, the authors show that PIPE outperforms SOTA methods, especially in generalization.", "questions": "See weaknesses.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper targets an important and practical challenge of editing unstructured, free-form knowledge, which is more complex than editing simple factual triplets.\n\n2. The proposed two-stage \"purge-and-imprint\" framework is intuitive, and separating the process of forgetting old information from learning new information is a promising direction.", "weaknesses": "1. The TANGLESCORE metric is defined using model outputs and internal states after an edit has been performed. However, the PIPE method claims to use TANGLESCORE to guide the edit itself. This is a circular dependency that the metric required to perform the edit can only be computed after the edit is complete, leaving a fundamental gap in the proposed methodology.\n\n2. The paper’s core purge-then-imprint framework has been explored in prior work. For instance, [1] also employs a similar two-stage process of first erasing outdated knowledge before introducing new facts. The current paper fails to discuss such relevant work, which overstates the novelty of its proposed framework.\n\n3. The paper also fails to discuss existing approaches quantifying editing difficulty. For instance, [2] demonstrated that the perplexity can serve as a strong indicator of editing difficulty. The authors do not mention this or other potential baseline metrics. This omission weakens the novelty and thoroughness of the proposed metric.\n\n[1] Enhancing Multi-hop Reasoning through Knowledge in Large Language Model Editing.\n\n[2] The Butterfly Effect of Model Editing: Few Edits Can Trigger Large Language Models Collapse.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Bu7sr1EXEJ", "reviewer": "Reviewer_76iD", "summary": "This paper proposes TangleScore, a metric intended to quantify the intrinsic difficulty of editing a given knowledge in an LLM, and a corresponding editing framework, PIPE, whose purge and imprint strengths are adaptively modulated by the proposed TangleScore. Specifically, TangleScore measures (i) the internal representation shift between model states associated with old vs. new knowledge and (ii) the semantic gap between answers before/after editing. Empirically, the authors show that higher TangleScore correlates with worse post-edit generalization on paraphrases. Based on this, PIPE applies stronger purging (unlearning) and more assertive imprinting on high-TangleScore (hard) instances using a gradient-bounded purge loss and a consistency-regularized imprint loss with an instance-wise weight. Experiments on UNKEBench, AKEW and KEBench across LLaMA and Qwen-family models seem to indicate that PIPE improves generalization over ROME/MEMIT/RECT/AlphaEdit and recent unstructured editors.", "questions": "Please refer to my summery of weaknesses.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "+ Overall I feel that tangleScore gives a practical, per-instance signal for how “entangled” an edit is, and PIPE uses it to adapt purge/imprint strength, which indeed turns a vague notion (e.g., “some edits are harder”) into an actionable control knob. \n\n+ Empirically, by introducing stronger purging (unlearning) and more assertive imprinting on high-TangleScore (hard) instances using a gradient-bounded purge loss and a consistency-regularized imprint loss, the method improves generalization on unstructured edits while preserving performance on structured edits and holding up model-wide abilities.", "weaknesses": "- The specific TangleScore form (choice of representation/output distances and ratio) feels ad-hoc and lack in-depth theoretical justification.\n\n-  Computing the score and running a two-stage edit add overhead, and it would be better if the authors can provide clearer cost–quality tradeoffs (runtime, memory, throughput) needed for large edit streams.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "FO4mQpCJ7H", "reviewer": "Reviewer_zBPp", "summary": "This paper addresses the generalization of edits for unstructured knowledge. The authors propose a metric, TANGLESCORE, to quantify the intrinsic difficulty of editing a specific knowledge instance. This metric is based on the internal representation shift and the output semantic gap between the old and new knowledge. Leveraging this insight, they introduce PIPE, a framework that adaptively modulates its editing strategy based on the TANGLESCORE. Experiments across four LLMs and three datasets show that PIPE outperforms baselines, particularly in generalization on paraphrased prompts and factual correctness.", "questions": "1.\tCould the authors elaborate on the design choice for the TANGLESCORE ratio? What is the intuition behind dividing the internal shift by the output shift? \n2.\tThe calculation of r_old and r_new relies on a specific prompt-answer template. How sensitive is the TANGLESCORE value to variations in this prompt template?\n3.\tIn Table 8, does the reported Time for PIPE include the pre-computation of TANGLESCORE?\n4.\tHow is the \"original key vector\" $\\tilde{k}_{i}^{orig}$ used in the knowledge purge function (Eq. 5) obtained?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The paper introduces the TANGLESCORE and diagnoses why unstructured KE fails. The conceptualization of editing difficulty as an intrinsic, quantifiable property of the model-knowledge is a valuable contribution.\n2. The PIPE method is a direct and logical consequence of the TANGLESCORE analysis. The two-stage \"purge-then-imprint\" paradigm is well-justified.\n3. The authors evaluate on multiple modern, open-source LLMs. The use of multiple datasets and the inclusion of structured KE demonstrates the method's applicability.", "weaknesses": "1.\tThe definition of TANGLESCORE as a ratio between internal representation shift and output semantic gap is not fully justified. Why this specific formulation (a ratio)? Why greater semantic gap of outputs causes smaller TangleScore? The paper would be stronger with a more rigorous theoretical or empirical justification for this specific choice.\n2.\tThe PR definition is unclear in eq3 and fig4. As stated “where γ = log λ_max/ log λ_min is the scaling factor, and λ_max and λ_min represent the maximum and minimum values,” what is the maximum and minimum for? Why need two hyper-parameter to set this γ? The rationale for these specific values is attributed only to \"preliminary exploratory experiments.\" Furthermore, λ_min only appears once, used to define the scaling factor γ, making its role ambiguous and the overall formulation seem arbitrary. This lack of clear justification for these critical, hard-coded values undermines the method's robustness.\n3.\tThe claim that TANGLESCORE is an \"intrinsic property\" is a strong one, but the analysis is based on showing the distribution remains similar pre- and post-edit. This doesn't necessarily mean the score for individual samples is unchanged.\n4.\tConfusing and contradictory analysis in fig.5(b). The analysis in Section 5.4, meant to show PIPE's effectiveness, is poorly presented and potentially contradictory. The method is under-explained. The Y-axis of Figure 5(b) is unlabeled (presumably 'Density'). More critically, the X-axis is labeled 'Probability(0.0-1.0),' while the text states 'negative log-probability' was used. These two metrics are inversely related. If the axis is 'Probability,' the observed rightward shift for PIPE would imply a higher probability of outputting old knowledge, directly contradicting the paper's claim of suppression. This ambiguity makes the entire analysis unverifiable. \n5.\tThe paper's central thesis is that existing methods fail on high-TangleScore samples and that PIPE is designed to solve this. However, the paper does not present the critical evidence: a plot of PIPE's performance versus TangleScore and improvements on hard knowledge against other methods. Without this, it is impossible to verify if PIPE's improvement comes from solving the \"hard sample\" problem, or from just being a better method on average.\n6.\tThe paper's mathematical notation is ambiguous and mathematically inconsistent. L_purge (Eq. 5) is explicitly defined for a single 'i-th knowledge item'. In contrast, L_consistency (Eq. 6) and L_learn (Eq. 8) are defined as summations over the entire batch. The final objective in Eq. 10 then adds these mismatched terms. This sloppiness makes the precise formulation of the final objective unclear. And the expressions like “current key output for the i-th query” “original key vector” are unclear to show how they are obtained.\n7.\tThe figures (fig.1 and fig.4) are blurry and fig.4 shows traces of post-processing of changing some characters to “TS”. Fig.5(b) lacks a legend. Line 292: “higher TANGLESCORE implies hard-to-edit knowledge…while higher TANGLESCORE denotes easier samples”. Line 342: “u denote the numbers of pre-training samples, tokens per input, and edited samples, respectively.” Eq.8 is under-explained.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "UletcOchJ3", "reviewer": "Reviewer_bDZe", "summary": "The paper addresses the challenge of unstructured knowledge editing in large language models, where existing methods often fail to generalize due to varying instance-specific editing difficulty. It introduces TangleScore, a metric that quantifies how strongly a fact is entangled with a model’s prior knowledge by combining internal representation shifts and semantic gaps between pre- and post-edit outputs. Building on this, the authors propose PIPE (Purge-Imprint Patch Editing), a two-stage adaptive framework that first purges outdated knowledge at an intensity proportional to the TangleScore and then imprints new knowledge using a dynamic loss balancing learning and stability retention. Experiments on UNKEBench, AKEW, and KEBench with LLaMA-2/3 and Qwen2.5 models show that PIPE improves generalization while preserving factual correctness and general abilities. Overall, the work provides both a diagnostic tool and an adaptive editing framework for more robust and interpretable unstructured knowledge editing.", "questions": "* TangleScore derivation: Could the authors clarify how the hidden-layer representations and answer distributions used in TangleScore are obtained?\n* TangleScore formulation: What is the motivation for dividing the hidden-state distance by the answer-distribution distance, and have alternative formulations been tested?\n* Generalization definition: What type of generalization is evaluated (e.g., paraphrase, reasoning, multi-hop), and how are these samples constructed in the benchmarks?\n* Multi-hop task: Could the authors clarify what the mentioned “multi-hop comprehension task” in Section 5.2 refers to?\n* Effectiveness of purge step: Have the authors measured the model’s behavior after the purge phase but before imprinting to confirm that the purge truly unlearns the targeted knowledge?\n* Relation to unlearning: If the imprint step were removed, would it be possible to employ PIPE as a standalone unlearning method?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "The paper addresses the highly relevant problem of editing unstructured knowledge in large language models. It is original in formulating editing difficulty as a quantifiable property of individual knowledge instances. The proposed TangleScore represents a clear conceptual advance by linking internal model entanglement to empirical editing outcomes. Building on this insight, the PIPE framework introduces an adaptive editing strategy that dynamically scales intervention strength based on the measured difficulty of each instance.\n\nThe experimental evaluation is thorough, covering multiple model families (LLaMA-2/3, Qwen-2.5) and diverse datasets (UNKEBench, AKEW, KEBench). It includes ablations, structured versus unstructured comparisons, and assessments of general ability retention using MMLU.\n\nOverall, TangleScore provides a valuable diagnostic tool for analyzing editability across models, while PIPE achieves consistent performance gains on the key challenge of generalizing unstructured knowledge edits. Together, they constitute a meaningful conceptual and practical advance in understanding and improving knowledge editing in LLMs.", "weaknesses": "While the paper presents an interesting and well-motivated approach, several aspects should be improved to strengthen its conceptual clarity and empirical rigor.\n\nFirst, the derivation of the TangleScore is insufficiently detailed and difficult to reproduce. It remains unclear how the hidden-layer representations used in the metric are obtained, whether they come from specific transformer layers, averaged across layers, or taken from a particular token position. Similarly, the definition of the answer distributions used in the Sinkhorn distance is vague. The paper does not specify whether these distributions are token logits, embedding vectors, or contextualized sentence representations. This lack of precision makes it difficult to interpret what the TangleScore is actually measuring.\n\nSecond, the form of the TangleScore function lacks theoretical justification. The authors divide the internal representation distance by the distance of the answer distributions, but no clear motivation or intuition is provided for this specific ratio. Ablation experiments testing alternative formulations, such as using only one of the two components or other combinations (e.g., $\\frac{1}{\\text{answer distance}}$ alone), would help validate this design choice and clarify the underlying rationale.\n\nThird, there is a conceptual inconsistency in Section 3.2. The authors first claim that the TangleScore is “_solely determined by the knowledge to be edited_” suggesting it is independent of the model, yet later state that the TangleScore depends on the model’s internal representations. This contradiction raises questions about whether TangleScore truly captures an intrinsic property of the knowledge instance or merely reflects model-specific behavior. A more precise discussion and controlled experiments varying model checkpoints could resolve this ambiguity.\n\nFourth, the definition of “generalization” in the experiments is unclear. The paper reports improvements in generalization metrics but never explains what type of generalization is tested. (e.g., paraphrase generalization, compositional generalization, or reasoning generalization). Even if the benchmarks (UNKEBench, AKEW) provide paraphrased samples, the authors should explicitly describe their structure and what constitutes success. Moreover, evaluating only a single type of generalization offers a limited view of editing robustness. The brief mention of a “multi-hop comprehension task” in Section 5.2 is particularly confusing, as it is unclear what data or setup is used. Clarifying and expanding these evaluations to include more challenging forms of generalization (e.g., multi-hop or reasoning-based edits) would significantly strengthen the empirical claims.\n\nFinally, while the proposed two-stage editing design (purge then imprint) is conceptually appealing, the evaluation does not isolate or verify the effectiveness of the purge step. All reported results focus on the final edited performance, leaving open whether the purge function truly removes the targeted knowledge before re-imprinting. An explicit evaluation (e.g., measuring model responses after the purge stage but before imprinting) would provide valuable evidence that the method actually unlearns the targeted knowledge.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)