ManiEdit: Sequential Unstructured Knowledge Editing for Language Models from a Manifold Perspective

Rui Liu; Chenheng Zhang; Haoxuan Li; Zhouchen Lin

cs.CLSep 27, 2026

ManiEdit: Sequential Unstructured Knowledge Editing for Language Models from a Manifold Perspective

Authors: Rui Liu, Chenheng Zhang, Haoxuan Li, Zhouchen Lin

Organizations: State Key Lab of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University · Institute for Artificial Intelligence, Peking University

Abstract

Large language models (LLMs) inevitably generate some incorrect or outdated content, necessitating efficient and precise mechanisms for continual knowledge updates. However, existing model editing methods struggle to sequentially edit unstructured long-form knowledge, suffering from severe edit forgetting and degradation of general capabilities. To address these challenges, we reframe knowledge editing from a manifold perspective, viewing it as a localized displacement of an edit sub-manifold within the global knowledge manifold. Under this formulation, the problem can be decomposed into two key questions: (i) how to identify representative edit points that effectively anchor the edit sub-manifold, and (ii) how to preserve the remaining manifold structure during the sub-manifold displacement process. Based on this perspective, we propose ManiEdit, a novel manifold-aware autoregressive editing framework consisting of two core components. Pivot Localization addresses the mediocre-point dilemma by identifying high-leverage pivots to anchor the edit sub-manifold. Manifold-Aware Preservation preserves different knowledge types through an energy-weighted penalty combined with recursive null-space alignment. Experiments on two base LLMs and four unstructured editing benchmarks demonstrate that ManiEdit achieves state-of-the-art performance, outperforming the strongest baseline by up to +27.81 BERTScore and +8.50 ROUGE-L, while maintaining near-original general capabilities across six representative downstream tasks. Our code is available at: https://github.com/Areyliu/ManiEdit

Figures & tables

Figure 1: Motivation for ManiEdit. Existing methods struggle with sequential unstructured knowledge editing, suffering from edit forgetting and general-capability collapse (left). In contrast, ManiEdit enables effective and reliable knowledge editing, preserving both edit knowledge and pre-trained general capabilities (right).

Figure 2: A manifold perspective of model editing. Left : The input representation manifold $\mathcal{M}_{\text{in}}$ . Retain keys (blue) distribute along the principal axes $\{\psi_{j}\}$ , forming a global sub-manifold $\mathcal{M}^{\text{retain}}_{\text{in}}$ ; edit-related keys (gold) form a local sub-manifold $\mathcal{M}^{\text{edit}}_{\text{in}}$ . Right : The output representation manifold $\mathcal{M}_{\text{out}}=W(\mathcal{M}_{\text{in}})$ . The edit operator $\mathcal{E}$ displaces the edit sub-manifold $\mathcal{M}^{\text{edit}}_{\text{out}}$ (gold) to its target $\mathcal{M}^{*,\text{edit}}_{\text{out}}$ (green), while the retain sub-manifold $\mathcal{M}^{\text{retain}}_{\text{out}}$ (blue) remains unchanged.

Figure 3: Overview of the ManiEdit paradigm. Pivot Localization identifies high-leverage semantic pivots to guide the segmentation of input samples, then the Manifold-Aware Preservation mechanism applies energy-weighted penalty and recursive null-space alignment to preserve general capabilities and historical edits.

Based on Qwen2.5-7B-Instruct
Method	UnKEBench				AKEW (Counterfact)				AKEW (MQUAKE)
	Ori		Para		Ori		Para		Ori		Para
	BERTScore	ROUGE-L	BERTScore	ROUGE-L	BERTScore	ROUGE-L	BERTScore	ROUGE-L	BERTScore	ROUGE-L	BERTScore	ROUGE-L
MEMIT	8.36	16.89	8.95	16.73	9.07	19.23	9.43	19.22	10.47	18.71	9.17	18.67
RECT	12.28	17.57	12.35	17.07	6.21	17.08	5.90	16.76	13.34	21.90	13.20	21.37
EvoEdit	25.79	16.08	21.60	15.16	35.89	24.56	19.85	20.95	52.28	26.35	44.51	21.76

Table 1: Comparison of ManiEdit with existing methods on the sequential unstructured model editing tasks. The best results are highlighted in bold, while the second-best results are underlined.

Figure 4: General capability accuracy on three GLUE tasks (SST-2, CoLA, MRPC) as the number of sequential unstructured edits increases. ManiEdit maintains near-original accuracy across all tasks, while baselines collapse rapidly.

Appendix figures & tables7 assets

Supplementary material from the paper’s appendix.

Appendix

Llama3-8B-Instruct
Method	Ori			Para			Sub
Method	BERTScore	ROUGE-L	ROUGE-1	BERTScore	ROUGE-L	ROUGE-1	ROUGE-L	ROUGE-1
SERAC	70.15	27.89	29.73	70.44	26.91	28.88	22.03	22.71
MELO	69.58	25.19	27.13	70.04	25.26	27.26	21.98	22.66
GRACE	73.02	34.34	36.17	70.11	25.42	27.43	22.41	23.09
ManiEdit	76.22	37.30	39.66	75.80	35.61	38.11	32.26	33.28

Appendix

Table 2: Comparison of ManiEdit with memory-based methods under the sequential unstructured editing setting on UnKEBench.

Llama3-8B-Instruct
Method	Ori			Para			Sub
Method	BERTScore	ROUGE-L	ROUGE-1	BERTScore	ROUGE-L	ROUGE-1	ROUGE-L	ROUGE-1
SERAC	69.22	23.20	24.88	42.97	15.25	16.58	37.36	38.35
MELO	69.69	20.30	22.22	52.09	19.11	21.04	34.42	35.42
GRACE	69.05	19.60	21.25	41.90	12.97	14.18	33.92	34.98
ManiEdit	78.50	37.24	39.98	53.88	28.73	30.80	40.05	41.18

Appendix

Table 3: Comparison of ManiEdit with memory-based methods under the sequential unstructured editing setting on the AKEW (Counterfact) benchmark.

Llama3-8B-Instruct
Method	Ori			Para			Sub
Method	BERTScore	ROUGE-L	ROUGE-1	BERTScore	ROUGE-L	ROUGE-1	ROUGE-L	ROUGE-1
SERAC	69.97	19.94	21.50	69.75	21.20	23.54	42.36	43.64
MELO	69.52	17.00	18.54	69.78	21.14	23.48	39.95	41.35
GRACE	81.82	52.57	53.37	69.82	21.13	23.45	39.97	41.38
ManiEdit	71.51	30.03	32.57	71.24	35.34	38.35	32.70	34.05

Appendix

Table 4: Comparison of ManiEdit with memory-based methods under the sequential unstructured editing setting on the AKEW (MQUAKE) benchmark.

Method	Qwen2.5-7B-Instruct						Llama3-8B-Instruct
	UnKEBench		AKEW (MQUAKE)		AKEW (Counterfact)		UnKEBench		AKEW (MQUAKE)		AKEW (Counterfact)
	ROUGE-1	ROUGE-L	ROUGE-1	ROUGE-L	ROUGE-1	ROUGE-L	ROUGE-1	ROUGE-L	ROUGE-1	ROUGE-L	ROUGE-1	ROUGE-L
MEMIT	16.54	16.35	23.06	22.82	26.57	25.80	1.39	1.35	10.33	10.00	13.09	12.80
RECT	15.52	15.31	26.10	25.58	24.39	24.10	15.00	14.63	17.52	17.38	16.19	16.13
UnKE	6.72	6.38	25.03	26.62	11.26	11.09	0.69	0.68	5.51	5.32	4.06	4.03
AnyEdit	15.63	15.29	21.63	21.14	21.82	21.37	15.84	15.62	21.01	20.43	13.22	12.98

Appendix

Table 5: Comparison of ManiEdit with existing methods on the sequential unstructured model editing tasks under the Sub-question split. The best results are highlighted in bold, while the second-best results are underlined.

Backbone	Benchmark	General-Capability Preservation	Edit-Knowledge Preservation	Original		Para
Backbone	Benchmark	General-Capability Preservation	Edit-Knowledge Preservation	BERTScore	ROUGE-L	BERTScore	ROUGE-L
Qwen2.5-7B-Instruct	UnKEBench	Null-space Projection	Recursive Null-space Alignment	43.69	24.12	41.85	23.64
		Energy-weighted Penalty	Regularization	46.73	29.14	46.20	28.46
		Null-space Projection	Regularization	37.08	30.90	37.99	31.45
		Energy-weighted Penalty	Recursive Null-space Alignment	76.63	39.43	74.97	38.07
	AKEW (Counterfact)	Null-space Projection	Recursive Null-space Alignment	45.76	27.54	30.49	24.60

Appendix

Table 6: Ablation of Manifold-Aware Preservation components on UnKEBench, AKEW (Counterfact), and AKEW (MQUAKE). The left component in each pair protects general pre-trained capabilities, and the right component protects sequentially edited knowledge.

Setting	MOSE				ManiEdit
	Ori		Para		Ori		Para
	BERTScore	ROUGE-L	BERTScore	ROUGE-L	BERTScore	ROUGE-L	BERTScore	ROUGE-L
@500	62.38	31.76	61.11	30.95	76.22	37.30	75.80	35.61
@1,000	54.65	20.57	55.56	19.80	77.00	34.99	75.66	34.61
$\Delta$	$-7.73$	$-11.19$	$-5.55$	$-11.15$	$+0.78$	$-2.31$	$-0.14$	$-1.00$

Appendix

Table 7: Long-horizon stress test on Llama3-8B-Instruct with UnKEBench (scores in %).

\Delta

denotes the change from 500 to 1,000 sequential edits.

Update	$\mathrm{leak}_{t}\downarrow$			$\mathrm{cum}_{t}\downarrow$
Update	Median	Mean	Max	Median
Without alignment ( $P_{t-1}=I$ )	9.02	8.62	16.0	8.72
ManiEdit	0.305	0.343	2.09	0.297
Reduction (median)	$29.6\times$			$29.4\times$

Appendix

Table 8:

\mathrm{leak}_{t}

and

\mathrm{cum}_{t}

over 500 sequential edits, with and without recursive null-space alignment.

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Sequential editing of structured knowledge in large language models allows targeted factual updates without retraining, yet existing methods often rely on complex regularization or constraint mechanisms whose necessity remains unclear. In this work, we systematically investigate the mechanisms underlying effective and stable sequential editing. Specifically, we first analyze the empirical success of AlphaEdit and establish, via a rigorous optimization analysis, the formal equivalence between one-time and sequential editing. Building on this insight, we generalize the equivalence to a broader class of editing objectives, demonstrating that stability emerges naturally from properly accounting for accumulated editing constraints, rather than from specialized regularization or null-space operations. We empirically confirm that many commonly used regularization strategies are unnecessary for reliable sequential updates. Furthermore, we extend our framework to handle conflicting edits, ensuring robust and consistent behavior under contradictory updates. Ultimately, our work provides Ariadne's thread through the labyrinth of sequential editing, charting a path toward simpler, more interpretable, and dependable knowledge updates. Our code is available at https://github.com/Wangzzzzzzzz/OTE-SE-Alignment.

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