LadderEdit: Edit-Level Residual Compression for Memory-Efficient Lifelong Editing of LLMs

Xiaobing Yu; Peijie Qiu; Jin Yang; Xuanzhao Dong; Weiwei Ma; Zhaoqi An; Xiaoqi Zhao; Xiaofeng Liu

cs.CLOct 8, 2026

LadderEdit: Edit-Level Residual Compression for Memory-Efficient Lifelong Editing of LLMs

Authors: Xiaobing Yu, Peijie Qiu, Jin Yang, Xuanzhao Dong, Weiwei Ma, Zhaoqi An, Xiaoqi Zhao, Xiaofeng Liu

Organizations: Yale University · Washington University in St. Louis · Icahn School of Medicine at Mount Sinai · Arizona State University

Abstract

Lifelong editing of LLMs requires storing thousands of edits after acquisition. A widely used family of approaches attaches one LoRA adapter per edit, which preserves behavior but grows linearly in storage. To address this challenge, we propose LadderEdit, a method that compresses each LoRA adapter after it is acquired. Each edit is first stored at low rank as a cheap sketch. We then check whether this sketch still satisfies the rewrite, generalization, and locality contract on probe prompts. Edits that pass keep the sketch; those that fail are promoted to a higher rank along a ladder until the contract is met. Because every edit retains some representation, coverage is maintained, and only hard edits consume more rank. Across ZsRE, CounterFact, and WikiBigEdit benchmarks on LLaMA-3-8B, Mistral-7B, and Qwen2.5-7B, LadderEdit tracks exact LoRA storage at 5.2x less memory and remains effective at 50,000 sequential edits.

Figures & tables

Figure 1: Performance comparison of LadderEdit, exact LoRA, and baseline methods across sequential editing tasks. LadderEdit usually tracks exact LoRA closely while reducing persistent memory, with small losses in some compressed settings.

Figure 2: LadderEdit insertion pipeline. We first fit an exact LoRA update for the incoming edit, then a candidate rank $\hat{r}_{i}$ from the update spectrum and exact-edit margin (one SVD, no behavioral cost). We further decompose the edit into ladder rungs: low-rank sketches at increasing rank. And finally we audit the predicted sketch against rewrite, generalization, and locality. If the audit passes, store at $\hat{r}_{i}$ ; otherwise promote along the spectral ladder until a passing rung is found.

Method	$T=100$				$T=500$				$T=1{,}000$				$T=2{,}000$
Method	Rel.	Gen.	Loc.	Avg.	Rel.	Gen.	Loc.	Avg.	Rel.	Gen.	Loc.	Avg.	Rel.	Gen.	Loc.	Avg.
	LLaMA-3-8B
FT	0.13 $\pm$ .004	0.11 $\pm$ .005	0.02 $\pm$ .001	0.09 $\pm$ .002	0.14 $\pm$ .005	0.12 $\pm$ .006	0.02 $\pm$ .001	0.09 $\pm$ .003	0.13 $\pm$ .005	0.12 $\pm$ .007	0.01 $\pm$ .001	0.09 $\pm$ .003	0.09 $\pm$ .006	0.08 $\pm$ .007	0.01 $\pm$ .001	0.06 $\pm$ .003
ROME Meng et al. (2022)	0.08 $\pm$ .003	0.08 $\pm$ .003	0.02 $\pm$ .001	0.06 $\pm$ .001	0.04 $\pm$ .002	0.04 $\pm$ .002	0.02 $\pm$ .001	0.03 $\pm$ .001	0.03 $\pm$ .002	0.03 $\pm$ .002	0.02 $\pm$ .001	0.03 $\pm$ .001	0.01 $\pm$ .001	0.01 $\pm$ .001	0.01 $\pm$ .001	0.01 $\pm$ .001
MEMIT Meng et al. (2023)	0.03 $\pm$ .002	0.03 $\pm$ .002	0.01 $\pm$ .001	0.02 $\pm$ .001	0.01 $\pm$ .001	0.01 $\pm$ .001	0.00 $\pm$ .000	0.01 $\pm$ .001	0.00 $\pm$ .000	0.00 $\pm$ .000	0.00 $\pm$ .000	0.00 $\pm$ .001	0.00 $\pm$ .000	0.00 $\pm$ .000	0.00 $\pm$ .000	0.00 $\pm$ .001
GRACE Hartvigsen et al. (2023)	1.00 $\pm$ .001	0.39 $\pm$ .009	1.00 $\pm$ .001	0.80 $\pm$ .003	1.00 $\pm$ .001	0.38 $\pm$ .011	1.00 $\pm$ .001	0.79 $\pm$ .004	1.00 $\pm$ .001	0.37 $\pm$ .012	1.00 $\pm$ .001	0.79 $\pm$ .004	1.00 $\pm$ .001	0.34 $\pm$ .013	1.00 $\pm$ .001	0.78 $\pm$ .004

Table 1: Q&A task results on the ZsRE dataset.

T

denotes the number of edits. Mean

\pm

standard deviation over three random seeds. Avg. is the arithmetic mean of Rel., Gen., and Loc. The best average performance per column is marked in bold .

Method

LLaMA-3-8B

Mistral-7B

T=100

T=500

T=1{,}000

T=2{,}000

T=100

T=500

T=1{,}000

T=2{,}000

Rel.

Loc.

Rel.

Loc.

Rel.

Loc.

Rel.

Loc.

Rel.

Loc.

Rel.

Loc.

Rel.

Loc.

Rel.

Loc.

0.42

\pm

.004

0.55

\pm

.001

0.25

\pm

.005

0.30

\pm

.001

0.12

\pm

.005

0.12

\pm

.001

0.05

\pm

.002

0.03

\pm

.002

0.39

\pm

.005

0.52

\pm

.001

0.22

\pm

.006

0.27

\pm

.001

0.10

\pm

.006

0.10

\pm

.002

0.03

\pm

.002

0.02

\pm

.001

ROME Meng et al. (2022)

0.28

\pm

.003

0.45

\pm

.002

0.08

\pm

.004

0.15

\pm

.002

0.02

\pm

.001

0.05

\pm

.002

0.00

\pm

.000

0.02

\pm

.001

0.25

\pm

.003

0.42

\pm

.002

0.06

\pm

.004

0.12

\pm

.003

0.01

\pm

.001

0.04

\pm

.002

0.00

\pm

.000

0.01

\pm

.001

MEMIT Meng et al. (2023)

0.35

\pm

.002

0.52

\pm

.001

0.12

\pm

.002

0.20

\pm

.001

0.03

\pm

.002

0.08

\pm

.001

0.01

\pm

.001

0.03

\pm

.002

0.31

\pm

.002

0.48

\pm

.001

0.09

\pm

.003

0.16

\pm

.001

0.02

\pm

.001

0.06

\pm

.002

0.00

\pm

.000

0.02

\pm

.001

GRACE Hartvigsen et al. (2023)

0.75

\pm

.001

0.96

\pm

.001

0.71

\pm

.001

0.96

\pm

.001

0.68

\pm

.001

0.96

\pm

.001

0.62

\pm

.001

0.95

\pm

.001

0.72

\pm

.001

0.95

\pm

.001

0.68

\pm

.001

0.95

\pm

.001

0.65

\pm

.001

0.95

\pm

.001

0.59

\pm

.001

0.94

\pm

.001

Table 2: Sequential editing results on CF.

T

denotes the number of edits. Mean

\pm

standard deviation over three random seeds. The best performance per column is marked in bold .

Method	$T={10{,}000}$				$T={50{,}000}$
Method	Rel.	Gen.	Loc.	Avg.	Rel.	Gen.	Loc.	Avg.
MEMIT Meng et al. (2023)	0.00 $\pm$ .000	0.00 $\pm$ .000	0.00 $\pm$ .000	0.00 $\pm$ .001	N/A	N/A	N/A	N/A
GRACE Hartvigsen et al. (2023)	1.00 $\pm$ .001	0.31 $\pm$ .017	1.00 $\pm$ .001	0.77 $\pm$ .006	0.99 $\pm$ .002	0.28 $\pm$ .019	1.00 $\pm$ .001	0.76 $\pm$ .006
WISE Wang et al. (2024a)	0.59 $\pm$ .027	0.57 $\pm$ .029	1.00 $\pm$ .001	0.72 $\pm$ .013	0.43 $\pm$ .030	0.41 $\pm$ .032	1.00 $\pm$ .001	0.61 $\pm$ .015
AlphaEdit Fang et al. (2025)	0.72 $\pm$ .023	0.77 $\pm$ .025	0.74 $\pm$ .015	0.74 $\pm$ .012	0.62 $\pm$ .026	0.60 $\pm$ .028	0.71 $\pm$ .017	0.64 $\pm$ .014
MEMOIR Wang et al. (2025)	0.72 $\pm$ .015	0.75 $\pm$ .019	0.83 $\pm$ .012	0.77 $\pm$ .009	0.66 $\pm$ .017	0.68 $\pm$ .022	0.73 $\pm$ .013	0.69 $\pm$ .010

Table 3: Editing performance on Qwen2.5-7B at long-horizon scale on WikiBigEdit. Mean

\pm

standard deviation over three random seeds.

Figure 3: Scaling reference on WikiBigEdit: average performance against persistent edit memory.

Diagnostic case	ZsRE	CF
Sketch-sufficient (rank-1 passes)	81.2%	76.4%
Detail-needed (rank $\geq 2$ required)	13.4%	16.8%
Residual-needed (rank $R_{\max}$ required)	3.1%	4.7%
Compression-regularized (sketch $>$ exact)	4.3%	6.7%

Table 4: Population structure of edit streams at

N=2{,}000

. Categories are determined by the behavioral audit on audit-split probes. Most edits are over-resolved by the full LoRA update.

Dataset	Policy	Memory	Comp.	Utility
CF	exact-cache (matched)	0.180	5.57	0.434
CF	rank-1 sketch only	0.125	8.00	0.753
CF	static mixed-rank	0.180	5.57	0.815
CF	dual-budget LadderEdit	0.180	5.57	0.862
ZsRE	exact-cache (matched)	0.178	5.63	0.420
ZsRE	rank-1 sketch only	0.124	8.09	0.765

Table 5: Budget-matched strict-memory frontier at

N=512

. LadderEdit preserves coverage and allocates residual fidelity through behavioral rung selection.

Panel of Figure 4 — Figure 4: Spectral-filter frontier analysis on ZsRE and CF at $N=512$ edits. The plots show the tradeoff between memory compression and behavioral contract utility for different rank tiers and allocation strategies.

Method	Time	Peak Mem.	Persistent	Serving path
	(ms)	(GB)	(GB)
LoRA (Exact) Hu et al. (2022)	86	18.4	100.93	exact-adapter lookup
MELO Yu et al. (2023)	91	18.6	100.93	retrieval
ELDER Li et al. (2024a)	95	19.1	0.08	MoE routing
MEMOIR Wang et al. (2025)	104	17.9	0.14	mask gate
LadderEdit	124	18.8	19.44	ladder lookup

Table 6: Cost decomposition at

T=10{,}000

on LLaMA-3-8B. LadderEdit reduces this storage by

5.19\times

relative to exact LoRA. Serving path summarizes the additional edit mechanism used at inference.

Appendix figures & tables25 assets

Supplementary material from the paper’s appendix.

Appendix

Setting	Rel.	Gen.	Loc.	Avg.	$\Delta$ vs. matched
Matched association (Exact LoRA)	0.95	0.91	1.00	0.95	—
Matched association (LadderEdit)	0.95	0.91	1.00	0.95	—
Contriever retriever (Exact LoRA)	0.91	0.86	0.99	0.92	$-0.03$
Contriever retriever (LadderEdit)	0.90	0.86	0.99	0.92	$-0.03$
E5-large retriever (LadderEdit)	0.88	0.85	0.99	0.91	$-0.04$
MPNet (frozen) (LadderEdit)	0.85	0.81	0.98	0.88	$-0.07$

Appendix

Table 7: Retrieval sensitivity on ZsRE / LLaMA-3-8B at

T=1{,}000

. The persistent-memory ratio is preserved under all retrieval settings in the table because the retriever is shared between exact LoRA and LadderEdit. Locality is barely affected (0.99

\rightarrow

0.98 under MPNet) because retrieved adapters still land on real edit directions rather than random directions.

Policy	Memory (norm.)	Pooled Avg.	Worst-edit Avg.
Fixed rank-1	0.125	0.85 (0.008)	—
Fixed rank-2	0.250	0.89 (0.007)	—
Fixed rank-4	0.500	0.90 (0.008)	—
Fixed rank-8	1.000	0.90 (0.007)	0.62
Uniform random rank	0.562	0.87 (0.010)	—
LadderEdit (audited)	0.19	0.95 (0.005)	0.74

Appendix

Table 8: Audit contribution against fixed-rank storage at

T=2{,}000

on ZsRE / LLaMA-3-8B across three seeds. Memory is normalized to exact LoRA. Pooled Avg. is the pooled evaluation Avg. across edits; worst-edit Avg. is the per-edit Avg. averaged over only the bottom 10% of edits. Standard deviations over three seeds are reported in parentheses when available.

Figure 5: Sensitivity of LadderEdit to the locality weight

w_{L}

on ZsRE / LLaMA-3-8B at

T=1{,}000

, with

w_{R}

and

w_{G}

held fixed. Left: average performance, peaking at the default

w_{L}=1.2

with a stable region across

[1.0,1.5]

. Middle: per-metric decomposition, showing that locality cliffs upward between

w_{L}=0.8

and

w_{L}=1.0

while reliability and generalization degrade for

w_{L}\geq 1.5

. Right: failure-mode decomposition, with locality failures dominating at low

w_{L}

and reliability or generalization rejections dominating at high

w_{L}

; the default minimizes the total failure rate.

Policy	Memory (norm.)	Avg.
Exact LoRA (FP16)	1.00	0.94
Exact LoRA (INT8)	0.25	0.93
LadderEdit (FP16)	0.19	0.91
LadderEdit (INT8)	0.05	0.90

Appendix

Table 9: Composition of LadderEdit with INT8 quantization at

T=2{,}000

on ZsRE / LLaMA-3-8B. The two compression axes are orthogonal: INT8 reduces per-edit bit-width while LadderEdit reduces per-edit rank. Composing them yields a

20\times

reduction over FP16 exact LoRA. The composed configuration (LadderEdit + INT8) reaches 0.90 Avg. at 0.05 normalized memory, dominating exact LoRA + INT8 alone (0.93 Avg. at 0.25 memory) on the cost axis.

Stored representation	Fraction of edits
Rank-1 sketch	79.4%
Rank-2 sketch	16.1%
Rank-3 sketch	1.8%
Rank-4 sketch	1.2%
Rank-5 sketch	0.7%
Rank-6 sketch	0.4%

Appendix

Table 10: Distribution of stored representations (N=1,000 edits at

T=1{,}000

on LLaMA-3-8B / ZsRE; sums to 100.0%) across the full

r{=}1

r{=}8

ladder. The majority of edits are served by the cheapest rung, and the distribution decays sharply: the top two rungs together cover

95.5\%

of edits, while only the remaining

\sim 4.5\%

requires ranks 3 or above. The audit promotes edits to higher rungs only when the low-rank sketch fails to satisfy the behavioral contract, which corresponds approximately to the detail-needed population of Table 4 . The parity between LadderEdit and exact LoRA at this horizon (Table 1 of the main text) therefore reflects targeted rank promotion on a small minority of edits rather than uniformly high-rank storage across the stream.

Dataset	Audit probes/edit	Val. probes/edit	Test probes/edit
ZsRE	2	1	2
CounterFact	3	1	3
WikiBigEdit	2	1	2

Appendix

Table 11: Per-edit probe counts after the three-way split. Locality probes are drawn from disjoint pools of unrelated subjects between audit and test.

Configuration	Rel.	Gen.	Loc.	Avg.
LadderEdit, strict split (main)	0.95	0.91	1.00	0.95
LadderEdit, leaky (audit=test)	0.99	0.97	1.00	0.99
Inflation gap	+0.04	+0.06	0.00	+0.04

Appendix

Table 12: Leaky-versus-clean comparison at

T=1{,}000

on ZsRE / LLaMA-3-8B. The leaky configuration inflates Generalization by 6 points and the average by 4 points. The numbers reported in the main paper correspond to the strict split.

Dataset	Case	Edit id	Energy	$\Delta U$	Gap	Interpretation
CF	Sketch sufficient	0	0.517	0.000	0.000	Rank-1 preserves the exact edit’s measured behavior, so higher resolution is unnecessary.
CF	Detail needed	9	0.590	-0.333	0.267	The sketch drops needed behavior; the audit should promote to a higher rung.
CF	Compression helps	14	0.474	0.333	0.000	Removing tail directions improves measured behavior, so exact LoRA is not always a behavioral ceiling.
CF	Exact limited	4	0.524	0.000	0.267	Both exact and sketch leave a contract gap; this is an edit-writer limitation, not a compression error.
ZsRE	Sketch sufficient	0	0.496	0.000	0.000	The cheap sketch is behaviorally indistinguishable from exact on the probes.
ZsRE	Detail needed	3	0.576	-0.167	0.100	Residual detail is needed for a hard edit that rank-1 cannot safely represent.

Appendix

Table 13: Representative edit-level diagnostic cases at

N=512

, seed 0, rank 1.

\Delta U

is the measured utility of the rank-1 sketch minus exact LoRA. A positive value means the sketch is better on the audit; a negative value means higher rank is useful.

	Audit threshold $\tau$ with $r{=}8$				LoRA rank $r$ with $\tau{=}0.05$
	$\tau{=}0.01$	$\tau{=}0.025$	$\boldsymbol{\tau{=}0.05}$	$\tau{=}0.10$	$r{=}4$	$\boldsymbol{r{=}8}$	$r{=}16$	$r{=}32$
Rel. ( $\uparrow$ )	0.73	0.86	0.95	0.81	0.78	0.95	0.88	0.74
Gen. ( $\uparrow$ )	0.82	0.88	0.91	0.85	0.81	0.91	0.89	0.83
Loc. ( $\uparrow$ )	0.99	1.00	1.00	0.94	0.99	1.00	0.97	0.92
Avg. ( $\uparrow$ )	0.85	0.91	0.95	0.87	0.86	0.95	0.91	0.83
Memory (GB)	4.21	3.97	3.84	3.62	2.18	3.84	7.32	14.18

Appendix

Table 14: Sensitivity of LadderEdit to the audit threshold

\tau

and LoRA rank

r

on ZsRE / LLaMA-3-8B at

T=1{,}000

. Avg. is the arithmetic mean of Rel., Gen., and Loc. Bold marks the default configuration. The default

(\tau{=}0.05,r{=}8)

sits at the peak of both sweeps: smaller

\tau

r

underfits the audit, larger

\tau

r

admits noisy edits that erode locality.

Probe bank size $\|\mathcal{P}\|$	Coverage	Probed edits		Unprobed edits		Overall
Probe bank size $\|\mathcal{P}\|$	Coverage	Avg. ( $\uparrow$ )	Failures (%)	Avg. ( $\uparrow$ )	Failures (%)	Avg. ( $\uparrow$ )
256	38%	0.95	1.2	0.78	14.6	0.84
1,024	64%	0.95	0.9	0.85	8.3	0.91
4,096	87%	0.95	0.8	0.89	4.1	0.94
16,384	96%	0.95	0.7	0.91	2.4	0.94
65,536	99%	0.95	0.7	0.92	1.8	0.95

Appendix

Table 15: Probe-coverage stress test on ZsRE / LLaMA-3-8B at

T=2{,}000

. We vary the audit probe-bank size

|\mathcal{P}|

and report performance on probe-covered and unprobed edits. Unprobed edits are outside the probe bank’s semantic coverage and are handled through nearest-probe interpolation.

Case	Prompt and target	Exact LoRA	Sketch rung	Ladder decision
ZsRE 29: sketch sufficient	Rewrite: Which is the position of Jules Basile Onambele? Target: winger. Generalization: What is Jules Basile Onambele’s position?	Jules Basile Onambele primarily plays as a winger or attacking midfielder.	Rank 1 gives the same rewrite and generalization output as exact LoRA.	Store rank 1. The full residual is unnecessary.
CF 11: sketch sufficient	Rewrite: Andreas Ivanschitz professionally plays the sport. Target: football. Generalization: After work Walther attended the Y.M.C.A. Andreas Ivanschitz, the	Andreas Ivanschitz is known for playing football (soccer) professionally. The generalization output remains incomplete.	Rank 1 preserves the same rewrite behavior and the same incomplete generalization response.	Store rank 1. A hard dataset still contains cheap compressible edits.
ZsRE 27: detail needed	Rewrite: What sports team was Stanislav Romanov a member of? Target: Spartak Myjava. Generalization: Which sports team was Stanislav Romanov’s?	Stanislav Romanov was a member of FC Spartak Myjava.	Rank 1 changes the value to FC Spartak Mytishchi, while rank 2 recovers FC Spartak Myjava.	Promote to rank 2. The edit needs a little more detail, but not exact storage.
ZsRE 24: compression helps	Rewrite: What kind of occupation does Karim-Mohamed Maamoun have? Target: architect. Generalization: What kind of occupation is Karim-Mohamed Maamoun?	Exact LoRA answers archaeologist on the rewrite and generalization prompts.	Rank 1 and rank 2 include archaeologist and architect in the rewrite output.	Store the sketch. Low-rank projection can filter harmful detail from the exact edit.

Appendix

Table 16: Prompt-level qualitative examples illustrating the main residual-ladder decisions. Outputs are shortened only for formatting. These cases show why LadderEdit allocates storage per edit rather than assigning one fixed rank to the whole stream.

Case	Exact LoRA	Sketches	Interpretation
CF 27, target Russian. Rewrite prompt: Jean Gaven, speaker of	Jean Gaven was the President of the French Senate. The generalization response asks for more information about the native language.	Rank 1 and rank 2 produce the same incorrect or uninformative behavior.	Exact-limited. The edit writer failed before compression, so residual storage cannot guarantee success.

Appendix

Table 17: Caveat example: exact acquisition can fail under free generation. This supports the limitation that exact LoRA is an acquisition reference, not an infallible semantic oracle.

Panel of Figure 6 — Figure 6: Additional audit diagnostics. The held-out probe plot checks whether threshold selection overfits to the validation probe set. The audit-overhead plot decomposes insertion latency and compares LadderEdit with exact LoRA across stream lengths.

Quantity	Value
Crossover horizon	$T\approx 2{,}000$
Exact LoRA @ $T=100$	86 ms/edit
LadderEdit @ $T=100$	93 ms/edit
Exact LoRA @ $T=50{,}000$	850 ms/edit
LadderEdit @ $T=50{,}000$	150 ms/edit
Speedup @ $T=50{,}000$	$5.7\times$

Appendix

Table 18: Insertion latency summary (A100-40GB).

Predictor	Utility	Audits/edit	Memory
Random rank proposal	0.81	2.31	0.22
Always rank-1, then promote	0.89	1.85	0.20
Spectral tail $T_{i}(r)$ only	0.88	1.32	0.19
Margin $\mu_{i}^{E}$ only	0.86	1.42	0.21
Spectral $\times$ margin (ours)	0.91	1.21	0.19
Oracle (exhaustive audit)	0.93	8.00	0.19

Appendix

Table 19: Predictor ablation at

T=1{,}000

on ZsRE / LLaMA-3-8B. Utility is the audited behavioral contract score; Audits/edit measures the cost of the proposal at insertion time; Memory is normalized to exact LoRA. Bold marks the configuration used in the main paper. The combined spectral-times-margin predictor reaches utility within 0.02 of oracle at

4.6\times

fewer audits per edit, while each factor alone trails by 0.03–0.05. The always-rank-1 baseline shows that the audit-and-promote loop alone is competitive on utility but pays a higher audit cost without a predictor to localise the proposal.

Dataset	$N$	Rank	Memory	Energy	Contract	False Ret.
CF	32	1	0.125	0.560	0.625	1.67
CF	32	2	0.250	0.747	0.656	0.00
CF	32	4	0.500	0.899	0.646	0.00
CF	32	8	1.000	1.000	0.646	0.00
CF	128	1	0.125	0.559	0.641	7.00
CF	128	2	0.250	0.744	0.662	1.67

Appendix

Table 20: Fixed-rank tier diagnostics for CF and ZsRE at

N=32

128

512

. Memory is normalized to exact LoRA. Retained energy is

1-\|\rho\|_{F}^{2}/\|\Delta^{E}\|_{F}^{2}

. False retirements count exact-pass edits that fail after compression.

Panel of Figure 7 — Figure 7: Swap-count diagnostics for ZsRE and CF at $N=512$ . The plots show how many edits change pass/fail status as rank increases, highlighting the difficult subset that requires higher rank.

Backbone	Method	Acc.	Gen.	Loc.	Avg.
Qwen2.5-14B	Exact LoRA	0.795	0.905	0.999	0.900
	LadderEdit	0.790	0.900	0.999	0.896
Qwen2.5-32B	Exact LoRA	0.821	0.923	0.999	0.914
	LadderEdit	0.816	0.918	0.999	0.911

Appendix

Table 21: LadderEdit on larger Qwen2.5 backbones. Accuracy (Acc.), generalization (Gen.), locality (Loc.), and their average are reported. LadderEdit remains close to Exact LoRA as the backbone increases from 14B to 32B parameters.

Cache policy	Full-stream utility
Random retention	0.18
FIFO	0.20
LRU	0.22
Learned retention	0.35
Learned retention + base fallback	0.41

Appendix

Table 22: Full-stream utility of exact-cache policies under the same capacity of 91 retained adapters out of 512 edits. Learned retention with base-model fallback is the strongest exact-cache baseline.

Dataset	Policy	Norm. memory	Utility
CF	Exact-cache	0.180	0.434
CF	Rank-1 for all edits	0.125	0.753
CF	Static mixed-rank	0.180	0.815
CF	LadderEdit	0.180	0.862
ZsRE	Exact-cache	0.178	0.420
ZsRE	Rank-1 for all edits	0.124	0.765

Appendix

Table 23: Strict-memory comparison on CounterFact (CF) and ZsRE. Utility is measured under approximately matched normalized persistent memory.

Method	Insert / edit	Peak GPU	Persistent memory
Exact LoRA	86 ms	18.4 GB	100.93 GB
LadderEdit	124 ms	18.8 GB	19.44 GB
Difference	+38 ms	+0.4 GB	$-81.49$ GB

Appendix

Table 24: Measured insertion and memory cost at

T=10{,}000

edits. Persistent memory refers to the retained edit bank, whereas peak GPU memory measures transient working memory during insertion.

Diagnostic property	ZsRE	CounterFact
Rank-1 sufficient	81.2%	76.4%
Rank 2 or higher required	13.4%	16.8%
Full acquisition rank required	3.1%	4.7%
Sketch passes while exact fails	4.3%	6.7%

Appendix

Table 25: Diagnostic rates describing edit-level rank requirements and compression behavior. The rows are diagnostic properties and are not assumed to form a mutually exclusive partition.

Selection mechanism	Exact LoRA Avg.	LadderEdit Avg.
Matched association	0.95	0.95
Contriever ( Izacard et al., 2021 )	0.92	0.92
E5-large ( Wang et al., 2022 )	–	0.91
Frozen MPNet ( Song et al., 2020 )	–	0.88

Appendix

Table 26: Performance under matched association and learned retrieval. Contriever is evaluated directly for both Exact LoRA and LadderEdit. Additional LadderEdit results with E5-large and frozen MPNet characterize sensitivity to the retriever.

Rank proposal	Utility	Audits / edit
Random	0.81	2.31
Always rank-1, then promote	0.89	1.85
Spectral tail only	0.88	1.32
Behavioral margin only	0.86	1.42
Spectral tail + margin	0.91	1.21
Exhaustive oracle	0.93	8.00

Appendix

Table 27: Rank-proposal ablation. The combined spectral-tail and behavioral-margin proposal approaches exhaustive rank search while requiring substantially fewer audits.

Probe coverage	Overall Avg.
38%	0.84
64%	0.91
87%	0.94
96%	0.94
99%	0.95

Appendix

Table 28: Sensitivity to semantic coverage of the behavioral audit probes.

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HoReN: Normalized Hopfield Retrieval for Large-Scale Sequential Model Editing

Yuan Fang, Yi Xie, Xuming Ran

1IXL Learning, Inc · 2Technical University of Munich · 3National University of Singapore

Knowledge Editing Memory-Augmented Language Models Hopfield Networks Model Editing Language Model Editing

Open paper AskPDF arXiv

Large language models encode vast factual knowledge that can become outdated or incorrect after deployment, yet retraining is prohibitively costly. This motivates lifelong model editing, which updates targeted behavior while preserving the rest of the model. Existing editors, both parameter-modifying and parameter-preserving, degrade severely as edits accumulate and struggle to generalize across paraphrases. We propose HoReN, a codebook-based parameter-preserving editor that wraps a single MLP layer with a discrete key-value memory. HoReN treats each codebook entry as both a knowledge key and a Hopfield stored pattern, retrieves edits by angular similarity on the unit hypersphere, and refines queries through damped Hopfield dynamics so paraphrases converge to the correct memory basin while unrelated inputs remain stable. HoReN achieves strong editing performance with consistent gains across diverse benchmarks spanning standard ZsRE, structured WikiBigEdit, and unstructured UnKE evaluations. Moreover, HoReN scales to 50K sequential edits on ZsRE with stable overall performance above 0.93, while prior editors collapse or degrade severely before reaching 10K. Our code is available at https://github.com/ha11ucin8/HoReN.

Abstract

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