Retain and Adapt: Auto-Balanced Model Editing for Open-Vocabulary Object Detection under Domain Shifts
Authors: Zixuan Duan
Fengyuan Lu
Xunzhi Xiang
Wenbin Li
Yang Gao
Qi Fan
Venue: ICLR 2026 Poster (2026)
Keywords: Open-Vocabulary Object Detection
Model Editing
Continual Learning
Knowledge Injection
Few-Shot Learning
Catastrophic Forgetting
Primary area: transfer learning, meta learning, and lifelong learning
TL;DR: We propose a hyperparameter-free auto-balanced model editing method that flexibly injects and learns new task knowledge into open-vocabulary detectors while preserving original capabilities, achieving strong adaptation without retraining.

Abstract: Recent advances in Open Vocabulary Object Detection (OVOD) have shown strong performance on standard benchmarks, but performance drops sharply under out-of-distribution (OOD) shifts. Continual learning offers a potential remedy by sequentially integrating new tasks, yet existing methods often struggle to balance retaining the pre-trained model capabilities with adapting to new tasks, and usually require retraining under specific task orders. To address these limitations, we observe that model editing naturally lends itself to this setting, as it enables efficient knowledge injection while retaining prior capabilities. Building on this insight, we introduce $\textbf{A}$utomatically $\textbf{B}$alanced $\textbf{M}$odel $\textbf{E}$diting ($\textbf{ABME}$), which injects new task knowledge into the powerful OVOD models while preserving the model’s original abilities. We first stores compact key–value representations with storage cost independent of task volume. Then we leverage the stored KV matrices to automatically balance the new and old knowledge for varying learning scenarios, 
supporting order-agnostic task insertion or removal without additional retraining. Experiments show that ABME consistently achieves a better trade-off between maintaining pre-trained performance and adapting to diverse OOD tasks compared to existing continual learning approaches for open-vocabulary object detection, and generalizes seamlessly across different models and task scales.

Published as a conference paper at ICLR 2026
RETAIN AND ADAPT: AUTO-BALANCED MODEL EDIT- ING FOR OPEN-VOCABULAN OBJECT DETECTION UNDER DOMAIN SHIFTS
Zixuan Duan, Fengyuan Lu, Xunzhi Xiang, Wenbin Li, Yang Gao, Qi Fan Nanjing University
ABSTRACT
Recent advances in Open Vocabulary Object Detection (OVOD) have shown strong performance on standard benchmarks, but performance drops sharply un- der out-of-distribun
(OOD) shifts. Continual learning offers potential remedy by sequentially integrating new tasks, yet existing methods often struggle to bal- ance retaining the pre-trained model capabilities with adapting new tasks, and usually require retraining under specific task orders. To address these limitations, observe that model editing naturally lends itself to this setting, as it enables efficient knowledge injection while retaining prior capabilities. Building on this insight, introduce Automatically Balanced Model Editing (ABME), which in- jects new task knowledge into the powerful OVOD models while preserving the model's original abilities. We first store compact key-value representations with storage cost independent of task volume. Then we leverage the stored KV ma- trices automatically balance the new and old knowledge for varying learning scenarios, supporting order-agnostic task insertion or removal without additional retraining. Experiments show that ABME consistently achieves a better trade-off between maintaining pre-trained performance and adapting to diverse OOD tasks compared to existing continual learning approaches for OVOD, and generalizes seamlessly across different models and task scales. Our code is available here
INTRODUCTION
promising paradigm that leverages Aasion-language pretraining to rec- ognize a wide range of object categories without exhaustive annotations (Gu et al. 2021: Li et al.) 2022b). Despite impressive progress in-distribution benchmarks, recent studies reveal that OVOD models still struggle under distribution shifts, such as novel domains or previously unseen categories, leading to substantial performance degradation (llyas al. 2024 Chhipa al. 2024). In practice, domain shift can arise from changes in image style, capture conditions, or resolution, which sig- nificantly affect the reliability of detection even when the object categories remain the same. This limitation poses a critical challenge for deploying OVOD systems in real-world scenarios, where adaptability diverse environments and continual integration of new knowledge are essential.
OVOD has emerged
The conventional approach for continuously extending OVOD models to handle new concepts in multiple OOD domains is continual learning, which incrementally integrates new tasks while re- ducing forgetting across the sequence. Yet, they generally do not explicitly consider the preitrained knowledge when balancing with the new capabilities required for multiple tasks, and they typically assume a fixed task order, limiting flexibility and efficiency: they are also highly order-sensitive. where small variations can yield large performance gaps (Lai et al.] [025): Hacohen & Tuytelaars 2024]. While few shot fine-tuning can perform well single few-shot dataset (Pan et al. 2025 Liu et al. 2025). it does not scale across multiple domains and frequently degrades base perfor- mance. These limitations motivate the search for
lightweight and flexible alternative.
Model editing (Mitchell et al. 2021 Fang et al. 2024) has recently emerged as a lightweight ap- proach for updating large pre-traines models, where small parameter modifications effectively inject new knowledge without retraining the entire model. Importantly, it enables the insertion of
Corresponding author: fanqi wnw...ddu.c..

---

Published as a conference paper at ICLR 2026
In- In-Domain
with In-domain
Other Methods
Pretrain first
and data
X Order sensitivity
Pretrain
X Heavy hyperparameter tuning
only
Multiple OOD tasks
Continual
learning
X Limited scalability
X of pre-trained capability
(+) Pretrained model
(b) Traditional Continual learning
Our Method
KV pairs
Model Editing
Order-agnostic
Extract
Injection
Preservation
Knowledge
Strong adaptabilitty
KW
High scalability
(c) Ours (Auto- Balanced Model Editing)
Preserve pre-trained capability
Comparison of paradigms for Open-Vocabult Object Detection (OVOD) under domain shifts. (a) Pretrained models fail on OOD tasks. (b) Traditional continual learning adapts sequen- tially but suffers from order sensitivity, tuning overhead, and forgetting. (c) Our Auto-Balanced Model Editing (ABME) injects knowledge via key-value statistics, automatically balancing new and old tasks, achieving order-agnostic, scalable, and adaptive learning.
Figure 1:
multiple new concepts while preserving the model's original capabilities. making it well-suited for continual extension in complex domains. In our OVOD few-shot experiments, we observe that fine-tuning only the Feed-Forward Network (FFN) parameters achieves performance comparable to full-model fine-tuning (see Table 11. suggesting that lightweight adaptation can be as effective as updating the entire model. Inspired by these experiments, we find that model editing provides natural paradigm for continuously extending new concepts to OVOD models. However, while model editing has been predominantly explored the context of large language models (LLMs), its adaptation to OVOD raises unique challenges that remain underexplored. As illustrated in Fig. [ our approach contrasts with traditional continual learning by achieving order-agnostic, scalable, and adaptive knowledge integration.
To enable model editing in OVOD, we fine-tune only the FFN layers on the support set to obtain a task-adapted model, then pass the new-concept data through it to record the input (keys) and output (values) at the edited FFN layer as KV pairs. We store compact KV matrices independent of task number, ensuring scalability without extra memory cost. Yet injecting new knowledge through these KV matrices still requires tuning hyperparameter to balance new and old knowledge across models and task scales, which is inefficient and impractical. To overcome this issue, we propose use the KV matrices themselves to automatically adjust this balance, eliminating the need for tedious hyperparameter tuning and enabling a general solution applicable across diverse models and task volumes. Our approach offers several advantages: it ensures Reliability, effectively incorporating new knowledge and achieving up to 96.4% of fine-tuned performance on novel tasks; guarantees Locality, by preserving 94.1% of the pre-trained model's original capability; and provides strong Flexibility, as we can quickly perform direct combination or removal of task-specific KV statistics without retraining, making adaptation order-agnostic and scalable across diverse domains.
Our main contributions are as follows:
To the best of our knowledge, we are the first to bring model editing into the OVOD task, and we propose principled way to construct key-value (KV) knowledge pairs for adapting new concepts.
Introducing model editing to OVOD:
Auto-balanced model editing: We design a data-adaptive balancing strategy that automat- ically reconciles new and old knowledge, maintaining robustness across different models and task scales without manual trade-off tuning.
Extensive validation and generality: We conduct comprehensive experiments on 19 few- shot datasets and validate our method on two distinct open- vocabulary detection models, Grounding DINO and GLIP, demonstrating its generality and scalability.
2

---

Published as a conference paper at ICLR 2026
2 RELATED WORK
Model editing. Model editing aims to incorporate new knowledge while preserving performance on unrelated tasks, and existing approaches fall into two categories. Methods that preserve the model's 's parameters avoid modifying weights, instead relying external mechanisms: IKE (Zeng ct al., 2023a) and MemPrompt (Madaan et al., 2022) edit via prompts, SERAC (Meng et al, 2022b) introduces counterfactual model, T-Patcher (Huang al., (023) adds correction neu- rons, and WISE (Wang et al., (024) uses dual memory with conflict-free sharding. By contrast, approaches that modify the model's parameters directly update internal representations: Knowledge Neurons (Dai et al. 2022) alter selected neurons, MEND (Mitchell et al. (021) employs hypernet- work, ROME (Meng et al. 2022a) and GLAME (Zhang al. 2024) use locate-then-edit strategies. AnyEdit (Jiag et al. (025) performs autoregressive sequential editing, and AlphaEdit (Fang et al.) 2024) constrains updates
the null space. Most of these methods focus on LLMs, where FFN layers are recognized as the primary locus of knowledge storage; our work extends this insight to OVOD, motivating an editing-based formulation for few-shot detection.
Continual learning. Continual learning addresses the challenge of catastrophic forgetting and can be grouped into three families: schearsal-based regularization- based, and architecture-based. Rehearsal- based approaches replay past knowledge using either stored exemplars or synthetic data, such as iCaRL (Rebuffi et al.) 2017), CCL-GM (Lavda et al. 2018). GSS (Aljundi et al. 2019).
and GDumb (Prabhu et al. 2020). Regularization-basel methods constrain parameter updates to protect previously acquired information. Examples include BSS (Chen et al. 2019). EWC (Kirk- patrick al., 2017), MAS (Aljundi et al.. 2018), and diffusion- based strategies (Jha et al. 2024). Architecture-baaee methods expand or reconfigure the network to accommodate new tasks, e.g..
DyTox (Douillard al., 2022), LMC (Ostapenko al., 2021), WSN (Kang et al., 2022). and Piggy- back (Mallya et al. (2018). Although effective, these approaches are typically designed for long-term term incremental learning and often require large-scale replay or architectural modifications. In contrast, our method targets few-shot OVOD and achieves knowledge retention efficiently by storing compact key-value statistics, without the need for exemplar memory or model expansion.
OVOD. OVOD has shown strong in distribution results by leveraging vision- language pretraining and semantic alignment (Li et al. 2022b Liu et al. 2024), yet recent studies show substantial degra- dation under distribution shifts with unseen categories or domains (Ilyas et al. 2024 Chhipa et al.,
2024). To address this, several few-shot fine-tuning methods have been explored: ETS (Pan et al,
2025 adapts parameter configurations from foundation models, Domain- RAG (Li et al. 2025)
trieves semantically and stylistically similar images for data synthesis, and CDTO (Fu et all 2022 applies feature alignment, reweighting, and domain prompting. While effective datasets, these approaches are task-speeific, requiring repeated fine-tuning that increases cost and may compromise prior performance.In contrast, we formulate few-shot adaptation model editing problem: instead of retraining, we inject task knowledge into FFN layers via compact KV statistics, enabling a single OVOD model to handle multiple tasks while retaining base-domain capability.
small
3 PRELIMINARY
3.1 MODEL EDITING IN LLMS
LLMs encode both factual and task-specific knowledge in their parameters, with recent studies showing that it is largely concentrated in the FFN layers (Meng et al. 2022b Fang et al. 2024). Concretely, the FFN at the [-th layer can be written as:
W a (Whn 7h' d)))
m
Here, W and Wi denote the input and output weight matrices of the FFN, respectively, while ? represents layer normalization and a the activation function. The terms a' and 1 correspond to the output of the attention block and the hidden state of the (1 - 1)-th layer. Prior work suggests triples rs) where denotes the subject, the
that knowledge in LLMs be abstracted relation, and o the object (c.g.. s = The longest river in the world", r = is", o = "the Nile River"). In general, the intermediate representation before the output projection is taken as the key k, while
3

---

Published as a conference paper at ICLR 2026
the projected output is defined as the value v, corresponding to (s) and (o), respectively, thereby forming key-value pairs (Geva et al. 2020). Building on this formulation, model editing seeks to replace original triples (s. with updated ones (s, where the new object o* is encoded through key-value pairs. Specifically, the new knowledge is represented by K2 E RRdoxa and Vi E Rd x which correspond to the key and value representations in the FFN output projection.
3.2 MODEL EDITING FORMULATION FOR OVOD
We formalize the OVOD few-shot model editing problem as follows. Let T = {T1. .2..... .2.....:77 Tr} be a set of T tasks, where each task т; corresponds an object detection problem sampled from datasets that are OOD with respect to the base OVOD model. In practice, these tasks often come from novel domains or contain unseen object categories, on which the pre-trained OVOD model typically performs poorly.
Each task is defined by small support set S; [(Tg و containing a few annotated examples, and a query set 2, for evaluation. We treat the adaptation to task тj as injecting new knowledge into the model, analogous to model editing in large language models, where the model must quickly incorporate information from the support set to improve detection accuracy on the corresponding query set. The practical goal of OVOD knowledge injection is twofold: to achieve Reliability, the model should minimize the detection loss on the query sets Qi, i.e., (f'; Q.); and to ensure Locality, it should simultaneously minimize the loss the base dataset Dusse i.e., (0'; Dmsse). In this way, the model adapts effectively to new tasks while preserving its original capability. In our editing setting, only the support sets S; are available for adaptation, while the query sets Q; and the base dataset Drese are used solely for evaluation and are not accessible during editing. This setting is more practically relevant than conventional continual learning, which emphasizes long-term retention but overlooks preserving the model's is original capability. These considerations motivate us to design an editing algorithm that efficiently injects new task knowledge while retaining the base model's capability, as described in Sec.
4 METHOD
In this section, we first introduce the construction and storage of new knowledge key-value pairs (Sec. [.I), then present the buto-balanced objective function (Sec. 4.2), and finally describe how to optimize this objective and summarize the overall procedure in Alg. (Sec. 4.3
4.1 KNOWLEDGE KEY- VALUE CONSTRUCTION
Traditionally, knowledge editing in LLMs (Meng et al., 2022b) is formulated under the key-value framework. Given a key l that encodes the subject-relation relation pair (s, the model retrieves the corresponding value v representing the object o. To inject new knowledge, existing methods usually keep the key k fixed, while replacing the associated value e with an updated value v" that encodes the target object o". In practice, v" is obtained by adjusting (e.g.. via gradient descent) so that the model is more likely predict o" when conditioned on (s) To obtain the new knowledge key- value pairs in the OVOD model, we adopt two-stage strategy consisting of fine-tuning and storage. In particular, we designate the parameter be edited, such as the output projection matrix Wout &doxd; in the FFN Eq. [ with its input and output defined as the key A and value v, respectively. During fine-tuning, for task 7;, we regard the output projection matrix Won. as the only learnable parameter while keeping all others frozen. The task-specific parameter set 9 thus corresponds to the adapted Wout, which is optimized with the following traditional detection objective:
min Loa(O S.),
(2)
Once the fine-tuned parameter 0; is obtained, we feed the support set S; into the corresponding fine- tuned OVOD model to extract the intermediate key-value representations, denoted as K; E R2 X and V, E Rd, However, the dimension n, can be extremely large; for instance, in vision backbone it may scale with the number of samples and patches, making it prohibitive to store the full K2 and V., In Sec. 4.3] show that keeping the entire matrices is unnecessary. Instead, it suffices to store the covariance and cross-covariance matrices, i.c., K. K; and K, V,, which are significantly more compact while preserving the information required for subsequent optimization.
4

---

Published as a conference paper at ICLR 2026
Algorithm OVOD Auto- BaloanBa Model Editing (Batch-wise Accumulation)
Input: Pre-trained OVOD model (all parameters θ), chosen editable sub-parameter Wo C θ (Wo E [Rdoxd1), ), task datasets 12
Output: Updated editable weight W
1: A f Odgxdo>
+ 0dpxd; $ - Odo
2: for t E (1,2,..... ... do
3: 0. 4- arg min Lut (1: BEWo
Fine-tune only editable Wo, keep others θ fixed
4:
for each mini-batch (ar, y) from S, do
5:
Forward with 9, at the edited FFN layer to obtain K₁ E IR xdo V/ E R A A K K
6:
accumulate K" K
7:
B B K V/
accumulate K KTV
8:
8 *-st
Σド Kolj,dol
accumulate 8;
9: _ +- diag(st/2)
D DI.
8,
1/4 1 1/2
10: + A +
11: W +- H-! H-I(B (%))
solve via SPD linear system
12: return W*
4.2 AUTO-BALANE EDITING OBJECTIVE DESIGN
In order to effectively leverage the constructed key-value representations, we first introduce a straightforward objective: to allow the weight matrix W to absorb knowledge while retain- ing previously learned information, we consider the following simple formulation:
min f(W) = |KW - VI? + A|A - Wolli: W
(3)
where Wo denotes the original parameter of the OVOD model. The key-value matrices (K, V) obtained from the previous step by concatenating the knowledge matrices across all tasks:
K = K1; K2:...; Kr] E IR nxd
V V1: V2: Vr] E IRaddd
(4)
where the total number of samples is n Σn i= with n, denoting the number of key-value pairs extracted from task 7;. The hyperparameter controls the trade-off between fitting new knowledge and preserving the old one.
Through this optimization, the model is encouraged to acquire new knowledge while simultaneously preserving previously learned information. Apparently, the hyperparameter À cannot be derived analytically but must instead be set heuristically and subsequently evaluated on both the query set and the base set to determine its suitability. Moreover, the choice of À is not universal: it must be re-tuned whenever the task setting changes or different OVOD model employed.
A natural starting point is the X-weighted fg regularizer in Fq. However, is task-dependent and requires re-tuning. To climinate this hyperparameter, we design a data-adaptive reweighting optimization objective:
min |KW V + |[[[W - Wo W
(5)
where I' E xddo is positive diagonal matrix
r = diag 1/4 1/4 $2
8//).
8i Σ
22
Intuitively, the regularization term || Wo applies weight of 8; to the squared & norm of the i-th row of (W - Wo), since T 8 1/2 In this way, the objective balances new fitting information and prior knowledge in data-adaptive manner, while more detailed discussion of the benefits of this design deferred to the Appendix B.3
OPTIMIZATION AND OVERALL ALGORITHM
From Eq. taking the derivative with respect W leads to linear system. Let H = K K K dd xo which is symmetric and positive semidefinite in general, and becomes positive definite as long T has strictly positive diagonal. The unique minimizer is:
5

---

Published as a conference paper at ICLR 2026
W = H (K (2
(6)
In implementation, we never compute H explicitly; instead, we solve the linear system HW* V V+I using a numerical solver (e.g., linalg.. solve). Moreover, computing the solution does not require storing the full key-value matrices. In practice, it suffices to maintain only the aggregated statistics K' K, K V, and I, which can be obtained by summing the corresponding task-specific quantities:
K -K K K
I m
Σ
r
This additive update rule is similar in spirit the matrix update in (Wang al. 2021), and the detailed proof of equivalence is deferred to Appendix B.2 Although the above expressions are written as summations over all tasks from 1 T for convenience, they hold for any subset or combination of tasks within ... Our method is summarized in Alg.
EXPERIMENT
5.1
IMPLEMENTATION DETAILS
We conduct experiments two cross-domain few-shot detection benchmarks, CDFSOD (Fu et al., 2024) and OD1n (Li et al. 2022a), covering 19 few-shot tasks under significant distribution shift. We follow the standard K-shot setting with K {1,5, 10, 30, 50J. Further details of the datasets and training setups are provided in Appendix C.T
Evaluation metric. Following CDFSOD and ODinW-13, age Precision (AP) metric, computed over IoU thresholds from 0.50 to 0.95 across all object scales, for evaluation. In addition, use the model's AP on the COCO dataset measure its ability to retain the original detection performance.
adopt the standard COCO-style Aver-
5.2 MAIN RESULTS
To assess the trade-off between preserving prior knowl- edge and adapting to new tasks, use two metries: Retention Ratio (RR), A /Aalll//aaiiiiie /APeriginal which mea- sures how well the model retains its original ability; and Adaptation Gain Ratio (AGR), peeiitd /Affiictuned which evaluates adaptation to new tasks relative to full fine-tuning, Here, subscripts denote task type (old/new), and superscripts the model variant (origi- mal/editeedfinetunedd)
Table Few-shot results on ODin 13. Avg denotes the average perfor- mance across all 13 datasets.
Method
Avg
AGR
new
Base Model
48.4
59.7
93.3%
Adam-NSCLL
54.7
59.0
99.8%
90.6%
Our
57.9
58.5
98.0%
95 99
Onaele
Tables and summarize base performance, rep- resentative baselines: EWC (Kirkpatrick et al. 2017). Adam-NSCL (Wang et al.] 2021), and SD-LoRA (Wu al. 2025 well as our editing method (Ours).
BWC
58.5
56.5
94.8%
93.0%
S-sbol
AdammNSCI.
SD-LaRA
58.4
55.6
93.1%
92.8%
Ours
61.0
$7.9
97.0%
97.0%
Orsele
62.9
Among them, Table is is our main experimental result.
BWC
61.3
56.7
95.0%
92.5%
sbol
Adam NSCL
97.5%
90.6%
For reference, we also include FFN fine-tune on each target dataset as an oracle upper bound.
SD-LORA
61.1
55.8
93.5%
92.2%
57.8
96.8%
Oracle
66.3
Effectiveness target tasks. We report AGR to sure how effectively each method approaches full fine- tuning. On CDFSOD-6. our method reaches 99% (1-shot), 97% (5-shot), 96% (10-shot), 96%
(30-shot), and 95% (50-shot), clearly higher than baseline methods. On ODinW-13 it achieves 96%
(1-shot), 97% (5-shot), and 93% (10-shot), again surpassing the baselines. These results indicate that our editing strategy retains near-fine fine-tuning adaptation while being more efficient than prior approaches.

---

Published as a conference paper at ICLR 2026
Table 2: Few-shot results on CDFSOD. Avg denotes the average performance across all target datasets. "Oracle" refers to the results obtained by independently fine-tuning the FFN on each individual dataset, serving as an upper bound for performance.
Shots
Method
ArTaxOr
ClipartIK
DeepFish
DIOR
NEU-DET
UODD
Avg
COCO
RR
AGR
Base Model
12.8
49.1
28.6
4.5
1.2
10.1
17.7
39.7
FWC
9.5
53.5
30.6
10.8
7.4
8.2
20.0
57.6
96.5%
90.9%
1-shot
Adam-NSCL
12.4
51.5
32.1
5.8
1.4
6.9
18.4
59.0
98.8%
83.6%
SD-LoRA
13.4
28.9
10.9
10.7
19.8
52.5
87.9%
90.0%
12.1
54.8
33.6
Oracle
55.8
31.9
13.5
EWC
33,3
54.4
35.7
19.5
20.0
23.4
31.0
57.1
95.6%
78.3%
5-shot
Adam-NSCI.
33.8
29.
73.5%
SD-LoRA
62.9%
69.4
57.9
37.6
26.1
20.2
19.5
38.5
95.5%
97.2%
Oracle
60.2
35.1
29.4
IWC
56.5
39.9
24.4
74.4%
10-shot
Adam-NSCL
72.2
52.8
34.6
74.8%
SD-LoRA
22.6
$2.6
32.0
18.2
20.5
24.2
28
52.7
88.3%
66.2%
Ours
71.6
59.5
38.7
32.4
22.8
20.9
41.0
56.8
95.1%
95.6%
Oracle
72.3
61.4
36.9
37.1
25.0
24.4
42.9
EWC
35.1
54,9
38.1
23.1
18.3
32.9
90.6%
67.3%
30-shot
Adam-NSCL.
75.1
53.5
37.5
13.2
7.6
32.6
57.9
97.0%
66.75
SD-LoRA
15.4
50.5
35.6
21.4
21.3
27.5
28.6
50.4
84.4%
58.5%
Ours
26,5
58.1
39.9
45.2
32.2
26.9
46.7
55.1
92.3%
95.5%
Oracle
77.8
61.1
44.3
49.7
35.1
25.6
.88.9
EWC
46.2
52.0
36.0
23.8
16.6
27.1
33.6
52.8
88.4%
66.5%
50-shot
Adam-NSCL
79.7
52.9
34.9
20.5
12.3
13.9
35.7
57.2
95.8%
70.7%
SD-LoRA
3.9
52.8
33.3
22.0
17.5
30.4
26.7
51.6
86.4%
52.95
Ours
79.5
58.6
35.4
51.2
34,5
28.9
48.0
54.5
91.3%
95.0%
Oracle
81.9
62.4
40.2
54.0
35.9
28.4
50.5
Preservation of prior capability. We further evaluate the Retention Ratio (RR) to measure how much COCO performance preserved af-
Table 3: Overall performance on all 19 datasets (CDFSOD-6 + ODinW-13). "Avg" denotes the average performance across all 19 datasets.
ter editing. Our method maintains high RR across different shots (most exceeding 95%), indicating that even when achieving very high AGR on new tasks, the model still retains the vast majority of its original COCO capability. This demonstrates that our edits not only adapt effectively to novel domains but also avoid catastrophic forgetting on the source dataset.
Shots
Methol
CDESOD
ODia
Avg.
COCO
RK
AGR
17.7
59.7
SD-LoRA
18.2
58.1
45.5
53.4
89.4%
94.2%
96.7%
Oracle
22.0
60.4
48.3
SD-LoRA
23.9
Ouns
37.3
6022
$3.0
58.0
97.2%
93.5%
10-hot
SD&LARA
25.0
614
49.9
$1.8
86.8%
84.7%
Flexibility. An additional advantage of our ap- proach lies in its flexibility. Without any extra training, we can directly combine the results from above and apply Eq, to integrate the capabilities across all 19 tasks. As shown in Table 3 our method achieves high AGR (mostly above 95%) on new tasks while maintaining strong RR (also above 95%) on COCO, indicating that a single edited model can effectively adapt to all 19 datasets without retraining.
39.6
93.5%
Oracil
42.8
66.3
58.9
5.3 ABLATION STUDY
To evaluate generality under different task volumes, we gradually increase the number CDFSOD target datasets by treating the first k E [l...., 6} as new tasks while keeping COCO the old task. For the manual-tuning baseline, we use 3 with fixed Ae {1, 5, 10, 15, 20) and compute (i) COCO mAP for old-task retention and (ii) the mean mAP across the k new tasks. We then average these results across all & values to obtain an overall old-task mean and new-task mean. In contrast, our method directly applies the auto-balanced objective in Eq. without any manual hyperparameters.

---

Published as a conference paper at ICLR 2026
New Tasis (tuaed A)
Old Task (tuned
Balamee
53
Task
52-
25.1
20.2
110
ArPaaX
Glipart
DaspPat
DIOR
NEL: DET
GOOD
Avorage
10
15
Grounding DINO
(b) mAP of SD-LoRA CDFSOD-6
Figure 2: (a) Manual tuning vs, Auto-Balanced Optimization, with the horizontal axis representing different values of x, (b) mAP of continual learning methods CDFSOD-6 across 20 random dataset orders. The error bars on SD-LoRA indicate the variance across different sequences.
Table 5: Continual learning methods combined with ours CDFSOD 10-shot. This combination leads to significant improvements on the new CDFSOD tasks while also enhancing the retention of the original COCO task performance.
Method
ArTaxOr
ClipartIK
DeepFish
NEU-DET
Avg
COCO
AGR
EWC
21.3
$6.5
39.9
24.4
23.3
25.7
31.9
55.5
93.0%
74.4%
EWC+Ours
73.3
59.3
39.6
31.6
23.8
22.3
41.7
55.7
93.3%
97.2%
SD-LoRA
22.6
52.6
32.0
18.2
20.5
24.2
28.4
52.7
88.3%
66.2%
SD-LoRA+Ours
69.5
$7.9
34.8
26.4
22.0
20.1
38.5
56.0
93.8%
89.7%
Oraele
72.3
61.4
36.9
37.1
25.0
24.4
42.9
Generality across task volumes. Fig. 2a shows that the red dashed line (Auto-Balanced) con- sistently exceeds the bars corresponding to fixed-X settings in terms of new-task mAP, while si- multaneously maintaining competitive COCO performance (right axis; compressed scale). This demonstrates that Auto-Balanced Optimization generalizes well across varying task volumes and outperforms careful manual tuning, all without the need for parameter search.
Order-agnostic learning. Fig. 2b evaluates continual learning methods on the six CDFSOD datasets by randomly shuffling their order and repeating the process 20 times. Results show that conventional continual learning is highly sensitive to task order, as indicated by the large error bars for SD-LoRA. In contrast, our method aggregates tasks according to Eq. 7 achieving stable perfor- mance regardless of task order.
Table 4: Ablation on exponent
As shown in Table we conduct an ablation study on the exponent a in r diag(sf, -8 .., sG), where s; - - kig The results indicate that smaller exponents (e.g., 1 or {) yield consistently better performance on CDFSOD-6 (5-shot) while maintaining comparable COCO accuracy.
in I CDESOD 5-shot.
CDFSOD
COCO
1
0
0
1/2
36.6
57.0
1/4
38.5
57.0
ABME combined with continual learning. Table 5 shows that when applying our method for model editing on top of conventional continual learning approaches, significant improvements are achieved. Under the CDF- SOD 10-shot setting, EWC+Ours improves by 9.8 mAP. SD-LoRA+Ours improves by 10.1 mAP, and further gains are also observed on the COCO old tasks. Moreover, we trained the GLIP model on CDFSOD under the 1/5/10-shot settings using our method. As shown Table 6 our method achieved an average RR of 97.9% and an AGR of 95.7% across the three settings, demonstrating its generalizability across different models.
1/8
38.5
56.9
Ablation on Editing Regions. Model editing in LLMs typically requires locating specific layers for knowledge injection. Following this, we screened FFN layers across different modules (e.g., backbone encoder-decoded Mitchell et al, (202I). As shown in Table 7 editing FFN layers across the entire architecture (Ours) achieves the best balance between new-task adaptation and old-task retention, outperforming edits restricted to specific sub-modules.
8

---

Published as a conference paper at ICLR 2026
Table GLIP few-shot results on CDFSOD. We apply our ABME method on GLIP under the 1, 5, and 10-shot settings. The results show that our approach achieves consistently high RR and AGR.
Shots
Method
ArTaxOr
ClipartIK
DeepFish
DIOR
NEU-DET
LODD
Avg
COCO
RR
AGR
Base Model
12.0
52.5
35.7
6.6
1.7
5.9
19.1
59.4
Ours
32.9
55.5
36.1
16.1
11.1
4,2
26.0
58.8
99.0%
98.1%
1-shot
Oracle
33.8
55.2
36.2
17.3
11.9
4.5
26.5
Ours
518
55.1
41.5
28.1
20.5
14.1
35.2
58.1
97.8%
94.6%
5-shot
Oracle
54.7
54.9
43.7
31.3
21.2
17.1
37.2
Ours
514
56.5
39.1
34.1
23.5
18.6
37.2
57.6
97.0%
94.4%
10-shot
Oracle
53.1
56.7
41.0
38.3
25.4
21.7
39.4
Table 7: Ablation on screening FFN layers different modules (Grounding DINO, 5-shot CDF- SOD). We report New-task mAP and COCO mAP. "Back." refers to FFNs in the Backbone, and "Enc-Dec" to FFNs in the Encoder-Decoder.
Edited FFNs
Back/Shalloo)
Back./mid)
Back.llast)
Back, (AlI)
Ene-Dec (All)
All FFNS (Ours)
New-task
23.4
22.3
20.6
35.1
36.2
38,5
Old-task 56.5
59.6
59,3
58.3
56.5
58.4
$7.0
Impact Editing Location (FFN vs. Self-Attention). To validate our design choice of targeting FFN layers, we compare the performance of ABME when applied to FFN versus Self-Attention (SA) layers. As shown Table g editing FFN layers yields a superior adaptation performance of 38.5 mAP on CDFSOD, outperforming SA editing (36.3 mAP) by 2.2 mAP. This confirms that FFN layers are the more effective site for injecting semantic knowledge worth noting that our method still recovers 93.6% of the independent fine-tuning performance (Layer Oracle) even on SA layers. This demonstrates that while FFN is the optimal location, our auto-balanced algorithm generalizes robustly across different network structures.
OVOD models. However,
Table 8: Comparison of editing FFN vs. SA layers CDFSOD (5-shot). "Layer Oracle" denotes the upper bound achieved by independently fine-tuning the specific layer type on each dataset.
Editing Target
CDESOD
Layer Oracle
% of Oracle
COCO
Base Model
17.7
59.7
Self-Attention Layers
36,3
38.8
93.6% 97.2%
57.8
FFN out Layers
38.5
39.6
57.0
5.4 SCALABILITY AND VERSATILITY ANALYSIS
Scalability to large-scale heterogeneous domains. To simulate extreme domain shifts and long- term adaptation, extended CDFSOD to 90 sequential tasks using ImageCorruptions (Michaelis et al. 2019). We specifically selected the most severe intensity (level 5 of 5) to challenge the model limits. As shown in Table 9 ABME achieves 23.8 mAP, closely matching the Oracle (26.4 mAP) while retaining 96.3% of the original COCO performance.
Generalization to CLiP-based Classification. To verify cross-architecture generalization, we ap- plied ABME to CLIP (Radford et al.] (021)) (ViT-B/16) (Dosovitskiy 2020) on 11 diverse datasets under 5-shot setting. As shown in Table TO our unified model achieves 74.1% accuracy, outper- forming specific continual learning baselines (ZSCL (Zheng et al. 20236), WiSE-FT (Wortsman et al. 2022)) and the Zero-shot Base (65.3%). recovers 992% of the Oracle(FFN out) perfor- mance (80.4%), demonstrating effective adaptation beyond object detection. Oracle (FFN-out) and Oracle (Full) refer to independent fine-tuning per dataset on FFN output layers and all parameters, respectively. Specific dataset details provided in the Appendix C.I
Visualization results. As shown Fig. 3 and Fig. the Base model (Grounding DINO pretrained) struggles under domain shifts, resulting in many missed detections, false positives, and heavily overlapping features. EWC and Adam-NSCL provide limited improvements in detection and fea- ture separation, but still fail to generalize well. In contrast, stronger detection of novel objects, reduces false detections, and produces more compact and clearly separated feature clusters, demonstrating superior robustness against domain shifts.
proposed ABME method achieves

---

Published as a conference paper at ICLR 2026
Table 9: Performance on 90 sequential tasks with extreme domain shifts (15 corruption types). De- tailed average performance across the 15 corruption types is provided in Table 12
Table 10: Generalization to CLIP on 11
classification datasets (5-shot).
Method
Avg Ace (%)
Base (Zero-shot)
65.3
Method
90 Tasks
COCO
ZSCL
67.4
Base Model
10.1
59.7
WiSE-FT
71.9
Adam-NSCL
20.8
57.2
Ours
74.1
Ours
23.8
57.5
Oracle(FFN out)
80.4
Oracle
26.4
Oracle(Full)
77.5
Base
EWC
Our
GT
Figure 3: Comparison of Grounding DINO pretrained model (Base), EWC, our proposed method
(Ours), and ground truth (GT) across different datasets.
(a) Base Model
(b) Adam-NSCL.
(c) Our
Figure 4: t-SNE visualization results of the Base model (Grounding DINO pretrained), Adam-
NSCL,, and our ABME method on the DIOR dataset, all trained on CDFSOD with 10-shot setting.
CONCLUSION
We proposed an auto-balanced model editing framework for OVOD that treats few-shot adaptation as knowledge injection into FFN layers and introduces method that eliminates the need for tedious hyperparameter tuning by automatically balancing new and old knowledge. Our method achieves performance close to full fine-tuning its compact key-value design supports flexible task combinations without retraining. We believe this bliting-based perspective offers lightweight and scalable alternative to traditional continual learning pipelines, and we hope it can inspire a new paradigm of continual learning for visual tasks, ultimately enabling robust and adaptive open-world perception systems.
new tasks while retaining most original capabilities, and
10

---

Published as a conference paper at ICLR 2026
ACKNOWLEDGEMENTS
This work is supported in part by the National Natural Science Foundation China (62192783, 62276128, 62406140), Young Elite Scientists Sponsorship Program by China Association for Science and Technology (2030NRC001), the Key Research and Development Program of Jiangsu Province under Grant (BE2023019) and Jiangsu Natural Science Foundation under Grant (BK20221441, BK20241200), The authors would like to thank Huawei Ascend Cloud Ecological Development Project for the support of Ascend 910 processors.
ETHICS STATEMENT
All authors have read and agree to abide by the ICLR Code of Ethics. This work does not involve interventions with human participants or personally identifiable information. We use only publicly available datasets under their original licenses and follow the terms of use. Potential risks and our mitigations are summarized below:
We do not collect release any personal data. When showing quali- tative examples, all images/videos are from public datasets; any sensitive content is filtered.
Privacy & Security.
Bias & Fairness. We report results multiple benchmarks and provide detailed settings to facilitate external auditing. We acknowledge possible dataset biases and encourage follow- up evaluation on broader demographics and domains.
Dual Use / Misuse. The method could be misused to enable undesired large-scale labeling or surveillance. To reduce misuse, we release only research artifacts (code/configs) and exclude any tools for scraping or re-identifying individuals.
Legal Compliance. We comply with licenses of all third-party assets (code, models, and datasets) and cite their sources. Any additional third-party terms are respected.
Research Integrity. We document preprocessing, training recipes, and evaluation proto- cols; random seeds and hyperparameters are provided to enable reproducibility.
Where applicable, institutional review information is withheld for double-blind review and can be provided after acceptance.
REPRODUCIBILITY STATEMENT
We provide detailed training configurations, including hyperparameters and optimization settings, in the main paper and Appendix. In addition, we will release code to ensure reproducibility, covering: (i) random seeds; (ii) full data preprocessing and splits: (iii) code structure with scripts reproduce the main tables and figures; (iv) checkpoints and logs for the primary models.
REFERENCES
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, and Tinne Tuytelaars. Memory aware synapses: Learning what (not) forget. In Proceedings of the European confer- ence on computer vision (ECCV), pp. 139-154, 2018.
Rahaf Aljundi, Min Lin, Baptiste Goujaud, and Yoshua Bengio. Gradient based sample selection for online continual learning. Advances in neural information processing systems, 32, 2019.
Xinyang Chen, Sinan Wang, Bo Fu, Mingsheng Long, and Jianmin Wang. Catastrophic forgetting meets negative transfer: Batch spectral shrinkage for safe transfer learning. Advances in neural information processing systems, 32, 2019.
Prakash Chandra Chhipa, Kanjar De, Meenakshi Subhash Chippa, Rajkumar Saini, and Marcus Liwicki. Open-vocabulany object detectors: Robustness challenges under distribution shifts. In European Conference on Computer Vision, pp. 62-79, Springer, 2024.
Damai Dai, Li Dong, Yaru Hao, Zhifang Sui, Baobao Chang, and Furu Wei. Knowledge neurons in pretrained transformers. In ACI., pp. 8493-8500 2022.

---

Published as a conference paper at ICLR 2026
Alexey Dosovitskiy. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.
Arthur Douillard, Alexandre Ramé, Guillaume Couairon, and Matthieu Cord. Dytox: Transformers for continual learning with dynamic token expansion. In Proceedings tfe IEEE/CVF conference on computer vision and pattern recognition, pp. 9285-9295, 2022.
Junfeng Fang. Houcheng Jiang, Kun Wang, Yunshan Ma, Shi Jie, Xiang Wang, Xiangnan He, and Tat-Seng Chua. Alphaedit: Null-space constrained knowledge editing for language models. arXiv preprint wrXiv22410.02355. 2024.
Yuqian Fu, Yu Wang. Yixuan Pan, Lian Huai, Xingyu Qiu, Zeyu Shangguan, Tong Liu, Yanwei Fu, Luc Van Gool, and Xingqun Jiang. Cross-domain few-shot object detection via enhanced open-set object detector. In European Conference on Computer Vision, pp. 247-264. Springer, 2024.
Mor Geva, Roei Schuster, Jonathan Berant, and Omer Levy, Transformer feed-forward layers are key-value memories. arXiv preprint arXiv:2012. 2020.
Xiuye Gu, Tsung- Yi Lin, Weicheng Kuo, and Yin Cui. Open-vocabulary object detection via vision and language knowledge distillation. arXiv preprint arXiv:2104.1392) 2021.
Guy Hacohen and Tinne Tuytelaars. Forgetting order of continual learning: Examples that are learned first are forgotten last. arXiv preprint arXiv:2406.09995, 2024.
Zeyu Huang, Yikang Shen, Xiaofeng Zhang, Jie Zhou, Wenge Rong. and Zhang Xiong. Transformer-patcher One mistake worth one neuron. ArXiv, abs/2301.09785. 2023.
Sadia Ilyas, Ido Freeman, and Matthias Rottmann. On the potential of open-vocabulary models for object detection in unusual street scenes. In European Conference on Computer Vision, pp- 201-217. Springer, 2024.
Saurav Jha, Shiqi Yang, Masato Ishii, Mengjie Zhao, Christian Simon, Muhammad Jehanzeb Mirza, Dong Gong. Lina Yao, Shusuke Takahashi, and Yuki Mitsufuji. Mining your own secrets: Dif- fusion classifier scores for continual personalization of text-to-image diffusion models. arXiv preprint arXiv:2410.00700, 2024.
Houcheng Jiang, Junfeng Fang, Ningyu Zhang, Guojun Ma. Mingyang Wan, Xiang Wang, Xiangnan He, and Tat-seng Chua. Anyedit: Edit any knowledge encoded in language models. arXiv preprint arXiv:2502.056/1 2025.
Haeyong Kang. Rusty John Lloyd Mina, Sultan Rizky Hikmawan Madjid, Jaehong Yoon, Mark Hasegawa-Johnson, Sung Ju Hwang, and Chang Yoo. Forget-free continual learning with win- ning subnetworks. In International conference machine learning, pp. 10734-10750 PMLR, 2022.
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska Baaaa--aaiinaa,. et al. Overcom- ing catastrophic forgetting neural networks. Proceedings of the national academy of sciences, 114(13):3521-35) -326,, 2017.
Guannan Lai, Yujie Li, Xiangkun Wang. Junbo Zhang, Tianrui Li, and Xin Yang. Order-robust class incremental learning: Graph-driven dynamic similarity grouping. In CVPR, pp. 4894 4904, 2025.
Frantzeska Lavda, Jason Ramapuram, Magda Gregorova, and Alexandros Kalousis. Continual clas- sification learning using generative models. arXiv preprint 1010v 2018.
Chunyuan Li, Haotian Liu, Liunian Li, Pengchuan Zhang, Jyoti Aneja, Jianwei Yang, Ping Jin, Houdong Hu, Zicheng Liu, Yong Jae Lee, et al. Elevater: A benchmark and toolkit for evaluating language-augmenten visual models. Advances in Neural Information Processing Systems, 35: 9287-9301, 2022a.
12

---

Published as a conference paper at ICLR 2026
Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang. Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al. Grounded training. In Proceedings of the IEEE/CVF conference on computer vision and pattemn recognition, pp. 10965-10975, 2022b.
pre-
Yu Li, Xingyu Qiu, Yuqian Fu, Jie Chen, Tianwen Qian, Xu Zheng, Danda Pani Paudel, Yanwei Fu, Xuanjing Huang, Luc Van Gool, et al. Domain-rag: Retrieval-guided compositional image generation for cross-domain few-shot object detection. arXiv preprini arXiv:2506.05872. 2025.
Tsung-Yi Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pp. 740-755. Springer, 2014.
Changhan Liu, Xunzhi Xiang, Zixuan Duan, Wenbin Li, Qi Fan, and Yang Gao. DON't NEED RE- TRAINING: A mixture of DETR and vision foundation models for cross-domain few-shot object detection. In The Thitt Annual Conference on Neural Information Processing Systems, 2025.
Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang. Jie Yang, Qing Jiang, Chunyuan Li, Jianwei Yang. Hang Su, et al. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. European conference on computer vision, pp. 28-55. Springer, 2024.
Aman Madaan, Niket Tandon, Peter Clark, and Yiming Yang. Memory-assisted prompt editing to improve GPT-3 after deployment. arXiv preprint arXiv:2201.06m0 060099 2022.
Arun Mallya, Dillon Davis, and Svetlana Lazebnik. Piggyback: Adapting single network to mul- tiple tasks by learning to mask weights, In Proceedings of the European conference on computer vision (ECCV), pp. 67-82, 2018
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. Locating and editing factual associations in gpt. In NeurlPS, pp. 17359-17377 2022a.
Kevin Meng, Arnab Sen Sharma, Alex Andonian, Yonatan Belinkov, and David Bau. Mass-editing memory a transformer. arXiv preprint arXiv:2210.07229, 2022b.
Claudio Michaelis, Benjamin Mitzkus, Robert Geirhos, Evgenia Rusak, Oliver Bringmann, Alexan- der S. Ecker, Matthias Bethge, and Wieland Brendel. Benchmarking robustness in object detec- tion: Autonomous driving when winter is coming. arXiv preprint arXiv: (907.07790 2019.
Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, and Christopher Manning. Fast model editing at scale. arXiv preprint arXiv:2110.11309 2021.
Oleksiy Ostapenko, Pau Rodriguez, Massimo Caccia, and Laurent Charlin. Continual learning via local module composition. Advances in Neural Information Processing Systems, 34:30298- 30312, 2021.
Jiancheng Pan, Yanxing Liu, Xiao He, Long Peng, Jiahao Li, Yuze Sun, and Xiaomeng Huang. Enhance then search: An augmentation-seard strategy with foundation models for cross-domain few-shot object detection. In Proceedings of the Computer Vision and Pattern Recognition Con- ference, pp. 1548-1556, 2025.
Ameya Prabhu, Philip HS Torr, and Puneet Dokania. Gdumb: simple approach that questions our progress in continual learning. In European conference computer vision, pp. 524-540.
Springer, 2020.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pp. PmLR, 2021.
Sylvestre-Alvin Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert. icarl: Incremental classifier and representation learning. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 2001-2010, 2017.
13

---

Published as a conference paper at ICLR 2026
Gobinda Saha and Kaushik Roy. Continual learning with scaled gradient projection. In Proceedings of the AAAI conference on artificial intelligence, volume 37, pp. 9677-9685. 2023.
Peng Wang, Zexi Li, Ningyu Zhang, Ziwen Xu, Yunzhi Yao, Yong Jiang, Pengjun Xie, Fei Huang, and Huajun Chen. Wise: Rethinking the knowledge memory for lifelong model editing of large language models. In NeurIPS, pp. 53764-53797, 2024.
Shipeng Wang, Xiaorong Li, Jian Sun, and Zongben Xu. Training networks in null space of feature covariance for continual learning. In Proceedings of the IEEE/CVF conference on Computer Vision and Puttern Recognition, pp. 184-193, 2021.
Zhiwu Wang. Yichen Wu, Renzhen Wang, Haokun Lin, Quanziang Wang, Qian Zhao, and Deyu Meng. Singular value fine-tuning for few-shot class-incremental learning. arXiv preprint arXiv:2503. 2025.
Mitchell Wortsman, Gabriel Ilharco, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo Lopes, Hannanch Hajishirzi, Ali Farhadi, Hongseok Namkoong, et al. Robust fine-tuning of zero-shot models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1959-79711 2022.
Yichen Wu, Hongming Piao, Long- Kai Huang, Renzhen Wang, Wanhua Li, Hanspeter Pfister, Deyu Meng, Kede Ma, and Ying Wei. Sd-lora: Scalable decoupled low-rank adaptation for class incre- mental learning. In ICLR, pp. 58337-5833555 2025.
Mengqi Zhang, Xiaotian Ye, Qiang Liu, Pengjie Ren, Shu Wu, and Zhumin Chen. Knowledge graph enhanced large language model editing. arXiv preprint arXiv:2402.13593 2024.
Ce Zheng, Lei Li, Qingxiu Dong, Yuxuan Fan, Zhiyong Wu, Jingjing Xu, and Baobao Chang. Can we edit factual knowledge by in-context learning? arXiv preprint arXiv:2305. 12740, 2023a.
Zangwei Zheng, Mingyuan Ma, Kai Wang, Ziheng Qin, Xiangyu Yue, and Yang You. Preventing zero-shot transfer degradation in continual learning of vision-language models. In Proceedings of the JEEE/CVF international conference on computer vision, pp. 19125-19138 2023b.
14

---

Published as a conference paper at ICLR 2026
A
THE USE OF LLMS
We used ChatGPP-44 to polish our manuscript, using the following prompt:
want you to act as an expert in scientific writing. will provide you with some paragraphs in English and your task is to improve the spelling, grammar, clarity, conciseness, and overall readability of the text provided, while breaking down long sentences, reducing repetition and increasing logic. You should artificial intelligence tools, such as natural language processing, rhetorical kncwledge, and your expertise in effective scientific writing techniques to reply. Provide the output as table in readable mode. The first column is the original sentence, the second column the sentence after editing, and the third column provides explanation of your edits and reasons. Please edit the following ng text in scient fic tone:
B THEORETICAL ANALYSIS
B. ANALYSIS OF OPTIMIZATION FUNCTIONS
The optimization problem is given by:
min W
|KW VII [[T(W - Wo)l
(8)
where I is diagonal matrix defined as:
r diag(s 1/4 1/4 82 s.... 1/4). 8t Σ ke
(9)
To find the optimal solution, we take the first-order derivative and it equal zero:
KT KT(KW- - V) + ( Wo) =
(10)
(K"K ) Wo) KTV- KWO
(11)
AW = (K* K + +1 - KT (V KKW)
(12)
B.2 MATRIX UPDATING RULE
We have the following expressions for K and V:
K
Vi
K₂
1/
K
V
KT
Vr_
For KT K, we can express as:
KT
K
Σ KF K₁
(13)
For KTV. have:

---

Published as a conference paper at ICLR 2026
KT
K
(14)
B.3 THEORETICAL ANALYSIS OF THE AUTO-BALANCING MECHANISM
The objective function is formally defined as: min
|| [KW - V)Ila [[ Wolli W
(15)
where the diagonal matrix is set I: s. feature energy of the imput key matrix K. In practice, the raw feature energy 8; inherently exhibits extreme variations across different feature dimensions and tasks (e.g., varying from 10a to 10 in our CDFSOD 5-shot experiments). This creates a severe scale imbalance for optimization: a fixed scalar À would be either too negligible for high-energy dimensions too restrictive for low-energy ones. To resolve this, we derived our design based on the optimization curvature to automatically normalize these scales.
1/4 with s; E, ki representing the accumulated
Feature-wise Adaptivity via Curvature Analysis. We address the optimization imbalance by ana- lyzing the Hessian (curvature) of the objective function L.
Data Term Curvature: The Hessian of the first term KW V with respect W is 2KT K. Suppose the input feature K has a magnitude scale Mg. The curvature of this term scales quadratically, i.e., (MM)). This creates extremely steep optimization landscapes for high-energy features, leading dominance in updates not balanced.
Adaptive Regularization: The Hessian of regularization term is 2r Since the feature energy &; scales with (MM), our design P s ((M)) (as 2 x /st Mg).
1/4 implies that 2 scales linearly with
This formulation achieves a critical balance mechanism:
The regularization strength 2 grows with feature magnitude (O(Ma)), pro- viding necessary constraints for strong features compared to fixed scalars.
Adaptivity:
Plasticity: The regularization grows slower than the data term (O(M&) ((M2)) This "sub-quadratic" scaling prevents the regularization from becoming over-rigid (which would 1/2 where 2 O(M2)), thereby allowing sufficient injection of new happen if a, 8
knowledge even for high-signal features.
C EXPERIMENT
state-of-the-art open- vocabulary object detector that unifies grounding with strong detection performance. We also evalu- on GLIP (Li al. (0022b), Lasion-language pre-training model that aligns detection with phrase grounding, in order to verily the generality of our auto-balance strategy across different models and task scales.
In experiments, we primarily adopt Grounding DINO (Liu 2024).
For fine-tuning, we update only the output layers of all FFN modules. The batch size is set to 8 for Grounding DINO and 2 for GLIP, and we use the AdamW optimizer with a learning rate of 10-4, Training is conducted for 18 epochs on most datasets, with slight adjustments under different shot settings to achieve better adaptation.
C.1 TRAINING DETAILS
Few-Shot Object Detection (CDFSOD) benchmark (Fu et al. 2024 uses MS MS-COCO (Lin et al. 2014) as the source domain and includes six heterogeneous tar- get domains: ArTaxOr, ClipartIK, DIOR, DeepFish, NEU-DET, and UODD. These domains differ substantially in visual style, resolution, and object distribution, providing a challenging setup for cross-domain transfer.
CDFSOD.
The
2

---

Published as a conference paper at ICLR 2026
Table 11: Few-shot results on CDFSOD 5-shot using Grounding DINO-B. The base model the pre-trained Grounding DINO-B without fine-tuning. Methods marked with denote the final model obtained by sequential fine-tuning on a single model, evaluated across all datasets.
Method
Ar TaxOr
ClipartIK
DeepFish
DIOR
NEU-DET
UODD
Average
COCO
Base model
12.8
49.1
28.6
4.5
1.2
10.1
17.7
59.7
FFN Wout only
69.2
60.2
35.1
29.4
22.2
21.5
39.6
Fully fine-tune
70.3
59.3
37.0
29.4
22.3
24.3
40.5
Fully fine-tune!
52.4
51.1
35.2
22.6
19.8
22.1
33,9
47.9
W-13. ODin (Li et al. 2022a) is a subset of the ELEVATER benchmark (Li et al. 2022a), consisting of 13 diverse object detection tasks drawn from various open-world datasets. The benchmark emphasizes robustness domain shifts and serves as a standard evaluation for open- vocabulary detectors.
Few-shot setting. Together, CDFSOD and ODinW-13 form 19 few-shot tasks with clear dis- tribution shifts, on which open-vocabulary detectors typically experience performance degrada- tion resembling out-of-distribution scenarios. We follow the standard K-shot protocol with K e {1,5, 10, 30, 50), where K labeled images per class are sampled as the support set and the re- maining images used for evaluation. For fair comparison, all baseline methods are consistently applied to the output layers across all FFN modules of the model.
EWC. For Elastic Weight Consolidation (EWC) (Kirkpatrick et al. 2017). we introduce a quadratic penalty on parameter updates, weighted by the Fisher information estimated from the previous task. The regularization coefficient is set to Agwe 10000. We use the AdamW optimizer with a learning rate of 10-4 and batch size 8, training for 18 epochs on tasks. The overall objective can be written as
( = (0) + AEWC
ΣΣ Fes (0: 0r,)
(16)
2
where (0) denotes the standard loss on the current task, Fi.; represents the Fisher information of parameter 0; estimated from task i,, and 0r,, is the optimal parameter value obtained after training on task This regularization term penalizes large deviations from previously important parameters, thereby mitigating catastrophic forgetting across sequential tasks.
Adam-NSCL For Adam-NSCL (Null-Space Continual Learning with Adam) (Wang et al. 202 we constrain parameter updates to the approximate null space of previously learned features, follow- ing the SVD-based formulation. Specifically, we select the null-space basis U/ associated with the smallest singular values of A/. and adaptively choose Al with diagonal entries À E {A À S a ) where X is the smallest singular value. In experiments, set the hyperparameter = 40. Optimization is performed using the Adam optimizer with a learning rate of x 10-4, batch size of 8, and training for 18 epochs most tasks.
For SD-LoRA (Spectral Decay LoRA) (Wu al. 2025), adopt the same adapter structure as LoRA but additionally apply spectral decay regularization on the low-rank updates to improve stability across sequential tasks. The hyperparameters are set as follows: rank r 16, initial spectral decay coefficient Oinit = 1.0, dropout rate 0.0. LoRA learning rate 0.002, and LoRA weight decay 0.0. Training is performed for epochs on most tasks with a batch size of 8.
SD-LORA.
Training Details for CLIP Experiments. We evaluated our method on 11 diverse classification datasets, organized in the following order: Aircraft, Caltech 101, CIFARIO0, DTD, EuroSAT, Flow- ers 102, Food101, MNIST, OxfordPets, StanfordCars, and SUN397. For optimization, we standard- ized the training duration to 500 iterations per dataset. Regarding the scope of parameter updates, ABME method specifically targets the output projection layers of all FFN blocks within the VTT-B/16 (Dosovitskiy (2020) encoder, whereas the baseline methods (ZSCL (Zheng et al. 2023b) and WiSE-FT (Wortsman al. 2022 involve full-model fine-tuning.
3

---

Published as a conference paper at ICLR 2026
Table 12: Performance comparison of different methods under various corruptions on CDFSOD 5- shot. All values represent mean Average Precision (AP) in percentage. Note that Oracle is obtained by independent fine-tuning on each corrupted dataset (averaged over total datasets). Contin- ual learning setting follows sequential training protocol according to the table rows; specifically, within each corruption, it trains on ArTaxor, Cl DeepFish, DIOR, NEU-DET, and UODD sequentially.
Corruption
Base
Adam-NSCL.
Ours
Oracle
gaussian noise
18.0
21.6
23.0
shot_noise
8.1
18.1
21.6
23.1
impulse noise
8.7
18.4
22.1
defocus,bil blur
11.6
23.0
25.7
28.2
gluss_buu blur
11.4
24.2
27.1
30.3
motion. blur
9.3
20.5
22.7
24.3
zoum_blur
2.7
6.2
6.9
10.7
9.6
20.0
23.0
24.9
frost
10.1
20.3
22.9
24.2
fog
14.2
26.7
29.6
32.4
brightness
12.9
25.5
28.8
31.6
20.5
21.8
26.2
franfic.ramsform
12.0
24.9
28.6
33.3
pixelate
12.2
24.5
28.2
31.6
jpeg-compression
11.5
22,5
25.9
28.2
Average
20.8
23.8
26.4
COCO
59.7
57.2
57.5
D ADDITIONAL BASELINE COMPARISONS AND RESOURCE EFFICIENCY
D.1 COMPARISON WITH STATE-OF-THE-AN METHODS
To further validate the effectiveness of ABME, we included two additional state-of-the-art continual learning baselines: SGP (Saha & Roy 2023) and SVFCI. (Wang et al. 2025). We evaluated all methods under the CDFSOD 5-shot setup. As shown in Table ABME significantly outperforms these recent approaches.
Table 13: Extended comparison CDFSOD 5-shot. We incorporated two additional baselines: SGP and SVFCL. ABME achieves the best trade-off between plasticity (New-task mAP) and stabil- ity (Old-task mAP).
Method
New-task mAP
Old-task mAP
RR f
AGR
EWC (2017)
31.0
57.1
95.6%
78.3%
Adam-NSCL (2021)
29.1
57.8
96.8%
73.5%
SGP (2023)
32.6
46.1
77.2%
82.3%
SD-LoRA (2025)
24.9
54.2
90.8%
62.9%
SVFCL (2025)
33.2
52.9
88.6%
83.8%
ABME (Ours)
38.5
57.0
95.5%
97.2%
Oracle
39.6
59.7
D.2 RESOURCE CONSUMPTION ANALYSIS
We compared the training time, GPU memory usage, and extra storage cost of all methods on the CDFSOD 5-shot setup. All models were trained on the same hardware configuration. As detailed in Table 14 ABME maintains constant resource efficiency comparable to standard FFN fine-tuning, requiring only fixed 1.3 GB storage independent of the task count. In contrast, baselines hibit varying overheads: EWC accumulates storage costs as tasks are added (0.7 + 3.4 GB), while Adam-NSCL. requires increased memory for subsequent tasks compared to the initial stage
4

---

Published as a conference paper at ICLR 2026
(1640 - 1731 MB). SD-LoRA suffers from linear memory growth and the longest training time. Furthermore, our final optimization step (solving Eq. 5) typically completes in <10 seconds, incur- ring negligible operational overhead.
Table 14: Resource consumption analysis CDFSOD 5-shot. For metries formatted "Start End" (e.g. 932 - 977), the values correspond to the consumption recorded during the training of the first task and the final task, respectively, indicating the variation in resource usage tasks accumulate.
Method
Training Time (s)
GPU Memory (MB)
Extra Storage (GB)
CDFSOD (New)
COCO (Old)
SD-LoRA
1121
932
977
24.9
54.2
EWC
1044
1640
31.0
57.1
Adam-NSCL
1640
1731
1.1
ABME (Ours)
1029
1640
1.3
38.5
57.0
Oracle (FFN-out)
1024
1640
39.6
CORE IMPLEMENTATION CODE
We provide the core Py Torch implementation of the Auto-Balances Model Editing (ABME) algo- rithm below. For clarity, we present the solver for single layer. The input stats dictionary contains the accumulated sufficiency statistics extracted from the support set:
The autocorrelation matrix of keys KT K IRdia
The cross-correlation matrix K" T V E gd Xdout,
sum x, sum y: Sum of input keys and output values (for bias handling).
Total number of support samples.
import torch
Storch.no_grade def solve_abme_ (M_old, b_old, stats, eps=1e-8):
***
Apply ABME update to a single linear layer (FFN output).
Args:
WLold: Original weights [cut_dim, in_dim] b_old: Original bias [out_ dim]
11
stats: Dictionary containing K'T K, KTT v, etc.
12
13
out_dim, in_dim = shold
Load Statistics
kk: K*T K [in, in], kv: K"T [in, out]
kk statsl'kk' kk' ].to (M old.dtype)
20
kv statsl'ke I (M dlype) [loodddddypee reshape (in dim, 1)
21
SX statsl' summ'mmmm
22
sy stats(' sum_y' (Ww.oddddyypp.oodded.. reshape (1, out_dim)
torch.tensor (stats dtype-R_old.dtype
24
25
26
# 2. Construct Augmented System (incorporating bias)
corresponds to the augmented K'T matrix
29
A terch.zeros, dim 1, in_dim. 1), dtype=N_old.dtet Al: :liiddim, dim] = kk
30
31
Al al:in_din dim, in_dim: = sx
A[in_dim: :in_dim] sx.t()
din_dim, in_dim]

---

Published as a conference paper at ICLR 2026
corresponds to the augmented K'T matrix B torch.zeros zeros (in_dim + 1, cut_dim), dtype=w_old.dtypog B[:in Bl:in_dim, :1 kv
36
37
B[in_dim:, :] sy
40
41
3. Prepare Original Parameters [WO; b0]
WO aug torch. (1M_old.t() b_old. dim) dim=0)
45
46
4. Compute Auto-Balanced Reqularization Matrix (Gamma") 22)
Get diagonal energy s_i (K"T K)
49
S kk.diag()
50
51
Numerical stability
floor () eps torch.clamp (s, min-fleor)
55
# Our design: Gamma - diag (s"(4)) -> Gamma 22 = diag(aqre (s))
F = ttrcc..zrooe (in_dim + 1,), dtype=i_oldd Regularization for weights
Pl: Pl:in diml s.sqrt
58
P [lin_dim] torch. (n) Regularization bias
59
60
5. Solve the Linear System
Objective: (A + Gamma 22 W* (B + Gamma "2 WD) r_r = A torch. diag (P)
64
# LHS Matrix RHS = B wo_aug P.unqquccze (1) # RHS Term
65
67
Solve using Cholesky or (terch, linalg. W M_new_auu torch. torch. sole (A_reg, RHS)
68
69
Extract Updated Weights Hias
72
73
W new = W. new _ug_eeugllla__aaaeeaee | :in dim, :1.t() lout _dim, in din] b. new = new aug jin dim, :]
return W_new, benew
Listing 1: PyTorch implementation of the ABME update for single layer.


Reviews:
[{"review_id": "ejQOGknNfv", "reviewer": "Reviewer_sVhG", "summary": "This paper tackles the challenge of maintaining OOD robustness in Open Vocabulary Object Detection (OVOD). The authors propose Automatically Balanced Model Editing (ABME) to efficiently integrate new task knowledge while preserving pre-trained capabilities. ABME stores compact key–value representations and automatically balances old and new knowledge without retraining. It supports order-agnostic task insertion and removal, overcoming common continual learning limitations. Experiments show that ABME achieves a better trade-off between adaptation and retention than existing methods.", "questions": "See Weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. This paper is well-structured and easy to follow.\n\n2. The idea of model editing through compact key–value representations and automatically balancing old and new knowledge is interesting and innovative.", "weaknesses": "1. In Eq. (5), what is the explicit advantage of the design of $\\Gamma$? How does it achieve data-adaptivity? Could the authors provide experimental evidence or theoretical insights to support this design choice?\n\n2. From Tables 1–3 and Table 6, why do RR and AGR decrease as the number of samples (shots) increases? This trend appears to contradict the common expectation that model performance should improve with larger data sample sizes. Could the authors clarify the reason behind this behaviour?\n\n3.  Please provide a comparison of the computational cost (e.g., memory usage, training time) of the proposed method versus existing baselines.\n\n4. Does the proposed ABME method generalize to other models and tasks, such as image classification with CLIP? I believe that including additional experiments on such tasks could strengthen the validity and generality of the proposed approach.\n\n5. A simple approach to balance old and new knowledge is to use residual connections of weights. Could the authors demonstrate the superiority of ABME compared to this baseline, either through empirical results or theoretical justification?", "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": "ylR7h8Kw2C", "reviewer": "Reviewer_6N8D", "summary": "This paper presents a method for open-vocabulary object detection that reframes adaptation as a model-editing problem. Instead of standard fine-tuning or rehearsal-based continual learning, the proposed approach leverages compact key–value statistics extracted from feed-forward layers to encode new task knowledge. The method automatically balances adaptation and retention through a data-driven regularization mechanism, eliminating the need for manually tuned hyperparameters. By maintaining only aggregated statistics, the system supports scalable and order-agnostic task integration without storing original data. Empirical results on multiple few-shot detection benchmarks show that the approach achieves strong performance in new domains while effectively preserving prior knowledge. Conceptually, the work bridges ideas from knowledge editing in large language models to vision-based detectors, offering a lightweight and interpretable alternative for cross-domain adaptation.", "questions": "In section 4.2, the paper replaces the pre-defined scalar regularization weight $\\lambda$ with a data-adaptive diagonal matrix $\\Gamma$, which scales each feature dimension according to the key-vector energy. While this design is intuitively appealing and empirically effective, the underlying rationale is only briefly mentioned, and the referred Appendix B.2 seems missing. Could the authors elaborate on the specific motivation for this formulation.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. Framing few-shot detection as a model-editing problem is conceptually elegant and provides a refreshing perspective that clearly distinguishes this work from conventional fine-tuning and continual learning paradigms. \n2. The proposed method delivers a precise and effective solution to the central challenge of balancing adaptation and retention, offering a well-grounded formulation that is both simple and practical. \n3. Extensive experiments on ODinW-13 and CDFSOD demonstrate consistent gains in adaptation performance while maintaining strong retention of prior knowledge, reinforcing the robustness and practical value of the proposed approach.", "weaknesses": "While the paper presents an elegant and well-motivated formulation, it inherits the core assumption from LLM-based editing that knowledge primarily resides in FFN layers. The direct application of this assumption to vision detectors may not be fully validated, as the distribution of transferable representations could differ across modalities. The paper would be strengthened by a more detailed analysis or empirical study investigating whether FFN weights indeed serve as the main locus of visual 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": {}}, {"review_id": "faBAIWkK6U", "reviewer": "Reviewer_oBvk", "summary": "An innovative Auto-Balanced Model Editing (ABME) framework is proposed and applied to the domain of open-vocabulary object detection. By efficiently fine-tuning FFN layers, it achieves performance comparable to full model fine-tuning while effectively mitigating the issue of catastrophic forgetting. This research provides a novel reference solution for domain adaptation problems, demonstrating certain academic value and application potential.", "questions": "1.\\begin{equation}\n\\min_{W} \\|KW - V\\|_F^2 + \\|\\Gamma(W - W_0)\\|_F^2,\n\\end{equation}\n\n\n\\begin{equation}\n\\Gamma = \\text{diag}\\left(s_1^{1/4}, s_2^{1/4}, \\ldots, s_d^{1/4}\\right), \\quad s_i = \\sum_t k_{ti}^2.\n\\end{equation}\nWhat is the rationale behind the specific definition of $\\Gamma$? Why is $s_i^2$ not the  $s_i$? Is it derived from the least squares method? Have you considered the Gaussian kernel?\n\n\n2.Can authors provide a comparison with more recent competitors.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.The idea that transitioning the model editing technology from LLM to domain adaptation tasks in OVOD is motivating.\n\n2.The proposed use of a data-driven regularization matrix to replace manual hyperparameter tuning is good.\n\n3.The paper demonstrates clear logic, rigorous theoretical derivation.", "weaknesses": "1.There is no ablation on the choice of edited regions, like which layers of the FFN. To my knowledge, model editing at different layers in LLM has a significant impact.\n\n2.The author did not provide the code for the paper, which to some extent reduces the reproducibility and practical impact of the study.\n\n3.The baseline set is limited. The baselines in this paper , like Adam-NSCL(2021),EWC(2017) are too old and outdated.", "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": "a6HpyXdanA", "reviewer": "Reviewer_mH4j", "summary": "The paper proposes ABME, which extends OVOD models to new domains without retraining. ABME injects new knowledge into FFNs through compact KV representations. Experiments on CDFSOD and ODinW-13 show that ABME maintains over 95% of base performance while achieving up to 97% adaptation gains on new domains, outperforming baselines like EWC, Adam-NSCL, and SD-LoRA.", "questions": "1. What are the limits of automatic balancing, and could it fail when domain shifts are extremely large or heterogeneous?\n2. How would ABME perform in fully online continual learning with hundreds of sequential domains?\n3. Can the KV-based editing be extended to multimodal or temporal models like video-language detection?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. ABME introduces model editing to OVOD for the first time.\n2. ABME are validated across 19 datasets and multiple models (Grounding DINO, GLIP) with consistent performance gains.\n3. The paper is well-written. The charts and formulas look clear and well-designed.", "weaknesses": "1. The auto-balancing mechanism lacks deeper theoretical analysis beyond empirical validation.\n2. The approach assumes that FFN layers store the majority of knowledge, which might not hold for all architectures.\n3. Detailed memory–accuracy trade-offs of KV storage are not analyzed.\n4. The paper lacks broader open-world or long-term continual settings", "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)