K-DeCore: Facilitating Knowledge Transfer in Continual Structured Knowledge Reasoning via Knowledge Decoupling
Authors: Yongrui Chen
Yi Huang
Yunchang Liu
Shenyu Zhang
Junhao He
Tongtong Wu
Guilin Qi
Tianxing Wu
Venue: NeurIPS 2025 poster ()
Keywords: structured knowledge reasoning
continual learning
large language models
Primary area: applications
TL;DR: The first work to study continual learning across diverse structured knowledge reasoning tasks

Abstract: Continual Structured Knowledge Reasoning (CSKR) focuses on training models to handle sequential tasks, where each task involves translating natural language questions into structured queries grounded in structured knowledge. Existing general continual learning approaches face significant challenges when applied to this task, including poor generalization to heterogeneous structured knowledge and inefficient reasoning due to parameter growth as tasks increase. To address these limitations, we propose a novel CSKR framework, \textsc{K-DeCore}, which operates with a fixed number of tunable parameters. 
Unlike prior methods, \textsc{K-DeCore} introduces a knowledge decoupling mechanism that disentangles the reasoning process into task-specific and task-agnostic stages, effectively bridging the gaps across diverse tasks. Building on this foundation, \textsc{K-DeCore} integrates a dual-perspective memory consolidation mechanism for distinct stages and introduces a structure-guided pseudo-data synthesis strategy to further enhance the model's generalization capabilities.
Extensive experiments on four benchmark datasets demonstrate the superiority of \textsc{K-DeCore} over existing continual learning methods across multiple metrics, leveraging various backbone large language models.

K-DE CORE: Facilitating Knowledge Transfer in Continual Structured Knowledge Reasoning via Knowledge Decoupling
Yongrui Ch Yi Huang Yunchang Liu Shenyu Zhang
Tongtong Wu Guilin Qi Tianxing Wu 'Shooo of Computer Science and Engineering, Southeast University. China
Junhao Hel
KKey Laboratory of New Generation Artificial Intelligence Technology and its Interdisciplinary Applications (Southeast University), Ministry of Education, China
JIUTIAN Research, China Mobile
*Department of Computer Science and Technology, Tsinghua University, China
'Department of Data Science & AI, Monash University, Australia {yongruichen, tianxingmu)@seu. cn, huangyi@chinamen com
Abstract
Continual Structured Knowledge Reasoning (CSKR) focuses on training models to handle sequential tasks, where each task involves translating natural language questions into structured queries grounded in structured knowledge. Existing gen- eral continual learning approaches face significant challenges when applied to this task, including poor generalization to heterogencous structured knowledge and inefficient reasoning due parameter growth tasks increase. To address these limitations, we propose a novel CSKR framework, K-DECORE, which operates with fixed number of tunable parameters. Unlike prior methods, K-DECORE introduces a knowledge decoupling mechanism that disentangles the reasoning process into task-specific and task-agnostic stages, effectively bridging the gaps across diverse tasks. Building on this foundation, K-DECORE integrates a dual- perspective memory consolidation mechanism for distinct stages and introduces a structure-guie guided pseudo-date synthesis strategy to further enhance the model's 's generalization capabilities. Extensive experiments on four benchmark datasets demonstrate the superiority of K-DECORE over existing continual learning meth- ods across multiple metrics, leveraging various backbone large language models.
Introduction
Structured Knowledge Reasoning (SKR) aims to translate the natural language question into structured queries over discrete, structured knowledge -such as relational databases, knowledge graphs, and dialogue states. is essential for wide range of real-world applications, including legal judgment prediction [.) clinical decision support (2 3 and financial analysis 44. Recent advances in Large Language Models (LLMs) have demonstrated impressive capabilities for structured reasoning when provided with appropriate prompting or fine-tuning [5] 71.
However, most existing methods operate under static assumption that SKR tasks singular in type and remain unchanged throughout both the training and deployment phases. This assumption is misaligned with weal-world scenarios, where models must continuously adapt reasoning tasks across various forms of structured knowledge. For instance, a virtual assistant like Siri or Alexa must
"Corresponding authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Task
Task k-1
Task k
AFM
D
Question
Quastion
Question
Schema Filtering
toble column
Unutication
SQL
TOP
LLM
5- Exp
government, agenc
founders
[+)
create_cal
6.a
singer: sang
SPRECT COUNT|yong
[+
IN-CREATE CALL
14 DUIDNNACTT Nitholes]
[+
DOIN fourders
IAND gevermeett agency
Try shp
Try gap
FROM singer
Ruge gap
BLOCTTTT Notapho
huge shp
LOIN Sounders) m. 06d
Query Building
(ANI gevernment, Aganss
T__
Catastrophic Forgetting
(.0 founders
ACT Nicholas
(.GI) (oundery) mn 05d-9)
ACT Natasha 3
Figure 1: a) Overview the continual SKR task. The backbone L.M is frozen and only the PEFT module if tunable. b) SKR based knowledge decoupling. Schema filtering refines the scope of given schema and impacts various SKR performance. The differences between schema filtering in these SKR tasks are minimal, making it potential for knowledge transfer.
dynamically adjust its knowledge base handle variety of tasks such as managing calendar events, to-do lists, and smart home controls, each characterized by distinct schemas and query languages. Therefore, is essential to empower the model leverage knowledge transfer across different tasks during the continual learning process [8].
Recent works to continual SKR have primarily focused designing the training strategy for single type of structured knowledge (e.g., continual text-to-SQL) 99 10) or employing parameter- However, these methods often fail to generalize across heterogeneous structured knowledge and suffer from parameter growth proportional to the number of tasks, ultimately hindering reasoning efficiency.
efficient fine-tuning (PEFT) techniques to allocate task-specific parameters 11
12 K
We observe that schema filtering, i.e., identifying schema elements relevant to query construction, (b)) is shared, reusable component across diverse SKR tasks and significantly influences overall performance. We hypothesize that decoupling pattern filtering from downstream stages and unifying its input-output format across tasks enhance robustness to task-specific variations. This consistency facilitates knowledge transfer and improves SKR performance without requiring parameter growth or task-specific reviews. For query construction stages with high task variance, their contribution to SKR depends on the structural richness of replayed queries. We propose to automatically synthesize structurally diverse replay samples, enabling more informative replay memory usage within the same capacity budget.
In this paper, we propose K-DECORE (Knowledge DEcoupling for COntinual REasoning). novel continual SKR framework. K- DECORE comprises a backbone LLM and two lightweight PEFT modules: schema filter and query builder, responsible for capturing task- task-specific and task-agnostic knowledge, respectively. This decoupled design fosters forward and backward knowledge transfer. To mitigate the model's tendency to forget previously learned tasks, K-DECORE incorporates dual- perspective memory mechanism (Section 3.2) designed to retain replay samples from both schema and query structure viewpoints. Specifically, the schema memory captures representative schema instances, ensuring the filter maintains comprehensive coverage across tasks, while the query memory emphasizes preserving diverse query structures inherent SKR tasks. Furthermore, to enhance the model's generalization to unseen patterns, we introduce a query synthesis strategy that generates novel structured queries for replay. We evaluate K-DECORE task streams comprising four diverse SKR tasks, where it consistently outperforms strong baselines and achieves state-of offte--o-thearr results across multiple metrics. In summary, the contributions of this paper include:
We propose novel continual structured knowledge reasoning framework, leveraging knowl- edge decoupling enable effective knowledge transfer across diverse tasks. To the best of our knowledge, this is the first work to explore continual learning across heterogeneous SKR tasks.
We propose a dual-perspective memory construction mechanism that specifically synthesizes pseudo queries for novel structures, with the goal of enhancing the L.LM's generalization capability.
We curate task streams from four SKR benchmarks and conduct comprehensive experiments. Em- pirical results demonstrate that K-DECORF consistently surpasses strong baselines across multiple evaluation metrics, establishing its effectiveness in continual structured knowlegde reasoning.

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Tank
Task k-1
Task K
Query Structure
Neem (AND (IOIN [C1]
Question
Quistion
API
JOIN ((C1)) [E1]]]]
Unification
Unification
Schema-guueee Memory NC;
Unitication
LUM
Replay
Synthesized Query Structure
LM
PEFT
LUM
DS-likk Schema
DB- like B Schema 0
DB like Schema
Scherma Filteriing
Schema
db8
doo
LUM
PEFT
PERT
LUM
PLFT
Synthesized Query
SQL
Replay
SPARQL
Query Building
LUM
PEFT
Samples
Structure-guice Momory M
Pseudo
Question
Figure 2: The left panel presents the K-DeCore training framework, organized into two key stages for each SKR task: schema filtering and query building, each supported by specialized PEFT modules. By unifying the schema, the framework aims bridge the gap between tasks, effectively enabling the knowledge transfer. The right panel illustrates the creation process of structure-guided synthetic pseudo samples, designed to offer more structurally diverse set of examples.
2 Preliminary
Structured Knowledge Reasoning Given a natural language question Q and accessible structured knowledge S, such as database, dialogue state, or a knowledge graph, the goal Structured Knowledge Reasoning is to generate a structured query V Formally, this can be represented as: y = (o(2, S), where J'o denotes L.L.-based reasoner parameterized by 0.
In this paper, we focus on three commonly used types of structured knowledge: a) A database represented as (C, T), where C Ci,,..,Cy denotes the column names and T {ti..... tm} denotes the table names. b) A knowledge graph is generally composed of aubject-reedicat-obiee triples, expressed as { (ss p, o) |s E E,p E R, EUI), where & is the of entities, R is the set of relations, and I' is the set of types. c) A dialogue state is represented as collection of predefined intents paired with their corresponding slots, indicated by { {[I1, $1), (In, Sn)} where each I; represents an intention and s, denotes the associated slots.
K SKR tasks, denoted by (D-, D2 ,...., DK), Each task DD' consists of a training set and a test set: D = D U D To encourage fo continually acquire reasoning capabilities over diverse types of structured knowledge, for tasks D* and D) (k + j), the corresponding structured knowledge S(D)) and S(Di) are of different types, i.e., Type(S(D*)) + Type(S(Di)). Our objective is to ensure that fe performs well on each Diu after sequentially learning on all D for all l {l...., K}.
Problem Formulation Let So be trained sequentially
test
K-DECORE
Figure illustrates the overall architecture of the proposed K-DECORE, Is built upon a backbone LLM with parameter 9, and incorporates three PEFT modules: Pa. Ps, and P Throughout training, the backbone parameters 0 remain fixed; only the parameters of the PEFT modules are updated, enabling efficient adaptation the task while preserving the pretrained knowledge of the LLM. The entire method can be divided into two parts: continual knowledge decoupling (Section 3.1 and perspeeeeee memory construction (Section 3.2 which are described in detail below.
3.1 Continual Knowledge Decoupling
Unlike traditional continual learning methods 13 14 in general fields, our proposed K-DECORE innovatively bifurcates the reasoning process over the structured knowledge into two distinct yet synergistic phases: schema filtering and query building. The schema filtering phase designed to distill the essential schema components S C necessary for constructing the final query y from

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the initial schema S. Subsequently. the query building phase focuses on generating the query y using both the original schema S and the refined schema S obtained from schema filtering. This decoupling strategy offers dual advantages: firstly, within-task pattern filtering has been empirically demonstrated enhance the performance of query generation by reducing the search space; secondly, the relatively stable output format of schema filtering across diverse SKR tasks enables the model to facilitate more robust knowledge transfer from preceding tasks during the schema filtering phase. In the following sections, we will delve into a detailed exploration of these two components.
3.1.1 Task-agnostic Continual Schema Filtering
K- K-DECORE trains an independent PEFT module, denoted Pa. for the schema filtering stage across all tasks. This module is integrated with the backbone LLM fe to predict the relevant schema set S*, i.e.&& S f(2, S; P.). During the training process for task k. the module P is initialized with parameters from the checkpoint of the preceding task, represented as P To ensure that P. perceives a consistent style akin to previous tasks while learning new SKR tasks, thereby mitigating forgetting due to training, the knowledge schema S from various SKR tasks is standardized into database-like (DB-like) unified schema representation. This standardization further reduces task- specific discrepancies and enhances effective knowledge transfer. The DB style chosen as the unified format for two primary reasons: (a) it is likely the most prevalent form of S and is familiar to LLMs, and (b) its straightforward relational structure simplifies the conversion of other schema types. Concretely, for each (Q, S is first converted to S = (Φ, )) where & = {ØI..... 610 denotes the set of abstract table names, and V = (vh..... M/v/ } denotes the set of abstract column names. Then, f0, Pa) predicts the useful schema f2 from Ω i.e., Ω f(, 2: 8 Pa), where 5 denotes the textual representation of S2:
② = Φ1 : w1,... .,. V Φ2 22....,, 4/ Øm Who w
(1)
During training, the following loss function is minimized to optimize Pa. while keeping 0 frozen:
L(D*; = Σ
log P(w Qi, Wi,
+ (
(2)
1=1
where P(2f
2r, W2, P.) denotes the probability of each token 23 generated by autore-
gression. To further mitigate the model's forgetting, we incorporate review loss L(MK'; P.)
computed on the memory M from task k,, which will be detailed in Section 3.2
How to convert to DB style? In this paper, various forms of structured knowledge can be easily transformed into S. For DB (T, C), each table name E 'T is mapped to Φ, while each column name E C is mapped to sb For a KG subgraph ((s, o) 8 E & p E R, o E & UT),, each entity type 7 E I' and each relation name 4 T are treated as ø. while each relation or property r R and each column name E C are treated as th For a dialogue state {[I,, $1),..., (In, },).. each intention I is considered as o, and each slot name s; is considered vb. See Appendix for details.
3.1.2 Task-specific Continual Query Building
Similar to schema filtering, K-DECORE trains another separate PEFT module, denoted as Po. specifically for the query building phase across all tasks. To avoid error propagation within the pipeline, we ensure that (e, Ps) also considers the complete schema S during reasoning, expressed as y' = f(Q, S* S; 0, Po). At this stage, the primary focus of the function f(0 Ps) is capture the semantics of the problem and the logical and structural features of queries associated with various SKR tasks during reasoning. As shown in Figure I the query structures for different SKR tasks highly task-specific and can vary significantll, which heightens the risk of forgetting at this stage. To address this risk, we include a small number of query samples from previous tasks during training. The following loss function is minimized to optimize Ps:
L(D*: Pb) - - Σ
Σ
log P(ys Q. St SK O, Σ L(M (LMK:OPP)) :.PP)).
1=1
where yo denotes the j-th token of the target query ), and M represents the memory dedicated to storing structural information of queries, which will be elaborated on in Section 3.2 Notably, here we directly use the raw schema format S to enable f(0, Pt)) to generate the executable queries.
3.22

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Algorithm Structure-guided Query Synthesis
Require: The training set of the k-th task, represented as D S, The set of all query structures present in Dis is denoted by My = ...,...., Ni J. while the set of all available schema sets is denoted by Ms (NaJ..
1: M
"eccddo 0
2: for IM Misndo < . do
3: St f RRNDDOM (Ms. 1². t RANDOM > Sampling schema and structures.
4: pseudo f ;));;)
> Synthesize pseudo query structure.
5:
J f- FiLLSCHEMA((i preudo: S*)
Fill the schema into the slots of the structure.
6: if EXECUTEE()+ S) "success" then
7:
Qt + f()+t ; P.P
Generate pseudo natural language question.
8:
M f M pscudo U(
9: end if
end for
11:
return M
pseudo
3.2 -ual-perspective Memory Construction
Schema-guided Memory Sampling. The goal of this sampling strategy is to construct memory M for each task D to relieve f(0, P.) from the burden of memorizing schema filtering knowledge. Since we have unified the representation of structured knowledge and tasks, the primary gap between different tasks lies in the domain of their schemas. Therefore, it is essential to select samples with representative schemas. Formally, for each training sample X' = (2, )) E Diiinn the process begins with the extraction of the relevant schema set S* from V. Subsequently, all samples Di partitioned into N clusters. The distance between two samples Xi and X₂ is defined by the cosine similarity di (Xi, X2) = cos (g(S)), g(S))), where g denotes an encoder-only LLM. Finally, the sample closest to the center of each cluster is selected, and (Q, S*) is added to M
Structure-guided Memory Construction. This strategy aims to preserve the sample set M with diverse query structure features, which are utilized for the rehearsal process of memory M consists of two parts, denoted as M = M U M First, M comprises representative samples selected from D Praiin This selection process analogous to the one used for constructing M with the main difference being that the sample distance within cluster is defined by d2(X1, X2) cos (g()i), )(}?))) Here, y denotes the structure of the query V which formed by substituting the schema elements with placeholders. The structure of the s-expression in Figure is represented (AND [Ti] (JOIN [C1] (JOIN ([c1]) [Ei]))), where [T1], [C1], and [E1] serve as placeholders for an abstract table, an abstract column, and an entity, respectively.
The
Unlike existing methods 99 10 which primarily preserve training-time structures (like M seen by fo. our K-DECORE employs M pasudo to boost zero-shot reasoning by introducing novel structures absent from D This is achieved through a structure synthesis process, detailed in Algorithm I The procedure begins by identifying the repertoire of query structures present in Piain denoted as My { ................................ ) }. and the available schema sets, .Ms = {Si .... S J. Iteratively, our backbone model fo is leveraged to generate novel structures: T' distinct structures y* My are sampled and synthesized into a new structure Peeeddo guided by carefully curated demonstrations. Here y D can be complex SQL queries with nested subqueries or S-expressions featuring multi- step compositional reasoning; detailed examples and prompts these synthesized structures can be found in the Appendix BB. This query structure pescudo is then instantiated into concrete query ) peeudd by populating it with a randomly sampled schema S* E .Ms. Only generated queries ) ypecddo that execute correctly, thereby ensuring legality and semantic validity, are retained. Finally, PEFT module, denoted as Pe, designed to generate the pseudo NLQ Q+ and is trained by optimizing
log P(gg P.).).
(4)
5

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where 2j denotes the j-th token of real Q. Notably, P. is tailored specifically for each task D Once MI is constructed, P serves as the initialization for training P Since P is constructed only once per task, it does not introduce the forgetting problem. To construct M we combined the real memory Mi with the synthetic memory M in varying proportions. In our experiments, we systematically investigated different synthetic sample percentage to identify the optimal balance.
4 Experiments
4.1 Experimental Setup
Datasets, We conduct experiments on four widely-used SKR datasets, covering DB, KG, DS reasoning. Spider [1] is a DB reasoning benchmark where each NLQ is translated into a complex SQL query. These queries often require multi-table JOIN operations, GROUP BY clauses, and nested subqueries, demanding sophisticated database reasoning capabilities. Complex WebQusstions (CWQ) [16] is a KG reasoning dataset where each NLQ corresponds to an executable SPARQL query. It involves up to 4-hop reasoning over a knowledge graph with complex constraints such as comparisons, aggregations, and nested conditions. GrailQA 17 is KG reasoning benchmark featuring NLQs that require complex multi-hop reasoning to build s-expressions over the Freebase knowledge graph. MTOP [8] is designed for multilingual semantic parsing in task-oriented dialogue systems. Each NLQ is parsed into a TOP representation, modeling nested intents and slots.
As detailed in Section 2 we utilize the aforementioned four datasets to construct three distinct sequential task streams, where each task Duain comprises a single dataset. The input and output spaces vary across different tasks, as illustrated in Figure 1. Additionally, to emulate scenarios with limited training data, each individual task D is constrained to |P I 1000 and |Pieal 300.
12 we employ three metrics to evaluate the performance of the methods: a) AA, = K E aCC), which assesses the overall accuracy across all tasks; b) BWT 1
Evaluation Metrics. In line with existing studies 13 1
K-1 ΣB=1 KK1 (acCk,K acck,)), which measures the extent of forgetting knowledge from previous tasks; c) FWT R Ek E (aCCI,* - accu,o). which evaluates the ability to forward transfer knowledge from past tasks to new ones. Here, acck.j represents the test accuracy on Di after training on DJ, and acC4,0 denotes the test accuracy on D only training on D
We conduct comprehensive comparison against the following three types of baselines: a) FINE-TUNING: Represents the performance of standard, vanilla model without any continual learning enhancements. b) Rehearsal-basee Methods: Includes methods such as EMAR [99 and SFNET (9, which require replaying real historical examples or utilizing additional unlabeled data. e) PEFT-based Methods: Covers techniques like PROGPROMPT 14], O-LORA A M.
Compared Methods.
C3 [12, and SAPT [33 Additionally, establish an upper- bound baseline, Multi-Tass where for each task D* the model fo trained jointly on all data of D (0:k)
train
Backbone LLMs. To thoroughly assess the performance across different backbone models, we employed three widely used LLMS of varying sizes: 1) T5-LARGE. 2) LLAMA3-8B-INSTRUCCY and 3) -WWEN2.5-BB-INSTRUGE
Most existing methods in the literature are tailored for encoder- decoder architectures, such as T5, which inherently limits their applicability to the current generation of LLMs, the majority of which adopt decoder-only design. Consequently. when employing LLaMA3 and QWEN2.5 the backbone, these methods are excluded from consideration due to architectural incompatibility. This distinction underscores the novelty of our approach, as it represents the first systematic exploration of leveraging decoder-only LLMs for the continual SKR task.
Implementation Details. Our experiments were conducted using a single NVIDIA RTX 4090 GPU. The hyperparameters were configured as follows (see Appendix [ for further details): a) We employed LoRA (00 as the PEFT module. b) The memory size for each task was set to [ 5 and [M = 5. c) The ratio of real to pscudo memory samples, |M& real to [MMecudol was maintained at 4 1. d) We used batch size of 12 and set the learning rate to 5 x 10-5, e) The number of training epochs was fixed at 5. f) T in Algorithm| D is set to 5. g) The backbone LLM for pseudo samples synthesis is set to
All of our data and codes are publicly available

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Table 1: Experimental results for comparison with baselines.
SKR streaml
SKR stream2
SKR stream3
Backbone
Method
BWT
FWT
AA
BWT
FWT
BWT
FWT
FINE- MUNNNG
2.9
-31.1
6.3
10.2
-240
7.6
6.8
23.3
4.2
EMAR [9]
12.6
18.5
4.8
12.5
19.5
7.2
8.7
20.6
3.0
O-L.RRA
1.4
-30.5
-2.3
7.8
-17.9
-5.4
5.4
-27.8
0.3
PROGPROMPT 14
9.7
-23.5
1.8
7.8
-17.9
4.6
15.9
19.3
1.9
T5-LARGE
SENET
27.3
-4.77
3.6
18.2
27.5
4.2
24.3
18.0
3.1
C3 12
26.6
30.8
4.2
29.8
3.2
SAPT Kk
18.5
15.8
3.7
24.5
10.2
5.4
20077
3.5
K-DECORE (Ours)
31.8
9.6
8.6
31.4
7.5
8.4
30.1
6.9
7.7
MULTII-ASK
37.4
9.1
8.8
38.4
12.6
10.1
36.6
11.5
6.8
22.8
29.4
4.2
26.3
22.9
1.8
16.4
29.1
1.6
EMAR 19
34.0
13.2
3.3
34.5
18.4
3.7
29.8
19.9
0.5
SENET
36.1
14.5
3.7
35.8
17.1
4.2
35.4
12.3
4.2
ILLAM33
C3
12
39.7
2.3
40.7
2.6
36.6
2.1
-B-INSTRUCT
K-DECORE (Ours)
40.5
-16.7
5.9
41.1
-7.1
6.1
37.0
-99.3
4.2
MULTI-TASE
54.5
5.7
10.0
54.7
12.8
5.1
54.3
12.2
4.8
FINE- UINE-TUNING
19.6
26.9
5.1
28.8
155.9
4.6
25.3
- 22.8
1.9
EMAR 10
35.6
9.2
5.4
35.4
10.8
5.3
30.7
- -14.8
2.0
SFNET
40.3
- 10.55
4.5
38.5
13.2
4.8
34.1
15.0
5.1
QWEN2.5
C5
12
38.9
1.9
38.8
1.8
35.9
2.6
-B-INSTRUCT
K- DEDDEORE (Ours)
43.2
-8.2 8.2
6.9
40.1
9.88
5.5
36.8
- -16.7
6.9
MULTI-TASK
52.0
9.2
10.4
51.8
9.9
9.7
55.0
15.6
7.3
4.2
Overall Results
Table presents comprchensive performance analysis of our proposed K-DECORE framework against several baselines across three continual SKR streams, utilizing various LLM backbones. With T5 as the backbone, K-DECORE uniformly outperforms all competing methods across all evaluation metrics. This superior performance is largely maintained when employing Llama3 and QWEN2.5 as backbones, where K D-ECORE establishes new state-of-the tttthhhertttt art results in both AA and FWT.
In contrast, while PEFT-based methods such C3 and SAPT achieve competitive results by ded- icating independent parameters to each task, their per-task efficacy is hampered by the inherent convergence limitations of prompt-tuning. K-DECORF, however, attains superior performance without introducing additional parameters, offering a more efficient and scalable solution through its novel knowledge decoupling mechanism. Furthermore, our synthetic sample generation strategy, by creating more diverse query structures, enhances the model's generalization capabilities, as evidenced by its strong FWT performance. Notably, since C3 trains and loads distinct PEFT module for each task, thereby precluding knowledge transfer to previous tasks, we do not report its BWT.
4.3 Ablation Study
To explore the contributions of each component of our proposed K- K-DECORE, we compared the performance of the following settings: a) w/o Decoupling: We omit the knowledge decoupling process and instead treat SKR as a standalone stage, In this configuration, we use single model fa equipped with one PEFT module P for inference, while maintaining the replay process unchanged. b) w/o Unification: To evaluate the contribution of the unified schema representation, we only used the original schema format specific to each SKR task during the schema filtering stage. e) w/o Replay: We employ continual knowledge decoupling without replaying any samples, which involves removing the second term in both Equations (2) and ( d) w/o Mít. We removed the memory M in the continual schema filtering, e) w/o My. We removed the memory MI, in the continual query building. f) w Random Memory: We randomly sample from D construct M and Mr.
Table 2 presents the results of our ablation study, investigating the contribution of key components of K- DECORE. Removing the entire replay mechanism specifically the query structure memory (w/o
7

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Table 2: Experimental results of ablation studies.
SKR streaml
SKR stream2
SKR stream3
Backhone
Method
AA
BWT
FWT
AA
BWT
FWT
AA
BWT
FWT
K- R-DECORE
40.5
16.7
5.9
41.1
17.1
6.1
37.0
19.3
4.2
w/o Decoupling
38.8
13.8
2.6
37.5
19.8
2.7
33.6
19.6
2.1
LLAMA3
W/o Unification
37.8
-17.7
3.4
37.1
- 15.22
0.7
34.6
13.77
-0.8
-B--INSTRUCT
w/o Replay
20.5
-1.2
4.3
28.8
- 33.4
6.8
17.9
-3.8
3.5
w/o .M
39.4
-17.1
5.1
38.2
20.99
6.2
35.6
-20.2
3.6
w/o .N
20.8
-42.1
5.3
28.6
32.8
5.3
18.5
44.5
3.7
W Random Mcmory
39.9
-17.4
5.6
37.7
20.6
5.4
36.3
-2..
3.9
K K-DECORE
43.2
-8.2
6.9
40.1
9.88
5.5
36.8
16.7
6.9
w/o Decoupling
37.9
8.8
5.4
39.3
5.5
4.0
36.1
17.1
4.8
QWEN2.5 -7B-INSTRUCT
w/o Unification
34.4
13.3
1.8
37.4
10.5
2.6
36.1
11.7
2.4
w/o Replay
16.8
4114
5.3
29.7
23.8
5.5
18.7
40.3
6.5
w/o M
42.8
99.7
5.3
39.5
-9.8
4.9
35.9
17.5
6.6
w/o M
19.1
39.2
6.0
28.3
24.4
16.3
43.8
6.8
w Random Memory
42.1
-7.7
5.3
39.4
11.8
6.3
36.2
17799
5.7
60
20
Kocccar
wo, Healay
440
9WT (N6)
FWT (%)
10-
Random Memorr
wo Ma
wn. Mc
we. Unitration
we. Decoupingg
-60
-10
Task Number
Task Number
Task Number
60
20
AA (6))
BWV (96)
PVT (963
40
20
i
2
-60
0
-10
2
3
Task
Number
Task
Figure 3: AA (%), BWT (%), and FWT (%) till the seen tasks after learning on each task, using LLAMA3-8B (top row) and QWEN2.5 5-B (bottom row). Solid lines represent the mean values across three distinct task sequences, while shaded regions indicate the standard deviation.
M!) leads to drastic performance drops in AA and significantly worse BWT, highlighting the critical role of memory, particularly the storage of diverse query structures, in mitigating forgetting and maintaining overall performance. Disabling the knowledge decoupling (w/o Decoupling) removing the unified schema representation (w/o Unification) also results in lower AA and reduced FWT; demonstrating that separating reasoning into distinct stages and standardizing schema representation are vital for effective knowledge transfer and handling heterogeneous tasks. While removing the schema memory (w/o M or using random memory sampling shows less severe degradation compared to removing query memory, performance still drops relative to the full model, confirming the benefits of dedicated schema memory and our proposed memory construction strategy.
4.4 Performance Till the Seen Tasks
3 presents the performance trajectory of the evaluated methods on previously seen tasks across different LLM backbones. Our proposed K-DECORE framework consistently demonstrates superior overall performance, with its advantage becoming more pronounced as the number of sequential tasks increases. The model's robust forward transfer capabilities, a key contributor to this success, can be attributed our strategies for aseudo-sample synthesis and continuous query construction, which collectively enhance its adaptability to new and unseen scenarios. An interesting trade-off revealed our ablation study. While omitting higher BWT, this apparent benefit comes at the cost of increased inter-task interference. This interference ultimately diminishes the model's s adaptability, leading to poorer performance FWT and overall AA, thereby underscoring the critical role of our decoupling strategy.
knowledge decoupling component results in

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22 5 AA/000
E OM
Morrory Size
Systette Sample Percentage 19)
0.2
Systheti Sample PercentAge (9))
0. Syntheto Samplt Perotstage (5)
0.5
0.5
Figure Peformance of K-DECORE with varing memory sizes and synthetic sample percentages.
Table 3: Experimental results on imbalanced task streams.
SKR stream1
SKR stream2
SKR stream3
Backbone
Method
AA
BWT
FWT
AA
BWT
FWT
AA
BWT
FWT
FINE-TUNINS TUNING
18.5
-1522
4.1
22.2
-11.8
26.3
-5.1
3.4
QWEN2.5 -7--NNTRUCCT
EMAR 19
28.2
3.1
5.4
30.7
1.1
28.3
3.2
K- K-DECORE (Ours)
31.8
10.9
8.5
8.5
30.1
6.5
4.5 Effects Varying Memory Sizes and Synthetic Sample Percentages
Figure shows the AA, BWT, and FWT of various methods across seen tasks using differrnt backbone LLMs. When the proportion of pseudo samples is relatively low, increasing the memory size tends to enhance AA and BWT. Conversely, with a fixed memory size, incorporating pseudo samples at proportion of 20% frequently yields optimal results in terms of AA, BWT, and FWT, outperforming scenarios with either no pseudo samples higher proportion of them. Interestingly, for FWT, larger memory capacity does not inherently translate to improved generalization capabilities.
4.6 Performance on Imbalanced Task Streams
To further assess our model's robustness in more realistic scenarios, we conducted an additional experiment simulating an imbalanced data stream, a common challenge where tasks have a non- uniform number of training samples. Specifically, we configured the training set with the following sample distribution: Spider (500), GrailQA (800), CombWebQ (300), and MTOP (600). Here we use QWEN2.5-77 -N-INRUU as the backbone model. As shown in Table 3 our method, KE- DECORE. demonstrates clear superiority in the imbalanced data stream experiment. It achieves the highest AA on all the streams, significantly outperforming the EMAR baseline.
4.7 Training Efficiency
Training Efficiency Comparison
Testing Elficiency Comparison
Training (seconds)
Texting Time (seconda)
60
Figure 5 iillsstrats the average training duration for each ap- proach across various tasks. No- tably, C3 exhibited consider- ably longer training time com- pared other methods, primarily due to its prompt tuning neces- sitating an extensive number of epochs. The primary source of time overhead for SAPT and O-LoRA lies in the merging process multiple LoRA modules. This bottleneck becomes increasingly pronounced as the number of tasks scales up. In contrast, our K-DECORF demonstrated favorable balance between efficiency and performance, with its training time only marginally exceeding that of the echearsal-based EMAR.
20a
KDeCome SAFT
C3
Lo
EMAR
D.eCCone SAFT
G3
O EMAR
Figure 5: Training and testing time of various methods.

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5 Related Work
5.1 Structured Knowledege Reasoning.
Structured Knowledge-intensive Retrieval (SKR) tasks, which include text-to-SQL 221, Knowledge Graph Question Answering (KGQA), and dialogue parsing, are centered converting natural language questions (NLQs) into structured, executable queries. the database domain (DB SKR), the process of translating NL.Qs into SQL queries has advanced significantly. Initial methods relied on specialized model architectures [22] and the use of intermediate representations [23]. More recently, the field has shifted towards leveraging Large Language Models (LLMs) enhanced with sophisticated techniques such as task decomposition, chain-of-the chain-of-thought prompting [24] 25]. and self- consistency (6]. This modern approach has led to substantial performance gains, as demonstrated in several recent studies (27 28 30). Similarly, SKR over knowledge graphs (KG SKR), or KGQA, has evolved from traditional semantic parsing [1 32 and embedding- based methods [33. Recent advancements, such as De (3 and KB-BINDER [5], now integrate the generation of logical forms with direct answer retrieval, markedly improving performance. In the domain of dialogue parsing, tasks like TOP (36] and MTOP [18 involve deciphering complex, multi-turn interactions. These have also been advanced by LLMs, which provide more nuanced understanding of the required structured representations.
While APEX [0]] and LECSP (37] also constitute related work, they were excluded from compar- ative analysis. These methods are specifically tailored for the text-to-SQL. task and do not generalize to the other SKR domains address. Furthermore, their computational overhead when implemented with LLM backbones exceeds the practical limits of our experimental setup.
5.2 LLM Continual Learning.
In the context of continual learning for LLMs 38) Eerameter-Effiieen Fine-Tuming (PEFT) methods, including prompt tuning 39), adapters [40, and LoRA [20], have become instrumental. These techniques are particularly effective for adapting models to new tasks with limited data [41 IO] by introducing a small number of trainable parameters while keeping the base model frozen. This parameter-efficient approach allows for the sequential integration of new knowledge, mitigating catastrophic forgetting and adapting to distribution shifts without the need for replaying historical data 12 13. A common strategy in continual learning is to train separate PEFT module for each new task. This compartmentalizes task-specific knowledge, effectively preventing interference between tasks 42 13 43
However, this one-to-one mapping of modules to tasks presents a significant drawback: it inherently struggles with generalization to novel samples that do not neatly fit into the learned task distributions, thereby limiting its practical utility in dynamic, real-world scenarios. In stark contrast, our proposed K-DECORF framework utilizes a fixed set of PEFT modules throughout the entire learning process. This design offers a more efficient and scalable solution for both training and inference in evolving, iterative environments.
Conclusion
In this paper, we introduced K-DECORE, a novel framework for Continual Structured Knowledge Reasoning (CSKR) that effectively addresses the challenges of catastrophic forgetting and parameter growth in heterogeneous structured knowledge environments. By leveraging knowledge decoupling, K-DECORE decouples schema filtering from query construction, enabling efficient knowledge transfer across diverse tasks without increasing model parameters. Our dual-perspective memory mechanism further enhances knowledge retention by maintaining replay samples from both schema and query structure perspectives, while our query synthesis strategy enriches memory content with complex structured queries. Through extensive evaluations on task streams from four SKR benchmarks, K-DECORE consistently demonstrated superior performance over strong baselines across multiple metries. However, due to time and cost constraints, we did not employ reasoning- bbsed LLMs (like QWEN3) as the backbone for our framework. This remains limitation of our current work. Future research will explore the integration of such models to potentially enhance the applicability and robustness of <-DECORE in real-world scenarios.
10

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7 Acknowledgements
This work was partially funded by National Nature Science Foundation of China (Grant No. U21A20488, No. 62376058, No. 62476058, and No. 6250072425), and funded by Southeast University- Mobile Research Institute Joint Innovation Center and the Southeast University Interdisciplinary Research Program for Young Scholars. We thank the Big Data Computing Center of Southeast University for providing the facility support on the numerical calculations in this paper.
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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: We propose novel CSKR framework, K-DECORE, which facilitates the knowledge transfer using knowledge decoupling.
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The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No
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is fine to include aspirational goals as motivation as long as is clear that these goals not attained by the paper.
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We discussed this in Section 6.
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A DB-style Input and Output Format
We emphasize that our DB-style unification designed to fully preserve all critical information, ensuring that the flattened representation remains faithful to the original structures. Below, explain how our design achieves this preservation while enabling effective covering across tasks.
For relation direction, our mapping explicitly preserves the subject-object orientation inherent in predicate-object) Specifically, we designate the content in the primary key of the transformed table as the subject, while the contents in other columns represent the corresponding objects. This structured encoding ensures that directional information is retained and can be reli- ably interpreted during query generation, preventing ambiguities that might arise from undirected representations.
triples
Regarding edge types, we incorporate them directly into the schema by prefixing each relation (or edge) with its type and treating as a dedicated column in the flattened table. This simple yet effective augmentation embeds the type information within the schema itself, allowing the model to access and utilize without requiring additional modifications to the backbone architecture.
For multi-level structures, such as hierarchical slots or nested tables, we adopt flattening techniques. This involves recursively expanding multi-level columns into single-level ones, ensuring that hierarchi- cal relationships are linearized but not discarded. Our framework is flexible enough to accommodate this, as the flattening occurs preprocessing and does alter the core continual learning mechanism.
More crucially, our DB-style unification is applied only during the schema filtering stage, where the primary objective is akin to entity linking making-namelly. identifying the relevant tables (entity types) and columns (relations) for the given query. In this context, not all granular details (c.g., full hierarchical depth) necessary, the filter's role is to select pertinent schema elements rather than reconstruct the entire original structure. The subsequent query builder module, operating on the filtered schema, then draw the preserved essential information.
A.1 GrailQA
Schema Filtering Stage:
INPUT:
Identify the exact schena conponent from the given scbena that would correctly answer the folloving question.
mook.serres_editaae aditor bnokk_ddition_ete ctttriioo__rrre__etteeeeeeee book.series
bookb boook_edttionseerre of book, book, edition, seriea editions, In, series, serlea_edite edittor question:
people's history christianity was edited by editor?
OUTPUT:
book.series_edee booi_ediiiooneeriie.ddii:
Query Building Stage:
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INPOT:
Genarate s-expreasion be used find the following question using the + knowledge graph schena itema provided.
achena:
people's history of christianity: 0.01brr book. series_editor wew.diiioo__rrrie_... book. wwcchooookkkttkaee adition srook_odition_eett aditions_ in_ti_s_sttttsseeee,, series_editor
book, book bcok__ddition_erteee
question:
people's history of christianity edited what series editor?
OUTPOT:
wew.eeeerieeeteeeee (JOIN editor srokk__eiiioneeereei n. n.012bphrj))
A.2
Spider
Schema Filtering Stage:
Identify the exact schena component from the given schena that would correctly answer the folloving question.
achena:
department department, id name creation ranking budget_ in_ billiona nun_emplon eun_eenloeess | head head_id born_state age nanagement departnent,_fr
tenporary actiing
question:
How many heads of the departments older than 56
OUTPUT: age
Query Building Stage:
Your tack is to convert the folloving question to an SQL query using the following database
achena:
department departaent__cc
crestion ranking budget, in bilbiooo eun_eeeeeeees bead head_id nane born_atate age nanagenent dopartment__16 bead_id tenporary_acting
question:
How nany dopartments
OUTPUT:
select count(+) from head where 56
A.3 CWQ
Schema Filtering Stage:
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INPOT:
Identify the schana component from the given achena that would corractly the + following question.
achema:
type.object type location. location location.location
containedby, contains, people_born. born_here comman.tppa topic notable_types geography. river nouth travel. trwal..tarrttt.atttaccioo. trg.object.proflle prominent_typa education. educationnl institution institttttiooooooio colore location. coattoo.ccuntzyy epokeagee.epoken, adminietrative
question: language is spoken in the country that has Southern Penineular?
QUTPUT: docation.countiy Sanguagea_spokenn adninistrative.do
Query Building Stage:
INPUT:
Given the list of schema itens, write SPARQL query that used find the the < follazing question.
acheua:
Southern Peninsula: n.08knfj contains, people_bera, ber__bbr___eeee here I common. topic notable_typer geograpby.river mouth I traval.touriate traval.
type.object type location.
sznee,, containedby,
near seve_ttaval_dee dewaalllddttnnation kgg kg..ojact.pprofil proninent _tpp oducation. oducationall institutin colors location. country languages_epoleen adhinistrativv_ddvlaaa
question:
language spoken country
Peninsular?
OUTPUT:
PREFIX na: <http://rdft http://ddf..rrabaee freebasee wwt.:/aww....///////aaeeeeeeeeee com/nn//////// DISTINCT 7EVBERE {7c
country adninietrative, diviiiios 08knfj
as: nel:ocation, country. country.languages saggaages_spcken ?z .)
A.4 MТOP
Schema Filtering Stage:
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INPUT:
Identify the exact schena component from the given achena that would correctly answer the folloving queetion.
achema:
in:get nessago, weather, slam, info_recipes, stories_pevs. reminder, recipes,
call_tine,
life_avent, cnfo_centact, contact, timer, reminder, date_time, age, sunrise, employer, employnent..tme. call_contact, oducation_tima, job, avatlabilsty, category_evant, undergrad, reminder, reninder,location, location, track_info info_mmie, sunset, matual_frinnd, attendee_event event, ceesage_contact, reminder_ aeiinder_enount, date_tine, event, detaile_neve. education_ dagres, najor, contact, life.event..ting lyrics_muic,, eirquelity, language, gender, group insserd_neeeage in:set unavailable, revp_yes, available, dofault_pporise cawnlll_prorder__meee
isvp_interested, pesuull_prooider_alllae reninder, alarm, tiner, playlist_munie in:create alarn, reninder, call, playlist_masie, nedía in:end_calll in:ignore_call
revp_eo in:delete
tiner in:question nevs, reninder_date_tteee date_time inipause music, timer sarie_truu_recije
in:play
inrupdate_call call in:update reniiderrrr en:ozezee_als alarn tasupdate_romindde
in:add time_ tiner, to.playlist_
inrehare, evennt im:prefer in:atart_ehuffle.
in:silence__ alamm in:svitch_call
in:upáste_tine incopéato_methoo.cc
in:prerieus_tre cttps/sssrrcck_csriisss. in:hold_call masakip_track_mu
in:update alarm masliko_maic in:restart, in:resura : tiner, call, music
in:merge_call insreplay muic in:loop_music in:stop music, shuffle_music
in: in:unloop_ mueic seuppdttereeiider.... location in: nessage, call in:updaterremindes
insrepeat all_msic,, all_off_masis corffattforwarrmmsst in:dialika_mnn misic in:disprater tn:help..rmmtnder in:follov_mast misic
in:revind
question:
Bas Angelika Kratzer video nessaged ne
OUTPUT:
IN:GET
NESSAGE
Query Building Stage:
IMPUT:
follewing language quary
Parsing (TOP)
representation using the following API specification.
API specification:
IN:GET :
NESSAGE, WEATHER, ALARM, INPO RECIPES, STORIES NEWS, REXINDER, RECIPES, EVENT, TIME,
LVEN_,VENT, INFO_CONTACT, CONTACT, TIMER, REMINDER, DATE_ TIME, SUNRISE, EMPLOYER,
TIME, JOB, AVAILABILITY, CATEGORY CALL, EMPLOYMENT_ TINE, CALL_CONTACE L0CATION, TRACK_IXPO IXPO_ MUSIC, SUNSET, RUTTULL_FRIENDS UNDERGRAD, CONTHDER_LOCATIEE
EDUCATION TIME,
ATTENDEE, EVENT, CONTACT, REMINDER, AMOUNT, DATE, TIME, _EE_TIIEEEETTT DETAILS, NEXS.
EDUCATION DECREE, MAJOR, CONTACT_ NETHOD, LIFE EVENT, TIME, LYRICS_ MUSIC, AIRQUALITY, LARGUAGE, GENDER, GROUP IN:SEND_MESR MESSAGE IN:SET URAVAILABLE, RSVP, YES, AVAILABLE,
DEFAULT PROVIDER MUSIC, RSVP. INTERESTED, DEFAULT PROVIDER, CALLING, RSVP ND IN:DELETE
TIMER IN: QUESTIINON NEMS, MUSIC IN:PLAY MUSIC, MEDIA END _ALL I IGHORE CALL
UN:UPDATE_ CALL IN-UPDATE,
TINE PAUSE MUSIC, TIMER ANSWER. CALL
I#: SNOOZE, ALARN UNDUAEE REMINDER, TODO I IN:IS. TRUE
TRUE RECIPES IN RENOVE, FROW, FRON_PLAYLISS PLAYLIST_ NUSIC
IE: TIME TINER, TO PLAYLIS MUSIC SHARE EVENT INEPRRR IN:START SEUFFLE_NUSE
DE:SILENCE ALARM IN:SWITCH CALL SUBTRACT TIMER UPDATE TIMER
IN: PI:PRVVIOUR TRACK, MUSIC IN WW:HOLD_CAR CALL SKIP.PP. TRACK, MUSIC UN:APA METHOD_ CALL.
ALARN
IN: MERGE CALL IN:REPLAY MUSIC IN:P MUSIC IN:STOP MUSIC, SHUFFLE_ NUSIC
UNLOOP NUSIC IN-UPDATE, REMINDER
CERTINDERRLLCATTIN LOCATION IN:CANCEL MESSAGE, CALL UP.ATEE.RERIIDDEE REMINDER
UPDATE,
RENIEN MUSIC IE:REPEAT MUSIC, OFF_FIII DN:FAST_FORVATT PORWARD_NUSEL
DISDIIIIEEEMME MUSIC DISPAERR
IN:FOLLOWMOSSH
queation:
Angeliks Xratzer messaged
OUTPUT:
[IN:GET [DN:GBT_MESSAGE MESSAGE CONTACT Angelika Kratzer
[SL:TYPE_COITTT)] CONTENT video
(SL:ABCIPIENT me
25

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B Prompts for Synthesizing Query Structures
B.1 Prompt for Pseudo Structure Synthesis
You are an expert in logical query expression synthesie, Your task to merge tuo simple query akelatons into single, complex skelaton that logically integrates their structure and neaning. The reault ahould preserve the samantics of both inpats and follos the same eyntax. Output only composed skeleton. or additionsl text.
structural style
include any explenation
Simple ekeleton 1: (exeeaaplle))
Simple skeleton 2:xexample22
Composed
B.2 Prompt for Pseudo Question Generation
You are an AI assistant that specializes in transforming structured query statements into fluent, language quastions. onn. and its associated achema context, and then urite corresponding question in clear, natural language that would retrieve the correct ansver the fornal query vere executed on database.
contextually accurate
given formal quary
question:
(query>
achema:
(schens)
Follav these steps carafully:
1. Vaderstand the Query Semantics:
Analyze query logic, including the target variables, the riltering conditiona, the joins, the structure of the query. Infer what specific information the query is interded retrieve.
2. Incorporate Schena Sonantics:
Use the provided achema elamenta (auch as table names, antity typas, or relation labels) to guide terminology and phrasing + phrases as necessary.
question. Nap schems componenta bunan-interpretable
3. Avoid Direct Translation:
Do not simply convert the query structure into a rigid, nechanical forn. Instead, ain for fluent, human night ask whan seaking the information.
natural-sounding question
Equivalence:
Ensure that the generated question is logically equivalent to the formal query in terns of the expected ansver. The question should not broaden, narrow, or alter the scope of the query.
6. Maintain Clarity and Brevity:
The question should unanbigusus and
preserving critical information
+
needed to aliga vith the query.
6. Output Format Constraint:
Only output the final quection, without any explenatory text, metadata, or formatting eymbole. The output should consist solely of single fluent question English.
B.3 Examples of Structure Synthesis
Structure 1: (ARGMAX [T1] (C1))
Structure
(AND [TI] (JOIN [C1) (E1)))
Synthetic Structure:
(ARGNAX (AND [71] (JOIN (C1) (E1))) [C2]
26

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Structure 1:
SELECT [c1] FROM [T1] WHERE [c2] [V1]
SELECT [C1] FROM [V] WHERE [c3] IN [V13;
Synthetic Structure:
SELECT
FROM (T1] WHERE [c1] IN (SELECT [C2] FROM [12) WHERE [c3]
[vi]);
C Hyperparameter settings
Our hyperparameter settings are as follows:
Table 4: Hyperparameter Configuration for Experiments
Hyperparameter
Value
GPU
NVIDIA RTX 4090
PEFT Module
LoRA 20
LoRA Target
gate proj, down_wwwogeegooo up_proj. proj. v_proj. k proj. o_ o_proj
LoRA Rank
LoRA a
16
Memory Size per Task
5, =
4:1 g
Real to Pseudo Memory Ratio
Batch Size
12
Global Batch Size
32
Learning Rate
x 10-6
Optimizer
Adam
Training Epochs
5
Maximum Input Length
1024
Maximum Output Length
256
Parameter T in Algorithm
27


Reviews:
[{"review_id": "EN7qvKK8dp", "reviewer": "Reviewer_Zwgd", "summary": "This paper introduces K-DeCore, a new continual learning framework for Structured Knowledge Reasoning across heterogeneous tasks. By decoupling reasoning into schema filtering and query building stages and leveraging a unified schema representation, the approach aims to facilitate knowledge transfer without increasing parameter count. A dual-perspective memory mechanism—covering schema and query structure—and a structure-guided pseudo-data synthesis strategy are incorporated to boost generalization and retention. Experiments on four SKR benchmarks with multiple LLM backbones show gains across accuracy, forgetting, and forward transfer metrics compared to baselines.", "questions": "Please refer to Strengths And Weaknesses", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n\n1. This paper clearly motivates that schema filtering is a common subtask across different SKR forms and proposes a systematic decoupling between schema filtering and query building. \n\n2. Unified schema representation: By mapping various structured knowledge forms (SQL, KG, dialogue) to a DB-style schema, the model reduces task heterogeneity in schema filtering. \n\n3. The core methodology, especially the knowledge decoupling and memory construction strategies, is clearly explained.\n\n\nWeaknesses\n1. All experiments constrain task train/test samples to fixed small sizes (1,000/300), which, while making experiments tractable, limits generality to much larger-scale or more realistic continual environments where distribution drift is more gradual or data is less segmented.\n\n2. The described DB-like mapping unifies most SKR schemas, but some structured knowledge forms may not map easily (e.g., hierarchical, nested, or graph-centric schemas with types or attributes that do not naturally correspond to columns/tables). \n\n3. The work highlights that most baselines cannot be directly ported to decoder-only LLMs, but the consequence is that comparative results with these (Llama3, Qwen2.5) have fewer baselines and thus weaker empirical competition, risking over-claiming.", "was_revised": true, "final_justification": "Here is a brief summary of my reasoning:\n\n* On Experimental Scale: My concern about the limited sample sizes was alleviated. The authors clarified that this setup aligns with existing benchmarks and, more importantly, provided data on the \"unseen schema rate\".\n\n* On Schema Mapping: The rebuttal provided a explanation of how the framework's mapping strategy handles complex, non-relational structures like graphs and nested schemas. \n\n* On Empirical Baselines: I commend the significant effort to address the initial weakness in comparative evaluation. By implementing and evaluating additional baselines (SFNet and Apex) for decoder-only models during the rebuttal period, the authors have demonstrated their method's better performance.\n\nOverall, the paper is better now. I have updated my final score to Borderline Accept.", "rebuttal": "We are very grateful to you for providing us with valuable feedback and suggestions for our paper. We will provide explanations and clarifications for each weakness and question.\n\n### **Weakness 1:**\nAll experiments constrain task train/test samples to fixed small sizes (1,000/300), which, while making experiments tractable, limits generality to much larger-scale or more realistic continual environments where distribution drift is more gradual or data is less segmented.\n\n\n### **Response to Weakness 1:**\n\nOur experimental setup aligns with established practices in existing continual learning studies, such as SAPT [1], C3 [2], and APEX [3]. For instance, SAPT employs a training set of 1,000 samples and a test set of 500. These works intentionally simulate real-world scenarios where labeled data is scarce, thereby underscoring the model’s robustness under data insufficiency—a common practical barrier in deploying continual learning systems.\n\nTo further validate the generalization capability of our method, we ensured that the training and test sets for each task on the stream are not identically distributed. For example, in tasks like Spider and GrailQA, we adhered to their original splits, which inherently introduce distribution shifts (e.g., varying query schema diversities between train and test) in continual environments. The detailed information of  is shown in the following table, where the unseen schema rate indicates how many schemas in the test set do not appear in the training set.\n\n| task name | # of training schema | # of test schema | unseen schema rate |\n|-----------|----------------------|------------------|--------------------|\n| GrailQA   | 934                  | 278              | 74.1%              |\n| MTOP      | 63                   | 35               | 8.6%               |\n| Spider    | 92                   | 70               | 87.1%              |\n| CompWebQ  | 310                  | 256              | 49.2%              |\n\n\nOur results demonstrate strong performance despite these constraints, suggesting broader applicability in real continual learning scenarios.\n\n\n### **Weakness 2:**\nThe described DB-like mapping unifies most SKR schemas, but some structured knowledge forms may not map easily (e.g., hierarchical, nested, or graph-centric schemas with types or attributes that do not naturally correspond to columns/tables).\n\n### **Response to Weakness 2:**\n**In fact, our framework is designed with the flexibility to handle such complex structures.**\n\n**1. On Graph-Centric Schemas (e.g., Knowledge Graphs):**\nOur mapping strategy has been empirically validated on complex, graph-centric schemas. We represent entities as distinct **tables** and their relations/attributes as **columns**. This approach effectively linearizes graph structures while preserving their core semantics. Our strong performance on KG-intensive benchmarks like **GrailQA** and **CompWebQ** directly demonstrates that this mapping retains the necessary information for complex, multi-hop reasoning.\n\nTo ensure no loss of critical graph properties, our mapping explicitly encodes:\n*   **Directionality:** We preserve the `subject-predicate-object` structure inherent in triples. The subject (e.g., an entity) is mapped to the primary key of its table, while its outgoing relations (predicates) and their corresponding objects are mapped to distinct columns. This preserves the directed nature of the edges, preventing ambiguity during query generation.\n*   **Edge Types/Attributes:** Type information is embedded directly into the schema at the column level. For instance, a relation is prefixed with its type (e.g., `location:born_in`), creating a dedicated, typed column. This schema-level encoding allows the model to leverage type information without any modification to the underlying architecture.\n\n**2. On Hierarchical or Nested Schemas:**\nFor hierarchical and nested structures, our framework is designed to accommodate established **schema flattening** techniques [1, 2]. This is a standard preprocessing step that linearizes nested data by recursively expanding multi-level fields into single-level columns (e.g., `user.address.city` becomes a column `user_address_city`). Since this transformation occurs before the data is presented to our model, it does not alter our core continual learning mechanism. \n\nWe will add this part to the appendix in the subsequent version.\n\n\n### **Weakness 3:**\nThe work highlights that most baselines cannot be directly ported to decoder-only LLMs, but the consequence is that comparative results with these (Llama3, Qwen2.5) have fewer baselines and thus weaker empirical competition, risking over-claiming.\n\n### **Response to Weakness 3:**\nOur initial baseline selection was guided by a significant technical consideration: many prior state-of-the-art methods [1,3] are intrinsically designed for the T5 encoder-decoder architecture. Their core mechanisms, such as specialized schema encoders or constrained decoding logic, are non-trivial to adapt to modern decoder-only LLMs without substantial re-engineering, which could compromise the integrity of the original methods. Therefore, we prioritized baselines that are either natively compatible with or have established adaptations for decoder-only models to ensure a fair and architecturally consistent evaluation.\n\nHowever, to directly address the reviewer’s concern and further strengthen our empirical claims, we have dedicated significant effort during the rebuttal period to implement and evaluate additional strong baselines on our target LLMs. Concretely, we implement SFNet[4] (2023) and Apex[3] (2025), which are relatively new and have good performance, with LLM backbones. The experimental results are shown in the following table.\n\n\n| Method | AA on stream1 | BWT on stream1 | FWT on stream1 | AA on stream2 | BWT on stream2 | FWT on stream2 | AA on stream3 | BWT on stream3 | FWT on stream3 |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| SFNet + Llama3-8B-Instruct | 36.1 | -14.5 | 3.7 | 35.8 | -17.1 | 4.2 | 35.4 | -12.3 | 4.2 |\n| Apex + Llama3-8B-Instruct | 39.2 | -11.2 | 2.1 | 40.5 | -12.6 | 3.2 | 33.4 | -12.7 | 3.6 |\n| Ke-DeCore + Llama3-8B-Instruct | 40.5 | -16.7 | 5.9 | 41.1 | -17.1 | 6.1 | 37.0 | -19.3 | 4.2 |\n| SFNet + Qwen2.5-7B-Instruct | 40.3 | -10.5 | 4.5 | 38.5 | -13.2 | 4.8 | 34.1 | -15.0 | 5.1 |\n| Apex + Qwen2.5-7B-Instruct | 41.8 | -7.5 | 3.9 | 39.6 | -8.9 | 4.1 | 35.5 | -14.2 | 4.8 |\n| Ke-DeCore + Qwen2.5-7B-Instruct | 43.2 | -8.2 | 6.9 | 40.1 | -9.8 | 5.5 | 36.8 | -16.7 | 6.9 |\n\n\nAs these new results demonstrate, our method maintains a significant performance advantage over a now-broader set of competitive baselines with LLM backbones.  We will incorporate these new results and discussion in the final version of the paper.\n\n\n- [1] Zhao, Weixiang, et al. \"SAPT: A Shared Attention Framework for Parameter-Efficient Continual Learning of Large Language Models.\" Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.\n- [2]. Chen, Yongrui, et al. \"Parameterizing context: Unleashing the power of parameter-efficient fine-tuning and in-context tuning for continual table semantic parsing.\" Advances in neural information processing systems 36 (2023): 17795-17810.\n- [3] Liu, Ruiheng, et al. \"Filling memory gaps: Enhancing continual semantic parsing via sql syntax variance-guided llms without real data replay.\" Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 39. No. 23. 2025.\n- [4] Chen, Yongrui, et al. \"Learn from yesterday: A semi-supervised continual learning method for supervision-limited text-to-sql task streams.\" Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 37. No. 11. 2023."}, {"review_id": "6KcqHlQUNL", "reviewer": "Reviewer_BVyM", "summary": "This paper proposes K-DECORE, a continual learning framework for Structured Knowledge Reasoning (CSKR) tasks, which involve translating natural language questions into structured queries over heterogeneous structured knowledge sources. The central idea is a knowledge decoupling mechanism splitting the reasoning process into schema filtering and query construction. K-DECORE maintains a fixed-size set of PEFT modules, utilizing dual-perspective memory and a strategy for synthesizing pseudo-queries to improve generalization and reduce forgetting. Experimental evaluation on four benchmarks spanning diverse SKR tasks and three LLM backbones shows that K-DECORE achieves consistent gains over existing continual learning baselines, as validated through multiple metrics and ablation studies.", "questions": "1. Could the authors provide a more detailed distribution of different problem types within the data stream? In scenarios where there is a significant difference between the training and test distributions, or where there is a large imbalance among different SKR categories, can the method still maintain robust performance?\n\n2. The performance drop after ablating certain components, such as Random Memory, appears to be minimal. Could the authors provide additional experiments or explanations to clarify this observation?\n\n3. As the proposed approach introduces several modules, it may significantly increase training cost. Although Section 4.6 compares training efficiency, the paper lacks sufficient implementation details. Could the authors provide additional experiments or elaborate further on the implementation aspects related to this comparison?", "limitations": "See Weaknesses and Questions", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths:**\n1. The experimental evaluation is thorough: methods are compared across three diverse task streams using three different LLM backbones, and baselines include a strong set of rehearsal-based and PEFT-based continual learning algorithms. Ablation studies dissect methodological contributions.\n2. The decoupled architecture, i.e., splitting schema filtering and query construction, offers a principled way to enable transfer and mitigate forgetting between heterogeneous tasks. The method shows a commendable level of innovation and delivers effective results.\n3. The paper is well-organized and clearly written, with illustrative figures and tables that effectively support the presentation.\n\n**Weaknesses:**\n1. It seems that the main experiments do not include a breakdown of the distribution of various SKR problem types across each stream, which could be important for interpreting the results more precisely.\n2. The necessity of the designed submodules in this work is not sufficiently demonstrated. In Figure 3, the performance differences between the curves are minimal. Similarly, in Table 2, K-DeCore achieves consistently the best performance only on the AA metric.", "was_revised": true, "final_justification": "I appreciate the authors' detailed rebuttal, which has resolved my concerns regarding the stream distribution and the effectiveness of the submodules. However, I believe the underlying significance of this work is somewhat limited, and this issue cannot be addressed through the rebuttal. Therefore, I have decided to maintain my score of 4.", "rebuttal": "We are very grateful to you for providing us with valuable feedback and suggestions for our paper. We will provide explanations and clarifications for each weakness and question.\n\n### **Weakness 1 & Question 1:**\nIt seems that the main experiments do not include a breakdown of the distribution of various SKR problem types across each stream, which could be important for interpreting the results more precisely.\n\nCould the authors provide a more detailed distribution of different problem types within the data stream? In scenarios where there is a significant difference between the training and test distributions, or where there is a large imbalance among different SKR categories, can the method still maintain robust performance?\n\n### **Response to Weakness 1 & Question 1:**\n\nIt is important to emphasize that the stream we constructed consists of four heterogeneous benchmarks (GrailQA, MTOP, Spider, CWQ), each of which presents a unique task order, thereby introducing different degrees of distribution shift and data imbalance:\n\n- SKR Stream 1: GrailQA → MTOP → Spider → CWQ\n- SKR Stream 2: CWQ → Spider → MTOP → GrailQA\n- SKR Stream 3: MTOP → GrailQA → CWQ → Spider\n\nCrucially, to assess true generalization in each SKR task, we ensure that the training and test sets within each task are non-IID. The degree of this challenge is quantified by the **unseen schema rate**—the proportion of schemas in the test set unseen during training. The statistics are detailed below:\n\n| task name | # of training schema | # of test schema | unseen schema rate |\n|-----------|----------------------|------------------|--------------------|\n| GrailQA   | 934                  | 278              | 74.1%              |\n| MTOP      | 63                   | 35               | 8.6%               |\n| Spider    | 92                   | 70               | 87.1%              |\n| CompWebQ  | 310                  | 256              | 49.2%              |\n\nThis demanding setup, particularly with the high zero-shot rates in GrailQA and Spider, allows for a nuanced analysis of how task heterogeneity impacts catastrophic forgetting and knowledge transfer. Across all three challenging streams, our method, K-DECORE, demonstrates remarkable robustness. Its decoupling mechanism consistently yields significant performance gains, highlighting its effectiveness in complex, non-IID continual learning environments. \n\n**To further assess our model’s robustness in more realistic scenarios, we conducted an additional experiment simulating an imbalanced data stream**, a common challenge where tasks have a non-uniform number of training samples. Specifically, we configured the training set with the following sample distribution: **Spider (500), GrailQA (800), CombWebQ (300), and MTOP (600)**. The results of this experiment are presented in the table below. Here we use Qwen2.5-7b-Instruct as the backbone model.\n\n| Method           | AA on stream1 | BWT on stream1 | FWT on stream1 | AA on stream2 | BWT on stream2 | FWT on stream2 | AA on stream3 | BWT on stream3 | FWT on stream3 |\n|------------------|---------------|----------------|----------------|---------------|----------------|----------------|---------------|----------------|----------------|\n| Fine-Tuning      | 18.5          | -15.2          | 4.1            | 22.2          | -11.8          | 4.8            | 26.3          | -5.1           | 3.4            |\n| EMAR             | 28.2          | -3.1           | 5.4            | 30.7          | -1.1           | 5.1            | 28.3          | -1.6           | 3.2            |\n| Ke-DeCore (Ours) | 31.8          | -10.9          | 8.5            | 33.5          | -8.5           | 7.3            | 30.1          | -6.5           | 6.2            |\n\nAs shown in the table, our method, Ke-DeCore, demonstrates clear superiority in the imbalanced data stream experiment. It achieves the highest AA on all the streams, significantly outperforming the EMAR baseline. \n\n\nWe will incorporate a full analysis of these results into the camera-ready version upon acceptance.\n\n\n\n\n### **Weakness 2 & Question 2:**\nThe necessity of the designed submodules in this work is not sufficiently demonstrated. In Figure 3, the performance differences between the curves are minimal. Similarly, in Table 2, K-DeCore achieves consistently the best performance only on the AA metric.\n\nThe performance drop after ablating certain components, such as Random Memory, appears to be minimal. Could the authors provide additional experiments or explanations to clarify this observation?\n\n### **Response to Weakness 2 & Question 2:**\n\nWhile the performance gap between our K-DeCore and the Random Memory baseline may appear modest in the averaged results of Figure 3 and Table 2, we wish to highlight the consistency and statistical significance of this improvement.\n\nTo provide a more detailed view, the table below presents the performance delta (K-DeCore AA - Random Memory AA) across three distinct task streams from Table 2. \n\n| Backbone Model                | $\\Delta$ AA(%) on SKR stream1 | $\\Delta$ AA(%) on SKR stream2 | $\\Delta$ AA(%) on SKR stream3 | avg $\\Delta$ | std $\\Delta$ |\n|-------------------------------|-------------------------------|-------------------------------|-------------------------------|------|------|\n| Llama3-8B-Instruct            | 0.6                           | 3.4                           | 0.7                           | 1.6  | 1.6  |\n| Qwen2.5-7B-Instruct | 0.4                           | 0.6                           | 0.9                           | 0.6  | 0.3  |\n\nThe results show that our full K-DeCore consistently outperforms the baseline in every single run. Crucially, the low standard deviation (std) demonstrates our method’s robustness against the randomness of task sequences, whereas methods like Random Memory can be more sensitive to it. This consistent, stable gain, validated across multiple permutations, underscores the reliability of our method.\n\n\n### **Question 3:**\nAs the proposed approach introduces several modules, it may significantly increase training cost. Although Section 4.6 compares training efficiency, the paper lacks sufficient implementation details. Could the authors provide additional experiments or elaborate further on the implementation aspects related to this comparison?\n\n\n### **Response to Question 3:**\n\nTo ensure a rigorous and fair comparison, all experiments were benchmarked under a consistent environment: a single NVIDIA RTX 4090 GPU, with T5 as the shared backbone model. We calculated the average training time per task by measuring the total wall-clock time from the beginning to the end of the training process for each method and then dividing by the total number of tasks. For all compared methods, we adopted the official hyperparameters specified in their respective papers.\n\nCorrespondingly, during testing, we counted the average inference time of each method on each task, from the start of inference of the first sample to the end of inference of the last sample. All methods adopted the VLLM framework for acceleration. \n\nFigure 5 in the manuscript demonstrates that our method does not add significant time cost during training or inference.\n\n We will incorporate this detailed breakdown into the appendix of our subsequent versions."}, {"review_id": "mV5JI1SxhA", "reviewer": "Reviewer_fB8B", "summary": "K-DECORE addresses challenges in Continual Structured Knowledge Reasoning by decoupling reasoning into task-specific (query building) and task-agnostic (schema filtering) components. It uses a fixed parameter model and integrates memory consolidation and pseudo-data synthesis for better generalization and reduced catastrophic forgetting. Experiments on benchmark datasets with LLM backbones show K-DECORE’s superiority over existing methods.", "questions": "Could you elaborate on why the \"limited convergence of prompt-tuning\" (for methods like C3 and SAPT) poses a constraint, and how K-DECORE's decoupled design inherently overcomes this issue, leading to its superior results without merely introducing additional parameters?", "limitations": "Yes.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strength:\n\n1. The paper makes a significant contribution by being the first to systematically explore continual learning across heterogeneous Structured Knowledge Reasoning tasks. This addresses a crucial real-world challenge where models need to continuously adapt to new reasoning tasks with varying structured knowledge types.\n\n2. The core idea of decoupling the reasoning process into schema filtering (task-agnostic) and query building (task-specific) is highly innovative and well-motivated. This design effectively bridges gaps across diverse tasks by identifying schema filtering as a shared, reusable component.\n\n3.  K-DECORE operates with a fixed number of tunable parameters, which is a major advantage over prior methods that suffer from parameter growth as tasks increase. This contributes to more efficient reasoning and scalability.\n\n4. The experimental results are comprehensive and convincingly demonstrate K-DECORE's superior performance across all evaluation metrics (AA, BWT, FWT) compared to strong baselines, including rehearsal-based and PEFT-based methods. The use of three different LLM backbones further strengthens the validity of the findings.\n\nWeakness:\n1. While K-DECORE significantly improves BWT compared to baselines like FINE-TUNING, the BWT scores for LLAMA3 and QWEN2.5 backbones are still negative (e.g., -16.7 for Llama3 Stream1, -8.2 for Qwen2.5 Stream1). This indicates that catastrophic forgetting, while mitigated, remains a persistent challenge. The BWT sometimes remains comparable to or slightly worse than some rehearsal-based methods (e.g., EMAR) in certain streams for Llama3.\n\n2. For the structure-guided query synthesis, the paper mentions that novel structures are synthesized \"guided by carefully curated demonstrations\" . While Appendix B is referenced for examples, more explicit details on demonstration would enhance reproducibility and understanding of this crucial step.", "was_revised": true, "final_justification": "The authors have addressed most of my concerns and I would like to keep my score.", "rebuttal": "We are very grateful to you for providing us with valuable feedback and suggestions for our paper. We will provide explanations and clarifications for each weakness and question.\n\n\n### **Weakness 1:**\nWhile K-DECORE significantly improves BWT compared to baselines like FINE-TUNING, the BWT scores for LLAMA3 and QWEN2.5 backbones are still negative (e.g., -16.7 for Llama3 Stream1, -8.2 for Qwen2.5 Stream1). This indicates that catastrophic forgetting, while mitigated, remains a persistent challenge. The BWT sometimes remains comparable to or slightly worse than some rehearsal-based methods (e.g., EMAR) in certain streams for Llama3.\n\n### **Response for Weakness 1:**\nFirst, it's important to emphasize that K-DeCore delivers significant improvements in BWT across all evaluated backbones compared to the Fine-Tuning baseline, regardless of the model (e.g., Llama3, Qwen2.5, or others). This consistent enhancement demonstrates that catastrophic forgetting is effectively alleviated, with our decoupled schema filtering and query building modules, combined with dual replay memories, enabling better retention of prior knowledge without parameter expansion. Moreover, when assessing the overall performance via the Average Accuracy (AA) metric—which captures the holistic effect across the entire task stream—K-DeCore outperforms **all compared methods**, including rehearsal-based approaches like EMAR. This suggests that while isolated BWT metrics may show residual forgetting, the net benefit in end-to-end accuracy underscores our method's superior generalization and task adaptability.\n\nTheoretically, we posit that mitigating catastrophic forgetting and achieving strong cross-task generalization often involve inherent trade-offs, particularly in parameter-efficient continual learning setups. For instance, methods like C3 can fully avoid forgetting by dedicating resources to isolated task representations, but this comes at the expense of high training costs, scalability issues, and limited flexibility for heterogeneous schemas—making them less practical for diverse, sequential tasks like those in our streams. In contrast, K-DeCore strikes a deliberate balance: by maintaining a frozen backbone and using lightweight, trainable LoRA modules with enriched replay, we achieve robust performance across tasks while keeping computational overhead low. Our negative BWT values, though present, are markedly better than naive fine-tuning and comparable to or better than EMAR in many streams, reflecting this optimized equilibrium.\n\nTo further substantiate this, our ablation studies (Table 2) show that removing key components like the dual memories leads to steeper BWT degradation, confirming their role in forgetfulness mitigation.\n\n\n### **Weakness 2:**\nFor the structure-guided query synthesis, the paper mentions that novel structures are synthesized \"guided by carefully curated demonstrations\" . While Appendix B is referenced for examples, more explicit details on demonstration would enhance reproducibility and understanding of this crucial step.\n\n\n### **Response for Weakness 2:**\nOur goal is to equip the model to generalize across the combinatorial space of unseen queries for a given schema. The two principles are:\n\n1. The demonstration set must be comprehensive by including at least one instance of every atomic structural component within the new schema. For a compositional semantic parsing schema like MTOP, this means including an example for each unique INTENT and SLOT type. For a Text-to-SQL/SPARQL schema, this includes each unique table/column and structural clause (e.g., ORDER BY, LIMIT, GROUP BY). This ensures the model has seen every basic building block it might need to use.\n\n2. The demonstration set must be minimal yet powerful by illustrating how atomic components can be combined. Instead of providing examples for every possible combination, we provide examples of simple structures and slightly more complex ones, teaching the model the rules of composition. The model is expected to learn to generate novel, more complex structures by recombining these observed primitives.\n\n\nTwo synthetic examples of MTOP and CombWebQ are shown below.\n\nMTOP:\n```text\nStructure 1: \n[INTENT_1 [SLOT_1 VALUE_1 ] [SLOT_2 VALUE_2 ]]\n\nStructure 2: \n[INTENT_1 [SLOT_1 VALUE_1 ]]\n\nSynthetic Structure: \n[INTENT_1 [SLOT_1 VALUE_1 ] [SLOT_2 VALUE_2 ] [SLOT_3 VALUE_3 ]]\n```\n\nMTOP:\n```text\nStructure 1: \nSELECT DISTINCT ?x\nWHERE {\n?x [COL_1] [ENT_1] . \n?x [COL_2] ?y .\n?y [COL_3] ?z .\n}\n\n\nStructure 2: \nSELECT DISTINCT ?x\nWHERE {\n?x [COL_1] [ENT_1] . \n?x [COL_2] ?y .\n}\nORDER BY ?y\nLIMIT 1\n\nSynthetic Structure: \nSELECT DISTINCT ?x\nWHERE {\n?x [COL_1] [ENT_1] . \n?x [COL_2] ?y .\n?y [COL_3] ?z .\n}\nORDER BY ?z\nLIMIT 1\n```"}, {"review_id": "ZJlUwaKDBr", "reviewer": "Reviewer_Hfgq", "summary": "The work studies continual structured knowledge reasoning, where a model must translate natural‑language questions into structured queries while it moves through a sequence of tasks that involve different forms of knowledge, such as relational tables, knowledge graphs, and dialogue states. Current continual learning methods either add new parameters for each task or generalize poorly across heterogeneous schemas. The authors introduce K‑DeCore, which keeps one frozen backbone language model and attaches two small, trainable modules: a schema filter that selects the relevant parts of a given schema and a query builder that forms the final query from the filtered schema. By separating these two stages, the approach transfers knowledge across tasks without increasing the parameter budget. K‑DeCore also maintains dual replay memories, one that stores representative schema examples and another that stores diverse query structures, and enriches them with synthetically generated queries to raise coverage.", "questions": "Please refer to the Strengths And Weaknesses.", "limitations": "Yes.", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n\n- The paper frames continual structured knowledge reasoning as a sequence of heterogeneous tasks and tackles it with a fixed‑parameter backbone plus two small LoRA modules, one for schema filtering and one for query building. By separating these roles, the design allows the same parameters to serve databases, knowledge graphs, and dialogue states without growing with the number of tasks, directly addressing the scalability problem that affects many prior rehearsal and adapter methods.\n\n- The knowledge decoupling idea is paired with a dual‑perspective replay memory: schema‑guided clusters preserve coverage of previously seen schema elements, while structure‑guided clusters store diverse query skeletons. This two‑view memory gives a clear, task‑specific reason for each stored sample and is lightweight, which keeps continual training practical on a single GPU.\n\n- Experiments span four public datasets, formed into three task streams, and the study tests three backbones.\n\n**Weaknesses**\n\n- Both schema‑guided and structure‑guided memories use cosine distance over embeddings from a separate encoder, which may leave important but embedding‑close cases unprotected.\n\n- Mapping every schema into a flat table format removes relation direction, edge types, and slot hierarchy. \n\n- The query-building generator stitches together random structure templates and schema fragments and then discards any sample that fails to execute. This generate-and-filter loop can bias memory toward structures that are easier for the SQL engine to validate rather than those that improve model generalization.", "was_revised": true, "final_justification": "My concerns have been addressed after reading the response. I would like to maintain my positive recommendation for this paper.", "rebuttal": "We are very grateful to you for providing us with valuable feedback and suggestions for our paper. We will provide explanations and clarifications for each weakness and question.\n\n\n### **Weakness 1:**\nBoth schema‑guided and structure‑guided memories use cosine distance over embeddings from a separate encoder, which may leave important but embedding‑close cases unprotected.\n\n\n### **Response for Weakness 1:**\nThe primary goal of employing cosine distance in both the schema-guided and structure-guided memories is to identify and prioritize samples that exhibit significant differences in their embedding space, thereby capturing a broad representation of the dataset’s overall distribution. This approach is grounded in the assumption that, **for samples with highly similar embedding semantics (i.e., those that are close in the embedding space), retaining just one representative instance is often sufficient for effective continual learning.** By focusing on diversity through distance maximization, we aim to select the most comprehensive set of exemplars within the constraints of a limited memory budget. This not only ensures coverage of common patterns but also, in theory, allows our method to emphasize long-tail samples—those rarer or more unique cases that might otherwise be overlooked in denser regions of the embedding space.\n\n### **Weakness 2:**\n\nMapping every schema into a flat table format removes relation direction, edge types, and slot hierarchy.\n\n\n### **Response for Weakness 2:**\n\nWe emphasize that our DB-style unification is designed to fully preserve all critical information, ensuring that the flattened representation remains faithful to the original structures. Below, we explain how our design achieves this preservation while enabling effective covering across heterogeneous tasks.\n\nFor relation direction, our mapping explicitly preserves the subject-object orientation inherent in triples (subject-predicate-object). Specifically, we designate the content in the primary key of the transformed table as the subject, while the contents in other columns represent the corresponding objects. This structured encoding ensures that directional information is retained and can be reliably interpreted during query generation, preventing ambiguities that might arise from undirected representations.\n\nRegarding edge types, we incorporate them directly into the schema by prefixing each relation (or edge) with its type and treating it as a dedicated column in the flattened table. This simple yet effective augmentation embeds the type information within the schema itself, allowing the model to access and utilize it without requiring additional modifications to the backbone architecture.\n\nFor multi-level structures, such as hierarchical slots or nested tables, we adopt flattening techniques inspired by related works[1,2]. This involves recursively expanding multi-level columns into single-level ones, ensuring that hierarchical relationships are linearized but not discarded. Our framework is flexible enough to accommodate this, as the flattening occurs preprocessing and does not alter the core continual learning mechanism.\n\nMore crucially, our DB-style unification is applied **only during the schema filtering stage**, where the primary objective is akin to entity linking—namely, identifying the relevant tables (entity types) and columns (relations) for the given query. In this context, not all granular details (e.g., full hierarchical depth) are necessary, as the filter's role is to select pertinent schema elements rather than reconstruct the entire original structure. The subsequent query builder module, operating on the filtered schema, can then leverage the frozen backbone to generate accurate queries, drawing on the preserved essential information.\n\nOur experimental results in Table 1 validate the robustness of our K-DeCore, demonstrating strong performance on the task streams over mainstream and widespread heterogeneous knowledge.  \n\nWe will explain in detail the transformation process of each structured knowledge in a camera-ready version once the paper is accepted.\n\n- [1] Min, Dehai, et al. \"Exploring the Impact of Table-to-Text Methods on Augmenting LLM-based Question Answering with Domain Hybrid Data.\" Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track). 2024.\n- [2] Zhang, Zhehao, Yan Gao, and Jian-Guang Lou. \"e5: Zero-shot hierarchical table analysis using augmented LLMs via explain, extract, execute, exhibit and extrapolate.\" Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.\n\n\n### **Weakness 3:**\n\nThe query-building generator stitches together random structure templates and schema fragments and then discards any sample that fails to execute. This generate-and-filter loop can bias memory toward structures that are easier for the SQL engine to validate rather than those that improve model generalization.\n\n\n### **Response for Weakness 3:**\n\nThe core rationale for incorporating successful execution as a filtering criterion is to guarantee that each pseudo-sample added to the memory is semantically and syntactically valid. We firmly believe that including incorrect or invalid samples would introduce harmful noise into the replay buffer, potentially degrading the model's ability to learn robust patterns across tasks. By filtering out failures, we prioritize high-quality augmentations that faithfully represent plausible query-schema interactions, thereby supporting effective knowledge transfer without polluting the training data. Importantly, this process does not inherently favor \"easier\" structures; instead, it enforces a baseline of executability, which aligns with real-world query requirements and encourages the model to generalize to diverse, valid scenarios rather than overfitting to artificial or erroneous ones.\n\nFurthermore, we clarify that our approach is not limited to SQL engine execution. We adapt the validation to the specific nature of each dataset: for queries on the CWQ dataset, we utilize a SPARQL engine to execute and verify SPARQL queries; for the GrailQA and MTOP datasets, we leverage the official evaluation code provided by the original papers to assess query legality, ensuring a tailored and accurate check. This multi-engine strategy broadens the scope of validated structures, reducing any dataset-specific biases and promoting a more comprehensive memory that captures heterogeneous query forms.\n\nWe will explain this section in detail in the camera-ready version once the paper is accepted."}]

Author rebuttal:

Meta-review: K-DeCore introduces a novel continual learning framework for structured knowledge reasoning tasks that decouples reasoning into task-specific and task-agnostic stages to facilitate knowledge transfer.  By mapping various structured knowledge forms (SQL, KG, dialogue) to a DB-style schema, the method reduces task heterogeneity in the task-specific schema filtering stage.  The framework incorporates a dual-perspective memory consolidation mechanism and a structure-guided pseudo-data synthesis strategy to enhance model generalization.  Experiments on four benchmark datasets and  large language models demonstrate K-DeCore's superior performance over existing continual learning methods across multiple metrics.  

In the discussion period, the authors addressed the various concerns raised by the reivewers, including the small experimental size and the applicability to complex, non-relational structures, while the reviewers mostly feel possitve about the discussion.
Decision: Accept (poster)