Dual-level Knowledge Alignment (DKA)

• Dual-level Knowledge Alignment is a design pattern that employs two distinct knowledge constraints to address failures in coverage and correctness.
• It applies dual optimization in diverse fields such as continual learning, fMRI decoding, and multimodal language tasks to refine both representation and behavior.
• Empirical results indicate that balancing semantic and relational alignments yields superior performance metrics compared to single-level alignment strategies.

Searching arXiv for the specified paper and closely related uses of “Dual-level Knowledge Alignment” / DKA. arXiv search query: id:([2507.09471](/papers/2507.09471)) OR "Cross-subspace Knowledge Alignment and Aggregation for Robust Continual Learning" Dual-level Knowledge Alignment (DKA) denotes a family of alignment strategies in which two complementary knowledge constraints are optimized jointly rather than collapsed into a single supervision signal. Across recent arXiv papers, the term appears in continual learning, long-form factuality, fMRI decoding, spatial transcriptomics, knowledge distillation, knowledge-based visual question answering, knowledge-graph-aligned language modeling, and continual medical image segmentation. The concrete meaning changes by domain, but the shared motif is stable: one level aligns a primary knowledge carrier, and a second level constrains relational validity, transferability, or truthfulness in a different space or at a different granularity (He et al., 13 Jul 2025, Li et al., 28 Sep 2025, Li et al., 8 Mar 2026, Zhang et al., 21 Nov 2025, Ding et al., 2020, Yu et al., 2024, Ye et al., 4 Jul 2025).

1. Terminological scope and representative uses

Recent usage does not define a single canonical DKA algorithm. This suggests that DKA functions as a recurrent design pattern: a method introduces two alignment targets, each intended to correct a failure mode left unresolved by the other. In some papers the two levels are semantic and distributional; in others they are explicit and implicit, per-sample and relational, or visual and textual. A closely related antecedent is the “knowledge alignment problem,” which frames alignment as bridging a user question and external groundings through clarifications, thereby making the external knowledge usable by the model (Zhang et al., 2023).

Paper	Domain	Dual levels
"CKAA: Cross-subspace Knowledge Alignment and Aggregation for Robust Continual Learning" (He et al., 13 Jul 2025)	PEFT-based continual learning	aligning intra-class feature distributions across different subspaces; learning a robust global classifier through a feature simulation process
"Knowledge-Level Consistency Reinforcement Learning: Dual-Fact Alignment for Long-Form Factuality" (Li et al., 28 Sep 2025)	long-form generation	factual recall / coverage; factual precision / truthfulness
"Duala: Dual-Level Alignment of Subjects and Stimuli for Cross-Subject fMRI Decoding" (Li et al., 8 Mar 2026)	cross-subject fMRI decoding	stimulus-level semantic alignment; subject-level distribution alignment
"Dual-Path Knowledge-Augmented Contrastive Alignment Network for Spatially Resolved Transcriptomics" (Zhang et al., 21 Nov 2025)	spatial transcriptomics	modality-to-knowledge alignment; knowledge-mediated modality alignment
"Multi-level Knowledge Distillation via Knowledge Alignment and Correlation" (Ding et al., 2020)	knowledge distillation	individual-sample alignment; cross-sample correlation
"Knowledge Acquisition Disentanglement for Knowledge-based Visual Question Answering with LLMs" (An et al., 2024)	KVQA	image-based sub-question; knowledge-based sub-question
"KaLM: Knowledge-aligned Autoregressive Language Modeling via Dual-view Knowledge Graph Contrastive Learning" (Yu et al., 2024)	KG-aligned autoregressive LM	explicit knowledge alignment; implicit knowledge alignment
"Dual-Alignment Knowledge Retention for Continual Medical Image Segmentation" (Ye et al., 4 Jul 2025)	continual segmentation	cross-network alignment; cross-representation alignment

2. Recurrent conceptual structure

A recurrent conceptual split in DKA is between coverage and correctness. In KLCF, “knowledge-level consistency” is defined as consistency between the aligned model’s “Expressed Knowledge” and the base model’s “Parametric Knowledge,” with a Dual-Fact Alignment mechanism that jointly optimizes factual recall and precision. The factual reward is

$$R_{\text{fact}}(\mathcal{A}_i)=\kappa\,R_{\text{recall}}(\mathcal{A}_i)+\lambda\,R_{\text{precision}}(\mathcal{A}_i)+\mu\,R_{\text{truth}}(\mathcal{A}_i),$$

where the first two terms operate against a fact checklist derived from the base model’s local knowledge boundary and the third scores the truthfulness of generated claims by a self-assessment module trained on SUPPORT and REFUTE claims (Li et al., 28 Sep 2025).

A second recurring split is between representation-level and behavioral alignment. KaLM makes this explicit: it combines an explicit knowledge alignment objective, implemented by dual-view KG contrastive learning over $(\mathcal{D}_{hr},\mathcal{D}_t)$, with an implicit knowledge alignment objective that casts triple completion as instruction-style language modeling. The joint objective is

$$\mathcal{L}_{KaLM}=\mathcal{L}_{exp}+\lambda\cdot\mathcal{L}_{imp}.$$

The first term shapes the LLM embedding space; the second teaches the model to generate the tail description from head-and-relation descriptions without sacrificing autoregressive behavior (Yu et al., 2024).

A third split is between local alignment and relational alignment. MLKD argues that traditional KD minimizes a KL divergence of softmax outputs and is therefore “knowledge alignment of an individual sample only,” whereas contrastive KD mainly transfers “knowledge correlation” between different samples. Its core formulation

$$\mathcal{L}=\lambda_1\mathcal{L}_{\mathrm{Align}}+\lambda_2\mathcal{L}_{\mathrm{Corr}}$$

treats per-sample teacher-student matching and batch-level relational structure as distinct but complementary knowledge levels (Ding et al., 2020).

In knowledge-based VQA, the same two-level logic appears as source-specific decomposition. DKA uses LLM feedback to decompose the original question into an image-based sub-question $q_i$ and a knowledge-based sub-question $q_k$,

$\{q_i,q_k\}=LLM(\mathcal{P},x_q),$

so that visual retrieval and external retrieval are aligned to different knowledge requirements instead of being coupled through the original question (An et al., 2024).

3. Language-model and retrieval-oriented formulations

In long-form factuality, KLCF operationalizes DKA around the base model’s parametric knowledge boundary. Offline, it samples base-model responses, extracts atomic claims $C(o_i)$, verifies them against a Wikipedia index, and retains SUPPORT-labeled claims as local factual parametric knowledge. These claims are condensed into a query-specific fact checklist $\Lambda(q_i)$. Online RL then computes checklist-based recall and precision, while a truthfulness reward model estimates $P(c\mid \text{True})$ for each extracted claim. The standard truthfulness reward is

$(\mathcal{D}_{hr},\mathcal{D}_t)$0

and the full RL objective combines factual reward, general preference reward, format reward, and a length penalty under GRPO. The paper emphasizes that online RL is external-knowledge-free even though external verification is used once offline to label the local knowledge boundary (Li et al., 28 Sep 2025).

KaLM uses a structurally different but conceptually analogous arrangement. For each triple $(\mathcal{D}_{hr},\mathcal{D}_t)$1, it forms a head-relation view $(\mathcal{D}_{hr},\mathcal{D}_t)$2 and a tail view $(\mathcal{D}_{hr},\mathcal{D}_t)$3, encodes both with the same autoregressive LLM, and aligns them through symmetric contrastive losses $(\mathcal{D}_{hr},\mathcal{D}_t)$4 and $(\mathcal{D}_{hr},\mathcal{D}_t)$5. The same parameters are then optimized by a triple-completion LM loss,

$(\mathcal{D}_{hr},\mathcal{D}_t)$6

which injects textual patterns of KG knowledge into the model. The explicit term targets alignment and uniformity in the embedding space; the implicit term targets generative behavior on knowledge-driven tasks (Yu et al., 2024).

In KVQA, DKA is “Disentangled Knowledge Acquisition from LLM feedback” rather than a loss-based alignment framework. After decomposition, PromptCAP answers the image-side requirement,

$(\mathcal{D}_{hr},\mathcal{D}_t)$7

and a retriever answers the external-knowledge requirement,

$(\mathcal{D}_{hr},\mathcal{D}_t)$8

The answering LLM then conditions on the original question, the disentangled caption, retrieved knowledge, and selected in-context examples. The two levels are therefore aligned at the query-formation stage rather than in a learned latent loss (An et al., 2024).

A related grounding formulation is MixAlign, which states the knowledge alignment problem as the mismatch between user question $(\mathcal{D}_{hr},\mathcal{D}_t)$9 and retrieved grounding knowledge $$\mathcal{L}_{KaLM}=\mathcal{L}_{exp}+\lambda\cdot\mathcal{L}_{imp}.$$0, and augments answering from $$\mathcal{L}_{KaLM}=\mathcal{L}_{exp}+\lambda\cdot\mathcal{L}_{imp}.$$1 to $$\mathcal{L}_{KaLM}=\mathcal{L}_{exp}+\lambda\cdot\mathcal{L}_{imp}.$$2 by introducing clarifications $$\mathcal{L}_{KaLM}=\mathcal{L}_{exp}+\lambda\cdot\mathcal{L}_{imp}.$$3. Although MixAlign does not use the DKA label, it anticipates later dual-level formulations by separating human–KB alignment from model–KB alignment (Zhang et al., 2023).

4. Representation-centric and multimodal formulations

In cross-subject fMRI decoding, Duala defines the two levels as stimulus-level semantic preservation and subject-level distribution alignment. Stimulus-level alignment is implemented through a triplet semantic alignment loss,

$$\mathcal{L}_{KaLM}=\mathcal{L}_{exp}+\lambda\cdot\mathcal{L}_{imp}.$$4

and a relational consistency loss that matches the new subject’s class-similarity matrix to a reference matrix aggregated from source subjects. Subject-level alignment is implemented by Subject-level Distribution Perturbation, which constructs category-wise means $$\mathcal{L}_{KaLM}=\mathcal{L}_{exp}+\lambda\cdot\mathcal{L}_{imp}.$$5 and subject-specific deviations $$\mathcal{L}_{KaLM}=\mathcal{L}_{exp}+\lambda\cdot\mathcal{L}_{imp}.$$6, then perturbs target-subject embeddings as

$$\mathcal{L}_{KaLM}=\mathcal{L}_{exp}+\lambda\cdot\mathcal{L}_{imp}.$$7

The framework therefore aligns semantic geometry and subject-specific neural variability simultaneously (Li et al., 8 Mar 2026).

In spatial transcriptomics, DKAN treats external biological knowledge as a cross-modal coordinator. Gene semantic texts are built from NCBI gene database entries, summarized with GPT-4o, embedded by BioBERT, projected, and refined by a transformer into $$\mathcal{L}_{KaLM}=\mathcal{L}_{exp}+\lambda\cdot\mathcal{L}_{imp}.$$8. These semantic features query image features $$\mathcal{L}_{KaLM}=\mathcal{L}_{exp}+\lambda\cdot\mathcal{L}_{imp}.$$9 and expression features $$\mathcal{L}=\lambda_1\mathcal{L}_{\mathrm{Align}}+\lambda_2\mathcal{L}_{\mathrm{Corr}}$$0 through cross-attention to produce $$\mathcal{L}=\lambda_1\mathcal{L}_{\mathrm{Align}}+\lambda_2\mathcal{L}_{\mathrm{Corr}}$$1 and $$\mathcal{L}=\lambda_1\mathcal{L}_{\mathrm{Align}}+\lambda_2\mathcal{L}_{\mathrm{Corr}}$$2, which are then aligned with a CLIP-style contrastive loss

$$\mathcal{L}=\lambda_1\mathcal{L}_{\mathrm{Align}}+\lambda_2\mathcal{L}_{\mathrm{Corr}}$$3

This yields one level of modality-to-knowledge alignment and a second level of knowledge-mediated modality alignment (Zhang et al., 21 Nov 2025).

In continual medical image segmentation, Dual-Alignment Knowledge Retention instantiates DKA with two HSIC-based modules. Cross-Network Alignment aligns bottleneck features of buffered samples between frozen teacher and current student by a linear-HSIC-derived cross-correlation matrix $$\mathcal{L}=\lambda_1\mathcal{L}_{\mathrm{Align}}+\lambda_2\mathcal{L}_{\mathrm{Corr}}$$4, and penalizes deviations of its diagonal entries from $$\mathcal{L}=\lambda_1\mathcal{L}_{\mathrm{Align}}+\lambda_2\mathcal{L}_{\mathrm{Corr}}$$5. Cross-Representation Alignment aligns current-network bottleneck features of buffered and current-domain samples by first mapping 3D feature tensors through a feature mapping block, then searching over feature pairings and maximizing nonlinear HSIC under the best permutation. The total objective is

$$\mathcal{L}=\lambda_1\mathcal{L}_{\mathrm{Align}}+\lambda_2\mathcal{L}_{\mathrm{Corr}}$$6

Here the two levels are explicitly inter-model and intra-model (Ye et al., 4 Jul 2025).

In continual learning with PEFT adapters, the CKAA abstract defines DKA as aligning intra-class feature distributions across different subspaces and learning a robust global classifier through a feature simulation process, so that the model can distinguish features from both correct and incorrect subspaces during training. Within the available abstract, this is the verified scope of the method’s DKA component (He et al., 13 Jul 2025).

5. Optimization behavior and empirical evidence

Across multiple domains, single-level variants underperform balanced dual-level variants. In KLCF, ablation shows that checklist-only optimization $$\mathcal{L}=\lambda_1\mathcal{L}_{\mathrm{Align}}+\lambda_2\mathcal{L}_{\mathrm{Corr}}$$7 increases recall metrics but weakens precision, while truthfulness-only optimization $$\mathcal{L}=\lambda_1\mathcal{L}_{\mathrm{Align}}+\lambda_2\mathcal{L}_{\mathrm{Corr}}$$8 peaks precision but sharply reduces recall; the balanced Dual-Fact setting $$\mathcal{L}=\lambda_1\mathcal{L}_{\mathrm{Align}}+\lambda_2\mathcal{L}_{\mathrm{Corr}}$$9 gives the best F1 and overall factuality. For Qwen2.5-14B trained from base, FActScore rises from $q_i$0 to $q_i$1, LongWiki $q_i$2 from $q_i$3 to $q_i$4, LongFact $q_i$5 from $q_i$6 to $q_i$7, and Factory $q_i$8 from $q_i$9 to $q_k$0 (Li et al., 28 Sep 2025).

Duala reports the same pattern. With only about one hour of fMRI data, it reaches $q_k$1 image retrieval and $q_k$2 brain retrieval on average across subjects, versus $q_k$3 and $q_k$4 for MindTuner and $q_k$5 and $q_k$6 for MindEye2. In ablation, SDP-only improves retrieval and reconstruction, $q_k$7-only strongly improves brain retrieval but slightly hurts image retrieval, and full Duala gives the best overall trade-off, including $q_k$8 image retrieval and $q_k$9 brain retrieval on subject 1 (Li et al., 8 Mar 2026).

DKAN likewise shows that both semantic knowledge and contrastive dual-path alignment matter. On HER2+, TRIPLEX obtains MAE $\{q_i,q_k\}=LLM(\mathcal{P},x_q),$0, MSE $\{q_i,q_k\}=LLM(\mathcal{P},x_q),$1, PCC(all) $\{q_i,q_k\}=LLM(\mathcal{P},x_q),$2, and PCC(HPG) $\{q_i,q_k\}=LLM(\mathcal{P},x_q),$3, whereas DKAN reaches MAE $\{q_i,q_k\}=LLM(\mathcal{P},x_q),$4, MSE $\{q_i,q_k\}=LLM(\mathcal{P},x_q),$5, PCC(all) $\{q_i,q_k\}=LLM(\mathcal{P},x_q),$6, and PCC(HPG) $\{q_i,q_k\}=LLM(\mathcal{P},x_q),$7. Ablation on STNET shows PCC(all) drops from $\{q_i,q_k\}=LLM(\mathcal{P},x_q),$8 to $\{q_i,q_k\}=LLM(\mathcal{P},x_q),$9 without gene semantic features and to $C(o_i)$0 without contrastive learning, indicating that semantics and dual-path alignment contribute differently but cumulatively (Zhang et al., 21 Nov 2025).

The same complementarity appears in continual retention and distillation. In Dual-Alignment Knowledge Retention on optic cup segmentation, REKD alone yields average Dice $C(o_i)$1 and BWT $C(o_i)$2, whereas CRA with pairing plus CNA reaches average Dice $C(o_i)$3 and BWT $C(o_i)$4. On prostate segmentation, the method reports average Dice $C(o_i)$5 and BWT $C(o_i)$6, exceeding the listed baselines. In MLKD, alignment alone and correlation alone are both beneficial, but alignment plus correlation performs better than either component alone across CIFAR-100, ImageNet, segmentation, and self-supervised settings (Ye et al., 4 Jul 2025, Ding et al., 2020).

KaLM provides an additional representation-level diagnostic. After training, sentence-level anisotropy on Wikitext-103 test sentences drops from $C(o_i)$7 to $C(o_i)$8, while KGC and generation-based KGQA improve. In the $C(o_i)$9-ablation on WN18RR, $\Lambda(q_i)$0 gives MR $\Lambda(q_i)$1, MRR $\Lambda(q_i)$2, Hit@10 $\Lambda(q_i)$3, and PPL $\Lambda(q_i)$4, whereas $\Lambda(q_i)$5 improves these to MR $\Lambda(q_i)$6, MRR $\Lambda(q_i)$7, Hit@10 $\Lambda(q_i)$8, and PPL $\Lambda(q_i)$9, indicating that implicit alignment improves both knowledge representation and language-model behavior at a moderate weight (Yu et al., 2024).

6. Misconceptions, limitations, and open directions

A common misconception is that DKA refers to a single standardized method. The literature instead uses the label for several non-equivalent constructions: factual recall plus truthfulness in long-form RL, semantic plus subject alignment in fMRI, explicit plus implicit KG alignment in autoregressive LMs, inter-network plus intra-network alignment in continual segmentation, and even query decomposition in KVQA (Li et al., 28 Sep 2025, Li et al., 8 Mar 2026, Yu et al., 2024, Ye et al., 4 Jul 2025, An et al., 2024). A second misconception is that “knowledge” always means external symbolic facts. In these papers it may also mean feature subspaces, cross-sample relations, neural-distribution statistics, gene semantics, or buffered bottleneck representations (He et al., 13 Jul 2025, Ding et al., 2020, Zhang et al., 21 Nov 2025).

The limitations are correspondingly heterogeneous. KLCF operates in strict closed-book QA, uses a static wiki snapshot, and remains response-level rather than process-level. Duala relies on category-based supervision and simplified distribution modeling based on per-category averages and element-wise variances. Dual-Alignment Knowledge Retention is currently formulated for 2D settings, depends on a replay buffer, and leaves 3D extension as future work. MixAlign focuses on tabular KBs, adds prompt and interaction overhead, and still shows a gap to oracle alignment when the number of irrelevant groundings increases (Li et al., 28 Sep 2025, Li et al., 8 Mar 2026, Ye et al., 4 Jul 2025, Zhang et al., 2023).

The forward direction suggested by these papers is therefore not a single “best” DKA recipe but a broader program of multi-constraint alignment. Proposed extensions include process-based factual rewards and step-wise factual alignment in KLCF, richer subject-level distribution modeling and hierarchical multi-level alignment in Duala, more heterogeneous knowledge sources in MixAlign, and 3D or adaptive layer selection in continual medical segmentation (Li et al., 28 Sep 2025, Li et al., 8 Mar 2026, Zhang et al., 2023, Ye et al., 4 Jul 2025). This suggests that DKA is best understood as a general strategy for separating two failure modes and binding them with two coupled alignment objectives, rather than as a fixed architecture or loss.