Knowledge Preservation & Unification

• Knowledge Preservation and Unification (KPU) is a framework that retains legacy data and integrates new information into a unified, computation-ready representation.
• It leverages techniques like domain-incremental learning, cross-lingual modeling, federated AI, and controlled vocabulary pipelines to ensure semantic interoperability.
• Empirical outcomes demonstrate substantial improvements in anti-forgetting and transfer efficiency, supporting applications from lifelong learning to digital archival.

Knowledge Preservation and Unification (KPU) encompasses methods and frameworks that guarantee the integrity, durability, and semantic interoperability of knowledge across space, time, organizational boundaries, or domain shifts while simultaneously enabling the synthesis and integration of disparate knowledge resources into a unified, computation-ready representation. KPU transcends mere data archiving or model persistence by emphasizing both the faithful retention of original knowledge (preservation) and the active harmonization of this knowledge to support generalization, transfer, and collective reasoning (unification). Research on KPU spans continual learning, federated AI, controlled vocabulary transformation, and semantic coding for cross-generational access.

1. Formal Foundations and Objectives

Formally, KPU frameworks address the challenge of learning, storing, and operating on knowledge such that past information is not forgotten or rendered inaccessible (preservation), while new knowledge is brought into a common representational space with earlier content (unification).

• In domain-incremental learning (e.g., Lifelong Person Re-ID), data arrive as a sequence of domains $D = \{D^1, D^2, ..., D^T\}$ with only the current domain accessible at each step. The objective is to preserve domain-specific representations and produce a cross-domain unified embedding, without reliance on archiving prior exemplars (Liu et al., 5 Aug 2025).
• In cross-lingual language modeling, KPU is formalized through unification metrics that quantify the similarity of facts expressed in different languages within model representations. Here, preservation corresponds to the retention of factual information, and unification measures the model's ability to ignore spurious features (e.g., language identity) when forming semantic vectors (Blum et al., 14 Aug 2025).
• In federated AI, KPU is mathematically grounded in a hierarchy of privacy-preserving computations. Each data owner retains local control, yet global learning occurs by securely merging distributed knowledge through cryptographic protocols, multi-level federated model training, and cross-domain knowledge graph fusion (Li et al., 2020).
• Controlled vocabulary digitization and semantic encoding frameworks, such as PDE and SKOS, implement KPU by transforming analog resources or free text into modular, globally addressable, and semantically self-sufficient data structures (Tsuyuki et al., 27 Jul 2025, Greenberg et al., 2021).

2. Methodological Architectures

Diverse architectures realize KPU objectives depending on the application context:

• Distribution-Aware Knowledge Unification and Association (DKUA) for Lifelong Person Re-ID integrates a shared backbone, a stack of frozen and trainable domain-style modules, distribution-aware transformation, knowledge-unification via adaptive weighted fusion, and cross-domain alignment via association and distribution matching. Notably, this architecture avoids distillation and exemplar rehearsal, instead leveraging statistical covariance matching to prevent distribution drift (Liu et al., 5 Aug 2025).
• The Permanent Data Encoding (PDE) framework achieves electrically independent KPU using fixed-length codes, public blockchain-anchored dictionaries, and a deterministic rule-based expansion process. PDE blocks are encoded as sequences of semantically explicit microtokens, facilitating decoding even on degraded physical media. Definitions are signed and globally discoverable, ensuring semantic resilience across generations (Tsuyuki et al., 27 Jul 2025).
• Federated AI (Knowledge Federation) frameworks partition KPU into four ascending levels: information-level federation (secure statistics), model-level federation (privacy-preserving multi-party joint model learning), cognition-level federation (ensemble learning over encrypted local embeddings), and knowledge-level federation (fusion/inference over exchanged structured knowledge graphs) (Li et al., 2020).
• Controlled vocabulary pipelines (e.g., LCSH-to-SKOS in Project Pipeline) employ staged digitization, mapping, persistent identifier minting (e.g., ARKs), and cross-ontology ingestion into a unifying search and tagging system (e.g., HIVE), ensuring historical computational resources remain harmonized and addressable in future scholarship (Greenberg et al., 2021).

3. Algorithms, Metrics, and Protocols

KPU systems deploy specialized loss functions, object functions, metrics, and protocols to operationalize preservation and unification:

• Lifelong vision models use total loss functions incorporating cross-entropy, triplet, knowledge-alignment, unified knowledge association, and distribution-based knowledge transfer terms:

$$L = L_{\mathrm{ReID}} + L_{\mathrm{KA}} + L_{\mathrm{UKA}} + L_{\mathrm{DKT}}$$

Each enforces preservation (of domain styles and statistical distribution) and unification (via cross-domain centers and affinity regularization) (Liu et al., 5 Aug 2025).

• Cross-lingual LMs define a Unification Score $U$, quantifying the ratio of average cross-language to intra-language cosine similarity for each fact. Mutual information metrics ($I(A; L)$ for attribute informativeness; $I(L; T)$ for language extractability from tokens) are diagnostic of spurious language separation and are used to guide data-processing or architecture choices that enhance unification (Blum et al., 14 Aug 2025).
• Federated AI protocols combine cryptographic primitives—additive homomorphic encryption, secure aggregation, private set intersection, label privacy-preserving computation, and optional differential privacy—so that global objectives (e.g., federated model training) are met under strict preservation constraints. The resulting global models, ensembles, or fused knowledge graphs are unified artifacts that integrate distributed knowledge without data leakage (Li et al., 2020).
• Semantic encoding and vocabulary pipelines use deterministic parsing and matching (BNF/regex specification, rule-based expansion, or inverted index searching) in conjunction with immutable identifiers and semantic resolvers for KPU enforcement (Tsuyuki et al., 27 Jul 2025, Greenberg et al., 2021).

4. Empirical Outcomes and Performance Characteristics

KPU systems demonstrate quantifiable gains in both retention of prior knowledge and effective cross-context generalization:

System/Domain	Preservation Metric	Unification/Generalization Metric	Key Results
Lifelong Person Re-ID	Anti-forgetting (Seen mAP/R@1)	Unseen mAP/R@1	DKUA achieves +13.9% mAP / +11.4% R@1 (Order 1), +14.3% mAP / +13.2% R@1 (Order 2) over SOTA on anti-forgetting; +13.6% mAP / +14.9% R@1 for generalization (Liu et al., 5 Aug 2025)
Cross-lingual LM	Unification Score $U$, R$^2_\mathrm{Lang}$	Cross-lingual accuracy	Unification score $U$ near 1 implies maximal transfer; balancing $I(A;L)$ and $I(L;T)$ drives U upward, supporting improved out-of-language generalization (Blum et al., 14 Aug 2025)
Knowledge Federation	Local privacy, encrypted gradient leakage	Global model accuracy, C-index	Joint credit scoring achieves empirical accuracy/C-index improvement of ~7% over local models, communication cost reductions of 80%, no raw data leakage (Li et al., 2020)
Pipeline/HIVE	100% mapping fidelity to historical records	Tagging/lookup response time, scalability	16,000 heading conversion in <1 hr, tagging of 50 KB text in <2 s, linear scaling for additional vocabularies, PIDs yield stable cross-platform access (Greenberg et al., 2021)
PDE	Human and machine readability under degradation	Dictionary integrity, semantic reconstitution	Enables knowledge reconstruction with minimal artifacts centuries later, regardless of electric or software context, blockchain ensures definition immutability (Tsuyuki et al., 27 Jul 2025)

Consistently, these KPU approaches outperform legacy exemplar-, distillation-, or centralized-data-dependent baselines in both anti-forgetting/retention and transfer/generalization metrics.

5. Use Cases and Application Domains

• Lifelong and continual learning: DKUA enables domain-adaptive models for person re-identification without storing prior samples, immediately extensible to object detection and segmentation tasks by repurposing per-task adapters (Liu et al., 5 Aug 2025).
• Disaster resilience and archival: PDE codes etched onto physical media can preserve medical protocols, maps, or schematics. Because PDE blocks and public dictionaries are readable without electronics, access persists across technological epochs (Tsuyuki et al., 27 Jul 2025).
• Federated industry applications: Bank-insurance credit scoring, where data sharing is infeasible, is enabled by model-, cognition-, and knowledge-level federation, yielding performance competitive with centralized training (Li et al., 2020).
• Digital humanities and library science: Project Pipeline’s conversion of historical vocabularies to SKOS and their unification in HIVE enables real-time metadata generation, cross-era terminology comparison, and robust semantic linking for large-scale scholarship (Greenberg et al., 2021).
• Multilingual NLP and LLMs: Addressing hallucinations in cross-lingual querying, controlled phase transitions in representation learning, and robust cross-lingual fact retrieval are achieved by optimizing KPU metrics and phase-aware model training (Blum et al., 14 Aug 2025).

6. Open Problems, Limitations, and Future Directions

Key open questions and future avenues for KPU research include:

• Dynamic capacity allocation in architectures such as DKUA, to support unbounded domain growth, and more efficient/robust covariance estimation or extension to non-Euclidean feature geometry (Liu et al., 5 Aug 2025).
• Dictionary governance and semantic drift in blockchain-anchored KPU schemes such as PDE, including the co-evolution of grammar, consensus mechanics, and branching/forking for domain-specific variants (Tsuyuki et al., 27 Jul 2025).
• Cross-ontology time-awareness and historical versioning, enabling time-travel semantics or temporally scoped queries in pipelines converting analog knowledge to SKOS (Greenberg et al., 2021).
• Scaling cognitive-level federation in federated AI to more complex distributed reasoning tasks while formally guaranteeing both privacy and representational unification (Li et al., 2020).
• Fine-grained intervention for spurious correlations in cross-lingual LMs, using KPU metrics to guide data balancing, tokenization, and synthetic data generation for better generalization and avoidance of language-induced information leakage (Blum et al., 14 Aug 2025).

A plausible implication is a trend toward integrated KPU frameworks, where technical advances in one domain (e.g., distribution-aware alignment, blockchain-based dictionaries) propagate to adjacent areas, enabling composite systems that unite technical robustness, privacy, cross-generational access, and semantic generalization.