Lifelong Free-text Knowledge Editing

• Lifelong Free-text Knowledge Editing (LF-Edit) is a paradigm for continuously updating LLMs via free-text input–output pairs that maintain reliability and specificity.
• It employs diverse methods such as key–value augmentation, adapter-based modularization, and memory fusion to ensure edits are effective, localized, and generalizable.
• Scalability and stability are achieved through novel techniques like gated retrieval, dynamic neuron masking, and closed-form interventions, minimizing catastrophic forgetting and interference.

Lifelong Free-text Knowledge Editing (LF-Edit) denotes the class of model editing methods that enable continual, scalable, and precise updates to the internal knowledge of LLMs using arbitrary free-text input–output pairs, without requiring full model retraining or introducing catastrophic forgetting or performance degradation on unrelated tasks. These systems must support sequences of thousands to millions of edit requests where each is an open-domain, possibly long-form natural language update, and must maintain model stability, generalization, reliability, and retention properties across this lifelong trajectory.

1. Formal Problem Definition and Core Desiderata

LF-Edit generalizes the parametric knowledge editing paradigm to the lifelong, free-text regime. Given a base LLM $f_{\theta_0}$ and an ordered stream of edit requests $S = \{ (x_{e}^i, y_{e}^i) \}_{i=1}^n$, each consisting of a free-text query and desired update, the system sequentially produces models $f_{\theta_1}, \ldots, f_{\theta_n}$ such that for every $i$, $f_{\theta_i}(x_{e}^i) \approx y_{e}^i$ and $f_{\theta_i}(x)$ remains stable for all $x$ unrelated to any edit in the prefix $S_{<i}$ (Cao et al., 4 Dec 2025).

The critical desiderata are:

• Efficacy: Each new edit is reliably manifested at inference time.
• Specificity (Locality): Behavior changes are minimal and restricted to edited knowledge; unrelated capabilities are preserved (Cao et al., 4 Dec 2025, Li et al., 4 Dec 2025, Li et al., 19 Aug 2024).
• Generalization: Success generalizes to paraphrases, synonyms, and compositionally related queries (Li et al., 19 Aug 2024, Liu et al., 25 Nov 2025).
• Retention: Prior edits remain reliable after subsequent modifications (edit trail robustness) (Cao et al., 4 Dec 2025, Li et al., 4 Dec 2025).
• Scalability: The approach accommodates thousands to millions of edits with stable runtime, parameter, and memory requirements (Gu et al., 20 May 2025, Fei et al., 24 Jul 2025, Liu et al., 25 Nov 2025).

Evaluation frameworks, such as MRLF-Bench (Cao et al., 4 Dec 2025) and WikiBigEdit (Thede et al., 7 Mar 2025), provide multi-rank assessments across recall, comprehension, compositional reasoning, and paraphrase generalization axes, highlighting the necessity of rigorous, real-world task settings.

2. Methodological Paradigms and Representative Architectures

Multiple orthogonal methodological axes have emerged in LF-Edit research, each with distinct memory, update, and architecture footprints.

2.1 Parametric Key-Value Augmentation

Approaches such as NeuralDB (Fei et al., 24 Jul 2025) represent explicitly edited facts as a neural key–value database integrated at a selected FFN layer, combined with a non-linear gated retrieval. During inference, whenever an LLM’s activation matches a stored fact's key above a similarity threshold, the corresponding residual is injected, otherwise the model remains unaltered. This design:

• Scales linear memory and compute in the number of edits ($m$).
• Preserves general model abilities via a hard gate ($g(\cdot)$) that restricts all modifications to precisely edited facts.
• Yields near-lossless performance on six general NLP tasks at up to $100,000$ concurrent edits.

2.2 Adapter- and Router-based Modularization

Methods including ELDER (Li et al., 19 Aug 2024) and RILKE (Liu et al., 25 Nov 2025) minimize interference by localizing edits to modular parameter subspaces:

• Mixture-of-LoRA (ELDER): Incorporates $N$ low-rank adapters per layer, assigns edit queries to adapter mixtures via a router network, and ensures semantic robustness by guiding similar edits to the same mixture. A deferral mechanism ensures original capabilities on unrelated queries.
• Representation Interventions (RILKE): Instantiates per-edit low-rank modules intervening in selected representation subspaces, with a routing network mapping queries (including paraphrases) to their appropriate interventions. Sharing clusters across edits compresses memory and increases scalability.

Both approaches yield continuous, robust adapter allocation with test retention and generalization sustained after thousands of edits.

2.3 Memory Fusion and Knowledge-Sharding

WISE (Wang et al., 23 May 2024) constructs a dual parametric memory—main (pretrained knowledge) and side (edited knowledge)—with a router selecting between them. Knowledge-sharding splits edits across subspaces, periodically merging them by conflict-aware fusion (e.g., Ties-Merge [Yadav et al. 2023]). This design resolves the "impossible triangle" of reliability, generalization, and locality and extends edit counts to several thousand.

EvoEdit (Cao et al., 4 Dec 2025) generalizes parameter merging by scoring the importance of each parameter post-edit (first-order Taylor-based drop-in-loss) and performing knowledge-driven fusion between original, prior-edited, and newly updated parameters, thus minimizing catastrophic forgetting over $>1000$ edits.

2.4 Non-Parametric and Retrieval-Augmented Approaches

RECIPE (Chen et al., 6 May 2024) and retrieval-augmented prompting systems eschew any weight changes, instead storing continuous prompt representations which are dynamically retrieved and prepended. A knowledge-sentinel thresholding mechanism determines applicability, preserving the unaltered LLM's output otherwise.

Experimental evidence (Chen et al., 6 May 2024, Thede et al., 7 Mar 2025) establishes that such approaches achieve $>90\%$ edit accuracy for up to $10,000$ edits, with no downstream drift, and competitive inference latency.

2.5 Sparse and Dynamic Neuron Masking

Neuron-specific masking (NMKE (Liu et al., 25 Oct 2025)) relies on entropy-guided dynamic selection of a minimal subset of high-attributive FFN neurons for each edit, combining knowledge-general and knowledge-specific neuron classes. Edits are confined to these dynamically determined neurons, sharply reducing off-target interference and shown empirically to preserve >90% edit/generalization rates over $>2000$ steps.

2.6 Training- and Memory-Free Closed-Form Methods

UltraEdit (Gu et al., 20 May 2025) demonstrates that scalable, per-edit closed-form interventions based on per-edit hidden states and gradients, combined via lifelong normalization, can deliver stable performance to the $10^{6}$-edit regime. Memory usage remains constant and parameter updates are analytically computed, bypassing both iterative training and external memory growth.

3. Failure Modes, Superposition, and Theoretical Limits

The scalability and reliability of LF-Edit are fundamentally constrained by properties of LLM internal representations:

• Knowledge Superposition (Hu et al., 14 Aug 2024): Empirically, key representations extracted from LLM layers are not mutually orthogonal in whitened space, manifesting heavy-tailed, high-kurtosis but nonzero off-diagonal dot products. The closed-form theory shows that interference between edited and unrelated knowledge scales linearly with the number of edits unless orthogonality holds.
• Toxicity Buildup and Flash (Hu et al., 16 Feb 2024): Sequential edits in a fixed layer cause norm blow-up and spurious parameter drift (toxicity buildup), and "pattern-unmatch" results in sharply over-amplified updates (toxicity flash) when the editing layer's key is unresponsive to the new fact.
• Impossible Triangle (Wang et al., 23 May 2024): Editing exclusively in main or side memory yields a trade-off between reliability, generalization, and locality that cannot be resolved under naive schemes.
• Norm-growth and Over-optimization (Gupta et al., 3 Feb 2025): Standard locate-then-edit methods exhibit continuous Frobenius norm-growth in edited matrices and overfitted activations, leading to catastrophic model collapse after several thousand edits.

4. Regularization, Memory Management, and Stability Mechanisms

To address failure modes and foster scalability, recent work introduces targeted regularization and novel memory paradigms:

• Early Stopping and Norm Constraints (Gupta et al., 3 Feb 2025): Most-Probable Early Stopping (MPES) halts activation optimization as soon as the edited output is most probable in all contexts, while an explicit Frobenius-norm constraint on parameter shifts bounds matrix growth, enabling $>10,000$ successful edits.
• Gated Retrieval (Fei et al., 24 Jul 2025): Explicit gating ensures that parameter modifications only activate on matching keys, preserving general abilities.
• Sparse Sharding and Merging (Wang et al., 23 May 2024): Subspace mask sharding and conflict-aware consensus merging spread edits without destructive interference and memory explosion.
• Deferral and Sentinel Thresholding (Li et al., 19 Aug 2024, Chen et al., 6 May 2024): Dynamic, Hamming- or similarity-based deferral mechanisms prevent spurious behavior changes, routing unrelated queries through the original model.

5. Empirical Benchmarks and Scaling Results

Large-scale benchmarks such as UltraEditBench (>2M edits) (Gu et al., 20 May 2025), MRLF-Bench (16,835 edits, multi-rank eval) (Cao et al., 4 Dec 2025), and WikiBigEdit (>500K edits) (Thede et al., 7 Mar 2025) provide systematic evaluation pipelines for both factual update and catastrophic forgetting assessment.

Key empirical findings include:

Method	Edits Supported	Efficacy (%)	Generalization (%)	Locality/Error	Downstream Drift	Memory Scaling
NeuralDB	100K+	95.5	90.2	35.1	<1% loss	O(m) KV store
UltraEdit	1M	81.7–85.3	76.8–80.8	>47	Negligible	Constant
ELDER	4K	>95	>90	<1	<1% loss	Fixed per edit
WISE	3K–10K	77	72	100	None (“impossible triangle”)	O(shards)
RILKE	1.5K	100	96 (BERT, Rouge-L)	<2% drop	<2% (MMLU)	O(k) (clusters)
RLEdit	20K	>88	>88	>73	<2% drop (GLUE)	O(traj. length)

Here, efficacy and generalization are measured on direct edits and paraphrases, respectively; locality measures correct outputs on off-target queries; memory scaling is quantified as a function of edit count.

6. Open Challenges and Future Directions

Despite considerable progress, several fundamental obstacles persist:

• Residual Interference: Universal knowledge superposition implies irreducible interference in deep models using parametric modification, barring lossless lifelong editing (Hu et al., 14 Aug 2024).
• Ultra-large Edit Capacity: While NeuralDB, UltraEdit, and some retrieval-augmented approaches scale orders of magnitude beyond early methods, the complexity–accuracy tradeoff and inference latency for $>10^6$ edits on 100B+ models remain open (Gu et al., 20 May 2025, Fei et al., 24 Jul 2025).
• Compositional Consistency and Reasoning: Multi-hop inference over composed or interdependent edits is challenging, with existing methods showing partial degradation (Thede et al., 7 Mar 2025).
• Efficient Memory Management: External memory and key–value stores require compact, hierarchical routing or cluster compression strategies to support practical deployment (Liu et al., 25 Nov 2025, Fei et al., 24 Jul 2025).
• Open-ended Free-text and Multi-modal Edits: Structural edits beyond simple facts—e.g., long-form, logical constraints, or modality-mixing—demand new abstraction and generalization techniques.

Anticipated directions include soft/differentiable routers (Fei et al., 24 Jul 2025), hierarchical index structures, adaptive edit assignment, advanced fusion and regularization algorithms, and broader benchmarks encompassing multi-hop, multi-modal, and cross-lingual editing settings.

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In summary, Lifelong Free-text Knowledge Editing synthesizes key–value augmentation, adaptive routing, modularization, regularized parameter fusion, dynamic memory management, and retrieval-augmented prompting into a set of scalable frameworks for maintaining continually up-to-date LLMs. Advances in regularization, dynamic adaptation, and empirical benchmarking continue to expand the feasible scale and reliability of LF-Edit while highlighting fundamental architectural and representational constraints that define the field's open technical frontiers (Fei et al., 24 Jul 2025, Li et al., 4 Dec 2025, Hu et al., 14 Aug 2024, Li et al., 9 Feb 2025, Liu et al., 25 Nov 2025, Li et al., 19 Aug 2024, Wang et al., 23 May 2024, Thede et al., 7 Mar 2025, Chen et al., 6 May 2024, Hu et al., 16 Feb 2024, Cao et al., 4 Dec 2025, Gu et al., 20 May 2025, Liu et al., 25 Oct 2025, Gupta et al., 3 Feb 2025).