Catastrophic Remembering

• Catastrophic remembering is the harmful persistence, accessibility, or overgeneralization of old information, causing systems to confuse tasks, retain obsolete instructions, or amplify attention to earlier events.
• Researchers study it through task-discrimination tests, diagnostic readouts, replay and generative memory, relevance mapping, adapter routing, prompt-growth measures, and digital-attention analysis.
• Effective mitigation requires selective protection, contextual addressing, explanation or rationale preservation, decoder alignment, controlled plasticity, and explicit mechanisms for revising obsolete knowledge.

Catastrophic remembering is a family of phenomena in which previously acquired information persists, is retrieved, or dominates adaptation in an undesirable manner. The term has no single standardized definition. In continual learning, it commonly denotes the counterpart of catastrophic forgetting: excessive retention or overgeneralization that impairs discrimination between tasks, contexts, or genuinely new inputs. Related uses describe retention of obsolete model behavior, preservation of decision evidence, selective persistence of examples, or even deliberate domain-specific forgetting used as a retrieval code. In digital collective memory, the related phenomenon is the reactivation and amplification of attention to earlier catastrophes after a new event. In agentic coding, catastrophic remembering denotes the accumulation of obsolete instructions whose rationales have been lost. Across these settings, the common issue is not simple memory loss, but the organization, accessibility, selectivity, and revisability of retained information.

1. Terminology and conceptual scope

Catastrophic remembering is best understood as a polysemous term spanning several research problems.

In continual learning, catastrophic forgetting describes performance degradation on earlier tasks after subsequent tasks are learned. Catastrophic remembering, in the sense introduced by Relevance Mapping Networks, is the complementary failure in which a model retains or generalizes old knowledge so broadly that it loses the ability to discriminate between task distributions, identify task boundaries, or recognize genuinely novel inputs (Kaushik et al., 2021). A model may therefore perform well on accumulated classification while treating new data as familiar or applying the wrong task-specific representation.

A second interpretation concerns selective persistence. Learning speed predicts which examples survive future interference: examples learned early and rapidly are more likely to remain correct, whereas examples learned late are more vulnerable. This produces a “last-in-first-out” forgetting pattern in which the model’s retained memory is biased toward simple or prototypical examples (Hacohen et al., 2024). Here catastrophic remembering refers less to excessive retention than to the structured and potentially undesirable selectivity of what is retained.

A third interpretation concerns accessibility rather than storage. Hidden representations may continue to encode old-task information even when the original classifier can no longer decode it. In this view, apparent forgetting is partly a failure to remember how to access an altered representation. Diagnostic readouts recover much of the lost performance, while only a smaller residual is not linearly recoverable (Anthes et al., 2023).

Other papers use related mechanisms without treating catastrophic remembering as a formal term. Relevance-based freezing preserves task-critical computational pathways and relevance signatures (Bogacka et al., 15 Feb 2025). Adapter systems use domain-specific forgetting as a retrieval signal, allowing masked reconstruction to identify which adapter matches the current stream (Swinnen et al., 29 May 2026). Recursive regression preserves old sufficient statistics and therefore introduces a strong stability bias, but does not directly test whether old knowledge becomes excessively dominant (Lai et al., 2024). In agentic coding, the term denotes retention of instructions that should be removed because the rationale authorizing deletion has decayed (Chakrabarti, 11 Aug 2026).

These meanings should not be conflated. Excessive task generalization, biased example retention, decoder misalignment, protected task pathways, domain-specific forgetting, and obsolete prompt accumulation are distinct mechanisms. Their shared concern is the management of information that persists across sequential updates.

2. Mechanisms of retention, interference, and accessibility

The dominant continual-learning formulation represents sequential learning as updates to one parameter state,

$\theta_k = U(\theta_{k-1},D_k),$

where $D_k$ is the current task data. Because earlier data are unavailable, information about old tasks must be compressed into the evolving parameter state. New updates can overwrite, distort, or overgeneralize the state.

The Context Channel Capacity framework interprets this as an information-routing problem. A sequential state-based learner is constrained by the information capacity of its parameters. If each independent task requires positive task-specific information, then retaining all tasks eventually conflicts with online learning and a parameter dimension bounded independently of the number of tasks. This is formulated as an Impossibility Triangle involving zero forgetting, online causal learning, and a bounded number of parameters (Cheng, 8 Mar 2026).

The framework defines Context Channel Capacity as

$C_{\mathrm{ctx}}=\max_{P(c)} I(c;\theta(c)),$

where $c$ is a context signal and $\theta(c)$ are prediction parameters generated from that context. Under the framework’s assumptions, zero forgetting requires

$C_{\mathrm{ctx}}\geq H(T),$

where $H(T)$ is task-identity entropy. A context pathway is insufficient if it can be bypassed by a larger context-free parameter route. CFlow illustrates this failure: although it accepts a context input, task information is primarily stored in a high-dimensional initialization $\theta_0$, producing effectively zero contextual dependence under Wrong-Context Probing (Cheng, 8 Mar 2026).

The same distinction between storage and access appears in representational diagnostics. For an old task $k$, the original readout $W_k^{(0)}$ is evaluated on the current representation $D_k$0, while a newly trained diagnostic readout $D_k$1 tests whether the task remains linearly decodable. The loss decomposes as

$D_k$2

Readout misalignment is the dominant component in the reported experiments. Procrustes alignment recovers approximately half of the gap between the original and diagnostic readouts, indicating that representational geometry is partially conserved but not rigidly preserved (Anthes et al., 2023).

A related stability–plasticity problem arises when importance mechanisms protect old knowledge. EXACFS estimates class-wise feature significance from squared loss gradients,

$D_k$3

and updates historical significance by exponential aging,

$D_k$4

Highly significant features receive stronger distillation constraints, whereas less significant features remain available for adaptation (Balasubramanian et al., 2024). The design explicitly avoids preserving every feature equally, because uniform preservation can impair plasticity.

3. Architectural and algorithmic approaches

Several approaches operationalize remembering through different internal objects: parameters, representations, explanations, relevance maps, adapters, sufficient statistics, or replay samples.

Relevance mappings and task separation

Relevance Mapping Networks learn task-dependent parameter masks. A task-specific effective weight is

$D_k$5

High-relevance parameters are stabilized for an old task, while low-relevance parameters remain plastic. Related tasks can share parameters; unrelated tasks can use distinct subnetworks. The Optimal Overlap Hypothesis rejects both complete sharing and complete separation, seeking task-dependent overlap (Kaushik et al., 2021).

RMNs also support task-free inference. A relevance-modified Welch $D_k$6-test is used for task-switch detection, while final-layer activations filter familiar data from candidate transition evidence. At inference, candidate task maps are evaluated and the map producing the largest response is selected. The reported task-free accuracies are $D_k$7 on Permuted-MNIST, $D_k$8 on Split-MNIST, and $D_k$9 on Sequential Omniglot. The paper treats these results as evidence against catastrophic remembering understood as loss of task discrimination, although it does not report a separate formal catastrophic-remembering metric.

Explanation-preserving replay

Remembering for the Right Reasons augments a replay buffer with stored explanations. For replay pair $C_{\mathrm{ctx}}=\max_{P(c)} I(c;\theta(c)),$0, the explanation-consistency loss is

$C_{\mathrm{ctx}}=\max_{P(c)} I(c;\theta(c)),$1

The model is encouraged to preserve not only the old label but also the visual evidence associated with the earlier prediction. Grad-CAM, vanilla backpropagation, and SmoothGrad are evaluated, with Grad-CAM producing compact spatial maps. On few-shot CUB-200 with ResNet-18, experience replay reaches $C_{\mathrm{ctx}}=\max_{P(c)} I(c;\theta(c)),$2 final accuracy, whereas ER combined with Grad-CAM RRR reaches $C_{\mathrm{ctx}}=\max_{P(c)} I(c;\theta(c)),$3 (Ebrahimi et al., 2020).

RRR also improves explanation localization. For ER, Pointing Game accuracy increases from $C_{\mathrm{ctx}}=\max_{P(c)} I(c;\theta(c)),$4 to $C_{\mathrm{ctx}}=\max_{P(c)} I(c;\theta(c)),$5, and Pointing-Game backward transfer changes from $C_{\mathrm{ctx}}=\max_{P(c)} I(c;\theta(c)),$6 to $C_{\mathrm{ctx}}=\max_{P(c)} I(c;\theta(c)),$7. The improvement is not uniform: TOPIC’s localization accuracy increases while its Pointing-Game backward transfer becomes slightly more negative, from $C_{\mathrm{ctx}}=\max_{P(c)} I(c;\theta(c)),$8 to $C_{\mathrm{ctx}}=\max_{P(c)} I(c;\theta(c)),$9. Explanation preservation therefore extends the notion of remembering but does not guarantee that the retained evidence is causally correct or human-aligned.

Generative replay and representation memory

ClaRe remembers class-conditioned latent distributions rather than fixed image exemplars. Its classification network and remembering network form a CVAE-inspired system:

$c$0

At later increments, the remembering network generates old-class samples, which are combined with real new-class data to retrain the classifier. The method addresses both forgetting and old/new class imbalance by generating as many old-class instances as needed. On MNIST, ClaRe achieves average accuracies of $c$1 for five classes and $c$2 for ten classes, compared with sequential fine-tuning at $c$3 and $c$4, respectively (Mohammadi et al., 2021).

The representation is not necessarily stored as an explicit external table of class means and variances. The paper describes class-conditioned latent knowledge as retained in the remembering network’s parameters. This reduces dependence on a fixed exemplar budget but introduces generation cost and dependence on the quality and diversity of the learned latent distribution.

Low-rank adapters and routing

Remembering Transformer combines a frozen ImageNet-21k-pretrained ViT, low-rank adapters, task autoencoders, sparse routing, and knowledge-distillation-based adapter fusion. A LoRA update has the form

$c$5

with rank $c$6. Autoencoder reconstruction loss selects the most similar adapter. When capacity is exhausted, a new adapter is trained to perform the current task while reproducing the selected old adapter’s outputs on representative replay samples.

On CIFAR10/5, the method reports $c$7 average accuracy using five adapters, $c$8 using three, and $c$9 using two. The trainable footprint decreases from $\theta(c)$0 million parameters with five adapters to $\theta(c)$1 million with three and $\theta(c)$2 million with two. Adapter fusion is therefore a lossy compression mechanism: it limits model growth but may imperfectly preserve old behavior (Sun et al., 2024).

A related domain-incremental method deliberately trains one LoRA adapter per domain and allows each adapter to forget other domains. At test time, masked-autoencoder reconstruction loss optimizes a coefficient vector over the adapters. The adapter mixture that reconstructs the current video stream best becomes dominant. This converts domain-specific forgetting into a retrieval signal rather than attempting to eliminate it (Swinnen et al., 29 May 2026).

Recursive sufficient statistics

CONCIL addresses continual learning in Concept Bottleneck Models by freezing the backbone and expressing concept and decision layers as ridge-regression problems over expanded features. The concept mapping is

$\theta(c)$3

Rather than retaining historical examples, CONCIL recursively updates inverse correlation matrices using the Woodbury identity. New data add terms to accumulated sufficient statistics instead of replacing old terms. Under fixed features, compatible dimensions, accurate recursive computation, and an adequate expanded linear representation, the intended solution is equivalent to centralized ridge regression over the union of phase datasets (Lai et al., 2024).

This produces what the paper calls “absolute knowledge memory,” but it does not directly establish resistance to excessive retention. A system that preserves all historical statistics may be less able to revise obsolete or contradictory mappings.

4. Empirical signatures and evaluation

Catastrophic remembering cannot be characterized by a single metric across all applications. The relevant measurement depends on whether the concern is task discrimination, decoder accessibility, selective example retention, prompt growth, or collective attention.

For task discrimination, RMNs use task-free classification and task inference through relevance maps. Remembering Transformer evaluates adapter selection without task identity, while ReReLRP uses LRP relevance signatures and a classifier chain. ReReLRP freezes high-relevance neurons and stores relevance-derived task representations. On task-aware medical evaluation, it reaches $\theta(c)$4 accuracy on BloodMNIST and $\theta(c)$5 on PathMNIST, with forgetting of $\theta(c)$6 and $\theta(c)$7, respectively (Bogacka et al., 15 Feb 2025). Its task-agnostic performance is lower in some settings, demonstrating that low forgetting does not guarantee reliable task recognition.

For representational accessibility, original-readout accuracy is compared with diagnostic-readout accuracy. The gap measures information that remains encoded but inaccessible to the original decoder. Procrustes alignment tests whether the change can be explained by translation, rotation, reflection, and uniform scaling. This methodology distinguishes readout misalignment, geometric deformation, and residual information loss (Anthes et al., 2023).

For example-level persistence, learning speed is defined as the fraction of training epochs during which an example is correctly classified,

$\theta(c)$8

In the central CIFAR-100 two-task experiment, the correlation between learning speed and the percentage of networks that remembered an example was $\theta(c)$9 with $C_{\mathrm{ctx}}\geq H(T),$0. With replay, the correlation between buffer size and the mean learning speed of remembered examples was $C_{\mathrm{ctx}}\geq H(T),$1 with $C_{\mathrm{ctx}}\geq H(T),$2 (Hacohen et al., 2024).

For prompt maintenance, catastrophic remembering is measured by excess prompt size,

$C_{\mathrm{ctx}}\geq H(T),$3

where $C_{\mathrm{ctx}}\geq H(T),$4 is the smallest prompt achieving maximal expected constraint satisfaction. Repository histories show a mean instruction-count increase of up to $C_{\mathrm{ctx}}\geq H(T),$5, a net increase of $C_{\mathrm{ctx}}\geq H(T),$6 instructions per commit excluding mass rewrites, and a deletion log-hazard slope of $C_{\mathrm{ctx}}\geq H(T),$7 per commit (Chakrabarti, 11 Aug 2026). In the inverted IFEval experiment, informative comments reduce excess size after 51 steps from $C_{\mathrm{ctx}}\geq H(T),$8 to $C_{\mathrm{ctx}}\geq H(T),$9, a $H(T)$0 reduction, while preserving comparable constraint satisfaction.

For collective memory, online attention is measured as excess Wikipedia views directed to older events after a current catastrophe. In aircraft-crash data, the combined secondary attention was $H(T)$1 million views versus $H(T)$2 million primary views, giving a secondary-to-primary ratio of $H(T)$3. Removing directly linked source–target pairs leaves a ratio of $H(T)$4 (García-Gavilanes et al., 2016). These measurements concern digital attention, not neurological memory or conscious recollection.

5. Data, rationale, and context as memory substrates

The approaches differ primarily in what they preserve and how that information is accessed.

Replay retains raw or representative examples. It reconstructs old training signals but consumes memory and may bias the model toward old data. Goldilocks improves replay by ranking examples according to learning speed and discarding both the fastest- and slowest-learned subsets. The buffer is sampled from intermediate-speed examples, which are considered “at-risk but salvageable” (Hacohen et al., 2024).

Generative replay retains a distributional approximation rather than examples. ClaRe generates old-class instances from class-conditioned latent representations, while Remembering Transformer uses representative exemplars when distilling old adapters. ReReLRP retains relevance records and task-specific structural information, though its reported implementation appears to preserve representative inputs or input-associated records as well. Its strongest defensible characterization is that it avoids replaying full raw examples as training data and avoids storing full-dimensional activation patterns (Bogacka et al., 15 Feb 2025).

Parameter and feature regularization retain importance information. EWC uses Fisher-weighted parameter constraints; EXACFS uses class-wise feature significance; RRR stores explanations; RMNs learn relevance masks; and ReReLRP hard-freezes relevant neurons. These methods differ in granularity. Parameter-level protection treats weights as the memory unit, feature-level protection preserves representations, and explanation- or relevance-level protection preserves computational evidence or task signatures.

Contextual architectures retain an addressing channel. HyperNetworks generate task-specific parameters from context rather than accumulating every task in a single sequential state. RMNs use relevance maps, Remembering Transformer uses autoencoder-based similarity, and gradient-context HyperNetworks use projected labeled mini-batch gradients. In the latter case, the oracle gap on Split-CIFAR-10 falls from $H(T)$5 percentage points to $H(T)$6 percentage points (Cheng, 8 Mar 2026).

Rationale is a distinct memory substrate. The agentic-coding study models an instruction as a constraint whose deletion may require evaluating interacting subsets of the prompt, with worst-case complexity $H(T)$7. Recording the reason at write-time is treated as an $H(T)$8 operation. Comments preserve the failure that motivated the instruction, hypotheses about its cause, recurrence information, falsified alternatives, and possible predecessor instructions. They do not become executable constraints and are visible only to maintainers (Chakrabarti, 11 Aug 2026).

These results suggest that “memory” in sequential systems is not a single object. It may consist of examples, parameters, feature geometry, explanations, relevance masks, sufficient statistics, task context, or rationale. Catastrophic remembering occurs when the retained substrate is too broad, too rigid, inaccessible, redundant, obsolete, or insufficiently addressable.

6. Limitations, trade-offs, and open problems

No reported approach eliminates all forms of catastrophic remembering or catastrophic forgetting.

A major limitation is the distinction between low forgetting and high performance. ReReLRP achieves very low forgetting on some datasets while exhibiting weak task-agnostic accuracy on others, including $H(T)$9 on Tiny ImageNet. EXACFS improves retention but shows that excessive distillation pressure can reduce plasticity when exemplar memory becomes larger. Increasing the exemplar count from 20 to 50 or 100 per class decreases performance under unchanged distillation strength (Balasubramanian et al., 2024).

Task recognition is itself fallible. RMNs rely on task-switch detection and relevance-map selection. Remembering Transformer selects the lowest-loss autoencoder but does not specify an independent novelty threshold, so a genuinely new task may be assigned to the least incompatible existing adapter. ReReLRP’s task classifiers can fail when relevance signatures are insufficiently distinctive. Test-time adapter retrieval can also confuse visually similar domains (Sun et al., 2024, Bogacka et al., 15 Feb 2025).

Architectural memory can grow even when data memory does not. RMNs store relevance maps for tasks. Remembering Transformer stores autoencoders and may retain adapters or representative replay samples. ReReLRP’s number of representatives can grow as

$\theta_0$0

Adapter fusion bounds one component of growth but is lossy. Contextual regeneration requires sufficient context bandwidth, expressive generation, reliable context inference, and a context pathway that cannot be bypassed (Cheng, 8 Mar 2026).

The evidence is also benchmark-dependent. Many results use curated image classification tasks, controlled task boundaries, or self-assembled domain sequences. CMP’s reported retention advantage is strong on a byte-level text protocol but is not reproduced as a sparse-versus-dense advantage on Split-MNIST (Atmuri et al., 20 Jul 2026). Its raw prediction quality remains substantially worse than the matched Transformer. Similarly, CONCIL’s absolute-knowledge claim relies on fixed or consistently expanded features, compatible dimensions, and recursive matrix computations; it does not test contradictory labels, obsolete concepts, or deliberate distribution conflict (Lai et al., 2024).

Collective-memory studies face a different inferential limitation. Wikipedia views measure public attention and navigation, not subjective memory. The two-phase decay model

$\theta_0$1

fits rapid exponential-like decline followed by a slower power-law tail, with switching points around 10–11 days after the page-view peak across five event categories (Igarashi et al., 2022). However, the model does not establish that the tail is generated by social interaction, nor that its parameters are universal psychological constants. Similarly, aircraft-crash view flow indicates coordinated digital attention but cannot determine whether visitors consciously remembered the target event (García-Gavilanes et al., 2016).

The open problem is therefore not merely how to remember more. It is how to retain the right information, maintain access to it, recognize when it applies, and revise it when it becomes obsolete. Several design principles recur across the literature:

• Contextual addressing: task or domain information must reach prediction parameters through a sufficiently expressive and structurally unavoidable channel.
• Selective protection: only task-critical parameters, features, explanations, or pathways should be stabilized.
• Representation–decoder coordination: preserving hidden information is insufficient if the readout becomes misaligned.
• Evidence preservation: labels and outputs may be inadequate when the underlying decision rationale also matters.
• Controlled plasticity: retention mechanisms should not prevent adaptation to genuinely new or conflicting information.
• Rationale preservation: in evolving instruction systems, the reasons for constraints should be recorded when they are still available.
• Explicit revision mechanisms: systems need procedures for detecting obsolete, redundant, or harmful retained information rather than relying on irreversible accumulation.

Catastrophic remembering is consequently best treated as a problem of memory governance. The central question is not whether a system retains information, but whether retained information remains discriminable, accessible, appropriately routed, proportionate to current requirements, and safely revisable.