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Paper Citation Record · LEDGER

Overcoming catastrophic forgetting in neural networks

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 25 inbound Pith citation observations for arXiv:1612.00796.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1612.00796 v2

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measured 0 of 0 reference resolution

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Source: paper_references, paper_reference_links

measured 25 of 25 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 25 of 25 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:45:58.730500Z

measured 0 of 1 external citation measurements

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Source: arxiv_reference, observed 2026-07-04T21:10:09.099856Z

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Outbound references

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Pith citing papers

Observation 12dc4e5d-517e-4be8-b922-eca5d5521e8c · inbound

Unlearning Isn't Deletion: Investigating Reversibility of Machine Unlearning in LLMs cites this paper.

Unlearning Isn't Deletion: Investigating Reversibility of Machine Unlearning in LLMs Overcoming catastrophic forgetting in neural networks

Reference 15

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arxiv_id, observed 2026-05-22T13:31:36.593047Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation c30f50f9-e31b-45d5-ab27-c615128012a5 · inbound

What is the role of memorization in Continual Learning? cites this paper.

What is the role of memorization in Continual Learning? Overcoming catastrophic forgetting in neural networks

Reference 29

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Observation de395f95-aaef-4230-869d-60a42899370f · inbound

The Future of Continual Learning in the Era of Foundation Models: Three Key Directions cites this paper.

The Future of Continual Learning in the Era of Foundation Models: Three Key Directions Overcoming catastrophic forgetting in neural networks

Reference 24

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Observation f5ae1dfe-e0c5-4f4a-83a1-e74db279766a · inbound

Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Unlearning Completeness cites this paper.

Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Unlearning Completeness Overcoming catastrophic forgetting in neural networks

Reference 40

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Observation 30c4ac92-cdf0-476a-b326-b62b96760ff7 · inbound

Universal Music Representations? Evaluating Foundation Models on World Music Corpora cites this paper.

Universal Music Representations? Evaluating Foundation Models on World Music Corpora Overcoming catastrophic forgetting in neural networks

Reference 59

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Observation 2df8197b-8b44-4da7-9b83-7add82c9c489 · inbound

Remember Past, Anticipate Future: Learning Continual Multimodal Misinformation Detectors cites this paper.

Remember Past, Anticipate Future: Learning Continual Multimodal Misinformation Detectors Overcoming catastrophic forgetting in neural networks

Reference 21

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Observation ba6b37c5-26a0-4d7f-a1fa-02d5dc3b1793 · inbound

Temporal Information Retrieval via Time-Specifier Model Merging cites this paper.

Temporal Information Retrieval via Time-Specifier Model Merging Overcoming catastrophic forgetting in neural networks

Reference 19

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Observation a2069725-81b2-47ba-a55d-9320e92613b9 · inbound

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning cites this paper.

LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning Overcoming catastrophic forgetting in neural networks

Reference 27

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Observation 4978a2e6-4819-4667-9c40-590e26f86048 · inbound

When Less is More: 8-bit Quantization Improves Continual Learning in Large Language Models cites this paper.

When Less is More: 8-bit Quantization Improves Continual Learning in Large Language Models Overcoming catastrophic forgetting in neural networks

Reference 14

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Preserving Fairness and Safety in Quantized LLMs Through Critical Weight Protection cites this paper.

Preserving Fairness and Safety in Quantized LLMs Through Critical Weight Protection Overcoming catastrophic forgetting in neural networks

Reference 15

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Improving Layout Representation Learning Across Inconsistently Annotated Datasets via Agentic Harmonization cites this paper.

Improving Layout Representation Learning Across Inconsistently Annotated Datasets via Agentic Harmonization Overcoming catastrophic forgetting in neural networks

Reference 4

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Observation d9013b75-0e17-451c-a101-806a1714de44 · inbound

State Representation and Termination for Recursive Reasoning Systems cites this paper.

State Representation and Termination for Recursive Reasoning Systems Overcoming catastrophic forgetting in neural networks

Reference 2

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MANGO: Meta-Adaptive Network Gradient Optimization for Online Continual Learning cites this paper.

MANGO: Meta-Adaptive Network Gradient Optimization for Online Continual Learning Overcoming catastrophic forgetting in neural networks

Reference 5

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation e44d9621-faa0-4734-9df9-65ab91ea9b9e · inbound

GoTTA be Diverse: Rethinking Memory Policies for Test-Time Adaptation cites this paper.

GoTTA be Diverse: Rethinking Memory Policies for Test-Time Adaptation Overcoming catastrophic forgetting in neural networks

Reference 19

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Observation 63d56a76-94be-4a8b-a4ce-5b10953b6a52 · inbound

Spectral Unforgetting: Post-Hoc Recovery of Damaged Capabilities Without Retraining cites this paper.

Spectral Unforgetting: Post-Hoc Recovery of Damaged Capabilities Without Retraining Overcoming catastrophic forgetting in neural networks

Reference 25

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Observation 84e7303f-19d8-4fd3-bc3e-67d81c9d0987 · inbound

Foundation-Preserving Adaptation via Generalized Rayleigh-Quotient Optimization cites this paper.

Foundation-Preserving Adaptation via Generalized Rayleigh-Quotient Optimization Overcoming catastrophic forgetting in neural networks

Reference 59

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Observation f39825e5-2dd2-4a81-b7aa-6e092b6d6394 · inbound

Training Prompt Matters: State-Adaptive Optimization for Robust Fine-Tuning cites this paper.

Training Prompt Matters: State-Adaptive Optimization for Robust Fine-Tuning Overcoming catastrophic forgetting in neural networks

Reference 5

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Observation 0f4b865e-f918-4d70-a53a-5e6b27d13ad0 · inbound

A Local Perturbation Theory for Cross-Domain Interference and Recovery in Multi-Domain RL cites this paper.

A Local Perturbation Theory for Cross-Domain Interference and Recovery in Multi-Domain RL Overcoming catastrophic forgetting in neural networks

Reference 14

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Observation 21bdae05-35a1-41b8-a6bb-50b7bff0e8c9 · inbound

TailLoR: Protecting Principal Components in Parameter-Efficient Continual Learning cites this paper.

TailLoR: Protecting Principal Components in Parameter-Efficient Continual Learning Overcoming catastrophic forgetting in neural networks

Reference 17

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 99d36691-e806-48ec-911f-6bd578f98887 · inbound

Substrate Asymmetry in User-Side Memory: A Diagnostic Framework cites this paper.

Substrate Asymmetry in User-Side Memory: A Diagnostic Framework Overcoming catastrophic forgetting in neural networks

Reference 51

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Observation b43aff0d-8ad7-4f8b-9b20-5ca1417da707 · inbound

Scaling Laws for Task-Specific LLM Distillation cites this paper.

Scaling Laws for Task-Specific LLM Distillation Overcoming catastrophic forgetting in neural networks

Reference 48

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Observation 2c8bbe8a-fc42-4a1d-8269-335321827cbf · inbound

Natural Ungrokking: Asymmetric Control of Which Rules Survive Pretraining cites this paper.

Natural Ungrokking: Asymmetric Control of Which Rules Survive Pretraining Overcoming catastrophic forgetting in neural networks

Reference 19

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Observation 81497779-d164-4e2c-92a4-2679e1741035 · inbound

Do Agent Optimizers Compound? A Continual-Learning Evaluation on Terminal-Bench 2.0 cites this paper.

Do Agent Optimizers Compound? A Continual-Learning Evaluation on Terminal-Bench 2.0 Overcoming catastrophic forgetting in neural networks

Reference 8

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Observation 8616c42b-de1b-41ed-b16f-b166b0dd805f · inbound

Memoir: Should a Model Write to Its Memory While It Thinks? cites this paper.

Memoir: Should a Model Write to Its Memory While It Thinks? Overcoming catastrophic forgetting in neural networks

Reference 14

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TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners cites this paper.

TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners Overcoming catastrophic forgetting in neural networks

Reference 19

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