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Paper Citation Record · LEDGER

Continual Learning in Transition

As of 10 August 2026, this Paper Citation Record lists 100 of 195 outbound references and 0 inbound Pith citation observations for arXiv:2608.06216.

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

pith.paper-citation-record.v1
2608.06216 v1

Coverage vector

measured 100 of 195 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:24:03.670173Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

100 of 195 outbound references displayed

  • verified exact2
  • verified fuzzy0
  • unresolved97
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a086feb9-9001-4f2b-a25b-30673fb55c3c · outbound

This paper cites GPT-4 Technical Report.

Continual Learning in Transition GPT-4 Technical Report

Reference 1

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source=pdf_text observed=2026-08-07T12:23:55.874551Z digest=sha256:401f2f7d421a94867255aec848a141137d6b8726ee30a753e7d00e033ccc6315

Observation a3168f7b-c16b-4c34-b3d8-394af4a57c80 · outbound

This paper cites ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools.

Continual Learning in Transition ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools

Reference 2

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source=pdf_text observed=2026-08-07T12:23:55.949386Z digest=sha256:f57920b28b77465d25dd29ffa702ac4bf314bd279e36a914e1733b0b069c6b51

Observation b32bce40-aca6-456c-be5b-ccf8ada52e45 · outbound

This paper cites Qwen Technical Report.

Continual Learning in Transition Qwen Technical Report

Reference 3

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source=pdf_text observed=2026-08-07T12:23:56.041993Z digest=sha256:9fdd436f4f8671f43f4acc23a45119d11157e62dccad6e1d0229cbb2acc54451

Observation 27de2026-da01-4b59-8860-64dacbb67698 · outbound

This paper cites DeepSeek-R1 incentivizes reasoning in LLMs through reinforce- ment learning.Nature, 645:633–638, 2025.

Continual Learning in Transition DeepSeek-R1 incentivizes reasoning in LLMs through reinforce- ment learning.Nature, 645:633–638, 2025

Reference 4

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Observation 5828c8e3-2003-410a-adef-e4e7edb38680 · outbound

This paper cites Kimi K2.5: Visual Agentic Intelligence.

Continual Learning in Transition Kimi K2.5: Visual Agentic Intelligence

Reference 5

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source=pdf_text observed=2026-08-07T12:23:56.325561Z digest=sha256:f437433814a2ae807fc799c24d6e95cffb0c0389174982cadd8095fe7444883e

Observation 3d18f883-d026-492a-beef-fc7b55f5866c · outbound

This paper cites GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models.

Continual Learning in Transition GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models

Reference 6

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source=pdf_text observed=2026-08-07T12:23:56.454414Z digest=sha256:72eb46f09e1593b56cae6051f00257255fdfaf44ead7696bf102535367d3b1fe

Observation a041e281-d498-40e8-903f-e2b4a8227331 · outbound

This paper cites GLM-5: from Vibe Coding to Agentic Engineering.

Continual Learning in Transition GLM-5: from Vibe Coding to Agentic Engineering

Reference 7

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source=pdf_text observed=2026-08-07T12:23:56.622369Z digest=sha256:b0eec7fbb7966520dcd1f5023b38de6e61dd1d44937aba8e26a8cc63b22f7a50

Observation 1c19941d-2112-4c66-9859-b15dd7b03022 · outbound

This paper cites ReST-MCTS*: LLM self- training via process-reward-guided tree search.

Continual Learning in Transition ReST-MCTS*: LLM self- training via process-reward-guided tree search

Reference 8

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source=pdf_text observed=2026-08-07T12:23:56.743078Z digest=sha256:6e9939170237427b5ce83a7f2ff6f0e1ae61c43a66134550d07fa446c098eec9

Observation ccc52d7c-a965-4c63-925c-eb2d98a2901d · outbound

This paper cites TDRM: Smooth reward models with temporal difference for LLM RL and inference, 2025.

Continual Learning in Transition TDRM: Smooth reward models with temporal difference for LLM RL and inference, 2025

Reference 9

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source=pdf_text observed=2026-08-07T12:23:56.810466Z digest=sha256:6244dcbae509265dfe2fb9ce95a664cd71614cb0d8bab6307912b781efd68034

Observation d6f58a78-07b7-4087-9e46-9547b07c6fd2 · outbound

This paper cites ReST-RL: Achieving Accurate Code Reasoning of LLMs with Optimized Self-Training and Decoding.

Continual Learning in Transition ReST-RL: Achieving Accurate Code Reasoning of LLMs with Optimized Self-Training and Decoding

Reference 10

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source=pdf_text observed=2026-08-07T12:23:56.891313Z digest=sha256:8a662e04b9fd30300641950cae5e510e58929639a716b0cbeae84e30c8270e13

Observation 0d81b20b-ad93-434b-aa69-6decec30d735 · outbound

This paper cites SceneGenAgent: Precise Industrial Scene Generation with Coding Agent.

Continual Learning in Transition SceneGenAgent: Precise Industrial Scene Generation with Coding Agent

Reference 11

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source=pdf_text observed=2026-08-07T12:23:56.981566Z digest=sha256:1ff9f209475aceb0eb0a591ab0f513afd42bd189acb5f573d02af43b2df979cc

Observation c1494071-7263-4bf0-a58b-790901473e8e · outbound

This paper cites MEMORYLLM: Towards Self-Updatable Large Language Models.

Continual Learning in Transition MEMORYLLM: Towards Self-Updatable Large Language Models

Reference 12

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source=pdf_text observed=2026-08-07T12:23:57.046101Z digest=sha256:91567e06b25b24d91ca6fd56a32cd0e85484b3391c767a623e5ee4c762572e5e

Observation 35c71bf5-1083-470c-bddb-664554c40540 · outbound

This paper cites Voyager: An Open-Ended Embodied Agent with Large Language Models.

Continual Learning in Transition Voyager: An Open-Ended Embodied Agent with Large Language Models

Reference 13

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source=pdf_text observed=2026-08-07T12:23:57.106462Z digest=sha256:6767fe08f6f25d74d2e69101aef373418387d23d19bf6e800feafa8de1dd453b

Observation bbb54716-99d8-4e64-abd4-51272961dd5a · outbound

This paper cites Reflexion: Language agents with verbal reinforcement learning, 2023.

Continual Learning in Transition Reflexion: Language agents with verbal reinforcement learning, 2023

Reference 14

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source=pdf_text observed=2026-08-07T12:23:57.214540Z digest=sha256:044830d3d283eea7f93452fab517bc3230107ef7695bde8d0767788620eecf36

Observation 4849a5d7-86da-4f3c-a9d0-ebba8114173b · outbound

This paper cites MemGPT: Towards LLMs as Operating Systems.

Continual Learning in Transition MemGPT: Towards LLMs as Operating Systems

Reference 15

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source=pdf_text observed=2026-08-07T12:23:57.286582Z digest=sha256:20e4e5bdc54420446e75b26588330fbd44405c03462e0e7ea9ee85cffd9f7baa

Observation a4b2af24-3c4e-49ad-9494-12bdd0b44a93 · outbound

This paper cites MemoryBank: Enhancing Large Language Models with Long-Term Memory.

Continual Learning in Transition MemoryBank: Enhancing Large Language Models with Long-Term Memory

Reference 16

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source=pdf_text observed=2026-08-07T12:23:57.385313Z digest=sha256:4ffe024301024ad4207a19fa12b0e08f1cf679fa1f2253b67591796d8dec73f0

Observation ca65fa84-61f6-43bd-9c32-f19010acdab6 · outbound

This paper cites AgentEvolver: Towards efficient self-evolving agent system, 2025.

Continual Learning in Transition AgentEvolver: Towards efficient self-evolving agent system, 2025

Reference 17

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Observation d672c99b-680a-4a56-885f-46c8b88db5bb · outbound

This paper cites Catastrophic interference in connectionist networks: The sequential learning problem.

Continual Learning in Transition Catastrophic interference in connectionist networks: The sequential learning problem

Reference 18

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source=pdf_text observed=2026-08-07T12:23:57.535621Z digest=sha256:ac5d423d6378aaff18136a545461e475d9e411746f079435f68d09c32240b5ea

Observation 67c5a7c1-5819-4fa6-94a7-c23485513dab · outbound

This paper cites Towards continual reinforcement learning: A review and perspectives.Journal of Artificial Intelligence Research, 75:1401–1476, 2022.

Continual Learning in Transition Towards continual reinforcement learning: A review and perspectives.Journal of Artificial Intelligence Research, 75:1401–1476, 2022

Reference 19

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source=pdf_text observed=2026-08-07T12:23:57.662594Z digest=sha256:cc35a3b3b0cf9aa3774135c861eb654a418ea4bdb5ac056f401f004affd7b297

Observation 9c53dd54-7c89-4b38-973d-c2acaefc40f0 · outbound

This paper cites A Comprehensive Survey of Continual Learning: Theory, Method and Application.

Continual Learning in Transition A Comprehensive Survey of Continual Learning: Theory, Method and Application

Reference 20

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source=pdf_text observed=2026-08-07T12:23:57.728002Z digest=sha256:5da340541b8e74379eada76532e1d8e98eb51a3b3b4e82f819681255240f98d0

Observation 2e8f8ff6-b6dc-471e-a402-a339a2980f05 · outbound

This paper cites Continual Learning for Large Language Models: A Survey.

Continual Learning in Transition Continual Learning for Large Language Models: A Survey

Reference 21

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source=pdf_text observed=2026-08-07T12:23:57.796801Z digest=sha256:c1818a17d4b3cae0ebee8c86e8ee52d9dd8b12b1cab364d46aa6764d3bcec477

Observation 29515515-4ca4-4c0f-9cd2-b5c21b23d969 · outbound

This paper cites Training language models to follow instructions with human feedback.Advances in Neural Information Processing Systems, 35:27730–27744, 2022.

Continual Learning in Transition Training language models to follow instructions with human feedback.Advances in Neural Information Processing Systems, 35:27730–27744, 2022

Reference 22

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source=pdf_text observed=2026-08-07T12:23:57.870320Z digest=sha256:97ec44ae741e3d6e23563f8bd73a5b16710dc0e2be07b441133188ba7e870028

Observation 0c7bcd99-5360-4260-b124-a510593d17da · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Continual Learning in Transition DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 23

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source=pdf_text observed=2026-08-07T12:23:57.965573Z digest=sha256:16bb5dea37a816edcd35bccb73fc0a5675a9f3bfd86344dfaab4fd3814e82c7e

Observation 91c32b69-b246-442b-8802-be4c4b3b595e · outbound

This paper cites RL’s razor: Why online reinforcement learning forgets less,.

Continual Learning in Transition RL’s razor: Why online reinforcement learning forgets less,

Reference 24

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Observation 39b7433e-f8ab-4d7b-b79d-dbdc1818fe97 · outbound

This paper cites On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes.

Continual Learning in Transition On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes

Reference 25

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source=pdf_text observed=2026-08-07T12:23:58.175658Z digest=sha256:c335cba6c51b089f2eef155beda3bff383383d4aa2222cbc3c88789dccc23bc5

Observation df64bea0-aef2-4ecb-a0b0-03a1f8bb6485 · outbound

This paper cites Self-distillation enables continual learning,.

Continual Learning in Transition Self-distillation enables continual learning,

Reference 26

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source=pdf_text observed=2026-08-07T12:23:58.244513Z digest=sha256:c75c6863562b55480b2b9279f23b910c918a5cdddee4a4d9e09ca0dc87112de6

Observation 71ad117f-0360-4945-a6c0-165d6e082e94 · outbound

This paper cites Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities.

Continual Learning in Transition Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities

Reference 27

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source=pdf_text observed=2026-08-07T12:23:58.404831Z digest=sha256:d3269a79ab190e3d40b2f3413d840714c5721a55a8d791c12472ccba7651e06c

Observation d9428f13-0b7b-4c7f-976d-f3548ea15244 · outbound

This paper cites Fine-Tuning Language Models with Just Forward Passes.

Continual Learning in Transition Fine-Tuning Language Models with Just Forward Passes

Reference 28

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source=pdf_text observed=2026-08-07T12:23:58.470982Z digest=sha256:0e70534d29d18eb0efe13b5cd687b879b02a57b8e13f9f1cc4de9b376d976c6f

Observation 663500d6-2451-473b-9759-a02a22cb444c · outbound

This paper cites Learning beyond gradients.

Continual Learning in Transition Learning beyond gradients

Reference 29

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source=pdf_text observed=2026-08-07T12:23:58.538584Z digest=sha256:99c5a03c7fbc0205b57008dd29b9bf84bdadae542d136f53237d56b338bcddae

Observation 4ab6b483-e09e-4374-bf62-a298c299340e · outbound

This paper cites Prompt- breeder: Self-referential self-improvement via prompt evolution, 2023.

Continual Learning in Transition Prompt- breeder: Self-referential self-improvement via prompt evolution, 2023

Reference 30

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source=pdf_text observed=2026-08-07T12:23:58.617698Z digest=sha256:18552984e4bf90d3749a6efd6caf3d14c11c3faaa9655dca64139e763ea64e55

Observation dcf11715-5b47-43cc-9d15-2ae1f86f4a12 · outbound

This paper cites Tent: Fully Test-time Adaptation by Entropy Minimization.

Continual Learning in Transition Tent: Fully Test-time Adaptation by Entropy Minimization

Reference 31

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source=pdf_text observed=2026-08-07T12:23:58.685565Z digest=sha256:291ad70e9d98e34683c5cb93f1361f593744cc0b7932c4320f41e9f273a0774f

Observation 6b41a975-3636-44f8-b0d9-81b25265478f · outbound

This paper cites Learning to (learn at test time): RNNs with expressive hidden states.

Continual Learning in Transition Learning to (learn at test time): RNNs with expressive hidden states

Reference 32

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source=pdf_text observed=2026-08-07T12:23:58.754740Z digest=sha256:862f1b6e8d2041ce6baec18f5f8fdf14005316875d9bc1cc5ca8a5c6d41d638f

Observation 3d6fe45c-3862-4e19-90ac-0998ee735e79 · outbound

This paper cites Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering.

Continual Learning in Transition Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering

Reference 33

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source=pdf_text observed=2026-08-07T12:23:58.820184Z digest=sha256:a6a44d3863bdb721f5b253b941d5a36fecd3a64aa7d85564cdd23d0d26125ad2

Observation d34e0bd3-94e4-4d7e-8769-f51342e7a26d · outbound

This paper cites An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks.

Continual Learning in Transition An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks

Reference 34

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Observation 9a79d460-e61a-4cec-91fe-1e46a0fca575 · outbound

This paper cites icarl: Incremental classifier and representation learning.

Continual Learning in Transition icarl: Incremental classifier and representation learning

Reference 35

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source=pdf_text observed=2026-08-07T12:23:58.963961Z digest=sha256:355d9ceda0b9febed2354aa7db50472f6433a8f302ffdfec15c62a9219836c76

Observation 9298bca4-c299-4cd3-817a-21f539edd278 · outbound

This paper cites Experience replay for continual learning.Advances in neural information processing systems, 32, 2019.

Continual Learning in Transition Experience replay for continual learning.Advances in neural information processing systems, 32, 2019

Reference 36

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source=pdf_text observed=2026-08-07T12:23:59.035847Z digest=sha256:8665249cb85f5c9fbdd346a2b7a3fefb76c667605d0b411f67cfa966912dd7b6

Observation b1c4df67-22e4-4879-9e84-be967d256d72 · outbound

This paper cites Infty engine: An optimization toolkit to support continual ai.GitHub repository, 2026.

Continual Learning in Transition Infty engine: An optimization toolkit to support continual ai.GitHub repository, 2026

Reference 37

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source=pdf_text observed=2026-08-07T12:23:59.115137Z digest=sha256:e5f3ef5886df48824977ac0a8a829f584c3caf0d016daee72cd0bd1397c2212f

Observation f15b780f-6e52-4820-995d-ccde78ffce39 · outbound

This paper cites Gradient episodic memory for continual learning.

Continual Learning in Transition Gradient episodic memory for continual learning

Reference 38

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source=pdf_text observed=2026-08-07T12:23:59.210343Z digest=sha256:2cbe2b2bcc82348d6555039a233dc5e175108d6f0bd80d4c8cfe2841fa6e44a4

Observation fb6b5239-92b1-4023-b155-49b8eae4a71b · outbound

This paper cites Orthogonal gradient descent for continual learning.

Continual Learning in Transition Orthogonal gradient descent for continual learning

Reference 39

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source=pdf_text observed=2026-08-07T12:23:59.280502Z digest=sha256:449b77638d34c99a566d67b8709537b6806799bdc88f20c2d080562f68cdfb66

Observation a183a6a2-90fd-4a31-a67d-551a36b04c9b · outbound

This paper cites Make continual learning stronger via c-flat.Advances in Neural Information Processing Systems, 37: 7608–7630, 2024.

Continual Learning in Transition Make continual learning stronger via c-flat.Advances in Neural Information Processing Systems, 37: 7608–7630, 2024

Reference 40

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Observation e03762d3-f9b1-4390-97e7-41124f1953eb · outbound

This paper cites A faster path to continual learning.

Continual Learning in Transition A faster path to continual learning

Reference 41

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source=pdf_text observed=2026-08-07T12:23:59.434986Z digest=sha256:6c57931d489d2a0a94f9907f095a9a3998277d2f6ee8c26a6939437d19b2e255

Observation 07469e33-953a-4ac3-ace0-1bf494901cba · outbound

This paper cites Rethinking the stability-plasticity trade-off in continual learning from an architectural perspective.ICML, 2025.

Continual Learning in Transition Rethinking the stability-plasticity trade-off in continual learning from an architectural perspective.ICML, 2025

Reference 42

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source=pdf_text observed=2026-08-07T12:23:59.507155Z digest=sha256:5ba7ded1bf3e160b1dee82f9ee36815f90ef3a162b1bec7deba420f8bbc43c03

Observation aadd07da-c19d-4ad4-85fa-bc11b3e0ddc9 · outbound

This paper cites Revisiting neural networks for continual learning: An architectural perspective.IJCAI, 2024.

Continual Learning in Transition Revisiting neural networks for continual learning: An architectural perspective.IJCAI, 2024

Reference 43

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source=pdf_text observed=2026-08-07T12:23:59.582658Z digest=sha256:899cb935c0028e0c85c8bfcb05f6c55a31c15060309c31b0b93ba3053649ee63

Observation 8b8ae0ed-a389-4096-b633-6043220e5583 · outbound

This paper cites Packnet: Adding multiple tasks to a single network by iterative pruning.

Continual Learning in Transition Packnet: Adding multiple tasks to a single network by iterative pruning

Reference 44

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source=pdf_text observed=2026-08-07T12:23:59.659278Z digest=sha256:0418ba4bf015a7f11fc6aee8f9ed0b3b15ee993e8a04edc3710276faa549411a

Observation 59ba7111-e3a9-4f35-a5b3-f3a0bc049814 · outbound

This paper cites Overcoming catastrophic forgetting with hard attention to the task.

Continual Learning in Transition Overcoming catastrophic forgetting with hard attention to the task

Reference 45

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source=pdf_text observed=2026-08-07T12:23:59.721481Z digest=sha256:d4f815ef5f3d6b00cda889c42ccd35cc1e86dd4655edd9d340460654209f2e57

Observation f34fa236-d6c7-450b-a6d5-2a5534cdd877 · outbound

This paper cites Progressive Neural Networks.

Continual Learning in Transition Progressive Neural Networks

Reference 46

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source=pdf_text observed=2026-08-07T12:23:59.826997Z digest=sha256:b7b132673f60e64196ced08b68c6ddaf9262451a508c1eaf358bd93e967086a2

Observation 47d18a91-aaec-464a-a672-72cfeafacdf3 · outbound

This paper cites Overcoming catastrophic forgetting in incremental object detection via elastic response distillation.

Continual Learning in Transition Overcoming catastrophic forgetting in incremental object detection via elastic response distillation

Reference 47

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source=pdf_text observed=2026-08-07T12:23:59.890559Z digest=sha256:439df62a810f0076aa9b5c85bf4fada68de69b43e8123b19ac1dc086136dd724

Observation 4c33d112-9409-4bae-baf3-906135b5f3bf · outbound

This paper cites Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, DharshanKumaran, andRaiaHadsell.

Continual Learning in Transition Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, DharshanKumaran, andRaiaHadsell

Reference 48

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source=pdf_text observed=2026-08-07T12:23:59.971052Z digest=sha256:f52195653a00a6eae191f5a63ef97f44a90545b2f37f5b8ce5ade5407b581aee

Observation f8aa660e-1405-4843-8b08-5a73e7c6d778 · outbound

This paper cites Continual learning through synaptic intelligence.

Continual Learning in Transition Continual learning through synaptic intelligence

Reference 49

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source=pdf_text observed=2026-08-07T12:24:00.035946Z digest=sha256:6ed380b7a994995ce286ca8932fbb6f107ed3cf19eae961a2a11051afd2de263

Observation b50ab61f-8f5c-49ce-9f0b-9f0c3ad604f6 · outbound

This paper cites Memory Aware Synapses: Learning what (not) to forget.

Continual Learning in Transition Memory Aware Synapses: Learning what (not) to forget

Reference 50

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source=pdf_text observed=2026-08-07T12:24:00.104575Z digest=sha256:1569863d1b421f88470aae55f204b0a14418b8a8725c9501c1248a408a40c696

Observation 94bd9f56-3a75-4049-aad6-5cd8cdbe5ea4 · outbound

This paper cites Learning without forgetting.IEEE transactions on pattern analysis and machine intelligence, 40(12):2935–2947, 2017.

Continual Learning in Transition Learning without forgetting.IEEE transactions on pattern analysis and machine intelligence, 40(12):2935–2947, 2017

Reference 51

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source=pdf_text observed=2026-08-07T12:24:00.164110Z digest=sha256:694675c2e869153dacb271b92a1d45d8aa05b895cc50c39619c1b6b8bb95fc50

Observation 15dc99fc-9770-442d-ba99-c3e8b7bf4550 · outbound

This paper cites Simple and Scalable Strategies to Continually Pre-train Large Language Models.

Continual Learning in Transition Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 52

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source=pdf_text observed=2026-08-07T12:24:00.233555Z digest=sha256:9d79a980ce6af55df12e80420d5b796f60790551d87e7c979f40fcbfb9ac6cca

Observation 4a738086-964e-4629-8543-ffb74bf71caf · outbound

This paper cites Richter, Quentin Anthony, Eugene Belilovsky, Irina Rish, and Timothée Lesort.

Continual Learning in Transition Richter, Quentin Anthony, Eugene Belilovsky, Irina Rish, and Timothée Lesort

Reference 53

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source=pdf_text observed=2026-08-07T12:24:00.282197Z digest=sha256:5e6fa09568ae5a2247a8e6b585aaee7a7eef912c243141ec09b86deafb5d863f

Observation 9990c7bd-4ecb-4d49-91de-8b683398fb09 · outbound

This paper cites Towards continual knowledge learning of language models.

Continual Learning in Transition Towards continual knowledge learning of language models

Reference 54

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source=pdf_text observed=2026-08-07T12:24:00.414609Z digest=sha256:ce74b2c35df1664a4cc18d9bdd3bb4914cb33747c504826b4a2ee78adcc0add9

Observation ac715759-d239-43d1-9aba-e34c5d6b4343 · outbound

This paper cites ELLE: Efficient lifelong pre-training for emerging data.

Continual Learning in Transition ELLE: Efficient lifelong pre-training for emerging data

Reference 55

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source=pdf_text observed=2026-08-07T12:24:00.481923Z digest=sha256:e83962ca685c78dd2198de7072bc24c6e2d6d65ee37401b6c95bceed0d634f4d

Observation 1cd9761b-abd8-499d-be45-479864f6c325 · outbound

This paper cites TimeLMs: Diachronic language models from twitter.

Continual Learning in Transition TimeLMs: Diachronic language models from twitter

Reference 56

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source=pdf_text observed=2026-08-07T12:24:00.549605Z digest=sha256:d09c5b74494a390e67b051a98a1cec96d11cdc63308dd11f2c3bab1daea8f5c4

Observation ef194a7d-33ba-4809-8043-33777f40cdd4 · outbound

This paper cites Large language model empowered recommendation meets all-domain continual pre-training.IEEE Transactions on Knowledge and Data Engineering, pages 1–14, 2026.

Continual Learning in Transition Large language model empowered recommendation meets all-domain continual pre-training.IEEE Transactions on Knowledge and Data Engineering, pages 1–14, 2026

Reference 57

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source=pdf_text observed=2026-08-07T12:24:00.619534Z digest=sha256:3bb89936c6a6fb7430dfca076ebc892ae2e98f11897345e2feabcebcb544375a

Observation 93213398-fd16-4fc4-9923-21217114f55c · outbound

This paper cites End- to-end test-time training for long context.arXiv preprint arXiv:2512.23675, 2025.

Continual Learning in Transition End- to-end test-time training for long context.arXiv preprint arXiv:2512.23675, 2025

Reference 58

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source=pdf_text observed=2026-08-07T12:24:00.694738Z digest=sha256:a3d3cc6b1653074a138f42dbab9d9fd1ae44437405baa7ed4a312d50ecf0ed65

Observation eb8f4ab8-e957-4ee9-845e-4210e86756f8 · outbound

This paper cites Titans: Learning to memorize at test time, 2025.

Continual Learning in Transition Titans: Learning to memorize at test time, 2025

Reference 59

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source=pdf_text observed=2026-08-07T12:24:00.762748Z digest=sha256:ba58f6d3c7f39c10ca8e888e6770e5d02fcc30bfba9895a9537b024c9baa378c

Observation 5ef1e059-7749-41e8-bf7c-af8e7853358a · outbound

This paper cites Orthogonal subspace learning for language model continual learning.

Continual Learning in Transition Orthogonal subspace learning for language model continual learning

Reference 60

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source=pdf_text observed=2026-08-07T12:24:00.846843Z digest=sha256:6aa4194a7835f57df52c9fd669ab95a67b6bfb5d28c3b2c337d5e48d24bbc184

Observation b273c3f6-9ced-4287-8e57-6081f09a49b7 · outbound

This paper cites Progres- sive prompts: Continual learning for language models.

Continual Learning in Transition Progres- sive prompts: Continual learning for language models

Reference 61

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source=pdf_text observed=2026-08-07T12:24:00.932533Z digest=sha256:32e6dbb3455dc15dc0eff2fd112ee2bcc75bc2952d24ee622c6fdf96833c05bd

Observation ce6dae2b-5941-4365-b07a-6810090871e7 · outbound

This paper cites LoRAMoE: Alleviating world knowledge forgetting in large language models via MoE-style plugin.

Continual Learning in Transition LoRAMoE: Alleviating world knowledge forgetting in large language models via MoE-style plugin

Reference 62

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source=pdf_text observed=2026-08-07T12:24:01.001460Z digest=sha256:ac5fce57c5584841a41db8902c82f7a64d40326f23f09ca733caab4dd94548f7

Observation 50ec8f39-657e-4ab7-ab57-aa047bc74fd0 · outbound

This paper cites SLIM: Let LLMs learn more and forget less with soft LoRA and identity mixture.

Continual Learning in Transition SLIM: Let LLMs learn more and forget less with soft LoRA and identity mixture

Reference 63

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source=pdf_text observed=2026-08-07T12:24:01.077568Z digest=sha256:c7aa108b5ffe3deab5aa32c8c45534b7dad3bc768d4efd2ce04b236b675086f0

Observation 81a7dd54-7470-44a4-8d2d-fd33e5827072 · outbound

This paper cites SAPT: A shared attention framework for parameter-efficient continual learning of large language models.

Continual Learning in Transition SAPT: A shared attention framework for parameter-efficient continual learning of large language models

Reference 64

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source=pdf_text observed=2026-08-07T12:24:01.141633Z digest=sha256:a5e93e397498663f646af7a2a7aa340188359932565d43b93d9a40c5104d8ff5

Observation 9340e362-4139-4868-bc85-79ed27494a1e · outbound

This paper cites Rehearsal-free modular and compositional continual learning for language models.

Continual Learning in Transition Rehearsal-free modular and compositional continual learning for language models

Reference 65

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source=pdf_text observed=2026-08-07T12:24:01.215273Z digest=sha256:348beaa5d9bc02f7133b13fc105e31a4e89084a4e4f03366e3fb5bd3c9149a97

Observation 16515c56-4f79-4f9c-bf31-d19d290a1035 · outbound

This paper cites InsCL: A data-efficient continual learning paradigm for fine-tuning large language models with instructions.

Continual Learning in Transition InsCL: A data-efficient continual learning paradigm for fine-tuning large language models with instructions

Reference 66

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source=pdf_text observed=2026-08-07T12:24:01.284601Z digest=sha256:efe4e694974a3c13fec7c3a5c6ada29503f89ec52e6feabbf20834796c342d98

Observation 1c32be99-da50-41c9-af41-94e920374354 · outbound

This paper cites Mitigating catastrophic forgetting in large language models with self-synthesized rehearsal.

Continual Learning in Transition Mitigating catastrophic forgetting in large language models with self-synthesized rehearsal

Reference 67

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source=pdf_text observed=2026-08-07T12:24:01.321774Z digest=sha256:cb65c637d321723793fe02d7a8122318482c0d7f93e2cb8439f405ead90efbc7

Observation 46896635-c248-4020-9e64-8c3b1396b817 · outbound

This paper cites SEEKR: Selective Attention-Guided Knowledge Retention for Continual Learning of Large Language Models.

Continual Learning in Transition SEEKR: Selective Attention-Guided Knowledge Retention for Continual Learning of Large Language Models

Reference 68

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source=pdf_text observed=2026-08-07T12:24:01.379622Z digest=sha256:c57cb71448b267ecf431d7ddaa807859c250d0b2fb55ccd98cfdb8d3089b663d

Observation 332988c2-13c3-4b61-9602-6cecf4b5fa7b · outbound

This paper cites AlphaEdit: Null-space constrained knowledge editing for language models.

Continual Learning in Transition AlphaEdit: Null-space constrained knowledge editing for language models

Reference 69

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source=pdf_text observed=2026-08-07T12:24:01.441706Z digest=sha256:be4006f4a82ae41eff9d076767b36908179470c8f87b37e523d455e1d49af596

Observation 69278eb6-a1b3-4f17-ac40-0519ca49c252 · outbound

This paper cites Norm anchors make model edits last, 2026.

Continual Learning in Transition Norm anchors make model edits last, 2026

Reference 70

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source=pdf_text observed=2026-08-07T12:24:01.505996Z digest=sha256:811d6e202e101689bfa0cd0cde216791ebc9c3291deb2df74a16bd58610ae360

Observation eefd7242-c73c-4245-838a-ce0ad08ec276 · outbound

This paper cites Yu, and Xiao-Ming Wu.

Continual Learning in Transition Yu, and Xiao-Ming Wu

Reference 71

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source=pdf_text observed=2026-08-07T12:24:01.582571Z digest=sha256:a42510db67af5f4a12ae3c47a44fcd49370f93bdf008c786c8c8426e02bbf53d

Observation d1add47a-8e93-4a60-8011-4ba09550886f · outbound

This paper cites Dynamic cross-modal prompt generation for multimodal continual instruction tuning, 2026.

Continual Learning in Transition Dynamic cross-modal prompt generation for multimodal continual instruction tuning, 2026

Reference 72

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source=pdf_text observed=2026-08-07T12:24:01.636619Z digest=sha256:db2bf31a8ca89bf41655201e0451ded5f198fc2507305ed3235c7e44fe8022b4

Observation 9dc3e717-1762-432d-8b03-7373b086500a · outbound

This paper cites CRAM: Centroid-routing and adaptive MoE for multimodal continual instruction tuning, 2026.

Continual Learning in Transition CRAM: Centroid-routing and adaptive MoE for multimodal continual instruction tuning, 2026

Reference 73

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source=pdf_text observed=2026-08-07T12:24:01.681607Z digest=sha256:bf8108a899df62f2b7fefe762e815fa1a25ddf77b75f1f3a5bc2ae19d44d5a0b

Observation d193ccf5-2547-481e-acde-b1af2e540463 · outbound

This paper cites an unresolved cited work.

Continual Learning in Transition Unresolved cited work

Reference 74

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source=pdf_text observed=2026-08-07T12:24:01.758523Z digest=sha256:51b56bc835065467dc7694059ae08566799160908b505dfa8404e4eabcfa60ea

Observation 2c3fd8f9-3ae1-4df7-b6c2-e057f8f47e0a · outbound

This paper cites Hidden forgetting in continual multimodal learning: When accuracy survives but grounding fails, 2026.

Continual Learning in Transition Hidden forgetting in continual multimodal learning: When accuracy survives but grounding fails, 2026

Reference 75

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source=pdf_text observed=2026-08-07T12:24:01.810088Z digest=sha256:3b932dbf4e6e0a93515be2996b3b6f244ede4eb1fdea9693274871d9c6e054ae

Observation 40c715b1-4354-4360-b44e-90fdb7e4bed2 · outbound

This paper cites Rethinking continual experience internalization for self-evolving LLM agents, 2026.

Continual Learning in Transition Rethinking continual experience internalization for self-evolving LLM agents, 2026

Reference 76

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source=pdf_text observed=2026-08-07T12:24:01.855684Z digest=sha256:d166b14c3e54f6cd1ee86500ca2e85b4a7caf4ef0991f7f51bc563c40f949c56

Observation 7a4674fa-cff8-4b8d-81de-274f4b1328bd · outbound

This paper cites Language models need sleep: Learning to self-modify and consolidate memories, 2026.

Continual Learning in Transition Language models need sleep: Learning to self-modify and consolidate memories, 2026

Reference 77

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source=pdf_text observed=2026-08-07T12:24:01.910782Z digest=sha256:15ee48deab4418ed402a96c9137b25512862d47722cabd7324f67ac64904ebbd

Observation 8127b4fa-ec9d-4139-851c-74c2f2d66dc0 · outbound

This paper cites Peam: Parametric embodied agent memory through contrastive internalization of experience in minecraft, 2026.

Continual Learning in Transition Peam: Parametric embodied agent memory through contrastive internalization of experience in minecraft, 2026

Reference 78

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source=pdf_text observed=2026-08-07T12:24:01.989198Z digest=sha256:1014542818a6f69d91de365cec25884d6f581b9a694f3ec0e9f4e2019891d3a1

Observation 1155cda4-c950-42ae-bb03-1f2ae78fbc2b · outbound

This paper cites Evolving-rl: End-to-end optimization of experience-driven self-evolving capability within agents, 2026.

Continual Learning in Transition Evolving-rl: End-to-end optimization of experience-driven self-evolving capability within agents, 2026

Reference 79

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Observation de1307cd-d190-4dc9-be38-b73d1e192982 · outbound

This paper cites A-MEM: Agentic Memory for LLM Agents.

Continual Learning in Transition A-MEM: Agentic Memory for LLM Agents

Reference 80

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source=pdf_text observed=2026-08-07T12:24:02.110057Z digest=sha256:b91a24a6330ebf6ea987d02e9d049bb7cefe9c51a5d7a781e04e1ca982acb064

Observation 209b7e98-fc8e-42c5-ba84-4261e8f7bbe8 · outbound

This paper cites HippoRAG: Neurobiologically inspired long-term memory for large language models.

Continual Learning in Transition HippoRAG: Neurobiologically inspired long-term memory for large language models

Reference 81

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source=pdf_text observed=2026-08-07T12:24:02.182840Z digest=sha256:cce34d5ef3f73c224956010e77870e4eea20f000af3ab8680f44ac501ab55905

Observation 16ee35a1-e5f5-4719-b645-966c515bf206 · outbound

This paper cites ExpeL: LLM agents are experiential learners.

Continual Learning in Transition ExpeL: LLM agents are experiential learners

Reference 82

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source=pdf_text observed=2026-08-07T12:24:02.276585Z digest=sha256:5979e31a7d275e2fbcb28c3a3b269a0217c1bfd0bd61417c7c52f84dc32458de

Observation 939306b4-f0d0-45f7-a14d-fd28713f15ef · outbound

This paper cites Agent workflow memory.

Continual Learning in Transition Agent workflow memory

Reference 83

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source=pdf_text observed=2026-08-07T12:24:02.357110Z digest=sha256:c290c7c3119d9730decfe8e385f3e92f87d97212cb0dd78daf4f2a6c011366b9

Observation b6dd804d-d317-4919-bd0f-4f53f97d3095 · outbound

This paper cites Mem0: Building production- ready AI agents with scalable long-term memory, 2025.

Continual Learning in Transition Mem0: Building production- ready AI agents with scalable long-term memory, 2025

Reference 84

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source=pdf_text observed=2026-08-07T12:24:02.423349Z digest=sha256:364b5d3a2f0fe42317184b0ea057028013609049babd54008b9cee9ac9daad3d

Observation da1d0a0d-f3dd-42ca-8d18-bdc23be405a0 · outbound

This paper cites Towards scalable lifelong knowledge editing with selective knowledge suppression, 2026.

Continual Learning in Transition Towards scalable lifelong knowledge editing with selective knowledge suppression, 2026

Reference 85

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source=pdf_text observed=2026-08-07T12:24:02.479748Z digest=sha256:5ff3e440ec07f7cba02af6d546237d78a7a1e70b47bfdf3484c547bbc2f6c17c

Observation d8d4abda-4311-4816-82d3-349ea0ce068a · outbound

This paper cites Forget to improve: On-device LLM-agent continual learning via budget-curated memory, 2026.

Continual Learning in Transition Forget to improve: On-device LLM-agent continual learning via budget-curated memory, 2026

Reference 86

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source=pdf_text observed=2026-08-07T12:24:02.546403Z digest=sha256:068e5bde915d9e9d99bec1b2c2ff7c7e2def8b5cbee94da7c51f040bec51895c

Observation 6da78fd9-aab6-4ffe-b2b1-addf0bc96a82 · outbound

This paper cites Collaborative multi-agent test-time reinforcement learning for reasoning, 2026.

Continual Learning in Transition Collaborative multi-agent test-time reinforcement learning for reasoning, 2026

Reference 87

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source=pdf_text observed=2026-08-07T12:24:02.614602Z digest=sha256:d6455edbd613325c5d46fd8b96351432ed8b1e0452cb7d8d7c23dd375d6c8d52

Observation 1a00528d-99c8-4f20-8def-fc1a21df99a4 · outbound

This paper cites Aging with GRACE: Lifelong model editing with discrete key-value adaptors.

Continual Learning in Transition Aging with GRACE: Lifelong model editing with discrete key-value adaptors

Reference 88

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source=pdf_text observed=2026-08-07T12:24:02.681676Z digest=sha256:10c81de00fec1d71b777723525913aebb6e1ac2c229d755d7baefd4fba5981a0

Observation 09a92607-39f3-411d-94c3-44adce04decc · outbound

This paper cites WISE: Rethinking the knowledge memory for lifelong model editing of large language models.

Continual Learning in Transition WISE: Rethinking the knowledge memory for lifelong model editing of large language models

Reference 89

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source=pdf_text observed=2026-08-07T12:24:02.757564Z digest=sha256:76c700cd61dc9774109c08ed6123d7e836c37ae38f193e9eb7ab77cf4c36e9c5

Observation f8923cc1-5db0-4688-839f-2254810ad8bf · outbound

This paper cites MemRL: Self-Evolving Agents via Runtime Reinforcement Learning on Episodic Memory.

Continual Learning in Transition MemRL: Self-Evolving Agents via Runtime Reinforcement Learning on Episodic Memory

Reference 90

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source=pdf_text observed=2026-08-07T12:24:02.834954Z digest=sha256:1c1005d37a23a2103ae72e31d1765a98ae7654abf77cf9aacae2e40c2668f87e

Observation e0f0a8c9-fbc7-492c-99f6-d3e338afbd37 · outbound

This paper cites Pan, Hinrich Schütze, Volker Tresp, and Yunpu Ma.

Continual Learning in Transition Pan, Hinrich Schütze, Volker Tresp, and Yunpu Ma

Reference 91

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source=pdf_text observed=2026-08-07T12:24:02.921739Z digest=sha256:1419b7c06e150a44c4408dc935c073b487d1a51c8bd74f1d8223f50a90cc7f33

Observation f836e2d7-6dde-4bd5-bbdf-c903e2c4bbdf · outbound

This paper cites Mem- α: Learning memory construction via reinforcement learning, 2025.

Continual Learning in Transition Mem- α: Learning memory construction via reinforcement learning, 2025

Reference 92

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source=pdf_text observed=2026-08-07T12:24:03.009454Z digest=sha256:01a61d8ebc8cfb2b61966cc25f518151485ad0abbe6bd4548c073aba57b70b6d

Observation 4781926c-d549-483c-80ff-88310e0553e5 · outbound

This paper cites Memory-R2: Fair credit assignment for long-horizon memory-augmented LLM agents, 2026.

Continual Learning in Transition Memory-R2: Fair credit assignment for long-horizon memory-augmented LLM agents, 2026

Reference 93

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source=pdf_text observed=2026-08-07T12:24:03.065529Z digest=sha256:fcb0b7e09b2f04d00a3b3d2ccffea687d6070fcea600722739a7b941c56df042

Observation 8dfccbbd-0c7a-4459-a207-221148305f78 · outbound

This paper cites MemBuilder: Reinforcing LLMs for long-term memory construction via attributed dense rewards, 2026.

Continual Learning in Transition MemBuilder: Reinforcing LLMs for long-term memory construction via attributed dense rewards, 2026

Reference 94

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source=pdf_text observed=2026-08-07T12:24:03.147804Z digest=sha256:e05070a1cd05cd4e18fff2cbea28110b690c67b044da42c59027a5f7400e6ef5

Observation 5a2bcd51-8448-48a0-b7bd-b1ce4755c6ad · outbound

This paper cites Memq: Integrating q-learning into self-evolving memory agents over provenance dags, 2026.

Continual Learning in Transition Memq: Integrating q-learning into self-evolving memory agents over provenance dags, 2026

Reference 95

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source=pdf_text observed=2026-08-07T12:24:03.259629Z digest=sha256:2b0b6bbc761cc6af59c72d4c7e3efccab009a402e8a16eddf498e2745db96f78

Observation 58e1c6ef-c7e1-46df-b90f-4ae7648f732a · outbound

This paper cites Marginal advantage accumulation for memory-driven agent self-evolution, 2026.

Continual Learning in Transition Marginal advantage accumulation for memory-driven agent self-evolution, 2026

Reference 96

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source=pdf_text observed=2026-08-07T12:24:03.329438Z digest=sha256:a383e4a2f9e8069212aec159604a0e3dd5698254eb9e3e48ed802077ecb9ad5f

Observation c8ce8d11-9629-4c18-9570-ce6bba0b37c2 · outbound

This paper cites Just-in-time reinforce- ment learning: Continual learning in LLM agents without gradient updates, 2026.

Continual Learning in Transition Just-in-time reinforce- ment learning: Continual learning in LLM agents without gradient updates, 2026

Reference 97

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source=pdf_text observed=2026-08-07T12:24:03.398196Z digest=sha256:35c6d46879ab204acb61c8804570dbdf34919935807963f80b0515a19a693a10

Observation 869eb844-642d-4196-8bc6-11cbc48c3216 · outbound

This paper cites Le, Samira Daruki, Xiangru Tang, Vishy Tirumalashetty, George Lee, Mahsan Rofouei, Hangfei Lin, Jiawei Han, Chen-Yu Lee, and Tomas Pfister.

Continual Learning in Transition Le, Samira Daruki, Xiangru Tang, Vishy Tirumalashetty, George Lee, Mahsan Rofouei, Hangfei Lin, Jiawei Han, Chen-Yu Lee, and Tomas Pfister

Reference 98

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source=pdf_text observed=2026-08-07T12:24:03.489277Z digest=sha256:a118f593970d0600f24c9a3a5a514eac31a62dc21b53348df5770812bcd12d38

Observation 7ed7fb0b-9dde-432a-a005-561edce8c089 · outbound

This paper cites Learning on the job: An experience-driven self-evolving agent for long-horizon tasks, 2025.

Continual Learning in Transition Learning on the job: An experience-driven self-evolving agent for long-horizon tasks, 2025

Reference 99

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source=pdf_text observed=2026-08-07T12:24:03.569556Z digest=sha256:e4a06f21ec541369eb8458194c018597da832190c1c919b485157c17eec812c2

Observation 7c26c397-16bb-4e96-9342-e90f7091ba2e · outbound

This paper cites Exg: Self-evolving agents with experience graphs, 2026.

Continual Learning in Transition Exg: Self-evolving agents with experience graphs, 2026

Reference 100

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source=pdf_text observed=2026-08-07T12:24:03.670173Z digest=sha256:0ec9821b9e8b10be6f72512ffb51f35521b96fe2d7315e6fbcf7af950a2ef214

Pith citing papers

No inbound Pith citation observations are available.