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Paper Citation Record · LEDGER

Continual Learning in Transition

As of 9 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:c728e06797b0d8455f832cadb4fdfeab54ee97c44d5e760c749c9bf922e6a9d1

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=sha256:4f3070a3e153088fe93b0534446d631e5ba46508e682aeaf82eb2d4ef5c16a8f

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=sha256:40c5d4f95a80e87b145748147edfd2c3635673879413531d42ea3683e5b8b9fb

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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source=pdf_text observed=2026-08-07T12:23:56.160890Z digest=sha256:6581162c4907c1dc0dc3c53ed980db2e56abd95cd7e134b3bff5810ae7e407ca

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=sha256:64a937ce3d98f047c6f5e77f434c13e42cd4c5e731a15bfb3faf23166f325767

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=sha256:241cea8e8a6fd78faffdf8823869c2b864bdc1895d7d4c747cef2170762a8b13

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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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:b4df53573e3bc0eed281c124206af88c9e5fa8b522bfa16262ed96651f0642bb

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=sha256:92abcaf1ba88e96b653fede58fc980691396f4bc163907fda942106ea202aa77

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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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:cff0cf884b15069ad8c488b8d17129b0b83f81eed6e3f109fbeb3741a339e90f

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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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:e39a9782631eed04b7c3fa2104cd4fea69b156cbaf1f2e6dbfa5c29b60690874

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=sha256:2ebbd7e13388f425aa2a90bddc771137773bbec8c7d1c964eaccf77b699892d4

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=sha256:93665ab4f9485cead314b15bb08b23f7313a732db1780fb252f8271e1f76b8e2

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=sha256:566d78b20534ace16f7a85217a0dcc18b6a5bd984c01e0e8d3a28b9cedff137b

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=sha256:7f3d9e57785f66d6403132c24afdd2865c421a196d0dbe950fe4ecdd68ebd358

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:d4f642822abbc5cec4da496ed69edc81fe5002bec100eaf53bf0125f1e196bda

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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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=sha256:0792aab21028082903195ee816b9c6a7cc082cc6666c85aaf7efc380ffd2ef4d

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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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=sha256:94173dccb0e329b081d7a39c37d4c22680742a55329326ff5189b0085fa6a2c1

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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source=pdf_text observed=2026-08-07T12:23:58.039841Z digest=sha256:89fbdc24cf30f18250c5f31f5a478708b24d4e1121b825531cc498966e91a71f

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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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=sha256:1c9b88a836bc9ae1f80013812e322af0e3cc53f98c93da1180faf3ee0961b43c

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:ca63f434e2e2e0fa333803c5b9ad357c2dde95cfa6f6476cd25d7e357ababc9c

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=sha256:1e5aeaa48e9e4550f8ea4626f6ade34e737c8716e03ccbf71cd81197a19e7c06

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=sha256:4253db389c3708c72b02468182fe543fa5e0d2b1aeb0322c83405bcf46d8afd7

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:a95ceaa14bb3271271e2602bb57ce8a477d9fccc7cd6275e67468202f4f7f100

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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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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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:a3cc0765b7f487691aa7b8f415c3ca24a7ebe38c9d0f01c9e17fa78a3f0bf622

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:f8023173ecfa94c48b345d9efe383f1572d916d27245bd973b935853c36f0ab3

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=sha256:5c335b17e2117dbe6df7ea06720cf37f44a1eedb4b7058c22a950415860d3b46

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:f3b13b8ba3c53a52b4d453a023c2342890533e2eb771d40bb08569b761dab7c6

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=sha256:4697c95568b43eb0c6d028002ca4a0b96659c8488caae13ce66b340e8d38f203

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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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

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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=sha256:184596c1e7d20193d908c388209a897ac3903cfb7b917f991417e06a95f305c2

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=sha256:1346126a7f82b0110880216580148a58aae82e6e3b3017e191dfc236b14f852f

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:bb659a35887d823ddec0f4c11da81d946d767aca50c2474aa8b9f81bbdf247a1

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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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:e72dc5ff6dd0b7ceb1d3a28f0c3134033825c9c20c565533dd266d23fab0b588

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=sha256:2a6070b3956753d0ff53020accc48c01e67a0aa2311ad50824d9bdfee24d404e

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:d654055b251316a16d55c54737bf7785fdc012f48f879e26b2071df53f447e0f

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:ff100e0402cdb558f3ad35c33c0e51837cede3bf105ce5d2510a33035409d358

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=sha256:0fdd23f1cbb2e39b0b4f3210bee5d4b21126d01ab914fe62915c4fd4ad3bc790

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:c1287b802168308e90ab485e2eebfe67c034d9d923767ad9fa0b314748d77e4c

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:ba32e96016ca3cf16b7ad2871cf6100adea7d990fa46bef275f542bc9a8411a5

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=sha256:2a2f3fde0d526a9a247fa8572d31f10330e8e09e9078ecb8162193f6dd576173

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:f09e8acc4060f3932495aa523d593098b42fb12551ff3567daf08194c8555b8f

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=sha256:7cf1380148be725b6b886237a270b04da7f4c7101ef4366078da52c844b5b924

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=sha256:457d0eae74dfa1e94dd5396b554325fa422f5733ce1118dd682b6c91cbe76936

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:ef3e63e644072fcdd6764a3109b3164e02bb93daeb4c75dd81d09a141a7086be

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:b32ae041498eb0ba219b5dd2d2c13193299a83d533865cab1cc1085c19d2cdc0

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:e650bfd8526fba959d19c6bebb36dadca2547d3504a5f4d8c2657ad5be8cff9f

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=sha256:2cfd7118a6a741798171c04b56d9159c9405d0a1fca07f8306f6ac8dec05ddc7

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:c3be64b421754c5f3c25424eba3037f3821334ab3c2e634f59c3a27ab8df8a8f

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=sha256:69b77ed0c648c173b21b16eae630b0d19c945a4ceec89651155bacc6d1e28118

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:f0ac294e6e4d6a1123dfd606cf4f18cb59ef2e4478e352349289275601b95757

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=sha256:542751a3ff5300049378f28292949b2b816326acd8f0a301c22536df45f9bd5b

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:d41387e5bebe8059987eaf2b1884ad085ed0610b15623931be6e3f22fea857cd

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=sha256:8eeda3712e80697feb7a03fa44cafb14c2413d3c9785f5898256acc2dde0448c

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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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=sha256:6cf0357580fd9c5e3be2ec1d728724f29d9b74add649a8859c0d793012fe9827

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=sha256:1cd7c27d04f68915d1a396140ec585ed6b6113f453d730bd021b9d11fb4c34bd

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:eb54e7a2dd7cebc5483c63a59125354f1dd70bed71938b73f80127c0a5c595a0

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=sha256:1296ab22ff36dcc8cce6489b96cdfc3585f13cc55a1d824849706e10542ea9a9

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=sha256:80b0dce1ac462c0b8207aa643ee33a0748af029721ecf05ae8d704e20b0ae748

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:b209b2250235860330e18dd166bb56c35967c561d8a26f1788720d5416926ba3

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:f5b116b4f969c8f8ba548230ad9cf2aa486b830c4e5b7aed577278be874f4aa8

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:b47359736fc8caf6e5f50f5d4a45d0416ee31b3b5bc9fabb3d95ea052cc98a15

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=sha256:8e6ebdf086ff1235248bed8436fabc2dee4765bce0e64208b486dba670fc53cf

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=sha256:34bb7fef444e31e16ad2c5f352e8b9dcb216148de1f24212ec5ca0bdc10cdf5d

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=sha256:721473e036d23a6b8735c920ca5ad3c61ec536b2470eb636d4a093bb9a50be69

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:e46f8b0678d657880a64b633521dd0716250f04bf5975f3cb39efc2eee025ef4

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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source=pdf_text observed=2026-08-07T12:24:02.050267Z digest=sha256:26b7d9ec25c369c4a9def81da5c1f05ef74808bcfd406293afb3c5274f13c33e

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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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=sha256:7df3231f20b273a0a17504a2cdbe3b2c4c044ee779bfc90a7c212cc3e715ca0a

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=sha256:5f9351ab887104d70c60666265766811f22b9ab77be77c4e0df3d78b9762c9ee

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=sha256:97371bb03c93573fe5f2ccc336548c5e98a1d96103bc10337a44dd49be46ab3f

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=sha256:2f11296e2302e7a66d7ab46a8f7d4ffcc4f8f9a85b064adfbd74eecff7976edf

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=sha256:735c8be83dd23e8ae1a262ceda427fa8dff75212eb1e02b5fcabfd9178d86509

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:fca33381ce161a8d6f2ffea26efa1235a1a43db96d48d383d2c51dd8d25235d8

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:e90cae6384ce54d5d647a393c09d7c3b8195e71d656de0152f6140adfa813eb1

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:f0108bc96e5f6d60f72c4afde24e441a3106ed1a61c3824cd3ee629276ba625e

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=sha256:579c07b95310fa267decaf52a272082bae7b965a23cfe99490f2523cb80429b7

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=sha256:595384fc7833a0ede88613d3979c161273c77cc500bb13100e87dab2ead0fa9f

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:a9c001dd2be58c60e95389572d1006ad9c12850b8be44694ccb6ec6443d63046

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=sha256:02b4f8b39473e91bdcd026b847578313a24074fac73a416bdefc710aded23ca3

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:f41fbb6e5b6661b7c3648efaa1f91c2636fec51c7ff056cfce1b9f4d214eea06

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=sha256:2e2112dcc304c6d3faeb46fef8ac6ebdda76ecd9e184559a8ccdc34fcdcb4e33

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=sha256:089f4e2166a0eb75612f291b43f22500958dba154d377f790c4121019ccb70f1

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=sha256:2d4f6c29e7bc1bbdabefa9d191c9b2ee0ea648e049b5076beb08eabb5bbd92dc

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:b92fc16cf6f51ee853e655fd8487177802332a4f9dc65c96bac8dac478e93952

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=sha256:3034216fa4855092fb28380ec3a9013ad4d89c0ee06b6fa4627f08a2a3c31994

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=sha256:93b0d845ebcb8e30d22d8fc38b5a03c5be3869072eeedb69ef68854e546e7d8c

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=sha256:62defbd485bdea1bdfb754e158e1e684a289ec4f4bae50e29aec42302c5f3ed0

Pith citing papers

No inbound Pith citation observations are available.