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Paper Citation Record · LEDGER

Continual Learning in Transition

As of 7 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 2026-08-07T06:34:17.273281+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:ae6a85541904ebccb59468dbb2b886306967d014ba533f5c50242395a2a53159

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=sha256:8a58b067af0e29fd86a902d804c789c1106666cd74364217f757fec220836957

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=sha256:20a8c23f0817ab3c232ff7db4cc0a5b79d309c6ec809b6da1e033a98a9d7fc64

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=sha256:8c58661a8bb41259b1928aa08aa61b74129f653b2f35792500c69f4046010f86

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=sha256:066ce61a94c05143eb63f0e4bca91a6f29a7227f13c680797b65041d144d5886

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:eb1cdac7e650668490835aba8c745bde2bcb82c0c3c8fb28050b67befbb4e3a1

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:c1644e5cfb2705d6469e86edbddd72424413ed5b0d3d0ca6c5467458fda26512

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:ef24074c83b39f43e9beb6209e0b7fa305b8b9fb0c35704d976f6cffeefb246a

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:fadb90e65a73e5586de02d09757d59b52519c2abaf23d4396bf074b6026458ed

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=sha256:9386ff3858f85e28b8a59643362bd0040a0e41c22ec5bd55162a2e1154b03614

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:d12e5cceba26e53d404bfbb09f931c2b826d4f4734d2718e49276f6c9db120fe

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=sha256:0b9c0ee7bc89e284bd666a10a390f4ec7be6276327383ae888668b2cc0c2fe83

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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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:f45e001357ac099e8e2ce3a84cdecb131a6705ff2b82631c6b40baa3ee3fa4bc

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:cdee27ff16ee51b0656e0c9aeda89be1e1949cb02536f779a2637acf309cc34e

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=sha256:2445fa2dd54a245ea72063a9860030cf72efacc0d75f62beb5abe50a09042527

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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source=pdf_text observed=2026-08-07T12:23:57.450650Z digest=sha256:eb62376986a1594b8d919321d49b0aac92ebd0b727787deb9292b85bb8e95f7c

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:e4b24d1aa2b50035358df62fef19b826613138c028a8194c1fddea28ddf003bc

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:af6fa754e8f6cbb8ed207031ac6066340ec28d381b057564047ddc18ae3a22eb

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=sha256:1f9723ee893814074487fd4d69c8f2a4d15be41fedc7ecfe5ffd5ead04085e1d

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:e1be8f83f99d4bc22d85d179a1bdc0aff35fefe3b065598676da7538b891c38f

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=sha256:431a5e69cd7404e79fcbe9cc41762bb41a279b9b7d89b9f914f032ee1d49abb0

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=sha256:6816286208e078c745046be889595757bf42c8fef758d47000da1cdbf7ac8a80

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:c02df62e00418282cb6c734b9d66f209fff06a0b8b354815bfa828bd061d6ea9

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:ad8d61aca8cfba3767bf20da20b664dcb48c6e496205c0c35767a9ed09c235cb

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=sha256:2ff3c66422dbf2f754e304fa112a043be153c8f1e5a72690f45b6bd0bdad154d

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:d38297d0d5e0c3d9f5a3567aa549d00528744845450a1a98b1927e244fa7ea33

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=sha256:7f5b604f8d738577f62cd8b0e86982835f340c55826a1c1e55fa389bdc7707b6

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:c05b9894605ed1fa9be136edbf41e9e4044c6a53a6ec5ddf87c22def08b908ca

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:b347d43e03eac5b64b286fc1910fc17a0aa77ba0d9a041fa352ab143179adb64

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=sha256:40f141d90e92ea2cea3e9086837617312cb0ef40ba0fb32b076606d542465d30

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=sha256:9cb1596af7de0627d5202abe5c161418b64d79916985eb8a1b4ad5b67513ec7c

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:dd359b92d171549f50d686265fbfc4ebd4e974489d7d21e8fca9c99ea6216c08

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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source=pdf_text observed=2026-08-07T12:23:58.889158Z digest=sha256:7336152b7da4414b9f26f5aa6686aa20f6e3be779acab2a09fa8152e1648fad9

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=sha256:749fee193378a5eec1b968173843ae552253db2394bfce222e56612261515a76

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:f3a9766a0fc42369277b5a11569159b7d524ba7ebae37001c3d18406b2fbedce

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=sha256:8bd09e81392b23d7ec90a5791d7b6976f31d7f812789aaf4663281bf60f52ca7

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:fd8caf6853d0f30f68df91357cdae9df9abe86f39948de596201ca12ae1b3d26

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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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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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=sha256:7510f072dafb3b767518bd11a9f8d661dc45d13f6f814e042c2d75b815038b78

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:a7aef9815208b8ad4f4bc68b28819afb186c50f4f44594ae9b22c96620437d83

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=sha256:1dd245ca5da0b9fec9efd6e1ed376558b9e31808c5187b11558ae85240268137

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=sha256:6e3e9d6f92941546ab7b522da32e226747dc1858479fc27846bd85424f218d90

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:e00564ae30184523fca9fc4f55b3ed5603f997d74adeb2b7a8eb36e88d14338e

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=sha256:23faea772978fbe5f9e9489fd818887ab232163d97bb36029caf183ce80c3034

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:e6f45c36c4b12b4a6f57b699fb97d7964820138f835443b879357dc50f0003b9

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=sha256:9b0e23c1d7d4303514b382f6dd24118d0ee46ce8a797d0500447b349318882e5

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=sha256:5d1569f8bf7adfbca14c3bf097a0ba37a3e5ee8fcf602352f2f114ce942170e3

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:b47ea08aba1c6ea5c4ed2da5f25689032ab5e3be3b6d411b4778b262472cf537

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:cc69f8105d3535a41466cf3d05e084435d741980a2cfa62826d033c76a8e50fd

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=sha256:64812b09d091f57894b065a2aa065b64f3576470f0a7728b988c8aa99f6929d4

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=sha256:2fc4d0f8cb9913656d28b73c011911646d85150c0ff72896b07956f2b8bb987f

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=sha256:336f247ccdfeb4f2741d973112e41a3df018f864d4045ac71bbcbca7ac4fa810

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:a0e2cd0e58106a89f32afeaca7775fd887c1e23e20156558d12fc8892585eb5a

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:efc2db77801754e7c20e237de39f327b7b314d9ca71c6d46150830a87120c4cb

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=sha256:128507279f3575171b2ff725c09c38fb02e8ca07bbcbc962f27aef17058ad9df

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:b315e5fcb8b924d6f86df21675376a70899e51ac42014e5f6df53ceb39c3fc6d

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:f831b0004095fe9549f08ee465d44b2dd0b645b37dea37687573c22d421d196b

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:a1d3d25d0d13076e40b5dace1a4ce9f692d3d3fb0314f2aceb36941dd0a7c0e7

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:e6f8da2ff1daead93ebd1dfc0221efddcabf07fbbb988e7009e531ff47027785

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=sha256:1f953f6a3aa77e36daf8499f900dab9664a52ba0045f9a5b572103a326a36401

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:a6fa08f20b93c6a93b03e20b48141a9b174b1cab0229fcd1c8bb6f2df638dc89

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:defa664ec9571d484775605b8121d69cf72d07cc79e5ea742513a78b899f8193

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:a6030afd923f4633fe3f599cb2f85cfc80c963697a011fff475c2d2f02c601d9

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=sha256:36bd79bf64f121ce4c444df03f6df9be3a58f6bb013c00489a5a146598d5c5f0

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=sha256:9cfa39a4cad59f759e970af92213ab94f28be034356f61591d95e756996fb627

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:b1b3011608db7010fdb672c1f302912e57fac0d0e3cef94b61f020e517026871

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:be22cc521af3b335467d996a10e49afb45e57b6503a50bcdb6f9bb871173e903

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:f7215f1613798c8dc4a433ed4b3e719c77048d938edc7776ef184fbb2cc623cd

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:e7c32fb1cb9a3c080fcdc51ff0cab740b9bdeb3aeee6cf9e5e2b3d2510a9228f

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=sha256:352c51f9922e38ee15938bd249ee50ffbbad5ffb084ce96e39d29e52d1ea8587

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=sha256:51416ca29e2d0a92b38c933a7342b1dbf3a9a667c6948c0a607f6cfc42f81187

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:b43280eb920c2a661ecdf1d76a2246c7aafe96f256736445975265aee65cafdc

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=sha256:5077307e2d60aaff814e09ad24801f70cc3f7762fa8ab5a9ca60882c362fe277

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:d3fb996a583848929901641333529177743ae75af4f3cf60b364288ec2e1fd7e

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=sha256:668eaeabc6ca81802926154ef97bcc09acc57d53109bd2297c7021bf0ee757a1

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:ca46d4219bde1302dc9f9b548e4b05b2d048a735c1a0764f443f737e34d2ffb4

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=sha256:66f8e458f9e9b6f10fa945dded287ba80524f6a06d31b5c5ed6fa5e3120205a7

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=sha256:44efb74bbd572ffab0c316252b051f791b8a71d0c9fbb65fa6ecb4c488952f00

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:b3c116aa99724119a7ba6d1feaa401f353136d27f36ef19213eac6710173a5e4

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:fd086152339f04a18e49ee6252821744611d5eef4b1e87a8c4d54b2465d54aa7

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=sha256:78c36fba5f4f4ea75e02b641606dc5a63172efa500892fe5984aab011a7eaf94

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=sha256:4f743268eb54a0e37e45c2f28e61a03c46f17ab9520c7f78dc2fff391372e625

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:d71a3d74bef6e9c8b52f3c5ad3c74e462a5386ad208ed4dfb736e774a6556556

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=sha256:24f9e2e3c4c798d62423839bf6a14e51a185e040953cf421ea10ae3ebe127e6f

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:e16342942e0c89f105679fc0d559bea62c1b9c313eeba68aa2d02aba6b7bceb1

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=sha256:3d16e5480e7e0e56e8b709c3ad2d42bf6e66621daedeb0b243558b04ad185770

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:b44280fc5cb24215596586864b7ca07ad7fe0d9d930310e1dcf5bbb6d10f87f9

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=sha256:4608b99a652e20c4f1f68d0668369612028bc942ecac1a10f558d4e4f8adf41f

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=sha256:5a45cd870fbfb06f88bdb2856bba4800a6f3d9c889a5477e4682425c9de857e5

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=sha256:28babbdfa1271160cb5d165b4df9705eb64da1e4b7e0eb46972d54220c3afeaa

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=sha256:244a412ea086018490505c8c9eb5181fde177c92da1d123a76baf71378d9b47c

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=sha256:0b5eed5c8a49975ff296535b751274e55436b2f316a5077a121735d94b84bf66

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:e3983587bad8ebb18ff2157dce3d21f3cdd65aa49ef5178a25810432631b3b12

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=sha256:0c30245bb01772d8270f457d1aa528ec8d0cd5aec106ae9ad76cd5d91033d5bb

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:c71d5b58025cc05f5a5dda49d7103def9a86daa08616832eaf4a282f418c64d5

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=sha256:0bfc66255c2e80c936049ef0bdb0126d00fc709db057dc5f3aa4e5671f889ce6

Pith citing papers

No inbound Pith citation observations are available.