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Paper Citation Record · LEDGER

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence

As of 7 August 2026, this Paper Citation Record lists 100 of 117 outbound references and 0 inbound Pith citation observations for arXiv:2506.13187.

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

pith.paper-citation-record.v1
2506.13187 v1

Coverage vector

measured 100 of 117 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:43:54.429969Z

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 117 outbound references displayed

  • verified exact1
  • verified fuzzy31
  • unresolved68
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4e0a7087-bcc0-4b2b-8203-0b86ef389586 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 1

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Observation 79f56404-f2f0-4ebd-aafd-c891f644d0a6 · outbound

This paper cites Improving language understanding by generative pre-training,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Improving language understanding by generative pre-training,

Reference 2

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Observation 078231ff-b815-4419-8ce0-f4dd28a8d524 · outbound

This paper cites Language models are few-shot learners,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Language models are few-shot learners,

Reference 3

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Observation 6a2977ca-3840-4e77-bac4-3d363ee91696 · outbound

This paper cites COMET: Commonsense Transformers for Automatic Knowledge Graph Construction.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence COMET: Commonsense Transformers for Automatic Knowledge Graph Construction

Reference 4

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Observation 0cd2d397-f8df-4c78-b46f-4a58d77e7e41 · outbound

This paper cites Fine-Tuning Language Models from Human Preferences.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Fine-Tuning Language Models from Human Preferences

Reference 5

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source=pdf_text observed=2026-08-07T00:43:42.262629Z digest=sha256:e06b9010707172350e746d971d46a5046c6e520f4a0b2f633d9f14e9ff0800ba

Observation 470da126-4227-4cf7-a245-fd92fa5a53da · outbound

This paper cites Parameter-efficient transfer learning for nlp,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Parameter-efficient transfer learning for nlp,

Reference 6

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Observation d6c5e5a8-f9ae-4426-a283-146fcab8f47c · outbound

This paper cites LoRA: Low-rank adaptation of large language models,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence LoRA: Low-rank adaptation of large language models,

Reference 7

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Observation 06c2021e-96b7-4b50-b07b-daea9d9a21ba · outbound

This paper cites Towards a unified view of parameter-efficient transfer learning,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Towards a unified view of parameter-efficient transfer learning,

Reference 8

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Observation c613ce88-b4d5-47ae-9015-d98bdd675a28 · outbound

This paper cites The power of scale for parameter-efficient prompt tuning,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence The power of scale for parameter-efficient prompt tuning,

Reference 9

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source=pdf_text observed=2026-08-07T00:43:42.870649Z digest=sha256:28fdd15271a4c39ca4727c1b36932b2a905e6deefab47dfebcabaf69f232ef6f

Observation 5b5d111a-35bb-4948-92d7-3d3dc1b921ec · outbound

This paper cites Prefix-tuning: Optimizing continuous prompts for generation,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Prefix-tuning: Optimizing continuous prompts for generation,

Reference 10

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Observation b1e740bc-5d3c-4c78-9221-67cc839eb6a3 · outbound

This paper cites Residual prompt tuning: improving prompt tuning with residual reparameterization,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Residual prompt tuning: improving prompt tuning with residual reparameterization,

Reference 11

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source=pdf_text observed=2026-08-07T00:43:43.216295Z digest=sha256:60a5995c705c229b4bf8df2e7c354a8d02f7c2717082b0ea0674763bf79ab231

Observation b2f57976-85c4-4386-9cc0-861a464054d7 · outbound

This paper cites Adaptive budget allocation for parameter-efficient fine- tuning,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Adaptive budget allocation for parameter-efficient fine- tuning,

Reference 12

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source=pdf_text observed=2026-08-07T00:43:43.291207Z digest=sha256:cfef2ba1481fb4eccc9fa0dd63ea08a0c18ec328747f6fdaba0ef9d08c923f84

Observation f2202779-1d69-40fa-828f-5f1e8e89cb22 · outbound

This paper cites DyLoRA: Parameter Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptation.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence DyLoRA: Parameter Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptation

Reference 13

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Observation 29f949df-8960-4b21-8b3f-a8334591c684 · outbound

This paper cites IncreLoRA: Incremental Parameter Allocation Method for Parameter-Efficient Fine-tuning.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence IncreLoRA: Incremental Parameter Allocation Method for Parameter-Efficient Fine-tuning

Reference 14

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source=pdf_text observed=2026-08-07T00:43:43.429659Z digest=sha256:0fc96e5ba83e9928c903363ccdf1c9309af4fd103324f44caf7766a15b6830a9

Observation de6400a7-d2f0-4f7c-9f39-af460740e612 · outbound

This paper cites Dora: Weight-decomposed low-rank adaptation,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Dora: Weight-decomposed low-rank adaptation,

Reference 15

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Observation d5b78b46-802e-43c3-b06a-47973eac256c · outbound

This paper cites Lora+: efficient low rank adaptation of large models,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Lora+: efficient low rank adaptation of large models,

Reference 16

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Observation a3e162fb-4ee5-475f-a20c-46227fa720f3 · outbound

This paper cites Vera: Vector-based random matrix adaptation,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Vera: Vector-based random matrix adaptation,

Reference 17

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source=pdf_text observed=2026-08-07T00:43:43.957025Z digest=sha256:b71ed1689f0ebc0d765d5e987c13581a91f06433e62705d54d14c3083be30a97

Observation 63941320-43f5-4db7-9455-b8752eadc724 · outbound

This paper cites Tied-Lora: Enhancing parameter efficiency of LoRA with weight tying.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Tied-Lora: Enhancing parameter efficiency of LoRA with weight tying

Reference 18

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source=pdf_text observed=2026-08-07T00:43:44.025509Z digest=sha256:fe4657e6140907e54b9c4b48441b5a8e0f41dbc6c96898fc975534632be1f100

Observation 8e2f3010-7400-45d6-be70-b059f51e2a07 · outbound

This paper cites Qlora: Efficient finetuning of quantized llms,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Qlora: Efficient finetuning of quantized llms,

Reference 19

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source=pdf_text observed=2026-08-07T00:43:44.213608Z digest=sha256:f39657a2c83d58d61b7889ae974f10993501e5bb68c8f42e04f59a1538e925bf

Observation 28de4d86-2c2b-4a60-b88b-a17bbe7a10a6 · outbound

This paper cites QA-loRA: Quantization-aware low-rank adaptation of large language models,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence QA-loRA: Quantization-aware low-rank adaptation of large language models,

Reference 20

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Observation 752f0f01-f8d6-4d84-bfe6-5f33013c99c8 · outbound

This paper cites Loftq: LoRA-fine-tuning-aware quantization for large language mod- els,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Loftq: LoRA-fine-tuning-aware quantization for large language mod- els,

Reference 21

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Observation 96f27793-f045-421f-bdc4-99b1652f1b50 · outbound

This paper cites LoRAPrune: Structured pruning meets low-rank parameter-efficient fine-tuning,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence LoRAPrune: Structured pruning meets low-rank parameter-efficient fine-tuning,

Reference 22

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source=pdf_text observed=2026-08-07T00:43:44.630189Z digest=sha256:e91f32031ff119e185982558a5dd686ed1bcf95f16a5fba9a60c96eaf4b2dde7

Observation abbdad79-ac6e-4298-83a8-d0393659d362 · outbound

This paper cites NOLA: Compressing loRA using linear combination of random basis,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence NOLA: Compressing loRA using linear combination of random basis,

Reference 23

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Observation cf34da90-e542-46a0-b2dd-8e872df59471 · outbound

This paper cites VB-loRA: Extreme parameter efficient fine- tuning with vector banks,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence VB-loRA: Extreme parameter efficient fine- tuning with vector banks,

Reference 24

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source=pdf_text observed=2026-08-07T00:43:44.870951Z digest=sha256:d0d81de2d90e1602e0df15479af339d2cfa3de1c16b08c0e09d10c31b5747674

Observation 807fec6d-af6a-4a73-87d3-626713b8bf2b · outbound

This paper cites The impact of initialization on lora finetuning dynamics,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence The impact of initialization on lora finetuning dynamics,

Reference 25

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source=pdf_text observed=2026-08-07T00:43:45.060890Z digest=sha256:5fbd3e046f5b88de4f850c24a21f8b1bf0770273b26696e5ac2faed9a988ca7c

Observation 7d63506e-f843-499f-b50b-437159d7e42f · outbound

This paper cites LoRA-XS: Low-Rank Adaptation with Extremely Small Number of Parameters.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence LoRA-XS: Low-Rank Adaptation with Extremely Small Number of Parameters

Reference 26

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Observation a76a5d8c-74c8-4af0-8331-784f3399e6cd · outbound

This paper cites Pissa: Principal singular values and singular vectors adaptation of large language models,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Pissa: Principal singular values and singular vectors adaptation of large language models,

Reference 27

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Observation 8b1272a1-45fe-4c00-b7ba-a6c3129d238a · outbound

This paper cites Latent retrieval for weakly supervised open domain question answering,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Latent retrieval for weakly supervised open domain question answering,

Reference 28

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source=pdf_text observed=2026-08-07T00:43:45.449976Z digest=sha256:0800205ab1a2b18bfc136453ef216643b8a21e00774800277d6d0d9eafd824f5

Observation cf4443ec-8d61-44b0-a3fa-34d21983a6e3 · outbound

This paper cites TriviaQA: A large scale distantly supervised challenge dataset for reading comprehen- sion,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence TriviaQA: A large scale distantly supervised challenge dataset for reading comprehen- sion,

Reference 29

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Observation 526d93c5-dacd-49ed-9eeb-23e6efc18627 · outbound

This paper cites Metamath: Bootstrap your own mathematical questions for large language models,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Metamath: Bootstrap your own mathematical questions for large language models,

Reference 30

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Observation 58dbdb54-eea4-4aa4-a6eb-864ee87877d5 · outbound

This paper cites Corda: Context-oriented decomposition adaptation of large language models for task-aware parameter-efficient fine-tuning,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Corda: Context-oriented decomposition adaptation of large language models for task-aware parameter-efficient fine-tuning,

Reference 31

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Observation 7d7b1ef5-d504-44a7-901c-5c8373173957 · outbound

This paper cites Towards theoretically inspired neural initialization optimization,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Towards theoretically inspired neural initialization optimization,

Reference 32

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source=pdf_text observed=2026-08-07T00:43:45.937895Z digest=sha256:5ce30564003879cbfaa9aadbe25419b8acd98818a7d1eb4de3a80e192cec6727

Observation 1db21544-2cd0-4497-acd2-1d50a9f1b370 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Training Verifiers to Solve Math Word Problems

Reference 33

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Observation 599a5b93-8a79-4c67-b1da-8ff56df37a76 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Evaluating Large Language Models Trained on Code

Reference 34

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Observation 5bdbe9b7-16c1-4ad6-9079-cc0aaa37d97a · outbound

This paper cites Program Synthesis with Large Language Models.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Program Synthesis with Large Language Models

Reference 35

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Observation 602b617c-fbab-4519-bd99-bf67ea370990 · outbound

This paper cites Judging llm-as-a-judge with mt- bench and chatbot arena,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Judging llm-as-a-judge with mt- bench and chatbot arena,

Reference 36

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source=pdf_text observed=2026-08-07T00:43:46.476352Z digest=sha256:6b422cdca1f0e4bde8184138f8f70f4b91a7a888e1856f728fbfab27e048bf89

Observation f36bbe2e-3f9b-4fea-93fb-b751b62710e4 · outbound

This paper cites Semantic parsing on freebase from question-answer pairs,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Semantic parsing on freebase from question-answer pairs,

Reference 37

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source=pdf_text observed=2026-08-07T00:43:46.552081Z digest=sha256:0bbc662aa613cdcdbf2d59ba0a92352d0f331b824be4a508e6b2c1f9d4ad50e8

Observation 1b35a448-2ec4-4534-ad0c-61e213788370 · outbound

This paper cites 8-bit optimiz- ers via block-wise quantization,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence 8-bit optimiz- ers via block-wise quantization,

Reference 38

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source=pdf_text observed=2026-08-07T00:43:46.694536Z digest=sha256:effb9b32d47d86623f252787224abbd02a27995b368dccd9195e864a4ac06319

Observation 57e5b943-0cd8-4089-850d-8f59633a7ea9 · outbound

This paper cites Visual instruction tuning,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Visual instruction tuning,

Reference 39

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source=pdf_text observed=2026-08-07T00:43:46.860234Z digest=sha256:6ad0acc87d098287a19f9ab1983f2de90d6e9722423179cddad93eba5423cb7b

Observation 403b8a25-f73d-4c1b-b29c-cefaf1047c40 · outbound

This paper cites Improved baselines with visual instruction tuning,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Improved baselines with visual instruction tuning,

Reference 40

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source=pdf_text observed=2026-08-07T00:43:47.032117Z digest=sha256:b439476352a967b6cae45cad0c4275293441a397d31e5130875bf81ff01096a7

Observation 95ba06a0-82e2-4b46-a708-7234b158b16f · outbound

This paper cites GPT-4 Technical Report.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence GPT-4 Technical Report

Reference 41

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source=pdf_text observed=2026-08-07T00:43:47.137729Z digest=sha256:60a65224e6303c54634a291996a48eda863c813227ee69146d102674b8d9552e

Observation ae745f12-f78d-4573-a2f9-a6f864f92021 · outbound

This paper cites Renaissance: A survey into ai text-to-image generation in the era of large model,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Renaissance: A survey into ai text-to-image generation in the era of large model,

Reference 42

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:43:47.270986Z digest=sha256:0f1e18fef003d4bf16100a8d47c4bb4412c0d1ef3c8cfbd79cfc0035aea2b1a5

Observation d5041a96-7ec2-48db-8b41-dd24fae9a6de · outbound

This paper cites Galore: Memory-efficient llm training by gradient low-rank projec- tion,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Galore: Memory-efficient llm training by gradient low-rank projec- tion,

Reference 43

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:43:47.411846Z digest=sha256:82118cfca11e12b5f4c52f626e1e7afb5ac23567a2c45f4dc8e9e5c0aa106840

Observation d99d4a52-9469-4c4a-9871-67aa73ac0cca · outbound

This paper cites Towards interpretable deep local learning with successive gradient reconciliation,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Towards interpretable deep local learning with successive gradient reconciliation,

Reference 44

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source=pdf_text observed=2026-08-07T00:43:47.556445Z digest=sha256:82ad49a4bd1cd888b90dd5e7cf8cd08cdf3787289c7e0f8e359c961eb4d68f6f

Observation 707d2288-3217-43a2-b8c1-ecb2b96c5137 · outbound

This paper cites Parameter-efficient fine-tuning of large-scale pre-trained language models,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Parameter-efficient fine-tuning of large-scale pre-trained language models,

Reference 45

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:43:47.682249Z digest=sha256:3df4d6d2003e55b306284117f055e6f6dc98ed3a2636789b2751a742da30c6e4

Observation 5bb92f7f-cf35-491a-a69c-cf8f49e6a57c · outbound

This paper cites Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 46

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:43:47.862146Z digest=sha256:312f44875fd40c7dde6baab7e963c5a540e33a9f6c257116a13c7fb1ef487c03

Observation d726ee3b-f17d-4d9f-a9d3-f934110b9edf · outbound

This paper cites Conditional adapters: Parameter-efficient transfer learning with fast inference,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Conditional adapters: Parameter-efficient transfer learning with fast inference,

Reference 47

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:43:48.045694Z digest=sha256:814b53f6c6a4b4c1051fed290e10ba62ee6558378955468e2becc880eb06e573

Observation 53c9bd6b-7b74-4246-b57d-314de8b9709c · outbound

This paper cites Parameter- efficient multi-task fine-tuning for transformers via shared hypernet- works,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Parameter- efficient multi-task fine-tuning for transformers via shared hypernet- works,

Reference 48

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:43:48.155607Z digest=sha256:98eb52c4fbd2ba666ccdd6f20d363af3922aae068cf17051ea976bfa89a431c5

Observation b308ec1f-3b87-4324-8f80-6cce7d6b255e · outbound

This paper cites Adapter- fusion: Non-destructive task composition for transfer learning,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Adapter- fusion: Non-destructive task composition for transfer learning,

Reference 49

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source=pdf_text observed=2026-08-07T00:43:48.267211Z digest=sha256:a0d9587d4e91ccfb210eb1f44bd3146f4e3a5e8667996a12dfdadee43f7126fa

Observation 8fd1b579-94d9-46dd-a095-911331733c5a · outbound

This paper cites Compacter: Ef- ficient low-rank hypercomplex adapter layers,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Compacter: Ef- ficient low-rank hypercomplex adapter layers,

Reference 50

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verified fuzzy
raw_fallback, observed 2026-08-07T00:44:09.439931Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:48.424299Z digest=sha256:75d82a7290038cc81e11803ce8412350661c51ac9beb1e353de9d0f2673ee08a

Observation fc840a86-b389-4978-9452-a3ce5b62694f · outbound

This paper cites SPT: Learning to selectively insert prompts for better prompt tuning,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence SPT: Learning to selectively insert prompts for better prompt tuning,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:09.121353Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:48.554856Z digest=sha256:e85632f2d569ef045be544447d13b5c6d56d9c8cff5ce7be872ec4e8005efb62

Observation 598468e7-7a14-429f-9b03-2f1120acd109 · outbound

This paper cites Measuring the intrinsic dimension of objective landscapes,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Measuring the intrinsic dimension of objective landscapes,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:08.778303Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:48.693707Z digest=sha256:7f75435a86a19677f811e6e9f77d657b918fec57eff24087cca3f5bf46001086

Observation 47218487-b391-4fa6-9710-2c135d10332c · outbound

This paper cites Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 53

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:43:48.811606Z digest=sha256:ea5153d80f706af84dae16d311186bfa811a502d387e631d72aebc3cb2460b93

Observation e23c0c18-8c86-4e49-aca5-d78808f26ce8 · outbound

This paper cites One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning

Reference 54

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:43:49.017057Z digest=sha256:7c0681a51dbe5c2a95b81aefad45935e8c6c4aecdff024a6c9d38947fc063335

Observation c4242758-8d1c-4712-9334-04e26cff2271 · outbound

This paper cites Controlling text-to-image diffusion by orthogonal finetuning,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Controlling text-to-image diffusion by orthogonal finetuning,

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:08.484502Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:49.185475Z digest=sha256:539e2639ef6fcd4162fc096becbfda97b709522f95ce6a3918def16eb59749a8

Observation b47ea63a-1d5f-443c-b30b-e31dd18b5807 · outbound

This paper cites LQ-loRA: Low-rank plus quantized matrix decomposition for efficient language model finetuning,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence LQ-loRA: Low-rank plus quantized matrix decomposition for efficient language model finetuning,

Reference 56

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verified fuzzy
raw_fallback, observed 2026-08-07T00:44:08.178882Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:49.294882Z digest=sha256:eb57cda669138e613de313937166896633fba753bf23b74f0ff75f68cc6824b9

Observation 5bdf4043-ac58-4559-abf6-d6b2ca1018a1 · outbound

This paper cites When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications

Reference 57

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:43:49.397014Z digest=sha256:3237164c470e02d18d7ed4287fd75a4e8e9468421a38e096cffc6a11c51933cb

Observation 2e5cc9b6-1a07-43ca-8599-e6d67a76d7e1 · outbound

This paper cites LoRAMoE: Alleviate World Knowledge Forgetting in Large Language Models via MoE-Style Plugin.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence LoRAMoE: Alleviate World Knowledge Forgetting in Large Language Models via MoE-Style Plugin

Reference 58

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source=pdf_text observed=2026-08-07T00:43:49.467029Z digest=sha256:0ea2071813c2b13710d4900baa1d5e2dbd44dd698e756040083993e99a98068e

Observation eda3c0dd-bf0b-47b8-bf5a-cd10c8eb27d5 · outbound

This paper cites MiLoRA: Harnessing Minor Singular Components for Parameter-Efficient LLM Finetuning.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence MiLoRA: Harnessing Minor Singular Components for Parameter-Efficient LLM Finetuning

Reference 59

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source=pdf_text observed=2026-08-07T00:43:49.601007Z digest=sha256:1169e071a722f3f4be22c49b64fd159dd12bef22913e9e8dd8237c1c198203c9

Observation 03cde7a9-0cf9-46f2-a7ab-89da80c5df7f · outbound

This paper cites Awq: Activation-aware weight quanti- zation for llm compression and acceleration,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Awq: Activation-aware weight quanti- zation for llm compression and acceleration,

Reference 60

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verified fuzzy
raw_fallback, observed 2026-08-07T00:44:07.805437Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:49.756684Z digest=sha256:53a7e6cb7362dcafad4e2f8224e5fa789c4a0ca7e514392c0001982d72d4f19d

Observation a04e5bdb-bc39-4688-ab60-6b098d39b26d · outbound

This paper cites ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models

Reference 61

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source=pdf_text observed=2026-08-07T00:43:49.873768Z digest=sha256:a15892a761a01e023a88e40a93a0b80ae56e88bd8393cd3c9fd7d12e8e0566a6

Observation 8b07358b-176a-4838-b29d-fe5e53fe0728 · outbound

This paper cites Owq: Outlier-aware weight quantization for efficient fine-tuning and inference of large language models,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Owq: Outlier-aware weight quantization for efficient fine-tuning and inference of large language models,

Reference 62

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verified fuzzy
raw_fallback, observed 2026-08-07T00:44:07.535099Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:49.986005Z digest=sha256:dedec6eace08053f87fe7ce5d3d7de1c33c8b7384cbefe93e0886992ca46ac17

Observation d01f5ad9-2c8a-48a6-9f1b-c39504544701 · outbound

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

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks

Reference 63

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source=pdf_text observed=2026-08-07T00:43:50.102023Z digest=sha256:bb011927288ab3caa593a685d7e623af69c6c32ec21057f9af8544299d845366

Observation 87027867-d6a8-425c-8508-c9b18e59dadf · outbound

This paper cites icarl: Incremental classifier and representation learning,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence icarl: Incremental classifier and representation learning,

Reference 64

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verified fuzzy
raw_fallback, observed 2026-08-07T00:44:07.221333Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:50.218398Z digest=sha256:5d98e9a60d10702c171a028ecb9597270965105d83317e4f1edbe1e74eabc949

Observation 6b665f51-91bf-4960-8c86-3c38aacf6644 · outbound

This paper cites Overcoming catastrophic forgetting in neural networks,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Overcoming catastrophic forgetting in neural networks,

Reference 65

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:43:50.319220Z digest=sha256:52ad64a96c963424bd8888501c9d3dd41c3698724c61b959f0515d685ad758d3

Observation ee3f5f4b-82f3-4791-941e-3d56b7576207 · outbound

This paper cites Continual unsupervised representation learning,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Continual unsupervised representation learning,

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:06.916826Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:50.412360Z digest=sha256:3ceca6f3dd95749ba21d671bbbb1f3a179269e522d77a75c308a0202c275d015

Observation 134abdc0-dac7-4e01-88bc-3b5265753b42 · outbound

This paper cites Gradient episodic memory for contin- ual learning,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Gradient episodic memory for contin- ual learning,

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:06.588358Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:50.500532Z digest=sha256:05666db89bb03c597b080724f9a1d568bebd3298d352d9dec39bd20de2bc884c

Observation 280d89fe-59c3-4d26-9f59-0120837c0f9c · outbound

This paper cites Neural collapse inspired feature-classifier alignment for few-shot class incremental learning,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Neural collapse inspired feature-classifier alignment for few-shot class incremental learning,

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:06.249090Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:50.639231Z digest=sha256:f2f99055124a4791885eee1aaaf86f2d664cb73a75e34ccf74a7ba71432678d5

Observation 923929a5-82ee-41d7-a95f-7c29e9c8ddf4 · outbound

This paper cites Neural Collapse Terminus: A Unified Solution for Class Incremental Learning and Its Variants.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Neural Collapse Terminus: A Unified Solution for Class Incremental Learning and Its Variants

Reference 69

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verified exact
local_arxiv, observed 2026-08-07T00:43:56.790321Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:50.778198Z digest=sha256:1825db4b7b5b4fde8061771d51104b3490dba0a278d71da69e7f88e421f415a0

Observation a823e471-a185-4420-b858-6d67bb28161c · outbound

This paper cites Enhancing online continual learning with plug-and-play state space model and class-conditional mixture of discretization,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Enhancing online continual learning with plug-and-play state space model and class-conditional mixture of discretization,

Reference 70

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verified fuzzy
raw_fallback, observed 2026-08-07T00:44:05.961176Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:50.924322Z digest=sha256:cbd2d2d3da8564128003f1b0ffb17527ee6c06d4d1edb88bd951c8956ac29f5a

Observation e50ad91a-f484-4ec4-9c77-3cc13776029c · outbound

This paper cites Learning without forgetting,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Learning without forgetting,

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:05.683857Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:51.025526Z digest=sha256:2a41d3ebefd543949bcf8cc2fa8d5924e63788afceda46439525b6454ad1667f

Observation 3840cfc6-1e3d-4eee-945c-062d2cf48c7b · outbound

This paper cites Learning a unified classifier incrementally via rebalancing,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Learning a unified classifier incrementally via rebalancing,

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:05.436484Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:51.120540Z digest=sha256:3ae43b3ce454de86e29306e9d0c3a1c4d7215ac3a55ca0577d1786954c8710e4

Observation 5854041b-f440-441f-81d0-210f7f0c49f9 · outbound

This paper cites Learning to learn without forgetting by maximizing transfer and minimizing interference,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Learning to learn without forgetting by maximizing transfer and minimizing interference,

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:05.104704Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:51.230271Z digest=sha256:c4f9e420ff4055beb8e86c768cae249edce79c39d85d08801197c25f4802fe49

Observation c88400c1-3b00-426a-89be-3423fa2745d5 · outbound

This paper cites Der: Dynamically expandable representation for class incremental learning,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Der: Dynamically expandable representation for class incremental learning,

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:04.843100Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:51.368743Z digest=sha256:83c94753e82eb5ec10879b9de090b22cc9180571a8116ef796df814581b379cb

Observation 10af06ab-1c71-4686-9b39-c95862ac5a4d · outbound

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

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Continual Learning for Large Language Models: A Survey

Reference 75

Resolution
unresolved
no resolver link, observed 2026-08-07T00:43:51.513729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:43:51.513729Z digest=sha256:d58de17be333722ba72a40fb71bdfbad31b4b0f2e252c92350a18c3f13802c30

Observation e2b33d26-f614-4f2f-88d2-49433c214497 · outbound

This paper cites Continual Instruction Tuning for Large Multimodal Models.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Continual Instruction Tuning for Large Multimodal Models

Reference 76

Resolution
unresolved
no resolver link, observed 2026-08-07T00:43:51.674160Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:43:51.674160Z digest=sha256:8d31820a2d33fbd1ea75093c478fa04a5d53ce3e09527c19e5ccdd791d9f3603

Observation 3b54e5bd-307d-4093-99c8-e7e7d3168f98 · outbound

This paper cites Investi- gating the catastrophic forgetting in multimodal large language model fine-tuning,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Investi- gating the catastrophic forgetting in multimodal large language model fine-tuning,

Reference 77

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:04.551931Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:51.853855Z digest=sha256:ed0cda399bc011c6258cf671525d5da62b424cef6d8d628ec5fc09badf56f0c4

Observation eb028ab5-6cda-46f2-a0f3-5d15df05cc09 · outbound

This paper cites Fine-tuned language models are continual learners,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Fine-tuned language models are continual learners,

Reference 78

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:04.279934Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:51.993008Z digest=sha256:179470c33d63932941c5ce74caa9e9be149e66fc449d7e6f054a43b0086e67c7

Observation af4c1015-42a6-40e1-bf9a-deff4a36f8bf · outbound

This paper cites Continual pre-training of large language models: How to re-warm your model?.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Continual pre-training of large language models: How to re-warm your model?

Reference 79

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:03.980770Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:52.131111Z digest=sha256:a166b14c2a16ff9d27bbc4a5ade639d236b81b96aa7980f0c0a64af8f88500f6

Observation 07dc000e-6f66-4c22-ae45-3bb023404480 · outbound

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

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 80

Resolution
unresolved
no resolver link, observed 2026-08-07T00:43:52.260130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:43:52.260130Z digest=sha256:4fbdf77605d57ee8fcafba546010c4fcc0b8d30da6240f4c8e5b3f094a91840f

Observation b573473a-ce02-49e1-b873-e91e21ac2724 · outbound

This paper cites LLaMA Pro: Progressive LLaMA with Block Expansion.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence LLaMA Pro: Progressive LLaMA with Block Expansion

Reference 81

Resolution
unresolved
no resolver link, observed 2026-08-07T00:43:52.400027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:43:52.400027Z digest=sha256:ebb2c8780a965923421286fdcf3f3eff4273e5c50ee6883ebab212885a227a17

Observation 52b5eab1-6738-49f9-a552-ba9669285cce · outbound

This paper cites Composing parameter-efficient modules with arithmetic operation,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Composing parameter-efficient modules with arithmetic operation,

Reference 82

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:03.673889Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:52.513377Z digest=sha256:8585fa24e235b9c2270117708dcc91111f78f0198cb94fecf876896e68512dcc

Observation a8e9b597-3d99-4a93-94ba-d5d62d14038b · outbound

This paper cites Language models are super mario: Absorbing abilities from homologous models as a free lunch,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Language models are super mario: Absorbing abilities from homologous models as a free lunch,

Reference 83

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:03.376211Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:52.620728Z digest=sha256:c1b8b1d740fcab93f6d490b3c77f36f2c99575261302425766e46590da2f7c4d

Observation f788424e-5026-405a-956c-ce29412f4424 · outbound

This paper cites Model tailor: Mitigating catastrophic forgetting in multi-modal large language models,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Model tailor: Mitigating catastrophic forgetting in multi-modal large language models,

Reference 84

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:03.050389Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:52.676699Z digest=sha256:dfdf0dca6075f9df07d0611be7203183c0affb7785cf2ca6e428e1a119c841fb

Observation 801abad0-1370-4dec-bda5-62e6a5d74dbb · outbound

This paper cites Language model compression with weighted low-rank factorization,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Language model compression with weighted low-rank factorization,

Reference 85

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:02.793373Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:52.774122Z digest=sha256:f6e6b21dbad9847d84a0b8a8deb11af147b0e130ce947228dee391f49ab057a5

Observation 73cf7f4b-9073-4ad7-b512-a9e32ebef77a · outbound

This paper cites SVD-LLM: Truncation- aware singular value decomposition for large language model compres- sion,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence SVD-LLM: Truncation- aware singular value decomposition for large language model compres- sion,

Reference 86

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:02.459725Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:52.874419Z digest=sha256:f0a6aa7c77c9af8be6d742212e35b02369523632d84e6a0674af9e295fcf0c66

Observation c418a7bf-e8f0-454b-a482-cf80ceabed3d · outbound

This paper cites Optimizing Singular Spectrum for Large Language Model Compression.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Optimizing Singular Spectrum for Large Language Model Compression

Reference 87

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unresolved
no resolver link, observed 2026-08-07T00:43:53.000581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:43:53.000581Z digest=sha256:f6e4786654f2973577933e5bc1bad6f69d583b830e9c4cae34b3c20ba27ef59f

Observation db661954-551f-4a82-a752-2519b7e7a25d · outbound

This paper cites Exploring post-training quantization in llms from comprehensive study to low rank compensa- tion,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Exploring post-training quantization in llms from comprehensive study to low rank compensa- tion,

Reference 88

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:02.172522Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:53.159615Z digest=sha256:693e8234e88d6617ee64bf5fe548cc74d4e9aff5fbc4497c4915d25595f845bb

Observation b72617bb-fbf8-479a-8199-33daa7d48306 · outbound

This paper cites SVDQuant: Absorbing outliers by low-rank component for 4-bit diffusion models,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence SVDQuant: Absorbing outliers by low-rank component for 4-bit diffusion models,

Reference 89

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:01.881213Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:53.296436Z digest=sha256:32058d5281f7d0435407e4e00518c8d13ee58fd65aa035c5060df739cac2fe7c

Observation dd930b33-efde-4281-900b-43e9ec7b923d · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,

Reference 90

Resolution
unresolved
no resolver link, observed 2026-08-07T00:43:53.440903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:43:53.440903Z digest=sha256:1869d0ff9dc4fe1b2b0e4941a4f119640afe5c364b25d475b4d1fe8be26be881

Observation 4069392f-eeff-48b6-a2de-8825e37f2205 · outbound

This paper cites Pointer Sentinel Mixture Models.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Pointer Sentinel Mixture Models

Reference 91

Resolution
unresolved
no resolver link, observed 2026-08-07T00:43:53.564627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:43:53.564627Z digest=sha256:329343c119ca6bf6182dffdee72db7a69687850dadce53121a12e996bc16e634

Observation 27ffa1e0-0fb4-4701-a2c8-176b54787184 · outbound

This paper cites Building a large annotated corpus of english: The penn treebank,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Building a large annotated corpus of english: The penn treebank,

Reference 92

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:01.647561Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:53.662985Z digest=sha256:4d6729fc194a6a246ef95bceb1c45854f65e3bc87333a6ca40f96b30526997e1

Observation 7fa2b942-2726-43d3-919e-e0f6e64417b6 · outbound

This paper cites Natural questions: a benchmark for question answering research,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Natural questions: a benchmark for question answering research,

Reference 93

Resolution
unresolved
no resolver link, observed 2026-08-07T00:43:53.752677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:43:53.752677Z digest=sha256:f13f4d57802adfe13f61d0d5cc5cca9d4df66c4a4ed1e4ca664765a29e48429c

Observation 239d11d4-a6c8-4c2c-b2e8-a4f8908f0264 · outbound

This paper cites Gpt3.int8(): 8-bit matrix multiplication for transformers at scale,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Gpt3.int8(): 8-bit matrix multiplication for transformers at scale,

Reference 94

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:01.393482Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:53.890048Z digest=sha256:c10fa05afddf24c040dd8969fd1e67c3502fb0e8554bb77b73938677861dc332

Observation 09b2abaa-5599-4420-a901-ce510db742a2 · outbound

This paper cites Smoothquant: Accurate and efficient post-training quantization for large language models,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Smoothquant: Accurate and efficient post-training quantization for large language models,

Reference 95

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:01.079579Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:54.001134Z digest=sha256:fd290d8d934db29512300c04127eccf460e1c3bd18c8b83ff05d7f072377e679

Observation 9c0de6f7-a178-4468-93e9-64621ff76a27 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 96

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unresolved
no resolver link, observed 2026-08-07T00:43:54.091090Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:43:54.091090Z digest=sha256:6bece4e969203120c10c27435effa61ce959d5fcc085cd707b87662eff0ee000

Observation c5352022-b6c0-494d-b56d-2096def99028 · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Gemma 2: Improving Open Language Models at a Practical Size

Reference 97

Resolution
unresolved
no resolver link, observed 2026-08-07T00:43:54.146385Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:43:54.146385Z digest=sha256:a2b3cd68706493c35e9036b1fcfbeac410867a9b261d01c76cebf7753b4dbf79

Observation 548cae34-c6b5-4ab7-ae0c-881582be1418 · outbound

This paper cites OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement

Reference 98

Resolution
unresolved
no resolver link, observed 2026-08-07T00:43:54.234553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:43:54.234553Z digest=sha256:7567673b95d876d689734c04832ae66c6a24a91bfd9bc2f65155c870925636e2

Observation 2a149dba-c1df-4c25-811c-62b3e5a3f958 · outbound

This paper cites WizardLM: Empowering large pre-trained language models to follow complex instructions,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence WizardLM: Empowering large pre-trained language models to follow complex instructions,

Reference 99

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:00.759259Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:54.330307Z digest=sha256:aa9bc35659bff9b4fa97230fffa055717d34e84b6d5f15df50c83dc8efa4202c

Observation a69e3f49-909e-4dcc-95fa-285996447255 · outbound

This paper cites Judging LLM-as-a-judge with MT-bench and chatbot arena,.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Judging LLM-as-a-judge with MT-bench and chatbot arena,

Reference 100

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:00.445069Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T00:43:54.429969Z digest=sha256:66ce2fed5561516ad7d53c94aa5fba5fc1b489e2ccbbcbeafe09b85e8ecb5959

Pith citing papers

No inbound Pith citation observations are available.