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Paper Citation Record · LEDGER

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging

As of 7 August 2026, this Paper Citation Record lists 82 of 82 outbound references and 2 inbound Pith citation observations for arXiv:2512.01461.

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

pith.paper-citation-record.v1
2512.01461 v2

Coverage vector

measured 82 of 82 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T19:16:04.999697Z

measured 84 of 84 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-14T21:12:06.989077Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-14T21:12:58.892326Z

Reference resolution

82 of 82 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved81
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  • malformed identifier1
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External citation measurements

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Outbound references

Observation da399615-8fbb-4b24-b938-fe22c4bfd0ef · outbound

This paper cites Git re-basin: Merging models modulo permutation symmetries.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Git re-basin: Merging models modulo permutation symmetries

Reference 1

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Observation b07ffe1c-0c09-4324-a646-bb87d8b19463 · outbound

This paper cites Qwen Technical Report.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Qwen Technical Report

Reference 2

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source=pdf_text observed=2026-08-03T19:15:50.873340Z digest=sha256:fee72077e03ccb694eb8b23a1f7157d424d9c84b0e07ad2982354dbee1b73aed

Observation 28dd684c-b709-4a27-964d-05dac9aaed0c · outbound

This paper cites Multitask learning.Machine learning, 28(1): 41–75, 1997.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Multitask learning.Machine learning, 28(1): 41–75, 1997

Reference 3

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source=pdf_text observed=2026-08-03T19:15:50.963658Z digest=sha256:090e822b233db3d8ced49aa06729a36322c107eb016a6baedecca52e9679b271

Observation e992de7c-3f6b-450a-8b90-b0b43539e11a · outbound

This paper cites Semeval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Semeval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation

Reference 4

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source=pdf_text observed=2026-08-03T19:15:51.095132Z digest=sha256:514125e7bc10de02e0a8ca9b7040b46dc5ff19f4176499ffe392ee31f77b24cc

Observation de5ec714-ec6f-4e0f-bc99-b65a1b40f4fc · outbound

This paper cites Fw-merging: Scaling model merging with frank-wolfe optimization.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Fw-merging: Scaling model merging with frank-wolfe optimization

Reference 5

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Observation 336b8f46-82db-4710-9d67-3687a94639ef · outbound

This paper cites Quora question pairs, 2018.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Quora question pairs, 2018

Reference 6

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source=pdf_text observed=2026-08-03T19:15:51.424928Z digest=sha256:36ddfa9a0ce5070310929e5507b98606dd42ae487757c5a4be9504c9ab324571

Observation 12efaed5-dd10-4d1d-ae86-4be31b08a428 · outbound

This paper cites Remote sensing image scene classification: Benchmark and state of the art.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Remote sensing image scene classification: Benchmark and state of the art

Reference 7

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source=pdf_text observed=2026-08-03T19:15:51.567201Z digest=sha256:56ad5882c2f779bb54e0f0b2246f32f49738f441e1e3e68a96ca6529a82cc763

Observation 9fcd8eea-b3b3-4733-b9c9-78019ba3f713 · outbound

This paper cites Describing textures in the wild.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Describing textures in the wild

Reference 8

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source=pdf_text observed=2026-08-03T19:15:51.756694Z digest=sha256:76a5bc50109d0d9318eeddf899dc9d6ddc30788fe560fe2435726d19815e892a

Observation b11c9aa3-2bd8-431e-b30a-b9e113337182 · outbound

This paper cites Model breadcrumbs: scalable upcycling of finetuned foundation models via sparse task vectors merging.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Model breadcrumbs: scalable upcycling of finetuned foundation models via sparse task vectors merging

Reference 9

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Observation ddbf7887-15fd-4f7d-a1d2-ee1b2829481c · outbound

This paper cites The mnist database of handwritten digit images for machine learning research.IEEE signal processing magazine, pages 141–142, 2012.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging The mnist database of handwritten digit images for machine learning research.IEEE signal processing magazine, pages 141–142, 2012

Reference 10

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Observation f6d345af-7335-4899-a85a-72d1b24c1874 · outbound

This paper cites Harmonizing and Merging Source Models for CLIP-based Domain Generalization.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Harmonizing and Merging Source Models for CLIP-based Domain Generalization

Reference 11

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source=pdf_text observed=2026-08-03T19:15:52.251953Z digest=sha256:857c82e2479ca575bef066ff113cd74331be956ef4bf4f40be51c304992e1520

Observation 21fea34f-8013-43e0-a42d-cb2833372c9d · outbound

This paper cites Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 12

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source=pdf_text observed=2026-08-03T19:15:52.462406Z digest=sha256:c8b5a057e59d1a2e9d3d63ee26b7d5a940b0fbb7c9ba2e74666220e404f2437b

Observation e046c146-1076-4729-a560-dee117416d40 · outbound

This paper cites Automatically constructing a corpus of sentential paraphrases.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Automatically constructing a corpus of sentential paraphrases

Reference 13

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source=pdf_text observed=2026-08-03T19:15:52.605455Z digest=sha256:3ea58250df17ffcfa1b08987e49f5d7270ee48171595f46fc8bb8a8139fd9cf2

Observation 7f29518c-dff4-4e75-94d7-1442fab13a4a · outbound

This paper cites Task singular vectors: Reducing task interference in model merging.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Task singular vectors: Reducing task interference in model merging

Reference 14

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source=pdf_text observed=2026-08-03T19:15:52.747149Z digest=sha256:c8b0cd23280cd88dde0c1eebb42a4f2c07514650de15598715a9d0b44a897474

Observation 61197950-567a-430a-b54f-b8d35421d587 · outbound

This paper cites The third pascal recognizing textual entail- ment challenge.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging The third pascal recognizing textual entail- ment challenge

Reference 15

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source=pdf_text observed=2026-08-03T19:15:52.891962Z digest=sha256:fa6b1fa206b5b4020d5733be3fcf672f976fee6662dce6da64b75dd4047bf07d

Observation a102c5cb-1c8b-4eda-a937-62edccec1e50 · outbound

This paper cites Gradient reweighting: Towards imbalanced class-incremental learning.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Gradient reweighting: Towards imbalanced class-incremental learning

Reference 16

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Observation bf17e717-a55c-4d3b-8fa1-4f7368dc1168 · outbound

This paper cites an unresolved cited work.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Unresolved cited work

Reference 17

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Observation 8f2e8975-5c84-4179-aad9-895c5c93b1a4 · outbound

This paper cites Measuring massive multitask language understanding.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Measuring massive multitask language understanding

Reference 18

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Observation b357f422-841c-42f5-8dd6-50e3593779be · outbound

This paper cites Emr-merging: Tuning-free high- performance model merging.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Emr-merging: Tuning-free high- performance model merging

Reference 19

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Observation d3b1d99c-8781-4863-8a2e-0550319ebfef · outbound

This paper cites Multi-granular spatio-temporal token merging for training-free acceleration of video llms.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Multi-granular spatio-temporal token merging for training-free acceleration of video llms

Reference 20

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Observation c362a8e0-5616-4ef7-9792-5103f5a6c4bc · outbound

This paper cites Editing models with task arithmetic.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Editing models with task arithmetic

Reference 21

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Observation d0af8a09-7bed-4837-922c-46280612bd72 · outbound

This paper cites Pytorch.Programming with TensorFlow: so- lution for edge computing applications, pages 87–104, 2021.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Pytorch.Programming with TensorFlow: so- lution for edge computing applications, pages 87–104, 2021

Reference 22

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Observation bd2ce9cb-5cdf-4b88-9cf2-7eb6549eac0c · outbound

This paper cites Dataless knowledge fusion by merging weights of language models.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Dataless knowledge fusion by merging weights of language models

Reference 23

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Observation 9a456770-13d9-4b6f-bece-974294769e1e · outbound

This paper cites Repair: Renormalizing permuted activations for interpolation repair.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Repair: Renormalizing permuted activations for interpolation repair

Reference 24

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Observation df19c80f-66dc-40ad-abb3-a13669cfc5ee · outbound

This paper cites Task vector quantization for memory- efficient model merging.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Task vector quantization for memory- efficient model merging

Reference 25

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Observation f3544bb3-a802-40fa-bb4c-c797cdc0f16e · outbound

This paper cites 3d object representations for fine-grained categorization.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging 3d object representations for fine-grained categorization

Reference 26

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source=pdf_text observed=2026-08-03T19:15:55.125850Z digest=sha256:bdf0a762a35704490ddcd96dbb2657a1e52f240e5bc9b69ceb4c6ce6c82f8390

Observation 7052d903-a01a-474d-b5a8-9058ffcca397 · outbound

This paper cites Singular value decompo- sition.Numerical analysis for statisticians, pages 129–142,.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Singular value decompo- sition.Numerical analysis for statisticians, pages 129–142,

Reference 27

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Observation 05e070d1-56fd-4993-a159-eabed10295ab · outbound

This paper cites Mitigating parameter interference in model merging via sharpness-aware fine-tuning.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Mitigating parameter interference in model merging via sharpness-aware fine-tuning

Reference 28

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Observation 034a1d56-e3f8-4bc8-bbcf-13b1e5d92356 · outbound

This paper cites Map: Low-compute model merging with amortized pareto fronts via quadratic approximation.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Map: Low-compute model merging with amortized pareto fronts via quadratic approximation

Reference 29

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Observation 52a0132b-2b31-4174-840b-1bfec81a6453 · outbound

This paper cites Model merging in pre-training of large language models.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Model merging in pre-training of large language models

Reference 30

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source=pdf_text observed=2026-08-03T19:15:55.407891Z digest=sha256:96ce7556d77d17f80319befaa974796db26225871a14ebc66d8c9e5d97686b02

Observation 1e4bced3-f827-4dee-9c27-6dcdab66b936 · outbound

This paper cites Truthfulqa: Measuring how models mimic human falsehoods.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Truthfulqa: Measuring how models mimic human falsehoods

Reference 31

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source=pdf_text observed=2026-08-03T19:15:55.473559Z digest=sha256:a995ff12234d8c58179862248e7f77606e65ed5f4d16de0e367a6f20163f8c5d

Observation 92ec3d1a-a429-409a-8b34-d11440dd8b04 · outbound

This paper cites 1bit-Merging: Dynamic Quantized Merging for Large Language Models.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging 1bit-Merging: Dynamic Quantized Merging for Large Language Models

Reference 32

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Observation 9c8dc007-c3d1-458d-bd2c-f43bb78300bf · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 33

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Observation d5af9a1b-a5b7-4cf3-8ce2-718930b74d08 · outbound

This paper cites Twin-merging: Dynamic integration of modular expertise in model merging.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Twin-merging: Dynamic integration of modular expertise in model merging

Reference 34

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Observation a11c0d9d-1620-497e-bd79-55f369567d98 · outbound

This paper cites No task left behind: Isotropic model merging with common and task-specific subspaces.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging No task left behind: Isotropic model merging with common and task-specific subspaces

Reference 35

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source=pdf_text observed=2026-08-03T19:15:55.993993Z digest=sha256:57378d6e5b1071f49a414130913b868e3ff7eb8647d2ef69fbfeac3bce2817a3

Observation 498a2ceb-3d32-492d-ae8f-88f4761b69d2 · outbound

This paper cites Merging models with fisher-weighted averaging.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Merging models with fisher-weighted averaging

Reference 36

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source=pdf_text observed=2026-08-03T19:15:56.131288Z digest=sha256:85d82c1913ed6242960ba2dae85c807e6624c7ee8814dd25548ba80fe96fd021

Observation c03961da-b987-40f1-bdb4-91d93cbc3706 · outbound

This paper cites Soft Merging of Experts with Adaptive Routing.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Soft Merging of Experts with Adaptive Routing

Reference 37

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Observation d8879641-fd29-461b-b695-df044e42c017 · outbound

This paper cites Reading digits in natural images with unsupervised feature learning.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Reading digits in natural images with unsupervised feature learning

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Observation 729470e4-0a3d-43f8-b2ef-70fcd5d70d4a · outbound

This paper cites Dawin: Training-free dynamic weight interpolation for robust adaptation.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Dawin: Training-free dynamic weight interpolation for robust adaptation

Reference 39

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Observation a3a5bdce-b087-418c-b5f2-92d9a423c79b · outbound

This paper cites Accurate and efficient low-rank model merging in core space.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Accurate and efficient low-rank model merging in core space

Reference 40

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Observation 3476cbb8-6728-4f84-8c35-0be6f294bf3c · outbound

This paper cites Bbq: A hand-built bias benchmark for question answering.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Bbq: A hand-built bias benchmark for question answering

Reference 41

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Observation bb995de4-9539-408f-bfdf-8733495f60c9 · outbound

This paper cites Less is more: Efficient model merging with binary task switch.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Less is more: Efficient model merging with binary task switch

Reference 42

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Observation c0fbedc3-a5b0-42a7-99ff-81e9cbcbe405 · outbound

This paper cites Mingle: Mixtures of null- space gated low-rank experts for test-time continual model merging.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Mingle: Mixtures of null- space gated low-rank experts for test-time continual model merging

Reference 43

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Observation ff65d78c-2bee-4ad8-889f-6c85b7262052 · outbound

This paper cites Language models are unsuper- vised multitask learners.OpenAI blog, page 9, 2019.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Language models are unsuper- vised multitask learners.OpenAI blog, page 9, 2019

Reference 44

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Observation cae3866d-f1c6-4bc6-b408-f0faaadf5531 · outbound

This paper cites Learning transferable visual models from natural language supervision.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Learning transferable visual models from natural language supervision

Reference 45

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source=pdf_text observed=2026-08-03T19:15:57.071460Z digest=sha256:a4b7410f4e8f16592a5f80f9bab7696e76ad42f8f82667ec5b6284fa48052bf0

Observation 1239da18-ec36-4d92-b49a-137665d5485f · outbound

This paper cites Squad: 100,000+ questions for machine comprehen- sion of text.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Squad: 100,000+ questions for machine comprehen- sion of text

Reference 46

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Observation 4ede5230-228f-4063-8972-68ede2ebd725 · outbound

This paper cites Recursive deep models for semantic compositionality over a sentiment treebank.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Recursive deep models for semantic compositionality over a sentiment treebank

Reference 47

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Observation 66839cdb-60ed-45e8-a490-1e2813a7af8c · outbound

This paper cites The german traffic sign recognition benchmark: a multi-class classification competition.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging The german traffic sign recognition benchmark: a multi-class classification competition

Reference 48

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Observation 6c41d9eb-5b8d-470f-a2a8-86f12c8bc214 · outbound

This paper cites Model merging with svd to tie the knots.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Model merging with svd to tie the knots

Reference 49

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source=pdf_text observed=2026-08-03T19:16:00.610131Z digest=sha256:4c06f4128f15434a4d26fe9be5f52c3307fe5511e91d6adbce3f41fb72630159

Observation b0d80eb1-baa0-43da-865e-fd4491aae670 · outbound

This paper cites Cat merging: A training-free approach for resolving conflicts in model merging.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Cat merging: A training-free approach for resolving conflicts in model merging

Reference 50

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source=pdf_text observed=2026-08-03T19:16:00.740601Z digest=sha256:ee70682db0a243ecd11cac30f606d156146f9f916b55b0155073a965ce8671e5

Observation 942444c8-e662-4279-88a3-4195dc3c13f3 · outbound

This paper cites Towards minimizing feature drift in model merging: Layer-wise task vector fusion for adaptive knowledge integration.arXiv preprint arXiv:2505.23859,.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Towards minimizing feature drift in model merging: Layer-wise task vector fusion for adaptive knowledge integration.arXiv preprint arXiv:2505.23859,

Reference 51

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Observation c71481e9-568d-47cb-991c-56ef88ffa65a · outbound

This paper cites Merging multi-task models via weight- ensembling mixture of experts.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Merging multi-task models via weight- ensembling mixture of experts

Reference 52

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source=pdf_text observed=2026-08-03T19:16:01.093238Z digest=sha256:c59bf22ddf8847abac0535bd62fa4577182621bec2b2f5541b67fdf9f8a83daf

Observation 34a8f182-8956-474d-b488-b29f84db6d57 · outbound

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

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 53

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source=pdf_text observed=2026-08-03T19:16:01.278500Z digest=sha256:74acbce3a4cc5f918ccf56a0ff0e5fe0bb47ba5242533c7875daf7485348a5ec

Observation 0cd60987-ede8-4b61-ab43-71b38f175ec0 · outbound

This paper cites Glue: A multi-task benchmark and analysis platform for natural language under- standing.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Glue: A multi-task benchmark and analysis platform for natural language under- standing

Reference 54

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Observation 8150e4dd-4c6e-4f5f-869b-0df38ca90866 · outbound

This paper cites Localizing task infor- mation for improved model merging and compression.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Localizing task infor- mation for improved model merging and compression

Reference 55

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Observation 51ee11c9-64d8-49d0-896a-aff217c76fd7 · outbound

This paper cites Neural network acceptability judgments.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Neural network acceptability judgments

Reference 56

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source=pdf_text observed=2026-08-03T19:16:01.794790Z digest=sha256:d65906a8ebe81e511b26f9ae15d43871c7b0a3ed8d0bad64950fcd08458bad82

Observation c30a9d63-26a5-46d9-8e7b-75a06c9735f5 · outbound

This paper cites A broad-coverage challenge corpus for sentence understanding through inference.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging A broad-coverage challenge corpus for sentence understanding through inference

Reference 57

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source=pdf_text observed=2026-08-03T19:16:01.935010Z digest=sha256:8dc84da7a2d6d1e35b2825f2c2909612f88ef0f83f8747389ca737c2073f339e

Observation 174a7012-9e9d-42ff-bbec-9149ee20cd49 · outbound

This paper cites HuggingFace's Transformers: State-of-the-art Natural Language Processing.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging HuggingFace's Transformers: State-of-the-art Natural Language Processing

Reference 58

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source=pdf_text observed=2026-08-03T19:16:02.135379Z digest=sha256:ffb48fdaa7418c053b04e3f7e1318bb965349d3849f675690cab99dd0a505006

Observation f240e85c-23a1-46eb-a660-daebd06cbf52 · outbound

This paper cites Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing in- ference time.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing in- ference time

Reference 59

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source=pdf_text observed=2026-08-03T19:16:02.337074Z digest=sha256:c09130cedec5372440fd1a9c150b927b9a7d58b253131c4d021e75aa642ed5f2

Observation c2d1c4e4-f8f6-4354-a062-694e60ca61ea · outbound

This paper cites Importance-based token merging for efficient image and video generation.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Importance-based token merging for efficient image and video generation

Reference 60

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source=pdf_text observed=2026-08-03T19:16:02.523080Z digest=sha256:3af30a10ff0d61a741dbaabe42ceb3f6c898e0329ecd7b63f7c9c2b0ed180e81

Observation 213a6f0b-0eb5-4db6-80a5-c72eca9ced60 · outbound

This paper cites Sun database: Large-scale scene recog- nition from abbey to zoo.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Sun database: Large-scale scene recog- nition from abbey to zoo

Reference 61

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source=pdf_text observed=2026-08-03T19:16:02.757883Z digest=sha256:d411a2c81adfa2da3c0a41566aa5a2c1e5e430460ce25edf372b4911b601c2b3

Observation 391f56a8-5815-4c73-a63d-cd86b227854e · outbound

This paper cites Ties-merging: Resolving interference when merging models.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Ties-merging: Resolving interference when merging models

Reference 62

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Observation 2b213b1d-01f6-4ef9-968e-152d905452f2 · outbound

This paper cites Calm: Consensus-aware localized merging for multi-task learning.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Calm: Consensus-aware localized merging for multi-task learning

Reference 63

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source=pdf_text observed=2026-08-03T19:16:03.006217Z digest=sha256:655b5d40c56d10282b6e4cc5d5bb35c7108f24067e4ad8f2818abe6c22bde10b

Observation 3622a0a5-e684-444d-b872-5a7c0238e7ce · outbound

This paper cites Adamerging: Adap- tive model merging for multi-task learning.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Adamerging: Adap- tive model merging for multi-task learning

Reference 64

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Observation 67bf4f89-086c-440e-81db-4a6f8af8f7f0 · outbound

This paper cites Continual model merg- ing without data: Dual projections for balancing stability and plasticity.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Continual model merg- ing without data: Dual projections for balancing stability and plasticity

Reference 65

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Observation 4396b531-c3f7-4eeb-8c30-25a36dfc386c · outbound

This paper cites Mix data or merge models? balancing the helpfulness, honesty, and harmlessness of large language model via model merging.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Mix data or merge models? balancing the helpfulness, honesty, and harmlessness of large language model via model merging

Reference 66

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source=pdf_text observed=2026-08-03T19:16:03.244174Z digest=sha256:70ad63425fa778e359de70e3affb38a18771f1eaf4a2f60ab3be4fedd7520b3e

Observation be94f38f-ed2a-4a70-b9de-248fdc155acf · outbound

This paper cites Merging Vision Transformers from Different Tasks and Domains.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Merging Vision Transformers from Different Tasks and Domains

Reference 67

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Observation 348c52c8-9e30-4b41-8a50-24c8f186c4d4 · outbound

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

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Language models are super mario: Absorbing abilities from homologous models as a free lunch

Reference 68

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source=pdf_text observed=2026-08-03T19:16:03.380629Z digest=sha256:5eb9dafb3dcb414b20da46e393457e0bd87ef56e6a3e60a953bd29ba9eb11bdf

Observation 7977c72d-a9ab-4e34-bd37-56107e8bc9b8 · outbound

This paper cites Robustmerge: Parameter-efficient model merging for mllms with direction robustness.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Robustmerge: Parameter-efficient model merging for mllms with direction robustness

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Observation 9e70b426-2ff4-490b-99b5-ee44a0eeda04 · outbound

This paper cites An overview of multi-task learn- ing.National Science Review, pages 30–43, 2018.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging An overview of multi-task learn- ing.National Science Review, pages 30–43, 2018

Reference 70

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source=pdf_text observed=2026-08-03T19:16:03.635239Z digest=sha256:4aad2d17c330dfce59e3c6cd2773b67c8de8dfba9bae27636a93d8f36ac39f6a

Observation 1b00b4b1-9c16-4e57-bb62-7c766c93ea30 · outbound

This paper cites A survey on multi-task learning.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging A survey on multi-task learning

Reference 71

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source=pdf_text observed=2026-08-03T19:16:03.727047Z digest=sha256:d245d20f18ab1b7ff673576085c88fbdbc7d17f6107201e14014c09b55a9ec0b

Observation e2ac7eb7-36e6-482f-9c40-d6155dbbcff3 · outbound

This paper cites Beyond training: Dynamic token merging for zero-shot video understanding.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Beyond training: Dynamic token merging for zero-shot video understanding

Reference 72

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source=pdf_text observed=2026-08-03T19:16:03.810134Z digest=sha256:6795a9749b0636c577989b095c05a5d0933205a2cf8d73ee012c9a3f51d6bc5f

Observation b2795d49-68cf-499e-a7f1-89948d08566e · outbound

This paper cites Merging loras like playing lego: Pushing the modularity of lora to extremes through rank-wise clustering.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Merging loras like playing lego: Pushing the modularity of lora to extremes through rank-wise clustering

Reference 73

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Observation 37368d30-7f3f-467c-b41c-a382c461aa48 · outbound

This paper cites Free-merging: Fourier transform for efficient model merging.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Free-merging: Fourier transform for efficient model merging

Reference 74

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source=pdf_text observed=2026-08-03T19:16:03.988962Z digest=sha256:5c0fc8cacfc53f22040cd9ef25e0d895553fdad0b68b90af95e71b60a569e962

Observation 5487cd89-048c-4cf7-b44c-597ac5832bb2 · outbound

This paper cites Aim: Adaptive inference of multi-modal llms via token merging and pruning.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Aim: Adaptive inference of multi-modal llms via token merging and pruning

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Observation 2556686e-7b97-4b14-8979-60530e573964 · outbound

This paper cites Metagpt: Merging large language models using model exclusive task arithmetic.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Metagpt: Merging large language models using model exclusive task arithmetic

Reference 76

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Observation b8094bb2-fba1-41df-a3e1-caa9fa85152a · outbound

This paper cites Hm3: Hierarchical multi-objective model merging for pretrained models.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Hm3: Hierarchical multi-objective model merging for pretrained models

Reference 77

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Observation 7d5aa789-7c83-4f32-9390-a487750bfb51 · outbound

This paper cites Remedy: Recipe merging dynam- ics in large vision-language models.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Remedy: Recipe merging dynam- ics in large vision-language models

Reference 78

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Observation b6b1bc46-69fc-4454-a575-24f6745b782f · outbound

This paper cites an unresolved cited work.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Unresolved cited work

Reference 79

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Observation cbd15223-86e5-4f2b-89b9-df7d0cda9260 · outbound

This paper cites Baselines for seen tasks • Individual Modelsrefer to task-specific models before merging.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Baselines for seen tasks • Individual Modelsrefer to task-specific models before merging

Reference 80

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Observation 3e61ff90-deff-4d4c-ae97-874850397e29 · outbound

This paper cites Datasets For visual classification tasks, we employ classification accu- racy as the evaluation metric.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Datasets For visual classification tasks, we employ classification accu- racy as the evaluation metric

Reference 81

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Observation d3158880-0e22-4ba3-b934-41709a028a37 · outbound

This paper cites More Backbones In addition to the backbones evaluated in the main paper, we also assess the performance of various methods on ViT-B/16, ViT-L/14, and GPT-2 backbones.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging More Backbones In addition to the backbones evaluated in the main paper, we also assess the performance of various methods on ViT-B/16, ViT-L/14, and GPT-2 backbones

Reference 82

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Pith citing papers

Observation e522c0d3-3187-4312-9b12-9b693b87bc25 · inbound

Explaining and Breaking the Safety-Helpfulness Ceiling via Preference Dimensional Expansion cites this paper.

Explaining and Breaking the Safety-Helpfulness Ceiling via Preference Dimensional Expansion Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging

Reference 31

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arxiv_id, observed 2026-06-30T03:17:25.261573Z

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Observation 19ecc26c-6536-4bfb-9a4c-3a81b4f2e090 · inbound

Explaining and Breaking the Safety-Helpfulness Ceiling via Preference Dimensional Expansion cites this paper.

Explaining and Breaking the Safety-Helpfulness Ceiling via Preference Dimensional Expansion Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging

Reference 31

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arxiv_id, observed 2026-06-30T03:17:25.261573Z

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