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Source: paper_references, paper_reference_links, observed 2026-08-03T19:16:04.999697Z
Paper Citation Record · LEDGER
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.
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Source: paper_references, paper_reference_links, observed 2026-08-03T19:16:04.999697Z
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Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-14T21:12:06.989077Z
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Source: arxiv_reference, observed 2026-05-14T21:12:58.892326Z
82 of 82 outbound references displayed
External citation measurements
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Observation da399615-8fbb-4b24-b938-fe22c4bfd0ef · outbound
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
Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Qwen Technical Report
Reference 2
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Observation 28dd684c-b709-4a27-964d-05dac9aaed0c · outbound
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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Observation e992de7c-3f6b-450a-8b90-b0b43539e11a · outbound
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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Observation de5ec714-ec6f-4e0f-bc99-b65a1b40f4fc · outbound
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
Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Quora question pairs, 2018
Reference 6
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Observation 12efaed5-dd10-4d1d-ae86-4be31b08a428 · outbound
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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Observation 9fcd8eea-b3b3-4733-b9c9-78019ba3f713 · outbound
Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Describing textures in the wild
Reference 8
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Observation b11c9aa3-2bd8-431e-b30a-b9e113337182 · outbound
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
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
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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Observation 21fea34f-8013-43e0-a42d-cb2833372c9d · outbound
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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Observation e046c146-1076-4729-a560-dee117416d40 · outbound
Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Automatically constructing a corpus of sentential paraphrases
Reference 13
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Observation 7f29518c-dff4-4e75-94d7-1442fab13a4a · outbound
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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Observation 61197950-567a-430a-b54f-b8d35421d587 · outbound
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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Observation a102c5cb-1c8b-4eda-a937-62edccec1e50 · outbound
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
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
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
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
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
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
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
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
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
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
Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging 3d object representations for fine-grained categorization
Reference 26
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Observation 7052d903-a01a-474d-b5a8-9058ffcca397 · outbound
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
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
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
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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Observation 1e4bced3-f827-4dee-9c27-6dcdab66b936 · outbound
Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Truthfulqa: Measuring how models mimic human falsehoods
Reference 31
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Observation 92ec3d1a-a429-409a-8b34-d11440dd8b04 · outbound
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
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
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
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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Observation 498a2ceb-3d32-492d-ae8f-88f4761b69d2 · outbound
Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Merging models with fisher-weighted averaging
Reference 36
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Observation c03961da-b987-40f1-bdb4-91d93cbc3706 · outbound
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
Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Reading digits in natural images with unsupervised feature learning
Reference 38
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Observation 729470e4-0a3d-43f8-b2ef-70fcd5d70d4a · outbound
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
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
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
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
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
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
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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Observation 1239da18-ec36-4d92-b49a-137665d5485f · outbound
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
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
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
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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Observation b0d80eb1-baa0-43da-865e-fd4491aae670 · outbound
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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Observation 942444c8-e662-4279-88a3-4195dc3c13f3 · outbound
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
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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Observation 34a8f182-8956-474d-b488-b29f84db6d57 · outbound
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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Observation 0cd60987-ede8-4b61-ab43-71b38f175ec0 · outbound
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
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
Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Neural network acceptability judgments
Reference 56
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Observation c30a9d63-26a5-46d9-8e7b-75a06c9735f5 · outbound
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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Observation 174a7012-9e9d-42ff-bbec-9149ee20cd49 · outbound
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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Observation f240e85c-23a1-46eb-a660-daebd06cbf52 · outbound
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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Observation c2d1c4e4-f8f6-4354-a062-694e60ca61ea · outbound
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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Observation 213a6f0b-0eb5-4db6-80a5-c72eca9ced60 · outbound
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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Observation 391f56a8-5815-4c73-a63d-cd86b227854e · outbound
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
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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Observation 3622a0a5-e684-444d-b872-5a7c0238e7ce · outbound
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
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
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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Observation be94f38f-ed2a-4a70-b9de-248fdc155acf · outbound
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
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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Observation 7977c72d-a9ab-4e34-bd37-56107e8bc9b8 · outbound
Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Robustmerge: Parameter-efficient model merging for mllms with direction robustness
Reference 69
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Observation 9e70b426-2ff4-490b-99b5-ee44a0eeda04 · outbound
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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Observation 1b00b4b1-9c16-4e57-bb62-7c766c93ea30 · outbound
Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging A survey on multi-task learning
Reference 71
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Observation e2ac7eb7-36e6-482f-9c40-d6155dbbcff3 · outbound
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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Observation b2795d49-68cf-499e-a7f1-89948d08566e · outbound
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
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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Observation 5487cd89-048c-4cf7-b44c-597ac5832bb2 · outbound
Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Aim: Adaptive inference of multi-modal llms via token merging and pruning
Reference 75
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Observation 2556686e-7b97-4b14-8979-60530e573964 · outbound
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
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
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
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
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
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
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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Observation e522c0d3-3187-4312-9b12-9b693b87bc25 · inbound
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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Observation 19ecc26c-6536-4bfb-9a4c-3a81b4f2e090 · inbound
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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