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Paper Citation Record · LEDGER

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation

As of 17 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2412.19098.

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

pith.paper-citation-record.v1
2412.19098 v4

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-25T08:07:09.574681Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:42:20.895955Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T15:42:25.988601Z

Reference resolution

57 of 57 outbound references displayed

  • verified exact6
  • verified fuzzy49
  • unresolved1
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5c0d3618-165b-415f-a98a-6e37ae0b9961 · outbound

This paper cites Multitask learning.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Multitask learning

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.146505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:afbae4502119f73279f9327b38d349d5952750751f3d250633cca3cea2ac207a

Observation 42972add-de58-4d34-8f96-d34ca5d952e2 · outbound

This paper cites S em E val-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation S em E val-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation

Reference 2

Resolution
verified exact
doi, observed 2026-05-25T08:10:31.328206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:bcaec7becd7227f084dc333984f90e55bdbb56527d68474a7037dd1a5f83a448

Observation 59ec2e81-6a2c-47ee-be65-32cf353dea06 · outbound

This paper cites Similarity and matching of neural network representations.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Similarity and matching of neural network representations

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.126280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:7ae53262a76b3d2eb350ec316f26f0a4d79ee8a3da0d57702354f99f31e87784

Observation 37b5b3e0-a835-4a93-95ba-f55a2c3b32b7 · outbound

This paper cites Model breadcrumbs: Scaling multi-task model merging with sparse masks.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Model breadcrumbs: Scaling multi-task model merging with sparse masks

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.122113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:bbc6fae855207e29bb7bcdf3a261e76955b552d6c4ae6f9198824ab28bf94ceb

Observation 6e315742-f885-4ebd-a0a7-debd6242014e · outbound

This paper cites DELLA-Merging: Reducing Interference in Model Merging through Magnitude-Based Sampling.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation DELLA-Merging: Reducing Interference in Model Merging through Magnitude-Based Sampling

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-25T08:10:31.432460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:0ac86b64cd3c1000caff31d18aee9c217f9e66c589e36dc591c6b5578c94549b

Observation 1b445f5a-54c6-4fe0-8bfd-780effa7b6af · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Imagenet: A large-scale hierarchical image database

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.138652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:7ea2dcc42587b82e17642ed4966fc5e73a0aca4714ee31d4b31bb00b38f99b93

Observation b23136c4-3797-4a1f-9f03-5f93aa275254 · outbound

This paper cites Automatically constructing a corpus of sentential paraphrases.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Automatically constructing a corpus of sentential paraphrases

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.064037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:038f4327df5ffe4e9943721e13f206b62b2c121b9af3091639bec4ae4a6b8a65

Observation f53501fc-e5da-4916-81a1-1e6eacedfd87 · outbound

This paper cites Parameter competition balancing for model merging.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Parameter competition balancing for model merging

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.028708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:b51124f1123f82804a1e70e396a3c3b67cb33131e066306d20e5d0603a619c62

Observation 425e1c4a-66a7-46af-acf3-672b8782a7a2 · outbound

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

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Task singular vectors: Reducing task interference in model merging

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.142438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:dd44f27a42d9256ae3b7d0c31ca1ef50df1dffeab763b4dd542ca446f90444f9

Observation 290655a1-6092-42bd-94ea-7620a2c40d8c · outbound

This paper cites The third pascal recognizing textual entailment challenge.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation The third pascal recognizing textual entailment challenge

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.158992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:51d56604b264300f08cdef8727b183edc55e8521c32999f52c8b447c9be3f84d

Observation e2598f84-86c6-4cea-8b7d-f5f5ace0889a · outbound

This paper cites Deep residual learning for image recognition.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Deep residual learning for image recognition

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.171018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:baa1feff21fd75ace448f61e4cb8de0d16097f22179a61d0bd2e22ad0f85bbae

Observation 98f8f421-efdd-44d4-9308-fab87bccb91c · outbound

This paper cites Benchmarking neural network robustness to common corruptions and perturbations.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Benchmarking neural network robustness to common corruptions and perturbations

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.105096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:9850445c4ae619a719236adf0fd86b20fd69494a53a22f00ecb4245b01a6b336

Observation a0027913-3644-4a9f-a208-25c25513025d · outbound

This paper cites Revisiting scalarization in multi-task learning: A theoretical perspective.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Revisiting scalarization in multi-task learning: A theoretical perspective

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.055499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:604f40ec6f62660d3ae1e099956d365ed7c6e046c1d24d39e632bfb1912bb2cc

Observation c0d3e910-5262-456d-b4c0-fb7d4defc901 · outbound

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

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Emr-merging: Tuning-free high-performance model merging

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.051878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:ecb0305edd4ce6d4932ebc5588f31f9e4b7d3c1e22c1ccbd22f4308bb6e2061d

Observation 8bbb8108-04c7-41fb-b1c4-ca656e9d2720 · outbound

This paper cites Patching open-vocabulary models by interpolating weights.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Patching open-vocabulary models by interpolating weights

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.068115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:9eb1f6290963457291720abfa7a8caa26bf000b8bd4fe7f0062b31f5e03ac623

Observation 4218a0e0-dd86-4e21-b02d-140c3f5f30dd · outbound

This paper cites Editing models with task arithmetic.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Editing models with task arithmetic

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.163008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:c0ecd1ef5aafe5902cc3395eadc6a0c9757feb8f87705a1c73bc2283251a8d4d

Observation a49c21e8-abfb-4f70-975f-45047b61d33e · outbound

This paper cites First quora dataset release: Question pairs.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation First quora dataset release: Question pairs

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.072059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:34241b4b92117c74affab63090601205666dffa4a5cc508ecdbc8c8fce2bc620

Observation b2279bb1-5d33-468b-be00-0c540709331b · outbound

This paper cites Fine-tuning attention modules only: Enhancing weight disentanglement in task arithmetic.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Fine-tuning attention modules only: Enhancing weight disentanglement in task arithmetic

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.024608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:be977dc5930ddab7ac56502cec8c0056a8180f3aa091e1075484e828fb039b01

Observation 2ba88148-3dec-439e-84fd-f341bc708b3e · outbound

This paper cites Multi-task learning using uncertainty to weigh losses for scene geometry and semantics.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Multi-task learning using uncertainty to weigh losses for scene geometry and semantics

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.032725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:91d404ce90775e9dc8cfc5931ba3f2ec9ff9fd6a9f0f92c096597a3ab0991399

Observation 9bbe592b-c5c5-488a-8003-49189400015e · outbound

This paper cites Adam: A method for stochastic optimization.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Adam: A method for stochastic optimization

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.020479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:e9c64ee784bd640bef2083d47dd00df59efdacd6f582eaf113060afbaab4ee61

Observation 5dab0b3c-6cb4-4ba5-808e-cea9478474e2 · outbound

This paper cites Overcoming catastrophic forgetting in neural networks.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Overcoming catastrophic forgetting in neural networks

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:31.998658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:021344716c317dc4d595e574ed471df72d09f48a8e1529e9b6cf569a8438fc59

Observation e5d29ffb-1088-48b6-9562-1c61fcb276be · outbound

This paper cites Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.040069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:a99466e2521185148d010c9555c2c9fb33fde7f62cf744c6936d5ffafb299d10

Observation 0aa623a7-b0fa-4572-825a-7145f915e49a · outbound

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

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 23

Resolution
verified exact
local_arxiv, observed 2026-05-25T08:10:31.438891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:2e72180cf182b4489781d46b6259b82336e1b57bd702ad8562529cb87e85c38c

Observation da321228-667a-48ea-8b90-8e050e8aa6ef · outbound

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

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation No task left behind: Isotropic model merging with common and task-specific subspaces

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.003052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:a7f15de864bd0170ca96a07e7c03f7137acd522c744ad08f0d338cfb4ad58955

Observation d61530d8-8381-426c-8160-d5e0b5330a67 · outbound

This paper cites Magmax: Leveraging model merging for seamless continual learning.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Magmax: Leveraging model merging for seamless continual learning

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.036535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:76acfd6f0f6e46a4e7ec48c06410e087499e6625ec26d388d9609f278f0d954d

Observation 33f019c1-cb3f-436f-a31e-0b3e597042aa · outbound

This paper cites Cross-stitch networks for multi-task learning.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Cross-stitch networks for multi-task learning

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.167029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:a88577d72076ce265568b69aa3ebbbbc62ee284e0541d155ffbef05ad309e530

Observation bde0b842-9a5c-4c18-b4b0-7d879e27b7d4 · outbound

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

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Reading digits in natural images with unsupervised feature learning

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.048356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:72635ef96f1b8bed76cc421ce69e3dde0fdf4153bcf6e9679062cc958ab5c2a8

Observation 5538999e-d1aa-4238-91b5-1be6c95734ba · outbound

This paper cites Towards calibrated robust fine-tuning of vision-language models.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Towards calibrated robust fine-tuning of vision-language models

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.109511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:ed229f22c42b220f7da66c0dd69ec738a872d726fd79e6623be27264240d222c

Observation d9997cb4-5fd9-465c-89c4-cb6c27254546 · outbound

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

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Dawin: Training-free dynamic weight interpolation for robust adaptation

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:31.986460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:41c99667a4fa47115a828be6b58543c35616f557812c1547a69d2c88feb67964

Observation baf03a84-0001-4d6c-bd67-d4713c7e9637 · outbound

This paper cites Task arithmetic in the tangent space: Improved editing of pre-trained models.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Task arithmetic in the tangent space: Improved editing of pre-trained models

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.044516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:86d56a7bd59a0374627cd93dfae5ad3eb53df6ca37b0877519077cdbac02ea72

Observation 38c5e189-466e-4fab-845d-865b7dfb8a01 · outbound

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

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Learning transferable visual models from natural language supervision

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.059913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:e478db3561e53ee34d8bf5d3877edc54e07f74ce582b85c6a84e24db89f3c33b

Observation 93bea45f-e468-4d3e-9e03-acd53386963b · outbound

This paper cites SQ u AD : 100,000+ questions for machine comprehension of text.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation SQ u AD : 100,000+ questions for machine comprehension of text

Reference 32

Resolution
verified exact
doi, observed 2026-05-25T08:10:31.333807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:c685540f5ffc3085afd7d3c478adf3c3d25b83f0dbbcc69ad71870172e67d95a

Observation f329c5ed-8ce1-4dc4-93af-34926ffe3153 · outbound

This paper cites Indoor segmentation and support inference from rgbd images.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Indoor segmentation and support inference from rgbd images

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:31.982382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:4c1f228ce113ef27532b4d0c210fc979cf2950e7ec88508fab35e89773076ef9

Observation 5626e355-70a2-40ec-9623-85c58760ab0f · outbound

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

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Recursive deep models for semantic compositionality over a sentiment treebank

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.093567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:fa0869fe97dc3152ea2e3fa82ca18f1f4763c3f33b64f36820f92921b4ca9218

Observation 434921f2-ff11-4bbd-ba8a-e608e53bfbc1 · outbound

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

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation The german traffic sign recognition benchmark: a multi-class classification competition

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:31.978496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:dd9e1e1de3027adb561836342c8479b7cb69347dc4d20bb46c2b6519f64ea6d2

Observation 13ad4411-434e-4dc7-907a-88749f4fb26c · outbound

This paper cites Fusionbench: A comprehensive benchmark of deep model fusion.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Fusionbench: A comprehensive benchmark of deep model fusion

Reference 36

Resolution
verified exact
arxiv_id, observed 2026-05-25T08:10:31.445479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:7c2c11948f03fcda38b4274eaebd270aecd7bd3b90ea154cbe429b03fcc6e806

Observation e58d3c02-d8eb-470c-99b5-adb7a122637c · outbound

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

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Merging multi-task models via weight-ensembling mixture of experts

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:31.974942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:fd4fb504decb35243ad94def80a195805e617797ad2be8b6148b8d86ccb85a13

Observation c2bebd73-d722-4c22-82b1-dec9d5eeca99 · outbound

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

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Glue: A multi-task benchmark and analysis platform for natural language understanding

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.134705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:cc002fac8b3f2d67a74dcb824037bace92429b4c39a9f90d90b15589b1d22a48

Observation b369c60c-ac16-43ea-b58a-8512e947cb7e · outbound

This paper cites Localizing task information for improved model merging and compression.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Localizing task information for improved model merging and compression

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:31.994427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:d8290e9bdd1ab25bfc88dfbeab25030d5f4e1333cf5c2ec0567b17d125c2873a

Observation 48849129-dabc-4f80-af6e-244697107ec3 · outbound

This paper cites Lines: Post-training layer scaling prevents forgetting and enhances model merging.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Lines: Post-training layer scaling prevents forgetting and enhances model merging

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:31.990916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:40433d7e9651ad2bc15db12a9cc3aff8e3c9a54d73f07c5ef7e0a2ebd5bcdc33

Observation f91be1db-6d77-4cd6-8881-c61d9eef8370 · outbound

This paper cites Neural network acceptability judgments.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Neural network acceptability judgments

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.007030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:ae84cba5890fa3dc7c002b9831d277889514d37633a94f77f6d17da2c24db4a2

Observation 004a9d51-2ef1-419b-b7eb-d07ba4ff6c25 · outbound

This paper cites Representation surgery in model merging with probabilistic modeling.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Representation surgery in model merging with probabilistic modeling

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.016163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:940e2ea2827bed24ce397fa87473cf2001fdadb0825ca7fdbcc435a30c4b6544

Observation 77b1b4b3-2142-4bdb-97c9-2867271e189f · outbound

This paper cites A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference

Reference 43

Resolution
verified exact
doi, observed 2026-05-25T08:10:31.338846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:f2f0663ff9736c4f11393cd0fbd54e9bf3cbf0b4bfb440844db680db87083de4

Observation 90e9b1ce-7544-4e7e-ada9-6a978d4b1cfb · outbound

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

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.175535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:56fd1f9b4d8852bf388bbb654d71575270efe022ed15f2fde4792cd51082f6ec

Observation d0a97d84-4878-44ff-9e1c-150921bdbf3b · outbound

This paper cites Robust fine-tuning of zero-shot models.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Robust fine-tuning of zero-shot models

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.151260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:1c770f83d77a51aa284987aea99ad3470d1a135830166f4146a8d4152e056926

Observation 86aafaa5-21d0-4ab6-a9c6-13f379638d3e · outbound

This paper cites Scalable model merging with progressive layer-wise distillation.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Scalable model merging with progressive layer-wise distillation

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.130872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:62f7004dca34c1f24b00e8fbf958d5c7554ee0c3e6bf73b10101391e632bf27a

Observation 34f540c9-dfd0-4f4b-b29f-06291f923623 · outbound

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

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Ties-merging: Resolving interference when merging models

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.155181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:bd813e802051a33731d3bfbcae229381627d9cdcc818a72e4842021bc2926605

Observation 6b4e472b-3d9f-4597-b080-2be8a6c06e66 · outbound

This paper cites Representation surgery for multi-task model merging.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Representation surgery for multi-task model merging

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.179713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:442c7d4d6eacf79954b80a93a41bc037f4477c8a4b642ce98fa7488301050c0a

Observation 80f991a5-dc83-44f7-b867-1c17a85b09ba · outbound

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

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Adamerging: Adaptive model merging for multi-task learning

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.113839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:e8b545c2e5e144548e279d8e0689f898f3fec9169c45f5dfe29c903bc4bab4f9

Observation 1b676484-d1f9-4659-b889-0ce6c9e74907 · outbound

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

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Language models are super mario: Absorbing abilities from homologous models as a free lunch

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.101179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:95749e0be3f6ad491cf15a422ee1edd752c022d399483c6991e5336661c37888

Observation 849e39f3-27dd-48c9-a47a-b3520b8a452c · outbound

This paper cites Gradient surgery for multi-task learning.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Gradient surgery for multi-task learning

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.089413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:d3cb043f4ee38dbf375a90d303909ea2c120979316f7e0208b3edda7429f0757

Observation c450277d-1634-4934-b990-dfb431fc8d86 · outbound

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

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Free-merging: Fourier transform for efficient model merging

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.097348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:e094a7079630ae333bc331fd14a05b112036b44a93eb3f43ad663ad1dac38d9a

Observation 7a250c8b-f079-4e1a-a4b8-0ce34ef54247 · outbound

This paper cites On the emergence of cross-task linearity in the pretraining-finetuning paradigm.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation On the emergence of cross-task linearity in the pretraining-finetuning paradigm

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.118199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:6601e24c4f1482e0f4b839b9f49596b97bbe7d195e713e9c279a63b3d7e008a0

Observation 836ee192-0162-4dc9-af63-d633f26d0585 · outbound

This paper cites write newline.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation write newline

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.081251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:70e494ccfa3e2da81eeb438320ebe35b9c9220cdc3883fa67f3396a9fed780ca

Observation ea10ab4d-5d6e-45b8-bff7-2499a35b0e00 · outbound

This paper cites @esa (Ref.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation @esa (Ref

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-05-25T08:10:32.076845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:fa1efd28b5f2db21a249d730118988530b9222f729ef2be6eb54bf18deaabab4

Observation c738aa8e-6b1c-4d2c-b24c-6faf282d795f · outbound

This paper cites an unresolved cited work.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Unresolved cited work

Reference 56

Resolution
unresolved
raw_fallback, observed 2026-05-25T08:10:32.085752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:30303965faaf12bb98ecbccd2f884628c5d817489390b65d5c72197957c6f292

Observation 2c98d765-267c-49bf-9101-c7b811d27da8 · outbound

This paper cites Most results are obtained on an NVIDIA RTX 4090 GPU, while experiments involving ViT-L/14 are performed on an NVIDIA RTX A6000 GPU.

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation Most results are obtained on an NVIDIA RTX 4090 GPU, while experiments involving ViT-L/14 are performed on an NVIDIA RTX A6000 GPU

Reference 57

Resolution
malformed identifier
arxiv_id, observed 2026-05-25T08:10:31.452248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T08:07:09.574681Z digest=sha256:23c4f75337c9ca6a0603787892a461452ca3900470c2526732dc445aa52bcdc8

Pith citing papers

Observation bbf018a9-c097-4be0-9c40-6836b3cc0816 · inbound

Local Mixtures of Experts: Essentially Free Test-Time Training via Model Merging cites this paper.

Local Mixtures of Experts: Essentially Free Test-Time Training via Model Merging SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation

Reference 33

Resolution
verified exact
local_arxiv, observed 2026-08-07T15:42:26.033203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T15:42:20.895955Z digest=sha256:e35602add38374d72a9eba6970dde459007684c71fd9bc06454e1482a79d7e60