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Paper Citation Record · LEDGER

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging

As of 10 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2607.20561.

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

pith.paper-citation-record.v1
2607.20561 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T20:28:05.077888Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

37 of 37 outbound references displayed

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External citation measurements

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Outbound references

Observation 81c91dfb-39b8-4012-b8f4-1c272b069ada · outbound

This paper cites Remote sens- ing image scene classification: Benchmark and state of the art.Proceedings of the IEEE, 105(10):1865–1883, 2017.

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Remote sens- ing image scene classification: Benchmark and state of the art.Proceedings of the IEEE, 105(10):1865–1883, 2017

Reference 1

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Observation 2cba4e13-8190-494a-8818-b82a135b6805 · outbound

This paper cites Describing textures in the wild.

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Describing textures in the wild

Reference 2

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Observation 036a5a66-7691-49b7-9551-5d398b93b98d · outbound

This paper cites An analysis of single-layer networks in unsupervised feature learning.

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging An analysis of single-layer networks in unsupervised feature learning

Reference 3

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Observation a86a8a29-5d88-420e-bf69-db464a26fa76 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging An image is worth 16x16 words: Transformers for image recognition at scale

Reference 4

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Observation d1512fa9-fdd9-4970-80ea-aaa2d909471d · outbound

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

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Task singular vectors: Reducing task in- terference in model merging

Reference 5

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Observation d6c9f186-aed4-4ee0-9968-be74c710370c · outbound

This paper cites an unresolved cited work.

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Unresolved cited work

Reference 6

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Observation 17a2558c-4344-467c-a017-3a488d248642 · outbound

This paper cites an unresolved cited work.

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Unresolved cited work

Reference 7

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Observation 9df15617-88f0-434d-a317-9d95e9881e66 · outbound

This paper cites Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark.

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark

Reference 8

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Observation d8368c25-91f5-428a-95ed-4df3cc4bc2bf · outbound

This paper cites Parameter-efficient transfer learning for nlp.

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Parameter-efficient transfer learning for nlp

Reference 9

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Observation 82a85e78-5cc9-4f13-b60c-6f8e0cf8aeb7 · outbound

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

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging LoRA: Low-rank adaptation of large language models

Reference 10

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Observation 861bcf2c-fa68-4ef3-811c-258685cc03f1 · outbound

This paper cites Lorahub: Efficient cross-task gen- eralization via dynamic lora composition.

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Lorahub: Efficient cross-task gen- eralization via dynamic lora composition

Reference 11

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Observation 5d8903a6-b3c5-4634-b3b3-7aa2c562726e · outbound

This paper cites Editing models with task arithmetic.

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Editing models with task arithmetic

Reference 12

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Observation e5fca836-b44f-4c92-8ebf-eb877765744e · outbound

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

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Dataless knowledge fusion by merging weights of language models

Reference 13

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Observation f968147c-6763-4c18-b4a3-e3af3b0d368d · outbound

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

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging 3d object representations for fine-grained categorization

Reference 14

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Observation c60b7143-ab8d-4208-841a-dea30668c8eb · outbound

This paper cites Learning multiple layers of features from tiny images.

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Learning multiple layers of features from tiny images

Reference 15

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Observation aba0b955-92f0-4202-ba13-8cec85740276 · outbound

This paper cites Lecun, L.

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Lecun, L

Reference 16

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Observation 17c55307-f22d-4182-8c6a-d5cf602bf094 · outbound

This paper cites Adarank: Adaptive rank pruning for enhanced model merging.

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Adarank: Adaptive rank pruning for enhanced model merging

Reference 17

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Observation 9ca9ef71-b17b-453e-8bef-2b15a016a9b3 · outbound

This paper cites When shared knowledge hurts: Spectral over- accumulation in model merging.

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging When shared knowledge hurts: Spectral over- accumulation in model merging

Reference 18

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Observation 826df131-a97d-41e3-ba35-d954cc10b9e3 · outbound

This paper cites Bagdanov, and Joost van de Weijer.

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Bagdanov, and Joost van de Weijer

Reference 19

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Observation b603b7f7-878f-4297-9044-20774824188a · outbound

This paper cites Merging models with fisher-weighted averaging.

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Merging models with fisher-weighted averaging

Reference 20

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Observation 740ee886-160c-45e6-a207-1e2a707560d8 · outbound

This paper cites an unresolved cited work.

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Unresolved cited work

Reference 21

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Observation b0cb7b6a-8db1-44cf-824c-0a45b43406c3 · outbound

This paper cites Automated flower classification over a large number of classes.

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Automated flower classification over a large number of classes

Reference 22

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Observation 1b42f2d3-d766-4f4b-8241-d6fb965ecfe8 · outbound

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

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Task arithmetic in the tangent space: Improved editing of pre-trained models

Reference 23

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Observation 5041fcb0-cb95-46e9-8870-81000a112d1d · outbound

This paper cites Parkhi, Andrea Vedaldi, Andrew Zisserman, and C.

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Parkhi, Andrea Vedaldi, Andrew Zisserman, and C

Reference 24

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Observation 109a12a4-c0f7-4d29-9432-6b21270f3e4b · outbound

This paper cites LoRA Soups: Merging LoRAs for Practical Skill Composition Tasks.

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging LoRA Soups: Merging LoRAs for Practical Skill Composition Tasks

Reference 25

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Observation fdf368fa-f94a-497c-83c8-b15180151f18 · outbound

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

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Learning transferable visual models from natural language supervision

Reference 26

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Observation bbfe9281-43eb-49c7-8014-0a5eac9ae402 · outbound

This paper cites Manning, Andrew Ng, and Christopher Potts.

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Manning, Andrew Ng, and Christopher Potts

Reference 27

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Observation 6b8f3bb2-a1b5-44eb-8b1c-b7e0bc9f8c3b · outbound

This paper cites Model merging with svd to tie the knots.ICLR, 2025.

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Model merging with svd to tie the knots.ICLR, 2025

Reference 28

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Observation 86ab25b5-78cd-41d8-ae6e-25a1527cae1b · outbound

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

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Localizing task infor- mation for improved model merging and compression

Reference 29

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Observation fba8650b-fb40-491c-97d2-540aa3837955 · outbound

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

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Reference 30

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Observation 330f74fa-d227-4f14-8941-542839c77e40 · outbound

This paper cites Fashion- mnist: a novel image dataset for benchmarking machine learning algorithms, 2017.

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Fashion- mnist: a novel image dataset for benchmarking machine learning algorithms, 2017

Reference 31

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Observation a028d8f7-fb61-4117-8154-7a34025419f3 · outbound

This paper cites Ehinger, James Hays, Antonio Torralba, and Aude Oliva.

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Ehinger, James Hays, Antonio Torralba, and Aude Oliva

Reference 32

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Observation 9520f835-5fa0-448e-950b-2bf30cdfa8c3 · outbound

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

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging TIES-merging: Resolving interference when merging models

Reference 33

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source=pdf_text observed=2026-08-01T20:28:04.726924Z digest=sha256:ae15741b5c8163ab0017108a8a35ef14619142843236c6dfb0630dc012eb6368

Observation 1a28e5d5-9f94-4eb6-bb43-51d508a92507 · outbound

This paper cites Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities.

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities

Reference 34

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Observation b9776303-2a59-4f90-840c-d9968af970c5 · outbound

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

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Adamerging: Adap- tive model merging for multi-task learning

Reference 35

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Observation 9fdf2a87-33b8-4611-acef-5c2debbb36e9 · outbound

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

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Language models are super mario: Absorbing abilities from homologous models as a free lunch

Reference 36

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Observation edf0132c-e387-4272-88eb-d0cf31d05a69 · outbound

This paper cites Dc-merge: Improving model merging with directional consistency.

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging Dc-merge: Improving model merging with directional consistency

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