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Paper Citation Record · LEDGER

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models

As of 18 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 3 inbound Pith citation observations for arXiv:2506.20629.

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

pith.paper-citation-record.v1
2506.20629 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:50:15.749239Z

measured 45 of 45 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T05:16:35.355130Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

42 of 42 outbound references displayed

  • verified exact2
  • verified fuzzy12
  • unresolved27
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b5db60fe-41bd-4e44-9518-a79662231a5a · outbound

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

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Lora: Low-rank adaptation of large language models

Reference 1

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source=pdf_text observed=2026-08-06T22:50:11.762337Z digest=sha256:e564b2d0b88090a8953cc4a7093151bfa3fced8e65d690c7a40b6e3cd553bc06

Observation bc146791-03eb-470b-ba9f-97bd0c413541 · outbound

This paper cites LoRA+: Efficient low rank adaptation of large models.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models LoRA+: Efficient low rank adaptation of large models

Reference 2

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raw_fallback, observed 2026-08-06T22:50:18.645387Z

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=pdf_text observed=2026-08-06T22:50:11.806899Z digest=sha256:e0ea062044c6243e5412064bd9387281fecb916a17df686f06b960369af39626

Observation d6846caf-cf64-4ca6-90dd-ea8b981b5217 · outbound

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

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Dora: Weight-decomposed low-rank adaptation

Reference 3

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

source=pdf_text observed=2026-08-06T22:50:11.866917Z digest=sha256:754cff3325c478a20312cdbb4546aaf094b42be935735e0e3ecc8334801c6387

Observation afdf1c00-ad44-45e4-ae95-dd2109535032 · outbound

This paper cites RA-LoRA: Rank- adaptive parameter-efficient fine-tuning for accurate 2-bit quantized large language models.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models RA-LoRA: Rank- adaptive parameter-efficient fine-tuning for accurate 2-bit quantized large language models

Reference 4

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T22:50:11.916739Z digest=sha256:a399b228023d523bbbb300bd8de07746bcd3e99cd2c7fdd295ed038013407aff

Observation 4f14b611-5324-426a-9f6a-8c236fabb752 · outbound

This paper cites Take Only What You Need: Rank Minimization as an Implicit Forgetting Regularizer in Continual Learning.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Take Only What You Need: Rank Minimization as an Implicit Forgetting Regularizer in Continual Learning

Reference 5

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source=pdf_text observed=2026-08-06T22:50:11.955707Z digest=sha256:37694e2427a2d89e6a0de12db8d84b46fd6297920b64833b6ba8b26cfaafa13f

Observation 411880e2-f4d0-4922-8149-838dee2eefcb · outbound

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

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models The impact of initialization on lora finetuning dynamics

Reference 6

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raw_fallback, observed 2026-08-06T22:50:18.283837Z

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=pdf_text observed=2026-08-06T22:50:12.015542Z digest=sha256:278fcece30e903d6b160205721a31629e9cafcee8a7f38c16d2656821c96e686

Observation 32f10d84-83ea-4ff9-8bb6-cbc0cb9fd109 · outbound

This paper cites AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Reference 7

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source=pdf_text observed=2026-08-06T22:50:12.149846Z digest=sha256:7fbdcbc7f738f3fb451ef46841ec7d6618ea01d2013433b0e6536b41a72b9e15

Observation dad788d7-cbe3-45db-ab3d-6b88285e0e49 · outbound

This paper cites Qlora: Effi- cient finetuning of quantized llms.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Qlora: Effi- cient finetuning of quantized llms

Reference 8

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T22:50:12.211265Z digest=sha256:947e5eb3884103a128bd422ed2b968084cc9f619b2a72180eb6890657d0cfda3

Observation 60eda5be-c04f-4910-a234-c24fe22a6fcd · outbound

This paper cites VeRA: Vector-based Random Matrix Adaptation.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models VeRA: Vector-based Random Matrix Adaptation

Reference 9

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source=pdf_text observed=2026-08-06T22:50:12.267784Z digest=sha256:eddbfb98173bde9b58ca2cd1814473b0eb6f69d89d4110d73323f76918704e73

Observation 5c6837c7-1911-4314-905f-1ef8bbbf7e3a · outbound

This paper cites LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning

Reference 10

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source=pdf_text observed=2026-08-06T22:50:12.331356Z digest=sha256:1a27307ef08a1bdab674ea73904c76c2532083ee7f31dc941f97eac364ef84d4

Observation ee0ed6d0-85d7-49d5-b489-a2954f848beb · outbound

This paper cites HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning

Reference 11

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source=pdf_text observed=2026-08-06T22:50:12.357927Z digest=sha256:fdc53ab4da9006ba924495ae49b6db4ab2a692639d648280da8ed3370f139186

Observation 6d5a7cc4-248e-4a91-8ea7-52f11502b57a · outbound

This paper cites MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning

Reference 12

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source=pdf_text observed=2026-08-06T22:50:12.408587Z digest=sha256:7a7edf2d8e73d61ae02ec82b02aaa45ad05e1a5c57397699202fb906d1349d67

Observation f9d36fd4-5867-4440-8523-58922a1ec4a0 · outbound

This paper cites A Note on LoRA.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models A Note on LoRA

Reference 13

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source=pdf_text observed=2026-08-06T22:50:12.474879Z digest=sha256:087dfc6d06322a1e4351e0a79a5efc2955e40052d8b359c28f9277672c1e278c

Observation 9cd9a7cb-07f0-49eb-bc91-7b730ad0733e · outbound

This paper cites Towards a Unified View of Parameter-Efficient Transfer Learning.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Towards a Unified View of Parameter-Efficient Transfer Learning

Reference 14

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source=pdf_text observed=2026-08-06T22:50:12.504083Z digest=sha256:ca9bb52a373709b2970de905c7df5dec671e9b23ff06fb01dc3691e578a469d7

Observation 383b47b1-d223-4a68-a1ec-16c743680418 · outbound

This paper cites The llama 3 herd of models, 2024.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models The llama 3 herd of models, 2024

Reference 15

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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=pdf_text observed=2026-08-06T22:50:12.614220Z digest=sha256:fc89e7ad6f1de06c9514e70c568888e28ca9508b62d8b678a36086af1327c6c9

Observation 6102ceff-bbf2-45f1-8b94-602a10a7f861 · outbound

This paper cites Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation

Reference 16

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source=pdf_text observed=2026-08-06T22:50:12.706998Z digest=sha256:bd7556a5d2ba5045d1279d6b39520407e3fa7b5dd6e9938db5e9814a11e505e3

Observation cd0794ff-9d5f-4fb4-be95-65d80cded92d · outbound

This paper cites Gradient-based parameter selection for efficient fine-tuning.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Gradient-based parameter selection for efficient fine-tuning

Reference 17

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

source=pdf_text observed=2026-08-06T22:50:12.818350Z digest=sha256:c67bd17bdda4982ec89578aea2c0758e1dd17bbdb0edd7d797dc9c8a1114449f

Observation 2da089bd-1adc-4d98-bc7a-aa29fc568acd · outbound

This paper cites Sensitivity-aware visual parameter-efficient fine-tuning.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Sensitivity-aware visual parameter-efficient fine-tuning

Reference 18

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source=pdf_text observed=2026-08-06T22:50:12.910209Z digest=sha256:dac30bd5f54fbbd785c6dbfecd0def3e1c100868022cddb94b150d4c96c55743

Observation ce9e4aa4-272e-4d60-b1a5-fefd85e3a4f3 · outbound

This paper cites an unresolved cited work.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Unresolved cited work

Reference 19

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source=pdf_text observed=2026-08-06T22:50:13.021420Z digest=sha256:7979b68332ca0aa84d2c2225249b170a47ae96bfbfae585b25130859ae1ee4d5

Observation ce83a540-3168-4779-9db0-909cbe759f6d · outbound

This paper cites Kingma and Jimmy Ba.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Kingma and Jimmy Ba

Reference 20

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

source=pdf_text observed=2026-08-06T22:50:13.218947Z digest=sha256:091af2cf600c540770cb971c0dc6f3f1695ab3b39f19ad8ccf0b9138d3babb94

Observation 8dc6addd-a51a-4ece-bbfc-5d095e255263 · outbound

This paper cites signSGD: Compressed Optimisation for Non-Convex Problems.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models signSGD: Compressed Optimisation for Non-Convex Problems

Reference 21

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source=pdf_text observed=2026-08-06T22:50:13.453468Z digest=sha256:ae570f9d10ed5fcc900c2b69991814dbf28939ea7eeae3959073e3effa27b475

Observation dd1a8385-e7d5-48c7-a0fd-26dbbedfddfe · outbound

This paper cites Visualising feature learning in deep neural networks by diagonalizing the forward feature map, 2024.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Visualising feature learning in deep neural networks by diagonalizing the forward feature map, 2024

Reference 22

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

source=pdf_text observed=2026-08-06T22:50:13.578167Z digest=sha256:75f2f5ff31e037f7c9a5a9d6de29f0ff3a239023e3a5e57b04496bf1e466af52

Observation 516add5c-cd9c-420e-80ee-e4bf65044507 · outbound

This paper cites Implicit Regularization via Neural Feature Alignment.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Implicit Regularization via Neural Feature Alignment

Reference 23

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source=pdf_text observed=2026-08-06T22:50:13.659226Z digest=sha256:b73bb5d5efa3b7c790b74380e2019ae41fd3a2902dc87d1fb580ec8bd283a8b5

Observation 75d14942-7f09-45ca-95e0-f31ddb2a1c04 · outbound

This paper cites Feature learning and signal propagation in deep neural networks.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Feature learning and signal propagation in deep neural networks

Reference 24

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

source=pdf_text observed=2026-08-06T22:50:13.754683Z digest=sha256:56c5e8ee85ca6db560a5ff20b9d9cd1c5539ccf82f09d51e8adb272a391728c4

Observation 18e2395b-0685-4b1d-bddd-cbc855c1caa0 · outbound

This paper cites Understanding and Minimising Outlier Features in Neural Network Training.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Understanding and Minimising Outlier Features in Neural Network Training

Reference 25

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source=pdf_text observed=2026-08-06T22:50:13.820346Z digest=sha256:58c9b58d43c0d9936b800ed29f7be2d995fb183a5785140189b82f015601ad02

Observation a23e82c6-11ae-4b8d-9364-b6de3b3eef0b · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Training Verifiers to Solve Math Word Problems

Reference 26

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source=pdf_text observed=2026-08-06T22:50:13.890990Z digest=sha256:aeac2dd5a233e668fbdee4885d4ad1e262929cccc0128596b15bc3491044f541

Observation 5183aeeb-7db3-4afc-abd7-7ba021bb41d9 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Evaluating Large Language Models Trained on Code

Reference 27

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source=pdf_text observed=2026-08-06T22:50:13.961350Z digest=sha256:3786ec70ad8dc467bee2afa7a34824f97f090b605da4d7c1edd32b673c051c01

Observation 447d596a-cfef-43ab-8d04-ea5ccab57416 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Measuring Massive Multitask Language Understanding

Reference 28

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source=pdf_text observed=2026-08-06T22:50:14.042709Z digest=sha256:3e054d78820cd36348bd3f3e870f17dacd96fb08632447a5c8eca67ade620157

Observation b3b6997b-fa6a-4013-b85e-751b70d60b89 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 29

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source=pdf_text observed=2026-08-06T22:50:14.160586Z digest=sha256:4f1d4c6a5da7060ec3c9c3f3c0c96c2e8d25966029c2ef8121b7f75b55decc71

Observation eb7b65fa-b8be-4540-86d1-5ccfe1ac932e · outbound

This paper cites Qwen3 technical report, April 2025.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Qwen3 technical report, April 2025

Reference 30

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raw_fallback, observed 2026-08-06T22:50:16.813184Z

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

source=pdf_text observed=2026-08-06T22:50:14.273874Z digest=sha256:2adfba247cc0a59b158fa0b35b72d80a500e84d6403cd7eb761205d7f82623fb

Observation 7e6ccd7b-320c-4252-ac65-c033859c8043 · outbound

This paper cites Gemma 3 technical report, 2025.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Gemma 3 technical report, 2025

Reference 31

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source=pdf_text observed=2026-08-06T22:50:14.336198Z digest=sha256:a8d4420b4e26727308a5a2f870a65e16aeea06c5941c2ac59fb56ae213941072

Observation 5db56f86-e897-44f6-9275-bec66881314d · outbound

This paper cites Adversarial nli: A new benchmark for natural language understanding.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Adversarial nli: A new benchmark for natural language understanding

Reference 32

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verified fuzzy
raw_fallback, observed 2026-08-06T22:50:16.682332Z

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=pdf_text observed=2026-08-06T22:50:14.417894Z digest=sha256:5a13ccb5596b1afdeddbe58a14798b0469ffbdb27d7f5cdde077022ea9a94f24

Observation 1f47f458-6a09-4c7a-af59-185ffdbe48e0 · outbound

This paper cites MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models

Reference 33

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source=pdf_text observed=2026-08-06T22:50:14.482792Z digest=sha256:9e14cf574be58bee395935022f26177ecc399fe69f25594860f628ce8ebb7b9d

Observation e070fe5c-7e87-41b8-8a10-5bf39df4fad6 · outbound

This paper cites Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning

Reference 34

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source=pdf_text observed=2026-08-06T22:50:14.576341Z digest=sha256:84009e27aa13688a392a77d590d5d5b6b479f087f77d4df5cd314b1acb6345ac

Observation 1be3cd60-22ba-4da2-b15d-2d4d881126db · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 35

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source=pdf_text observed=2026-08-06T22:50:14.684254Z digest=sha256:b24171dae1550c52865df8a9e6b70cee33ef657794de1afd0d8ba9449dea0009

Observation f0915151-274c-4caf-ba34-072f6be67ad8 · outbound

This paper cites Rae, Oriol Vinyals, and Laurent Sifre.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Rae, Oriol Vinyals, and Laurent Sifre

Reference 36

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T22:50:14.881189Z digest=sha256:c6dee95b54f86b382acc842990dee896a428ee67b7f92e8a8faf877b0ec8a0c0

Observation e33a345d-cbf0-45e2-bc08-32823397018e · outbound

This paper cites Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

Reference 37

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no resolver link, observed 2026-08-06T22:50:15.138441Z

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source=pdf_text observed=2026-08-06T22:50:15.138441Z digest=sha256:210447144905de0956e04c107ecaafdca398556bab6484cb1247e5e0db707728

Observation 885b4b84-f9cf-4666-89b9-1eddf68da088 · outbound

This paper cites On the impact of the activation function on deep neural networks training.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models On the impact of the activation function on deep neural networks training

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:50:16.534788Z

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=pdf_text observed=2026-08-06T22:50:15.503504Z digest=sha256:f46f1482582fc78236fea8bd29f06f053c56a98b6a5d0dea220f362f6cb5ffa2

Observation 182b4bf2-086e-478b-a7e5-4997fd1f11a4 · outbound

This paper cites Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation

Reference 39

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no resolver link, observed 2026-08-06T22:50:15.749239Z

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source=pdf_text observed=2026-08-06T22:50:15.749239Z digest=sha256:f45c2532ddcd540ceb5b43b0845455856243b8edf51ad4530aed3e09f7532993

Observation a332630d-d8ed-4262-a49f-6f32e235eb1e · outbound

This paper cites Adam: A Method for Stochastic Optimization.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Adam: A Method for Stochastic Optimization

Reference 2017

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no resolver link, observed 2026-08-06T22:50:13.370576Z

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source=pdf_text observed=2026-08-06T22:50:13.370576Z digest=sha256:c024f39b658d0e5e2c86415123999a925aa052925b0b88a6cc181ca973fe1cd3

Observation 6b15f366-c0a1-41dd-8aa3-21ed137496a3 · outbound

This paper cites Feature Learning in Infinite-Width Neural Networks.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Feature Learning in Infinite-Width Neural Networks

Reference 2022

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unresolved
no resolver link, observed 2026-08-06T22:50:13.117590Z

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source=pdf_text observed=2026-08-06T22:50:13.117590Z digest=sha256:d913c4f770f9e388fbff8dc953e5135d06399933f0db1fb6d1881ec73b0c6376

Observation 8decc2cc-64c6-4c65-89a0-bc6e92d4e8e4 · outbound

This paper cites an unresolved cited work.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Unresolved cited work

Reference 2024

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unresolved
raw_fallback, observed 2026-08-06T22:50:18.095224Z

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=pdf_text observed=2026-08-06T22:50:12.072489Z digest=sha256:ee5914450c6b529d43636b00a83384951d81a81e45f7116621cd3c943f115390

Pith citing papers

Observation b39c9925-8b32-4b65-8f19-cfb6d7b58af3 · inbound

Data-Efficient Adaptation of LLMs via Attention Head Reweighting cites this paper.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models

Reference 20

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no resolver link, observed 2026-08-02T05:16:35.355130Z

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source=pdf_text observed=2026-08-02T05:16:35.355130Z digest=sha256:ba6bcd230e85722c7e299044b59c3b03f568f9e5ebaf05c6691afbfd2c502300

Observation a91d7e92-debe-4d2e-9bb7-a94f201777d4 · inbound

Non-vacuous Generalization Bounds for Reinforcement Learning with Verifiable Rewards cites this paper.

Non-vacuous Generalization Bounds for Reinforcement Learning with Verifiable Rewards PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models

Reference 23

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no resolver link, observed 2026-08-02T02:01:50.780452Z

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source=pdf_text observed=2026-08-02T02:01:50.780452Z digest=sha256:66944ea868649c0d4b51765b12551d7c957be1be2148ab8088ecd1e797f3ccc8

Observation ab7739c6-94ab-4006-b8c5-da9c222a1270 · inbound

PoLoRA: A Preconditioned Orthogonalized LoRA Optimizer cites this paper.

PoLoRA: A Preconditioned Orthogonalized LoRA Optimizer PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models

Reference 2025

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unresolved
no resolver link, observed 2026-08-01T17:32:33.530247Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-01T17:32:33.530247Z digest=sha256:caf3d0c4dac40cc12e3d27ff6be5ff562a99370b6efd81302ead3af0ee405e59