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Paper Citation Record · LEDGER

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades

As of 18 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2505.13515.

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

pith.paper-citation-record.v1
2505.13515 v1

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:53:40.901530Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

49 of 49 outbound references displayed

  • verified exact0
  • verified fuzzy19
  • unresolved29
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bd4ad752-6391-4fad-bf9e-313b2f1791bc · outbound

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

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Lora: Low-rank adaptation of large language models,

Reference 1

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verified fuzzy
raw_fallback, observed 2026-08-15T20:53:41.718652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation cedfbf7f-4c86-45fe-b0c1-3a546a3f87d6 · outbound

This paper cites Gemini nano with the google ai edge sdk,.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Gemini nano with the google ai edge sdk,

Reference 2

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raw_fallback, observed 2026-08-15T20:53:41.706365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 0f24a756-a7db-4faa-b410-cf2ad3d57906 · outbound

This paper cites Autodroid: Llm-powered task automation in android,.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Autodroid: Llm-powered task automation in android,

Reference 3

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raw_fallback, observed 2026-08-15T20:53:41.694492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 1c4093a5-1570-46f4-90d6-8d1fa35d6df1 · outbound

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

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 4

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no resolver link, observed 2026-08-15T20:53:40.050666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 5808cff3-bc9b-4d4a-914c-3f7ee3b4c828 · outbound

This paper cites The llama 4 herd: The beginning of a new era of natively multimodal ai innovation,.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades The llama 4 herd: The beginning of a new era of natively multimodal ai innovation,

Reference 5

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raw_fallback, observed 2026-08-15T20:53:41.682404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:53:40.054581Z digest=sha256:a38ab83f941ae262fa2855723aeb4126207a16d27db6f6c43ae9fa20d65bfe01

Observation 9af0a624-2e65-4ea0-8aad-621611f56598 · outbound

This paper cites Qwen2 Technical Report.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Qwen2 Technical Report

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:53:40.062683Z digest=sha256:576563243769556bb562d7470f4639b3a51150bcb9f2dd9d12c523dd28752fc8

Observation ec238f76-7385-4460-a57f-abd4e68ecd3b · outbound

This paper cites Qwen2.5 Technical Report.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Qwen2.5 Technical Report

Reference 7

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no resolver link, observed 2026-08-15T20:53:40.066937Z

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source=pdf_text observed=2026-08-15T20:53:40.066937Z digest=sha256:071279644634f751b0728d7c5e915f82a002c9450ebaec1def18061b18b57ab8

Observation 4d6812f9-af14-4749-85a2-6bc07d87b5f9 · outbound

This paper cites Fwdllm: Efficient federated finetuning of large language models with perturbed inferences,.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Fwdllm: Efficient federated finetuning of large language models with perturbed inferences,

Reference 8

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raw_fallback, observed 2026-08-15T20:53:41.602515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:53:40.071023Z digest=sha256:1b273214dbd1338ca5c7eeb67e3ac99a39a12ffb6e699c0dc216630e45df2519

Observation 402c309d-c82c-4202-a9fd-fbb0c5b9ba01 · outbound

This paper cites Understanding the Performance and Estimating the Cost of LLM Fine-Tuning.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Understanding the Performance and Estimating the Cost of LLM Fine-Tuning

Reference 9

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T20:53:40.127210Z digest=sha256:3cb361bdaa9b907d7e71e73071ea7d3eadec5adae0c35f184906474ec4dd326f

Observation fcdaf1f0-c098-4dd0-8535-145ffb50383b · outbound

This paper cites Energy and policy considerations for modern deep learning research,.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Energy and policy considerations for modern deep learning research,

Reference 10

Resolution
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raw_fallback, observed 2026-08-15T20:53:41.591008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 6fbcb595-7940-4915-a299-f73b761a6840 · outbound

This paper cites What you can cram into a single vector: Probing sentence embeddings for linguistic properties.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades What you can cram into a single vector: Probing sentence embeddings for linguistic properties

Reference 11

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:53:40.296746Z digest=sha256:0a8778df3b3537e6e80e18be2af2986adc5fe4e03ba82362fac806232fc5cd9f

Observation 5acd7c73-e324-408f-880b-4189569c5a85 · outbound

This paper cites Towards automated circuit discovery for mechanistic interpretability,.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Towards automated circuit discovery for mechanistic interpretability,

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:53:40.300591Z digest=sha256:13002a012ceef77c10608d3ce08ba5513a795057cde0d6b54fe634d2e0016009

Observation 2abbdd45-f22f-4e35-a253-9d745e4e3e43 · outbound

This paper cites Locating and editing factual associations in gpt,.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Locating and editing factual associations in gpt,

Reference 13

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no resolver link, observed 2026-08-15T20:53:40.304266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:53:40.304266Z digest=sha256:899878fa52595674c8db28c8ac4c5d8b357bf8d5c7bd44122975cffd1bf47945

Observation 17ec31c2-1b85-456e-9680-7724f52eb97d · outbound

This paper cites Analyzing Transformers in Embedding Space.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Analyzing Transformers in Embedding Space

Reference 14

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no resolver link, observed 2026-08-15T20:53:40.308235Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:53:40.308235Z digest=sha256:a8ad49f94be6bc2502371ce54281300a4982ebf5011c9a09141571e10fc57319

Observation cbd9c696-4bb5-4003-8ef4-1d1e160bc00d · outbound

This paper cites A practical review of mechanistic inter- pretability for transformer-based language models,.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades A practical review of mechanistic inter- pretability for transformer-based language models,

Reference 15

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:53:40.463467Z digest=sha256:91c8f66b28f001a5cfba95a4a920632a61b9dd2d3aa63154a2fb2a455f5aae31

Observation ab68f86a-cd23-42bc-8dbc-409da302d13e · outbound

This paper cites Parameter-efficient transfer learning for nlp,.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Parameter-efficient transfer learning for nlp,

Reference 16

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raw_fallback, observed 2026-08-15T20:53:41.568184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 52ac10f5-79fd-431a-9417-9a103be0f409 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 17

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:53:40.470797Z digest=sha256:fe05aa8768fc7bac254c6a0654317db7c4c1650a062f05bf6e176611b6583327

Observation 4d4b508a-7a4d-4402-9e8d-2561efdd5534 · outbound

This paper cites Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning,.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning,

Reference 18

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raw_fallback, observed 2026-08-15T20:53:41.493900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:53:40.474278Z digest=sha256:513fd1c80466711a95b756295b205decd0194819e0106d171db2575df091570b

Observation 4f3cc393-edd7-4ab1-8fad-7f6ead94b381 · outbound

This paper cites Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models,.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models,

Reference 19

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T20:53:40.477857Z digest=sha256:fc326ad79a3ea765568c9a1ca724c0fe644c8af0645a7e551264e449a719d1b0

Observation 29c1693d-6621-4153-9ed9-7044956c3c23 · outbound

This paper cites Training neural networks with fixed sparse masks,.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Training neural networks with fixed sparse masks,

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:53:40.480574Z digest=sha256:ca57e5d428cd005db6f2a7d29b0109f44c6b91f248f853555f7f7e2cff50223c

Observation 4869b0a9-088b-419e-a9e2-ea51bee306ff · outbound

This paper cites Input-Tuning: Adapting Unfamiliar Inputs to Frozen Pretrained Models.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Input-Tuning: Adapting Unfamiliar Inputs to Frozen Pretrained Models

Reference 21

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Unavailable: canonical work link unavailable.

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Observation b19c0a4e-1247-419d-a443-e8b7360acebf · outbound

This paper cites Adaptive budget allocation for parameter-efficient fine-tuning,.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Adaptive budget allocation for parameter-efficient fine-tuning,

Reference 22

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raw_fallback, observed 2026-08-15T20:53:41.477998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 882ef4f4-83fe-4a2f-91c6-cf2db27a4aec · outbound

This paper cites Losparse: Structured compres- sion of large language models based on low-rank and sparse approximation,.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Losparse: Structured compres- sion of large language models based on low-rank and sparse approximation,

Reference 23

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raw_fallback, observed 2026-08-15T20:53:41.468645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:53:40.552318Z digest=sha256:af675275e8e7b5ded28b2fde2889b8b171db8a937c29357a954bbddd076dcd1a

Observation 0aafc8e1-00d0-481b-8873-7d13589ae3e7 · outbound

This paper cites DoRA: Weight-Decomposed Low-Rank Adaptation.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades DoRA: Weight-Decomposed Low-Rank Adaptation

Reference 24

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Observation 2500a492-31a7-4a9e-9280-ed8b9c9133f7 · outbound

This paper cites PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models

Reference 25

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source=pdf_text observed=2026-08-15T20:53:40.629590Z digest=sha256:06e5fb7f5757311334a1c8360748caa1e44d22b11e659ccbd55bbd600ce304d9

Observation 688a283c-dfc8-400d-86ce-fac20e372f71 · outbound

This paper cites LoRA+: Efficient Low Rank Adaptation of Large Models.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades LoRA+: Efficient Low Rank Adaptation of Large Models

Reference 26

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:53:40.633738Z digest=sha256:d19fd228aa78a96ab30af7f102b532a6b70c37d61863031de19d87bc21d5a378

Observation dd089d45-b55d-4be5-b6f5-7cbe70b7e641 · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,

Reference 27

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:53:40.638318Z digest=sha256:659a160ca99b25b121e6650400ec3392de3c79c0b0252fdb91d6c5ff3d44a1a8

Observation f5d8d0f1-527d-408c-8240-7212c0b432d2 · outbound

This paper cites Do wide and deep networks learn the same things? uncovering how neural network representations vary with width and depth,.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Do wide and deep networks learn the same things? uncovering how neural network representations vary with width and depth,

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:53:41.453775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:53:40.641562Z digest=sha256:3a41b87d4e31a8bfe209c78df8106927fbe26b07b5c45d945ac0c74f71896bb6

Observation d616ca39-6378-42b1-a059-b4654876c6bf · outbound

This paper cites Similarity of neural network representations revisited,.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Similarity of neural network representations revisited,

Reference 29

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verified fuzzy
raw_fallback, observed 2026-08-15T20:53:41.442475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:53:40.645471Z digest=sha256:9bd95f8011beb9ed2deff13b00bbef8e7139688700e71e1b59b93126d82c0437

Observation 9206f83c-c6de-4476-a6f3-dc7752cc2084 · outbound

This paper cites Feature selection via dependence maximization,.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Feature selection via dependence maximization,

Reference 30

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raw_fallback, observed 2026-08-15T20:53:41.430666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:53:40.648645Z digest=sha256:8d166ae92aba8cfa2861522a4f4a8ef8da4ff436507a128e89e7f9d3cd9b965f

Observation b03d06ae-85dd-4285-9708-00dd3aa11171 · outbound

This paper cites Post selection inference with kernels,.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Post selection inference with kernels,

Reference 31

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verified fuzzy
raw_fallback, observed 2026-08-15T20:53:41.366897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:53:40.651931Z digest=sha256:66ff5754d0a59eac8ead16d726ebc0d21dd8559e4cf457c941dad3d67f8f8e66

Observation 79aa1f88-a06f-44a5-9600-d996fb18e693 · outbound

This paper cites On the Variance of the Adaptive Learning Rate and Beyond.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades On the Variance of the Adaptive Learning Rate and Beyond

Reference 32

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:53:40.655389Z digest=sha256:592df00e22cea42670575e91eb4c6b8573e28e047f20f03e51e5fd1c2bd7cd28

Observation ad798297-1856-4131-8fa4-5fa141b08f0b · outbound

This paper cites Algorithms for the assignment and transportation problems,.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Algorithms for the assignment and transportation problems,

Reference 33

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verified fuzzy
raw_fallback, observed 2026-08-15T20:53:41.300859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:53:40.678994Z digest=sha256:7fb58df732fe40dbd2542dd6391e92164b0c67d18083c0d6e4d49f7aee7d82e8

Observation f9e864fc-b82a-4415-bc6a-a031e6770e76 · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 34

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:53:40.739626Z digest=sha256:29d126c560026543329769674f9bf7d375cfa57facfe5bb50eb88c8ab65b67cd

Observation 9e2a1d31-315e-4fb1-8f25-2ac4532671c7 · outbound

This paper cites Piqa: Reasoning about physical commonsense in natural language,.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Piqa: Reasoning about physical commonsense in natural language,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:53:41.289375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:53:40.742811Z digest=sha256:4341c1e2c521d89195f490f4d8d2152583d6ee0ac7200a29391a9cecd52b4762

Observation ac72b228-8601-421c-991f-2ab2e8bf4c03 · outbound

This paper cites SocialIQA: Commonsense Reasoning about Social Interactions.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades SocialIQA: Commonsense Reasoning about Social Interactions

Reference 36

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unresolved
no resolver link, observed 2026-08-15T20:53:40.746002Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:53:40.746002Z digest=sha256:8831ebf650bd03e6a3fb408c8be2d8a94a3219e137ba759fbd5fda3dc9dbbc3d

Observation 296bdada-cc06-4ebc-84fe-fce73f8799ad · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 37

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:53:40.749613Z digest=sha256:fbc374daf49d5174fd2324179af4e0d48a3af1fc8c328f53ae63f5bb3b17e378

Observation d7c19b76-0091-4840-9c0b-9664660c202b · outbound

This paper cites Winogrande: An adversarial winograd schema challenge at scale,.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Winogrande: An adversarial winograd schema challenge at scale,

Reference 38

Resolution
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no resolver link, observed 2026-08-15T20:53:40.752501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:53:40.752501Z digest=sha256:75bbb45cc92a96428cccfc39e19d2bbf06c7ac44f87bb1bf0fad09a803b5544e

Observation 5dd7fe16-6b04-4254-b159-e0b370339803 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 39

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no resolver link, observed 2026-08-15T20:53:40.756750Z

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source=pdf_text observed=2026-08-15T20:53:40.756750Z digest=sha256:b74ea233bd201fdb3814a106e2380a486a8dc088dfba5e28909f19b52d367eb8

Observation 703b33df-441b-48d6-a22f-9c1c25bf8df8 · outbound

This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 40

Resolution
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no resolver link, observed 2026-08-15T20:53:40.760507Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T20:53:40.760507Z digest=sha256:39840557d055b2da736345edbe8b8abb5d956aace4808a34ec240711dd74190c

Observation 7372b5ac-a46e-4e88-9f14-d710beb1c2ce · outbound

This paper cites LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models

Reference 41

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source=pdf_text observed=2026-08-15T20:53:40.763827Z digest=sha256:e9de5002fc5dedc250bbd3fd793e946e4425b112324ef81ddc8f46130366dfd4

Observation 3de47173-8e2a-446d-b479-98c10dfcfcb4 · outbound

This paper cites Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems

Reference 42

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no resolver link, observed 2026-08-15T20:53:40.767352Z

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source=pdf_text observed=2026-08-15T20:53:40.767352Z digest=sha256:99e31aab156c23d7e8c9d4427700f9f676e1af39f30d1c6e8bdbf6f7d0706d25

Observation 41ecdc2a-5262-473d-8dc4-6ab5e93ea9e2 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Training Verifiers to Solve Math Word Problems

Reference 43

Resolution
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no resolver link, observed 2026-08-15T20:53:40.827411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:53:40.827411Z digest=sha256:2237b82c007d520c8991f29e3bd08172a8474b51569f5fe75275be55a6f75f8d

Observation a06b2f0c-c767-43d7-95aa-6ba368ed7019 · outbound

This paper cites Mawps: A math word problem repository,.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Mawps: A math word problem repository,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:53:41.272427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:53:40.886946Z digest=sha256:5e8937ec56c5881487bb7c4581d627183e9bc82900c7b621959326059d848e96

Observation 7c2a6ff2-d18d-4d18-a2bc-017cc5ea72a6 · outbound

This paper cites Are NLP Models really able to Solve Simple Math Word Problems?.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Are NLP Models really able to Solve Simple Math Word Problems?

Reference 45

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no resolver link, observed 2026-08-15T20:53:40.890593Z

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source=pdf_text observed=2026-08-15T20:53:40.890593Z digest=sha256:ae7a1ddd777ff24e514b838ef6ed8dd704176df52fd54d0791b67beb777f9b69

Observation 31c1d88e-1433-4490-8ade-6e9bc0fdfe37 · outbound

This paper cites Chain-of- thought prompting elicits reasoning in large language models,.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Chain-of- thought prompting elicits reasoning in large language models,

Reference 46

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no resolver link, observed 2026-08-15T20:53:40.894217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:53:40.894217Z digest=sha256:395d8855293bd6806facff5b23bdc5e3bb6f23d93a465d27e2dc24f707cbd3dd

Observation 77fa7f03-bce1-48bc-9c76-5e7028e0027c · outbound

This paper cites Transformer layers as painters,.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Transformer layers as painters,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:53:41.254615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:53:40.898185Z digest=sha256:6633c10f26a190439e9c2b8683187bb9031783a43c2cab63173acce519a4415e

Observation 3d11f053-48fa-4359-a656-83af5bd25c4f · outbound

This paper cites Insights on representational similarity in neural networks with canonical correlation,.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Insights on representational similarity in neural networks with canonical correlation,

Reference 48

Resolution
malformed identifier
raw_fallback, observed 2026-08-15T20:53:41.243321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:53:40.901530Z digest=sha256:5691b1fdb9be6a6febbf7a586d25e41dcaeefedf57a89d23cffb553d75a66b2c

Observation 63334ae4-867e-4b03-a34d-963a5c971462 · outbound

This paper cites Available: https://ai.meta.com/blog/llama-4-multimodal-intelligence/.

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades Available: https://ai.meta.com/blog/llama-4-multimodal-intelligence/

Reference 2025

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:53:41.612408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:53:40.058477Z digest=sha256:07653c43eaf6527025976d1b3a6a2f59957c5bd33da66ff59889be6121b59a3c

Pith citing papers

No inbound Pith citation observations are available.