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Paper Citation Record · LEDGER

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades

As of 21 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 2026-08-21T06:32:19.484+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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+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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No event found in the named queried sources as of 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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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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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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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=pdf_text observed=2026-08-15T20:53:40.062683Z digest=sha256:420e422cd393285077d4dfb0ef976fb105932131f6394804d1f036c5a1e87a28

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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source=pdf_text observed=2026-08-15T20:53:40.066937Z digest=sha256:40aa649487a61c1d7b1b00612bbd0497936462a3ffc35ea099f8c53620478b5b

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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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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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=pdf_text observed=2026-08-15T20:53:40.127210Z digest=sha256:7d02148b5aa979ac24b2c77a26b82dd3993037f57b2847f917c78f02d71eb6a6

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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+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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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=pdf_text observed=2026-08-15T20:53:40.300591Z digest=sha256:6250a8acc4c90f7735cf90ca8d2dd347aaa8a947ba0bcf441c71cc0f9acf3a84

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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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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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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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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+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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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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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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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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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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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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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 2026-08-21T06:32:19.484+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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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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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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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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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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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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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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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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 2026-08-21T06:32:19.484+00:00.

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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 2026-08-21T06:32:19.484+00:00.

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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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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 2026-08-21T06:32:19.484+00:00.

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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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Unavailable: canonical work link unavailable.

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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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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 2026-08-21T06:32:19.484+00:00.

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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:d950d1c149a19ad98814d5d07c54efdbbd47abb2a21af56f909db21dbaf7840d

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

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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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:53:40.742811Z digest=sha256:ed323d56599562df0b128a3b2f31d8f3ad5c2fe060a81d23a1d219cb947d5f7b

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
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:53:40.746002Z digest=sha256:39ebe2bd56810960a0f044c5f46b54b889bbac4cd91ae919be9237a5dc260b92

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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source=pdf_text observed=2026-08-15T20:53:40.749613Z digest=sha256:987f99170ea229f8d84a92536b91bf79a6e6b70220226599c6042f4eb35321a8

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

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source=pdf_text observed=2026-08-15T20:53:40.752501Z digest=sha256:f05b05df7c85b1ddb06ff67955edb767752be00674cc676ead2ac94d65e54f8c

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=sha256:158a29943c3cb74788bfb0213b71740b42c436cb74e9778d755c217418129f92

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

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

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source=pdf_text observed=2026-08-15T20:53:40.760507Z digest=sha256:27a74ac5ec53e593a863692621d7ce0833675ab0dcdd830b3ee11f65e087fb6e

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=sha256:52606e47e4223e3c15789411e21c9e83b152acb927c7b446728bb0661b6ed704

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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source=pdf_text observed=2026-08-15T20:53:40.767352Z digest=sha256:cac45247473747783defbd21663ed9cddb6d6dbca459a005eab22272dbbf820f

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

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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=sha256:52eaef884deb03aedc39077cb3c4caf616793ac91997530a1d482a2166d5ee44

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:53:40.886946Z digest=sha256:681dfbb8fed5fd1aa3632137d3405dea36732bfc6310880fdb784416dffa578f

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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unresolved
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=sha256:5025dbdec520df17b36aa2e8cfae99c8006fb8c6399c3119683300718b0f81d8

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

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source=pdf_text observed=2026-08-15T20:53:40.894217Z digest=sha256:00c2fa6d5f82d4f563ec84ad16bf08ba3de94e6b394d37fc0fffac7c81b52779

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

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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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:53:40.898185Z digest=sha256:bb330df9a5801f50ec1d58471c99dfeb0e5ba2504781bb77e51bede37753f643

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

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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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:53:40.901530Z digest=sha256:6a87d95359e903b9ce649ad6ba4c9d3f2f3db7b7a3ed1cdba9b8ef55fbd3ee1c

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

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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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T20:53:40.058477Z digest=sha256:d72a5d7cdcb61a087010c3de5cee7e5584e5e851df7bbe278908b31006a96aad

Pith citing papers

No inbound Pith citation observations are available.