Pith. sign in

Paper Citation Record · LEDGER

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models

As of 16 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2506.21119.

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

pith.paper-citation-record.v1
2506.21119 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:43:57.676619Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

38 of 38 outbound references displayed

  • verified exact0
  • verified fuzzy14
  • unresolved24
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation abbaafb5-47ca-4e6e-8204-6edc4b75d414 · outbound

This paper cites Language models are unsupervised multitask learners,.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models Language models are unsupervised multitask learners,

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-06T22:43:54.496122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:43:54.496122Z digest=sha256:0bb486d5535e960b10a5b4c523536527cd9b96fd8b0ce8a16769688e020fa4ce

Observation 07a9e4c5-cdec-4ed7-a0b1-9fe7e053b84d · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-06T22:43:54.555315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:43:54.555315Z digest=sha256:4d7ee23ba058bad72fb616965675c27b7e7cbee6ce3f26eab282b3a141e41f4b

Observation 8ad8ccb0-ea1b-4375-b9d7-2afeb9c10006 · outbound

This paper cites Language models are few-shot learners,.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models Language models are few-shot learners,

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:43:59.277860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T22:43:54.631038Z digest=sha256:26fc190f63acab298a1a56fb8707f56d3f6de1e21195a7a9fd487b3f401c367a

Observation d4ac90af-af9f-4e87-aecd-174b63fbf6a4 · outbound

This paper cites What does bert learn about the structure of language?.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models What does bert learn about the structure of language?

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:43:59.171646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T22:43:54.709449Z digest=sha256:d113f70e5010096886213ecc8e63a0c9db69f0ca6a3c52ed7abd96dd50d774fd

Observation 3bb52e1d-1e0c-48df-9426-8c374e29671d · outbound

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

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models Parameter-efficient transfer learning for nlp,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:43:59.080451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T22:43:54.780801Z digest=sha256:5af82240da7f88349837c42082083a0863e6f0d78809e11bb4c7b4a97827e70c

Observation 28330c3b-b41b-4d09-a427-46dc4e59961a · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-06T22:43:54.856658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:43:54.856658Z digest=sha256:389b48f54d3d988ead70524a3185bc746c0cfaf1e34a61316111d27c262a9fbe

Observation 30cedd7f-f3fa-4203-b5d9-d128ae3ce4e7 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-06T22:43:54.942510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:43:54.942510Z digest=sha256:c05033c980a99e46f3f3db35fd9f4bc7b1b3ce274cb7a1d90832d444fb1be935

Observation bbcf38d5-472a-430a-a371-4672daac4cd2 · outbound

This paper cites A fully progressive approach to single-image super-resolution,.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models A fully progressive approach to single-image super-resolution,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:43:58.959448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T22:43:55.023840Z digest=sha256:dd3e2e27b1d70ebb75ab8ac80e554c3beee2aa06284ffa6ffc0a738ad7663eb4

Observation 344cb963-e5ee-4e3e-984e-95b0f026f74e · outbound

This paper cites Cascade ef-gan: Progressive facial expres- sion editing with local focuses,.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models Cascade ef-gan: Progressive facial expres- sion editing with local focuses,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:43:58.893600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T22:43:55.087858Z digest=sha256:5c61186db690ea1a32ce97331c378f1a340006fb2aa3ee3eed23cad1838a5e8f

Observation 4ad88754-a7ae-44f7-a5d3-c5c22a59157d · outbound

This paper cites Progressive Growing of GANs for Improved Quality, Stability, and Variation.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models Progressive Growing of GANs for Improved Quality, Stability, and Variation

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T22:43:55.153780Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:43:55.153780Z digest=sha256:b53d1b4bb927651a6ff3226fc4b28bdfdc3a9ab792741c20b9afa518e316a672

Observation 102cdc13-1f51-4980-b90c-c43aacbf7044 · outbound

This paper cites GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-06T22:43:55.238949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:43:55.238949Z digest=sha256:a6d424549bdba4262d03f045133ffd41eca0f13eeba256c75a8f99b27ce9cf36

Observation 092f8973-323d-4d37-8e73-58e90505cbaa · outbound

This paper cites SQuAD: 100,000+ Questions for Machine Comprehension of Text.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models SQuAD: 100,000+ Questions for Machine Comprehension of Text

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-06T22:43:55.309762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:43:55.309762Z digest=sha256:2d74aa37c863462c912a4eb3ce3193c42c773b778b630e9bd47371be9cd006b7

Observation 68d0b7a0-6bc3-4b68-bc00-a4e26238e82a · outbound

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

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models,

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-06T22:43:55.395121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:43:55.395121Z digest=sha256:efab528c23a5a6b7a80e5962a168c6511f7e9dc88c85142cc0a49e0924765fde

Observation 3ba9485f-680a-4e58-ab44-3f863d6d6278 · outbound

This paper cites Transformers: State-of-the-art natural language processing,.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models Transformers: State-of-the-art natural language processing,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:43:58.803014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T22:43:55.473863Z digest=sha256:9d5f419807277602651e8cd60f1396b3af27cad9e3733bf425be68615649fddf

Observation 540795af-bf38-4a4d-ad66-f2bb2bb438d0 · outbound

This paper cites Parameter-Efficient Transfer Learning with Diff Pruning.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models Parameter-Efficient Transfer Learning with Diff Pruning

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-06T22:43:55.559780Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:43:55.559780Z digest=sha256:cf3781aca0a7fa77ce4548b70a671087d10b075df71fd6e71484ef9b7e373360

Observation a849b09e-cb07-4129-9624-7a31dcf34253 · outbound

This paper cites Semi-supervised sequence learning,.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models Semi-supervised sequence learning,

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:43:58.717111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T22:43:55.630566Z digest=sha256:4c88a7f8213e0c4baee6c65495f1411190713ecfb7eb8a1cd11efce1f29bd1fa

Observation 888ecb68-2034-4121-94ce-223274090a19 · outbound

This paper cites Universal Language Model Fine-tuning for Text Classification.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models Universal Language Model Fine-tuning for Text Classification

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-06T22:43:55.768195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:43:55.768195Z digest=sha256:00c4a0830b1627637f3088c0d814b238863ff3a241bf1327870afeb645a7a03b

Observation d03f7099-3624-40fe-937a-35c325fa38b9 · outbound

This paper cites Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-06T22:43:55.838312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:43:55.838312Z digest=sha256:071afd009d4d732b8f800ce09898947e6802c4d7153acca89bad0685dd311ce4

Observation 87f0ca40-2f52-4ab4-a0a5-6d7f0f36c035 · outbound

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

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-06T22:43:55.933587Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:43:55.933587Z digest=sha256:dfd9f358b55175ce181e67ba17557c640cd0cb2582726fb7bb14237a1c3fbcb6

Observation aeaabf64-8447-4ba7-bfc7-c8eb9975c8a3 · outbound

This paper cites HuggingFace's Transformers: State-of-the-art Natural Language Processing.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models HuggingFace's Transformers: State-of-the-art Natural Language Processing

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-06T22:43:56.009343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:43:56.009343Z digest=sha256:653dd58551ade35c8c9194c3eca03cac919ce784b57c0b2cf19c6b87c805fa44

Observation fb516b74-a77b-4e23-bfec-6586408d4daa · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-06T22:43:56.122231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:43:56.122231Z digest=sha256:979c423d033084e3b2f7f79f573adc854f1b220d4e4bd41125317d4445705020

Observation 657b5f7f-bbe0-4ff8-8fe8-1d5169a95163 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to- text transformer,.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models Exploring the limits of transfer learning with a unified text-to- text transformer,

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:43:58.615961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T22:43:56.231227Z digest=sha256:ff2743d1336530b754d9659753ad6d0b4a89c338f17f3cc28e41fd534bb0c911

Observation a57bced4-5871-47f7-ab5c-06b8c8a85b68 · outbound

This paper cites LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-06T22:43:56.321335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:43:56.321335Z digest=sha256:0cc37d42c791d7431a9968a4639c2b384c52fa841fb76185b339785c03bf947c

Observation 20309761-7744-4cec-a52f-8d48e61ff41e · outbound

This paper cites P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-06T22:43:56.429128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:43:56.429128Z digest=sha256:da4ccdd69cc29de2c8b9bb515fa8c6638e5ec3f19ceda7286705dde619cc11e5

Observation 0845c449-6ef0-47dc-926c-c39b1b25ddcf · outbound

This paper cites Qlora: Efficient fine- tuning of quantized llms,.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models Qlora: Efficient fine- tuning of quantized llms,

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:43:58.523578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T22:43:56.534072Z digest=sha256:a6754576caaeaa553f48b898e6058bf9a6b73ed1da13cb512980db52169a638b

Observation e1e9e9c6-d05f-4fc0-afcc-ce558b722729 · outbound

This paper cites Efficient fine-tuning of bert models on the edge,.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models Efficient fine-tuning of bert models on the edge,

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:43:58.427281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T22:43:56.593075Z digest=sha256:84b63546850b511ac210750e1896acb981286be6bed9c8fc5a2f864eb7b60cfe

Observation ae79f3b4-2bb1-4f47-9bf3-611533647027 · outbound

This paper cites KronA: Parameter Efficient Tuning with Kronecker Adapter.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models KronA: Parameter Efficient Tuning with Kronecker Adapter

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-06T22:43:56.720662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:43:56.720662Z digest=sha256:402d105aa7910167dc27c7831d8e4f451eb79048120620d6d5a4d6e9ec252722

Observation 77be7424-62ea-4223-9e45-eff2d9152b24 · outbound

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

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-06T22:43:56.838471Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:43:56.838471Z digest=sha256:61a0ae9d41ddd186522a8892d3cde0c810387b942a4a63eb29241386c0555796

Observation e91bdede-7018-40cb-b6cb-d4e37e1cbb0d · outbound

This paper cites Gpt under- stands, too,.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models Gpt under- stands, too,

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:43:58.360184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T22:43:56.892261Z digest=sha256:ea79429028c3c129aeed832dee26c18a6d6775f5fff7df0fceeb8288dd2dc008

Observation bdd604d9-f30a-41d6-b851-712dc5ac77a0 · outbound

This paper cites DoRA: Enhancing Parameter-Efficient Fine-Tuning with Dynamic Rank Distribution.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models DoRA: Enhancing Parameter-Efficient Fine-Tuning with Dynamic Rank Distribution

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-06T22:43:56.984753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:43:56.984753Z digest=sha256:c77c680b476a4b46611f8fecb5b2dd76d89c1bba1d7e8afff7e5ab05f9e33e32

Observation 61e1fc3e-2278-4268-84bd-8b3801a2f69a · outbound

This paper cites Pre-trained models for natural language processing: A survey,.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models Pre-trained models for natural language processing: A survey,

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:43:58.247631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T22:43:57.073628Z digest=sha256:9a57ed7235081af78f16ccc7e5139ebd11cb11455407202f81e0b7110f83c6d3

Observation c0c91efa-4d49-4eb9-84c3-dc29f7e3c41a · outbound

This paper cites Sparsegpt: Massive language models can be accurately pruned in one-shot,.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models Sparsegpt: Massive language models can be accurately pruned in one-shot,

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-06T22:43:57.137739Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:43:57.137739Z digest=sha256:ee16227b7e12ec64f91213da07b1fff25edf567bda97bb3f75616723afd2be3f

Observation e97925db-e852-4d2f-8edd-c1ab04d79807 · outbound

This paper cites A Simple and Effective Pruning Approach for Large Language Models.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models A Simple and Effective Pruning Approach for Large Language Models

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-06T22:43:57.236566Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:43:57.236566Z digest=sha256:38538ae2d00beee7c51cabca8b750b9c383d683040d9d1d92f2270dc2a809b33

Observation 484685a3-1924-4953-847d-9ed7f5478ec7 · outbound

This paper cites Gpt3. int8 (): 8-bit matrix multiplication for transformers at scale,.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models Gpt3. int8 (): 8-bit matrix multiplication for transformers at scale,

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:43:58.108206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T22:43:57.317176Z digest=sha256:530cf1973b1208009b5b505e54c45d665904d9989ff43f5c29d9f6eb4b20ecb6

Observation 36bfd8de-f8c2-4a01-ab36-f0d3724f9906 · outbound

This paper cites LLM-FP4: 4-Bit Floating-Point Quantized Transformers.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models LLM-FP4: 4-Bit Floating-Point Quantized Transformers

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-06T22:43:57.403719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:43:57.403719Z digest=sha256:185cc443e64b5841787bdbbaa798cc1ec00edd58bf753a4f5970d2e5d47b6dff

Observation c7f62867-610e-4667-afb2-589ecf686dbb · outbound

This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-06T22:43:57.511902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:43:57.511902Z digest=sha256:4aac4961fb96d084b58d471357aaa0d29bc73723fbc85672088d1684776712c2

Observation 82774ef6-98a6-4c22-984d-658b7ba796ba · outbound

This paper cites Deep residual learning for image recog- nition,.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models Deep residual learning for image recog- nition,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:43:58.024900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T22:43:57.577608Z digest=sha256:1e2107d5ea201e404d23455a6c2cc3a63a8b79c1a7243181ae13067e32dad3a0

Observation 452e4000-a90a-450a-965c-a47a6691813d · outbound

This paper cites Layer Normalization.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models Layer Normalization

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-06T22:43:57.676619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:43:57.676619Z digest=sha256:45602a9b2c81f2f168d43ed71ee77a37768180a6d81079538e221af28d757363

Pith citing papers

No inbound Pith citation observations are available.