Pith. sign in

Paper Citation Record · LEDGER

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs

As of 13 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:2411.08244.

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

pith.paper-citation-record.v1
2411.08244 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:51:53.829961Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:58:19.975935Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-12T15:58:20.284574Z

Reference resolution

36 of 36 outbound references displayed

  • verified exact3
  • verified fuzzy20
  • unresolved13
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 45f0908e-240a-4abe-b799-5a12d0751163 · outbound

This paper cites FL-NAS: Towards Fairness of NAS for Resource Constrained Devices via Large Language Models.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs FL-NAS: Towards Fairness of NAS for Resource Constrained Devices via Large Language Models

Reference 1

Resolution
verified exact
local_arxiv, observed 2026-08-12T21:51:54.047121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T21:51:53.664480Z digest=sha256:e89c3ff1e8c397c7abd732625445481afa8f5ed1790f269457ad35c66f6778d5

Observation bdb4ab77-afb4-414c-a2bc-cc04d9c01920 · outbound

This paper cites Language models for online depression detection: A review and benchmark analysis on remote interviews.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs Language models for online depression detection: A review and benchmark analysis on remote interviews

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-12T21:51:53.669949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:51:53.669949Z digest=sha256:227284e5ace975d8a74bcf6e5b5a02af6d665b0206fc696d8005d7e972c97c90

Observation 79824e8b-255d-483d-9468-2f285916c029 · outbound

This paper cites When Automated Assessment Meets Automated Content Generation: Examining Text Quality in the Era of GPTs.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs When Automated Assessment Meets Automated Content Generation: Examining Text Quality in the Era of GPTs

Reference 3

Resolution
verified exact
local_arxiv, observed 2026-08-12T21:51:54.026968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T21:51:53.675006Z digest=sha256:34b82dbfca64dede07fdee10c5d545ab4433752ee9de2f9e5654bacfd89ab376

Observation f1d4e9d8-ea92-494f-b6ae-d3985408f647 · outbound

This paper cites Privacy issues in large language models: A survey, 2023.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs Privacy issues in large language models: A survey, 2023

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:51:54.450481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T21:51:53.680667Z digest=sha256:1e82f19836ce22d6a8e996eb3e413c67949b2f8a2cc8663c591f0c8ee0abe657

Observation 9413a066-1052-4ba5-b613-abfa6bc11e74 · outbound

This paper cites Embracing large language models for medical appli- cations: Opportunities and challenges.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs Embracing large language models for medical appli- cations: Opportunities and challenges

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:51:54.436093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T21:51:53.686231Z digest=sha256:9208b6a94bc0eb501b0a5501de0e73562ffb65a53f65ba3b202e7fe19c6a7982

Observation 46ce3f11-f0b8-41a7-8b8c-096255d5d624 · outbound

This paper cites Can large language models be good companions? an llm-based eyewear system with conversational common ground, 2023.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs Can large language models be good companions? an llm-based eyewear system with conversational common ground, 2023

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:51:54.421707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T21:51:53.691194Z digest=sha256:b0932f66e85a61c7d507bb2411ca3e5f7911d2c64d9452d7419b3663217556f5

Observation 624b52b1-db71-4acc-bd5b-9ac8d7afb6bf · outbound

This paper cites Personal llm agents: Insights and survey about the capability, efficiency and security, 2024.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs Personal llm agents: Insights and survey about the capability, efficiency and security, 2024

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:51:54.406466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T21:51:53.696343Z digest=sha256:93aeed744e946c7978a1a31ccc736d4955b566d9ffacfdf181b946ef19e798fe

Observation f82687c7-7926-4e79-8a61-38938e2fa67c · outbound

This paper cites Robust Implementation of Retrieval-Augmented Generation on Edge-based Computing-in-Memory Architectures.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs Robust Implementation of Retrieval-Augmented Generation on Edge-based Computing-in-Memory Architectures

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-12T21:51:53.700620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:51:53.700620Z digest=sha256:a5831e8d048b830c7f81c52956675c2705dc87375621dfec8fe5e7d2f01f3e1a

Observation 81f0bc23-a841-4d76-8f49-db8feff54f6c · outbound

This paper cites Enabling On-Device Large Language Model Personalization with Self-Supervised Data Selection and Synthesis.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs Enabling On-Device Large Language Model Personalization with Self-Supervised Data Selection and Synthesis

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-12T21:51:53.705212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:51:53.705212Z digest=sha256:405fd812e35b47f841dc18a70a5c7d9fd4601602f17e3296164dd2c4d6a405f7

Observation f1df41f8-c269-4cab-a1f0-f3523f94bf02 · outbound

This paper cites Empirical Guidelines for Deploying LLMs onto Resource-constrained Edge Devices.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs Empirical Guidelines for Deploying LLMs onto Resource-constrained Edge Devices

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-12T21:51:53.709981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:51:53.709981Z digest=sha256:e28ff8a36ac98d6574b5f5af1bf061dc2fa1541cb1cf5b2b33e3ee9dd87f3b19

Observation edc43395-1479-4264-b2c3-1e52648cfc39 · outbound

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

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-12T21:51:53.714992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:51:53.714992Z digest=sha256:45fae24d440112ea981e2aa68efe34da4ee27cb2a2f4408b8c019f3687ec67a6

Observation bdc504eb-cd04-4e2f-9770-a446da4bf93a · outbound

This paper cites DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-12T21:51:53.720335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:51:53.720335Z digest=sha256:1faeb004b7178d767b180959436c3878ff247607cf8edfb0de12e33c5f2340e4

Observation 4caab6b3-81eb-40e3-b29f-94ff42a4a3a6 · outbound

This paper cites PI-Whisper: Designing an Adaptive and Incremental Automatic Speech Recognition System for Edge Devices.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs PI-Whisper: Designing an Adaptive and Incremental Automatic Speech Recognition System for Edge Devices

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-12T21:51:53.725495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:51:53.725495Z digest=sha256:9650d2c95acedc818ae1f48d3a724bf68a3f5837d61ab4673b002c420e6ecf9e

Observation 865e366c-7131-451c-a569-57c894b6fed6 · outbound

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

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-12T21:51:53.729940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:51:53.729940Z digest=sha256:26d6547664a6afc60b1cfe1231f6b919bee4289acc49b98d3ebbaf229fba15f7

Observation 330dfcd8-7bee-4f6f-a7f0-a9cd139e6242 · outbound

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

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-12T21:51:53.734667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:51:53.734667Z digest=sha256:28e6e5e2867491abda06f5f81c08f8f049c72925865cca177b3f0a7d9bd7afc1

Observation 79fc790a-ea39-40c6-936c-a6d35ed4668b · outbound

This paper cites Ferroelectric compute-in-memory annealer for combinatorial optimization problems.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs Ferroelectric compute-in-memory annealer for combinatorial optimization problems

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:51:54.391868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T21:51:53.740001Z digest=sha256:dceb72bf9589a75710410d59e62d5cfc03ee41c09f88fa0a4fb7c44149e64bc8

Observation 998c2eb4-16b7-4932-8267-78ea5ff356f8 · outbound

This paper cites A crossbar array of magnetoresistive memory devices for in-memory computing.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs A crossbar array of magnetoresistive memory devices for in-memory computing

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-12T21:51:53.745011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:51:53.745011Z digest=sha256:b9a52ea1fdf2ca29dbb693506d2e2d992f390309dbf73f94642bcbfc829b3a23

Observation 47704d3a-3ff6-4a67-a8ba-c306ab0d6680 · outbound

This paper cites A compute-in-memory chip based on resistive random-access memory.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs A compute-in-memory chip based on resistive random-access memory

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:51:54.369153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T21:51:53.750050Z digest=sha256:ec227db3d804661d47fb6baa94fee9cf90789aaba9fdb2d195295d00e8d41652

Observation 62ab764a-6de7-40dd-8bdc-28163e49aa5d · outbound

This paper cites The future of ferroelectric field-effect transistor technology.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs The future of ferroelectric field-effect transistor technology

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:51:54.355425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T21:51:53.754052Z digest=sha256:c528279c8560855e735cecea44f5ceedcccb72db3db14ab69e68337213c46ccc

Observation c0755687-e17b-4655-b4fe-7440a95940af · outbound

This paper cites Swim: Selective write-verify for computing-in-memory neural accelerators.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs Swim: Selective write-verify for computing-in-memory neural accelerators

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:51:54.341891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T21:51:53.758326Z digest=sha256:e8db77c0288fef86980cac66d8f291a568a3f8b616273b625c5d8d86870cb4ee

Observation 537e0bb0-6403-443a-96f6-4ac4e6b7f6c5 · outbound

This paper cites Uncertainty modeling of emerging device based computing- in-memory neural accelerators with application to neural architecture search.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs Uncertainty modeling of emerging device based computing- in-memory neural accelerators with application to neural architecture search

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:51:54.327909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T21:51:53.762567Z digest=sha256:d02fd3e8645503843f492611301a6228bf8ddd97daff3b05d0138bd33759ed25

Observation ea9a9508-5b5b-4f05-ac57-36ace3f647db · outbound

This paper cites Signal and noise extraction from analog memory elements for neuromorphic computing.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs Signal and noise extraction from analog memory elements for neuromorphic computing

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:51:54.313692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T21:51:53.766646Z digest=sha256:56bead9ead9ed424424d495763f9fdb105aa0d36c6002b2b373e56e0b80df416

Observation 9228a07c-9f46-4e6d-929c-d63576a5d87e · outbound

This paper cites On the reliability of computing-in-memory accelerators for deep neural networks.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs On the reliability of computing-in-memory accelerators for deep neural networks

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:51:54.299020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T21:51:53.770992Z digest=sha256:63fa39206a5f4bb73c3517b3da1780647954009bb7096d0f50b1310f6d64dea7

Observation ff688744-d7d9-46b7-bcca-ceaca98247cf · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-12T21:51:53.775252Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:51:53.775252Z digest=sha256:057996b5f7213f9a1a73a31de51c00ae96820971e31fca8305fb36a10c2b46f0

Observation 24f2346b-bcf9-401b-ae7d-c9d5d5c9b868 · outbound

This paper cites Text similarity estimation based on word embeddings and matrix norms for targeted marketing.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs Text similarity estimation based on word embeddings and matrix norms for targeted marketing

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:51:54.283432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T21:51:53.779813Z digest=sha256:f742d05c8a8572829e2fe0e9c19f6030c62115e2c8a685188e0aaf2c6a9f9ed6

Observation e5e1d08f-9f3b-422c-b8eb-66d528bc71f7 · outbound

This paper cites From word embeddings to pre-trained language models: A state-of-the-art walkthrough.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs From word embeddings to pre-trained language models: A state-of-the-art walkthrough

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:51:54.269582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T21:51:53.784019Z digest=sha256:9fde7bd96ed0da24f010ba423dec858380e8497bd53200baf72dbdf60a83b782

Observation e924376e-ef0f-4d66-a5d8-e1695804f9d0 · outbound

This paper cites Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-12T21:51:53.788312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:51:53.788312Z digest=sha256:30b70f6b245a5242f759875b1fe62dea049d8181fb611e6753b475112f6a574e

Observation 1b10c531-d4c1-4ebe-93e4-ee9eea6e8027 · outbound

This paper cites Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms

Reference 28

Resolution
verified exact
local_arxiv, observed 2026-08-12T21:51:53.871048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T21:51:53.792834Z digest=sha256:d8cdd8c5a4ca6dca51b4a008cbf4767e2767509d4448d1d3fc1967fb775c0d80

Observation c6869326-cd5a-47d7-8aae-69574d6e8b3c · outbound

This paper cites Fully hardware-implemented memristor convolutional neural network.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs Fully hardware-implemented memristor convolutional neural network

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:51:54.255231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T21:51:53.797616Z digest=sha256:a083be5383cfbe074603c538e4ebacd76f14ce2269ae667db5409df685dc4f7e

Observation 5bc9bd68-1441-4517-9c64-eea67e275857 · outbound

This paper cites Architecture-circuit-technology co-optimization for resistive random access memory-based computation-in-memory chips.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs Architecture-circuit-technology co-optimization for resistive random access memory-based computation-in-memory chips

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:51:54.240991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T21:51:53.801851Z digest=sha256:38b70b26bbd424ac42027c83ac52ab7c3f950be471f1fb9fe4f53a4c11c34c54

Observation 62b3d589-a134-4fc7-a278-2fd12f57e921 · outbound

This paper cites Switching pathway-dependent strain-effects on the ferroelec- tric properties and structural deformations in orthorhombic hfo2.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs Switching pathway-dependent strain-effects on the ferroelec- tric properties and structural deformations in orthorhombic hfo2

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:51:54.127987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T21:51:53.806841Z digest=sha256:5daa552580dab40911a5a297bb283dc88b3fdee96cb3afa17aaa0a9b9779932b

Observation d7062846-becd-4a00-925b-3668a2fdfc7a · outbound

This paper cites Rouge: A package for automatic evaluation of sum- maries.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs Rouge: A package for automatic evaluation of sum- maries

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-12T21:51:53.811969Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:51:53.811969Z digest=sha256:70425ec8f5ac282e9423b7cfa30ee111d578d6ad8369e41fbd4a16f801da093d

Observation ad30537c-956f-43d6-b748-a0eac66a377d · outbound

This paper cites Cxdnn: Hardware-software compensation methods for deep neural networks on resistive crossbar systems.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs Cxdnn: Hardware-software compensation methods for deep neural networks on resistive crossbar systems

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:51:54.104676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T21:51:53.816052Z digest=sha256:3788628306a29c4fcc114e0483e389479c4f44264946f76678a4b19186ee9a55

Observation 55e863a2-3b7c-47b6-9275-49c750c274b4 · outbound

This paper cites Correctnet: Robustness enhancement of analog in- memory computing for neural networks by error suppression and com- pensation.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs Correctnet: Robustness enhancement of analog in- memory computing for neural networks by error suppression and com- pensation

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:51:54.089545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T21:51:53.820297Z digest=sha256:2c18f3439060047860babea5eac7b104e663599073b4f045c5f48c3d6e5feec2

Observation ba0afc39-d0d5-4180-b85a-24d5ed505513 · outbound

This paper cites Learning binary codes for maximum inner product search.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs Learning binary codes for maximum inner product search

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:51:54.075567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T21:51:53.824583Z digest=sha256:31179a1b2eba4aa1718b6a939c46e6dbdd503c220a6cd3a431462cffdae13d6a

Observation 32d5d28c-e1da-47fb-a75a-a9eb462c5401 · outbound

This paper cites Dnn+ neurosim v2.

NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs Dnn+ neurosim v2

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:51:54.061992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T21:51:53.829961Z digest=sha256:76e80b5fe9af286c18acf3e04a59bab854daa50a540a511d8a1882e27eb957d3

Pith citing papers

Observation d352fe30-9af2-4a9f-ad32-654d02ee4c2c · inbound

Tiny-Align: Bridging Automatic Speech Recognition and Large Language Model on the Edge cites this paper.

Tiny-Align: Bridging Automatic Speech Recognition and Large Language Model on the Edge NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs

Reference 25

Resolution
verified exact
local_arxiv, observed 2026-08-12T15:58:20.291466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T15:58:19.975935Z digest=sha256:caed1309e0f84de7aeba9723eea724db55a5aeeb53ddc4624620c7a7fa923c58