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Paper Citation Record · LEDGER

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design

As of 7 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2507.16226.

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

pith.paper-citation-record.v1
2507.16226 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:18:33.226462Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

26 of 26 outbound references displayed

  • verified exact1
  • verified fuzzy13
  • unresolved12
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 38fe39e5-87c5-40bf-8a60-c00e38e2c130 · outbound

This paper cites Overview on signing and whitelisting for intel® software guard extensions (intel® sgx) enclaves,.

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design Overview on signing and whitelisting for intel® software guard extensions (intel® sgx) enclaves,

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:18:37.892637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T15:18:29.455944Z digest=sha256:34592db84ccc8f2457ffec79c1cf763cfc4ee243c25ecdbdcae9b2cf9518e4b3

Observation 56bca6af-600b-465c-9789-522389a8acd6 · outbound

This paper cites Privacy-Preserving Inference in Machine Learning Services Using Trusted Execution Environments.

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design Privacy-Preserving Inference in Machine Learning Services Using Trusted Execution Environments

Reference 2

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verified exact
local_arxiv, observed 2026-08-06T15:18:33.989902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T15:18:29.577471Z digest=sha256:7c90c66320bf1bd1b8379817b3005dcd14867b387878a9329d25799ea606f482

Observation 4c90974c-f4e9-4ad9-a18f-f850be9adbf5 · outbound

This paper cites {SOTER}: Guarding black-box inference for general neural networks at the edge,.

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design {SOTER}: Guarding black-box inference for general neural networks at the edge,

Reference 3

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verified fuzzy
raw_fallback, observed 2026-08-06T15:18:37.664099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T15:18:29.646954Z digest=sha256:896f0395ed84a8fa5d4fc4d7d66bd356fa522b0302589db4f7028f901252a514

Observation c0f80270-9edc-467c-9847-829faf49a82c · outbound

This paper cites Shad- ownet: A secure and efficient on-device model inference system for convolutional neural networks,.

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design Shad- ownet: A secure and efficient on-device model inference system for convolutional neural networks,

Reference 4

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verified fuzzy
raw_fallback, observed 2026-08-06T15:18:37.511282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T15:18:29.716753Z digest=sha256:c6ce2afd899e905c1e690503d7e8f4ee9e8004572c97ecafd04511810ed6057d

Observation c4cb6ffe-cee9-4fc7-b794-8d6e356ea60a · outbound

This paper cites Intel® trust domain extensions,.

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design Intel® trust domain extensions,

Reference 5

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verified fuzzy
raw_fallback, observed 2026-08-06T15:18:37.301055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T15:18:29.795507Z digest=sha256:69e234801a3613521c2483cf9aa06acd35f419708c54b68a5e875ec3e94f4d82

Observation 7878f728-7106-4c56-990e-58c55b281ffe · outbound

This paper cites Language Models are Few-Shot Learners.

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design Language Models are Few-Shot Learners

Reference 6

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unresolved
no resolver link, observed 2026-08-06T15:18:29.861202Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:18:29.861202Z digest=sha256:aa4290b18d9d90f9bddfad066478ed604bdf730ecfcecf3e6307d93d07cdc4dd

Observation e41ebf95-f6ec-41d3-8cec-eadbc390f311 · outbound

This paper cites Introducing gemini: Our most capable ai model,.

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design Introducing gemini: Our most capable ai model,

Reference 7

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verified fuzzy
raw_fallback, observed 2026-08-06T15:18:37.121749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T15:18:29.957931Z digest=sha256:2d6f12de0ab97db7be4e5b3697082a0adcf04afb6adeecf081415d7882abe33e

Observation 9f0f936d-5f9d-4590-8b5f-1faf9d33819e · outbound

This paper cites Understanding oversubscribed memory management for deep learning training,.

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design Understanding oversubscribed memory management for deep learning training,

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-06T15:18:30.100236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:18:30.100236Z digest=sha256:2632aabc3da7938e013dcfee5c0e795efb78ed4a7b80ab14df383b9d25c8d8ef

Observation 39edf039-1237-448c-9dfb-2f963898d2ef · outbound

This paper cites Llm4sechw: Leveraging domain-specific large language model for hardware debug- ging,.

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design Llm4sechw: Leveraging domain-specific large language model for hardware debug- ging,

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-06T15:18:30.229333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:18:30.229333Z digest=sha256:39104ccac01cd7e5c7cd01b1833989e5dc9273b3a346a7daab85970d3c059887

Observation 9de40d77-faf0-47a7-9960-427e64357163 · outbound

This paper cites Socurellm: An llm-driven approach for large-scale system-on-chip security verification and policy generation,.

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design Socurellm: An llm-driven approach for large-scale system-on-chip security verification and policy generation,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:18:36.863473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T15:18:30.437055Z digest=sha256:b5f4d5f4c4a6a472a46e938d4dd895a40eebc760d8d37b219ae4c957d0790857

Observation 62bdaa85-f27c-4898-ae50-4be6b6ddbab9 · outbound

This paper cites DeepSeek LLM: Scaling Open-Source Language Models with Longtermism.

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-06T15:18:30.606838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:18:30.606838Z digest=sha256:702ad516572f81923ce68ad6dab2ff16fb0dce183c8cf7523aec00880e4f3f8d

Observation a7f2e570-9cd1-4c70-991f-6cb7a909252f · outbound

This paper cites Evaluating the Performance of the DeepSeek Model in Confidential Computing Environment.

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design Evaluating the Performance of the DeepSeek Model in Confidential Computing Environment

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-06T15:18:30.753891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:18:30.753891Z digest=sha256:311f4778366d3cf0eca832c2944afc9a8ba1a02a0cda347b742bc295ada1688c

Observation 8b78001d-34f2-4163-81af-f98d8d449ef7 · outbound

This paper cites Amd memory encryption: Sev, sme, and sev-es,.

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design Amd memory encryption: Sev, sme, and sev-es,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:18:36.559807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T15:18:30.959440Z digest=sha256:860c8bc6aa06bc1f9e3b8af48d32e60dfaca570ec72f684a5a3654895a6edb4c

Observation 36715200-f50d-42db-bdc8-73ae07b54d0b · outbound

This paper cites Building a secure system using trustzone technology,.

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design Building a secure system using trustzone technology,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:18:36.116743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T15:18:31.114548Z digest=sha256:fcf7e8b1be9bb70fcb380cf1655fd1c7f8a29ba36588f6fedfa30338ff1454ba

Observation bc893fd0-7d4f-4a99-b11d-bf318f8694dc · outbound

This paper cites Drgpum: Guiding memory optimization for gpu-accelerated applications,.

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design Drgpum: Guiding memory optimization for gpu-accelerated applications,

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-06T15:18:31.465912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:18:31.465912Z digest=sha256:0fe1180321ee0c38cdc22fa1673a8a7dd474d46530be96cbd9d5a10882bfae8b

Observation 721b3e9e-48cc-492d-b227-31bb7cbf5f9b · outbound

This paper cites Exploring parallel implemen- tation of sphincs+ using advanced vector extensions (avx) sets,.

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design Exploring parallel implemen- tation of sphincs+ using advanced vector extensions (avx) sets,

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:18:35.406566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T15:18:31.642475Z digest=sha256:c5120d68bdeb45d1d743ae68030a8464c55c34df666243d9b862b7e44517724b

Observation a655a722-cc93-4882-a2ba-e97cabebe391 · outbound

This paper cites Forest: Access-aware gpu uvm management,.

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design Forest: Access-aware gpu uvm management,

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-06T15:18:31.789878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:18:31.789878Z digest=sha256:f741918834f76d21ee5e8b783cb52272c0d4e55d513a7376074323c1612241c2

Observation e4c26fad-3f22-42ff-bae9-868526e280bf · outbound

This paper cites Marvel: Multi-agent rtl vulnerability extraction using large language models,.

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design Marvel: Multi-agent rtl vulnerability extraction using large language models,

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-06T15:18:31.951883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:18:31.951883Z digest=sha256:5d9745587d9904cc49503ef2b6a337bd0729a55c802e31f51d766d1959e3c7cb

Observation ae678ca3-2e87-471e-9d3f-5e95a97c04b1 · outbound

This paper cites Spiced: Syntactical bug and trojan pattern identification in a/ms circuits using llm-enhanced detection,.

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design Spiced: Syntactical bug and trojan pattern identification in a/ms circuits using llm-enhanced detection,

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:18:35.106587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T15:18:32.123975Z digest=sha256:15bfd8cefcf437dab44604ac6e4ba76701e48ad200d9ccb2bd36e0db13c8649f

Observation 22748f23-c89c-4c16-93ae-7d383587576e · outbound

This paper cites ThreatLens: LLM-guided Threat Modeling and Test Plan Generation for Hardware Security Verification.

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design ThreatLens: LLM-guided Threat Modeling and Test Plan Generation for Hardware Security Verification

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-06T15:18:32.308082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:18:32.308082Z digest=sha256:0c8ba8b069d2b0e93e6f7bca6ed6a944b5b5f12469a45b94298893494a82ab50

Observation 9cb330cc-8432-48f2-84b3-27be1ed27123 · outbound

This paper cites Is ChatGPT a General-Purpose Natural Language Processing Task Solver?.

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design Is ChatGPT a General-Purpose Natural Language Processing Task Solver?

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-06T15:18:32.454770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:18:32.454770Z digest=sha256:ecae55756666b887ef0855bda73b14723911121556a26aff3aea957c6d512132

Observation 19111194-d89d-4754-ad24-cd56f3781925 · outbound

This paper cites Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT.

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT

Reference 22

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unresolved
no resolver link, observed 2026-08-06T15:18:32.670079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:18:32.670079Z digest=sha256:d77abdc67cb9dc4009f83585b0469b4b998b4522d7a79390fdda1fd047c1b1e9

Observation 1b5ba101-d8cc-43c2-a9ff-0d02cc4c72df · outbound

This paper cites A Survey of Large Language Models.

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design A Survey of Large Language Models

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-06T15:18:32.860151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:18:32.860151Z digest=sha256:6e4d43a013a3e43b8367e013e9361eecaf016e0c69d8c8e03c58804a9e2fbcb1

Observation dff96290-8dfa-493e-8663-7e667da419c8 · outbound

This paper cites A generalize hardware debugging approach for large language models semi-synthetic, datasets,.

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design A generalize hardware debugging approach for large language models semi-synthetic, datasets,

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:18:34.715000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T15:18:33.007298Z digest=sha256:6b6397efbb67bad9a7844b166d5b471174fef4eacbd0fe85e63f41b3d23e2911

Observation 5b160add-63d5-4da3-a973-5a7436074378 · outbound

This paper cites The case for 4-bit precision: k- bit inference scaling laws,.

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design The case for 4-bit precision: k- bit inference scaling laws,

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:18:34.368103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T15:18:33.226462Z digest=sha256:699cccdda8c64c8255001a3061a6751cfec91818141aa04d30b7588ef917b65d

Observation e03e6319-a7ed-4031-80e3-354adece1a1a · outbound

This paper cites Available: https://documentation-service.arm.com/static/ 5f212796500e883ab8e74531.

Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design Available: https://documentation-service.arm.com/static/ 5f212796500e883ab8e74531

Reference 2020

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:18:35.734803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T15:18:31.301136Z digest=sha256:4d50c181d21eb9f48bd394d07f09a5b8ecd93addbbb07cba81e0c08f673f1594

Pith citing papers

No inbound Pith citation observations are available.