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Paper Citation Record · LEDGER

Keyword Matters: Unveiling the Energy Sensitivity of On-Device LLM Prompting

As of 8 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2607.22568.

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

pith.paper-citation-record.v1
2607.22568 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T12:37:17.291925Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

12 of 12 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved12
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 48429db2-feac-49a7-a626-ad75f9877fc7 · outbound

This paper cites Efficient Memory Management for Large Language Model Serving with PagedAttention.

Keyword Matters: Unveiling the Energy Sensitivity of On-Device LLM Prompting Efficient Memory Management for Large Language Model Serving with PagedAttention

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-02T12:37:16.449904Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T12:37:16.449904Z digest=sha256:341e942641ea24daf6bc96d7c9c71b4c2d0184afed2b80b5ded6d9d5eaecccd6

Observation a383f0f7-8c5a-46b9-a699-9d4fb217e1bf · outbound

This paper cites InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2511–2522, Singapore.

Keyword Matters: Unveiling the Energy Sensitivity of On-Device LLM Prompting InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2511–2522, Singapore

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-02T12:37:16.580769Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T12:37:16.580769Z digest=sha256:2e8ff48f86d457e80deca401c3a9fe272ec3ea8d052a094be170858f3bbc4b25

Observation ebdd4da8-e202-44f9-a867-ed678b1f55a6 · outbound

This paper cites https: //ai.meta.com/research/publications/the-l lama-3-herd-of-models/.

Keyword Matters: Unveiling the Energy Sensitivity of On-Device LLM Prompting https: //ai.meta.com/research/publications/the-l lama-3-herd-of-models/

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-02T12:37:16.648197Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T12:37:16.648197Z digest=sha256:134602bc84b4434dd6af30649c62d0ae40a6e1a9789e7c05d293bacce00e5f0d

Observation e4096334-4b22-4090-9078-46e289559268 · outbound

This paper cites https://github.com/mlc- ai/mlc- llm.

Keyword Matters: Unveiling the Energy Sensitivity of On-Device LLM Prompting https://github.com/mlc- ai/mlc- llm

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-02T12:37:16.687157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T12:37:16.687157Z digest=sha256:f0ee19bc8ac49e9f866413631f58cdfc0323b1c39b05d3098efbcd88fc649b3e

Observation 956d658c-2e88-4ebf-bd9f-3ee2d8ee99a2 · outbound

This paper cites https://github.com /NVIDIA/TensorRT-LLM.

Keyword Matters: Unveiling the Energy Sensitivity of On-Device LLM Prompting https://github.com /NVIDIA/TensorRT-LLM

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-02T12:37:16.786832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T12:37:16.786832Z digest=sha256:74b2aaa6bba60c7654609e8a0fcfa23403fc9fc532682e4fd6f313952ca8dde9

Observation 4d8993ea-a3fc-4965-a412-14f8b52c6580 · outbound

This paper cites https://github.com/tatsu-lab /stanford_alpaca.

Keyword Matters: Unveiling the Energy Sensitivity of On-Device LLM Prompting https://github.com/tatsu-lab /stanford_alpaca

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-02T12:37:16.911184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T12:37:16.911184Z digest=sha256:6a53588176bcec1d11873db9697ebc08012bc1a8d9e1f9727854b8bc5f213676

Observation 3a5fbe97-0626-4325-88b0-98fbd653379e · outbound

This paper cites https://busine ss.yelp.com/data/resources/open-dataset/.

Keyword Matters: Unveiling the Energy Sensitivity of On-Device LLM Prompting https://busine ss.yelp.com/data/resources/open-dataset/

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-02T12:37:17.163584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T12:37:17.163584Z digest=sha256:68edd0ddabcb4f227fb29b1f8996a9e9a8e0f849dbc840a8ac209439b28f95b2

Observation 663dc144-b379-45bd-80b0-0467f7879f59 · outbound

This paper cites Large Language Models Are Human-Level Prompt Engineers.

Keyword Matters: Unveiling the Energy Sensitivity of On-Device LLM Prompting Large Language Models Are Human-Level Prompt Engineers

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-02T12:37:17.291925Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T12:37:17.291925Z digest=sha256:e982be320eb00f247eec4db719df126a8f533fac718f132a5b1ba6dace3567e8

Observation 178af1ac-c2a3-42f9-96c5-b38c5b027e9a · outbound

This paper cites Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models.

Keyword Matters: Unveiling the Energy Sensitivity of On-Device LLM Prompting Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-02T12:37:16.293093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T12:37:16.293093Z digest=sha256:d97f346f45d16a6903cd2fba43ca822430a14c660bd08c2b122a8d14caf8c4af

Observation 93af7e13-a114-4135-89cb-5923ae512070 · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

Keyword Matters: Unveiling the Energy Sensitivity of On-Device LLM Prompting Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-02T12:37:17.042022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T12:37:17.042022Z digest=sha256:82003ee1dc2e2914d900e5a9d3fed1bd2a9092c53d2bbd99b3306f67b2ab51e2

Observation fc77ecf9-1083-4ac9-aae9-c72572a57d35 · outbound

This paper cites Active Prompting with Chain-of-Thought for Large Language Models.

Keyword Matters: Unveiling the Energy Sensitivity of On-Device LLM Prompting Active Prompting with Chain-of-Thought for Large Language Models

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-02T12:37:16.379427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T12:37:16.379427Z digest=sha256:1300302789078c0a1e36ce87a7b633b207d6fb579190bd483d6babf20e7e164b

Observation c38036c8-6617-4fa2-abb6-0c6715b6b375 · outbound

This paper cites MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases.

Keyword Matters: Unveiling the Energy Sensitivity of On-Device LLM Prompting MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-02T12:37:16.511857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T12:37:16.511857Z digest=sha256:647f512d478c108354edd935534c00bdebe5c88b0396b0527953942ed246fb62

Pith citing papers

No inbound Pith citation observations are available.