Paper Citation Record · LEDGER

Purifying Large Language Models by Ensembling a Small Language Model

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2402.14845.

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

pith.paper-citation-record.v1
2402.14845 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:28:39.908954Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T08:15:32.159590Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 01cd7306-ed8f-49d8-8e2c-0319ce5158a1 · inbound

Copyright-Protected Language Generation via Adaptive Model Fusion cites this paper.

Copyright-Protected Language Generation via Adaptive Model Fusion Purifying Large Language Models by Ensembling a Small Language Model

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-11T19:34:17.685371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f936d18d-dbe3-4c4b-91cd-a835713d27dd · inbound

Ensembling Large Language Models with Process Reward-Guided Tree Search for Better Complex Reasoning cites this paper.

Ensembling Large Language Models with Process Reward-Guided Tree Search for Better Complex Reasoning Purifying Large Language Models by Ensembling a Small Language Model

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T11:08:56.420484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e412d72e-990f-4846-b9d3-f9aedcdc5b8e · inbound

Fast Large Language Model Collaborative Decoding via Speculation cites this paper.

Fast Large Language Model Collaborative Decoding via Speculation Purifying Large Language Models by Ensembling a Small Language Model

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T19:34:42.813056Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41afe9f5-1b51-4670-a20c-46740993cac8 · inbound

Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety cites this paper.

Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety Purifying Large Language Models by Ensembling a Small Language Model

Reference 178

Resolution: verified exact; arxiv_id, observed 2026-05-23T04:42:34.273583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0afaa0b6-8393-4dc2-ac6f-a68e749f1f9b · inbound

Harnessing Multiple Large Language Models: A Survey on LLM Ensemble cites this paper.

Harnessing Multiple Large Language Models: A Survey on LLM Ensemble Purifying Large Language Models by Ensembling a Small Language Model

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:25:19.472917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 36e6e7af-cb4d-45a9-af8f-ebb002a1212f · inbound

Embodied-R: Collaborative Framework for Activating Embodied Spatial Reasoning in Foundation Models via Reinforcement Learning cites this paper.

Embodied-R: Collaborative Framework for Activating Embodied Spatial Reasoning in Foundation Models via Reinforcement Learning Purifying Large Language Models by Ensembling a Small Language Model

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-16T12:28:39.908954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99eee69b-f3c3-4e1f-bc88-3456e743810b · inbound

Exploring the Role of Large Language Models in Cybersecurity: A Systematic Survey cites this paper.

Exploring the Role of Large Language Models in Cybersecurity: A Systematic Survey Purifying Large Language Models by Ensembling a Small Language Model

Reference 116

Resolution: unresolved; no resolver link, observed 2026-08-16T11:23:51.556416Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8272eb5-e824-4597-9bae-6824a73f320a · inbound

Position: Enough of Scaling LLMs! Lets Focus on Downscaling cites this paper.

Position: Enough of Scaling LLMs! Lets Focus on Downscaling Purifying Large Language Models by Ensembling a Small Language Model

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T04:39:14.067054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36b86ce5-fb26-402b-bf48-1cf0d81e97ac · inbound

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges cites this paper.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Purifying Large Language Models by Ensembling a Small Language Model

Reference 177

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:48.510089Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71476e70-499a-4fa5-87a2-841cdf8965a7 · inbound

Investigating Training Data Detection in AI Coders cites this paper.

Investigating Training Data Detection in AI Coders Purifying Large Language Models by Ensembling a Small Language Model

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T14:51:53.784719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e6a646f-f871-41ac-b2c3-340961643164 · inbound

Rethinking LLM Ensembling from the Perspective of Mixture Models cites this paper.

Rethinking LLM Ensembling from the Perspective of Mixture Models Purifying Large Language Models by Ensembling a Small Language Model

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:26:06.418115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1fc063a1-37b4-4685-9163-9ac18c936977 · inbound

Rethinking LLM Ensembling from the Perspective of Mixture Models cites this paper.

Rethinking LLM Ensembling from the Perspective of Mixture Models Purifying Large Language Models by Ensembling a Small Language Model

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-01T08:15:32.163277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c80601c5-0b89-4d94-b080-20dce5474eec · inbound

Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration cites this paper.

Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Purifying Large Language Models by Ensembling a Small Language Model

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-03T13:59:16.459134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.