Paper Citation Record · LEDGER

MELTing point: Mobile Evaluation of Language Transformers

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2403.12844.

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

pith.paper-citation-record.v1
2403.12844 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:05:48.926708Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T07:59:39.583265Z

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 a438da52-656c-4ca8-a43a-23cab28744c1 · inbound

Generative AI on the Edge: Architecture and Performance Evaluation cites this paper.

Generative AI on the Edge: Architecture and Performance Evaluation MELTing point: Mobile Evaluation of Language Transformers

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T18:19:50.957530Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 065c10a3-5aab-48d4-a43f-97272c48523c · inbound

Edge-First Language Model Inference: Models, Metrics, and Tradeoffs cites this paper.

Edge-First Language Model Inference: Models, Metrics, and Tradeoffs MELTing point: Mobile Evaluation of Language Transformers

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T15:03:08.584216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0e3b06d-2744-44fa-8edf-29b05baf09ab · inbound

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices cites this paper.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices MELTing point: Mobile Evaluation of Language Transformers

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T20:05:48.926708Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18f3af40-13bb-42c4-9eff-b4f9f24831d5 · inbound

Toward Zero-Egress Psychiatric AI: On-Device LLM Deployment for Privacy-Preserving Mental Health Decision Support cites this paper.

Toward Zero-Egress Psychiatric AI: On-Device LLM Deployment for Privacy-Preserving Mental Health Decision Support MELTing point: Mobile Evaluation of Language Transformers

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-10T09:28:39.016789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5c1d5bcd-b44c-4c50-a7f9-0acdb3bc3f94 · inbound

Does Mixture-of-Experts Actually Help Inference on Consumer and Edge Hardware? An Empirical Study cites this paper.

Does Mixture-of-Experts Actually Help Inference on Consumer and Edge Hardware? An Empirical Study MELTing point: Mobile Evaluation of Language Transformers

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-04T07:59:39.584867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bf8a915b-0f3a-4814-ba0a-1b2468a03a1e · inbound

Does Mixture-of-Experts Actually Help Inference on Consumer and Edge Hardware? An Empirical Study cites this paper.

Does Mixture-of-Experts Actually Help Inference on Consumer and Edge Hardware? An Empirical Study MELTing point: Mobile Evaluation of Language Transformers

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-12T13:05:17.273287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.