Paper Citation Record · LEDGER

Cache & Distil: Optimising API Calls to Large Language Models

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2310.13561.

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

pith.paper-citation-record.v1
2310.13561 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:56:56.962993Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T16:17:08.774095Z

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 169a8a96-4c7c-412b-b493-9b7f2b174c7d · inbound

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

Harnessing Multiple Large Language Models: A Survey on LLM Ensemble Cache & Distil: Optimising API Calls to Large Language Models

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:25:19.664915Z

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.

Observation ddbcf273-44eb-4338-99b8-695ce6e04f59 · inbound

Towards Efficient Multi-LLM Inference: Characterization and Analysis of LLM Routing and Hierarchical Techniques cites this paper.

Towards Efficient Multi-LLM Inference: Characterization and Analysis of LLM Routing and Hierarchical Techniques Cache & Distil: Optimising API Calls to Large Language Models

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T05:56:56.962993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc66c43f-6e5f-4ec2-a714-5f6bf1e1a73d · inbound

Brevity is the soul of sustainability: Characterizing LLM response lengths cites this paper.

Brevity is the soul of sustainability: Characterizing LLM response lengths Cache & Distil: Optimising API Calls to Large Language Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T05:09:10.941521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 365e0782-d255-4921-932b-1544368270a9 · inbound

From Sampled Outcomes to Capability Distributions: Rethinking Supervision for LLM Routing cites this paper.

From Sampled Outcomes to Capability Distributions: Rethinking Supervision for LLM Routing Cache & Distil: Optimising API Calls to Large Language Models

Reference 93

Resolution: verified exact; arxiv_id, observed 2026-07-02T16:17:08.775430Z

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.