Paper Citation Record · LEDGER

FLM-101B: An Open LLM and How to Train It with $100K Budget

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2309.03852.

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

pith.paper-citation-record.v1
2309.03852 v3

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 2026-08-11T06:34:44.6726+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T13:09:46.206921Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T20:40:36.493769Z

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 86954125-3ecc-4c15-a639-bebb87bb40ac · inbound

A Survey of Large Language Models cites this paper.

A Survey of Large Language Models FLM-101B: An Open LLM and How to Train It with $100K Budget

Reference 104

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:46:39.931708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a2fa818a-4d7f-4cf1-bb8c-3a0c9b2e0819 · inbound

Deploying Foundation Model Powered Agent Services: A Survey cites this paper.

Deploying Foundation Model Powered Agent Services: A Survey FLM-101B: An Open LLM and How to Train It with $100K Budget

Reference 152

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:46.206921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3c8182b-3ff7-4c20-929b-79fa92b1f6ad · inbound

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models cites this paper.

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models FLM-101B: An Open LLM and How to Train It with $100K Budget

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-10T11:47:24.857799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 551eae61-8d32-4e6f-a272-2dc974a09f53 · inbound

Beyond Sunk Costs: Boosting LLM Pre-training Efficiency via Orthogonal Growth of Mixture-of-Experts cites this paper.

Beyond Sunk Costs: Boosting LLM Pre-training Efficiency via Orthogonal Growth of Mixture-of-Experts FLM-101B: An Open LLM and How to Train It with $100K Budget

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-21T20:40:36.496377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.