Pith. sign in

Paper Citation Record · LEDGER

Scaling Expert Language Models with Unsupervised Domain Discovery

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2303.14177.

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

pith.paper-citation-record.v1
2303.14177 v1

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 2026-08-10T06:31:04.303077+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T17:01:41.068255Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T19:46:11.396256Z

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 f473fae4-055d-4194-bc25-5ab2ba8a8be2 · inbound

MergeME: Model Merging Techniques for Homogeneous and Heterogeneous MoEs cites this paper.

MergeME: Model Merging Techniques for Homogeneous and Heterogeneous MoEs Scaling Expert Language Models with Unsupervised Domain Discovery

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-09T17:01:41.068255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T17:01:41.068255Z digest=sha256:f3ddc7f10e7f83bff7178fb862d494021f4bd48ea442f35c737f7cae82bef769

Observation c461b8aa-20e5-4472-aed0-0a501f763488 · inbound

When One LLM Drools, Multi-LLM Collaboration Rules cites this paper.

When One LLM Drools, Multi-LLM Collaboration Rules Scaling Expert Language Models with Unsupervised Domain Discovery

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-08T22:33:09.292800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T22:33:09.292800Z digest=sha256:a60d9ffadc5449901b0ff365cb3d97893d98f3e64891b3ceb8721c53c10d1256

Observation b700499a-34f2-4a2c-ac60-b4c67ed46b52 · inbound

Local Mixtures of Experts: Essentially Free Test-Time Training via Model Merging cites this paper.

Local Mixtures of Experts: Essentially Free Test-Time Training via Model Merging Scaling Expert Language Models with Unsupervised Domain Discovery

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-07T15:42:19.320715Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:42:19.320715Z digest=sha256:42516401f07051285abb7659925c0afbf044c2563f0ef2c697f86f6f530d26bf

Observation dea4f49c-d8e5-4686-86dd-5cdaa69a665a · inbound

Rethinking Data Mixture for Large Language Models: A Comprehensive Survey and New Perspectives cites this paper.

Rethinking Data Mixture for Large Language Models: A Comprehensive Survey and New Perspectives Scaling Expert Language Models with Unsupervised Domain Discovery

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-07T13:33:50.715368Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:33:50.715368Z digest=sha256:d2710e718b5bb73838ddd332e5ae93e64dd26f17b1673530b185390d73e1359f

Observation 4ae30110-bb3e-4e18-a4d6-5bbae9eb14ae · inbound

FlexOlmo: Open Language Models for Flexible Data Use cites this paper.

FlexOlmo: Open Language Models for Flexible Data Use Scaling Expert Language Models with Unsupervised Domain Discovery

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-06T18:57:16.056141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:57:16.056141Z digest=sha256:e933dee6c0bf5d10181e7165961801054d44a797fc4c88e773de78eb93b7cfe7

Observation b18a3884-6931-4579-99ec-079050cc2964 · inbound

Parameter Alignment Mitigates Catastrophic Forgetting in Multilingual Expert Language Models cites this paper.

Parameter Alignment Mitigates Catastrophic Forgetting in Multilingual Expert Language Models Scaling Expert Language Models with Unsupervised Domain Discovery

Reference 33

Resolution
verified exact
arxiv_id, observed 2026-07-01T19:46:11.397719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-06-28T21:59:33.573611Z digest=sha256:11c2785489ecc898815a5928b6f13379c6286a36b6ae53d4ee6b5c58fd5ed5fe