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Paper Citation Record · LEDGER

M6-T: Exploring Sparse Expert Models and Beyond

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2105.15082.

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

pith.paper-citation-record.v1
2105.15082 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:40:01.546388Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

14
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 0f3ca32e-7bb1-432a-b69c-915a911baa30 · inbound

DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models cites this paper.

DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models M6-T: Exploring Sparse Expert Models and Beyond

Reference 164

Resolution
verified exact
arxiv_id, observed 2026-05-13T01:07:22.233647Z

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.

source=arxiv_source observed=2026-05-13T01:07:22.166595Z digest=sha256:272895a8834b994b4dab4aebfaaf382f5b96260572d9cc744394874d47439613

Observation 0c8373a6-2710-4d9c-ad0b-523a863bc711 · inbound

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models cites this paper.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models M6-T: Exploring Sparse Expert Models and Beyond

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-16T12:40:01.546388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:40:01.546388Z digest=sha256:de3026557b0f4b40cd68ff79b338d96f7831f454080654946a303707bad82b09

Observation ea7a8c66-e388-4263-897b-46e884175c77 · inbound

TimeExpert: An Expert-Guided Video LLM for Video Temporal Grounding cites this paper.

TimeExpert: An Expert-Guided Video LLM for Video Temporal Grounding M6-T: Exploring Sparse Expert Models and Beyond

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-06T05:30:37.167043Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T05:30:37.167043Z digest=sha256:7e4197c71651e6d2d30f27ac60a57cc167141d8ffb62599383bfcc569f03f471