Pith. sign in

Paper Citation Record · LEDGER

Language Modeling Using Tensor Trains

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2405.04590.

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

pith.paper-citation-record.v1
2405.04590 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:38:17.050922Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T05:32:14.323680Z

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 fb0ceaae-c594-4c00-bfbf-8c20c819f28a · inbound

No-Free-Lunch Theories for Tensor-Network Machine Learning Models cites this paper.

No-Free-Lunch Theories for Tensor-Network Machine Learning Models Language Modeling Using Tensor Trains

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-11T20:38:17.050922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:38:17.050922Z digest=sha256:8e7fa56f7e948ff111e6b340661690e921204683c55a49ca41c9b80d0b158f8f

Observation 2c762558-3ba4-41b1-b00c-18b4db91416a · inbound

Advantages of density in tensor network geometries for gradient based training cites this paper.

Advantages of density in tensor network geometries for gradient based training Language Modeling Using Tensor Trains

Reference 26

Resolution
verified exact
local_arxiv, observed 2026-08-11T05:32:14.327599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T05:32:13.912425Z digest=sha256:03c260ba46b69d9a5c706b71145ece97097349211c914880d97e3a99b0f65257