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Paper Citation Record · LEDGER

Tensor Train Low-rank Approximation (TT-LoRA): Democratizing AI with Accelerated LLMs

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2408.01008.

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

pith.paper-citation-record.v1
2408.01008 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 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:23:59.916996Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T01:46:26.904000Z

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 8819e99e-d458-49e4-a45b-e06a76dc0eab · inbound

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models cites this paper.

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models Tensor Train Low-rank Approximation (TT-LoRA): Democratizing AI with Accelerated LLMs

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-10T20:23:59.916996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:23:59.916996Z digest=sha256:3e34a69e2a1fc6fc3715e2be9d944bf596a8054fd36d6b8e0c09f803c0782cfc

Observation e7127106-5e7b-4562-a8e3-dbbcb822a4f9 · inbound

Rethinking the Role of Tensor Decompositions in Post-Training LLM Compression cites this paper.

Rethinking the Role of Tensor Decompositions in Post-Training LLM Compression Tensor Train Low-rank Approximation (TT-LoRA): Democratizing AI with Accelerated LLMs

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-07-02T01:46:26.906217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-06-28T11:31:25.851340Z digest=sha256:d6b29035d981f45cab098168ae21cd49b99dc8cde27094111486927077681d79