Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Faster Language Models with Better Multi-Token Prediction Using Tensor Decomposition
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2410.17765.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T21:38:34.615153Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T21:47:28.176395Z
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 63302489-244a-41a6-ad7d-cbf2bcfede47 · inbound
On multi-token prediction for efficient LLM inference cites this paper.
On multi-token prediction for efficient LLM inference Faster Language Models with Better Multi-Token Prediction Using Tensor Decomposition
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T21:38:34.615153Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b98260e6-5202-40dd-b5ef-d99b3dd77e9f · inbound
Tensorizing Engram: Sharing Latents Across N-Gram Embeddings is Beneficial in LLMs cites this paper.
Tensorizing Engram: Sharing Latents Across N-Gram Embeddings is Beneficial in LLMs Faster Language Models with Better Multi-Token Prediction Using Tensor Decomposition
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T21:47:28.177756Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 55cd9e8d-2216-4b67-a402-2075e554d33d · inbound
Tensor-Train Joint Modeling for Few-Step Discrete Diffusion cites this paper.
Tensor-Train Joint Modeling for Few-Step Discrete Diffusion Faster Language Models with Better Multi-Token Prediction Using Tensor Decomposition
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-07-11T23:56:55.728852Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 644f7d2e-0fea-4ea6-92c5-e67268a081b4 · inbound
Tensor-Train Joint Modeling for Few-Step Discrete Diffusion cites this paper.
Tensor-Train Joint Modeling for Few-Step Discrete Diffusion Faster Language Models with Better Multi-Token Prediction Using Tensor Decomposition
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T08:49:47.245513Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.