Pith. sign in

Paper Citation Record · LEDGER

Inference-Friendly Models With MixAttention

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2409.15012.

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

pith.paper-citation-record.v1
2409.15012 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 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T17:50:17.669634Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T17:55:13.970681Z

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 5f20c078-b77e-4a93-88df-8644af026dd5 · inbound

TurboAttention: Efficient Attention Approximation For High Throughputs LLMs cites this paper.

TurboAttention: Efficient Attention Approximation For High Throughputs LLMs Inference-Friendly Models With MixAttention

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-11T17:50:17.669634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T17:50:17.669634Z digest=sha256:e1ec2e48cfcb00cb190aefdcb510b9aa37eb1d653c0947d109c3c6dfba4372dd

Observation 0279807a-455a-42b3-9ac9-00da505464b2 · inbound

TPLA: Tensor Parallel Latent Attention for Efficient Disaggregated Prefill and Decode Inference cites this paper.

TPLA: Tensor Parallel Latent Attention for Efficient Disaggregated Prefill and Decode Inference Inference-Friendly Models With MixAttention

Reference 31

Resolution
verified exact
local_arxiv, observed 2026-08-05T17:55:14.064205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T17:55:09.267444Z digest=sha256:799b0602fc61589ecb71d76fb1fcf0c72499e759633f3879b0b74dbfac214d5f