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Paper Citation Record · LEDGER

Accumulator-Aware Post-Training Quantization for Large Language Models

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2409.17092.

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

pith.paper-citation-record.v1
2409.17092 v2

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 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T05:04:32.420368Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T05:04:32.643749Z

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 05086e11-5298-49d8-a767-4674412e0fa5 · inbound

Unified Stochastic Framework for Neural Network Quantization and Pruning cites this paper.

Unified Stochastic Framework for Neural Network Quantization and Pruning Accumulator-Aware Post-Training Quantization for Large Language Models

Reference 6

Resolution
verified exact
local_arxiv, observed 2026-08-11T05:04:32.650879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T05:04:32.420368Z digest=sha256:ce7d97947b73a1431d0af69d0f7246ccbdbe0a77d4d365ec3c562d2a47b7fe4a

Observation cded7dda-db24-4bd5-bb3e-491f194772e9 · inbound

When Can Depth Replace Precision? A Resource Theory of Quantized Neural Computation cites this paper.

When Can Depth Replace Precision? A Resource Theory of Quantized Neural Computation Accumulator-Aware Post-Training Quantization for Large Language Models

Reference 125

Resolution
unresolved
no resolver link, observed 2026-07-30T23:38:38.555815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T23:38:38.555815Z digest=sha256:fbde0169c4dc574972cb75a3d8ff5511eb46ca8bf6f3351de757f40e02780f64