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Paper Citation Record · LEDGER

LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2411.00136.

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

pith.paper-citation-record.v1
2411.00136 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 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:49:03.916941Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T06:31:10.409785Z

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 89ecc77c-05b5-476d-8f20-896aca0967b4 · inbound

Understanding the Performance and Power of LLM Inferencing on Edge Accelerators cites this paper.

Understanding the Performance and Power of LLM Inferencing on Edge Accelerators LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-07T04:49:03.916941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:49:03.916941Z digest=sha256:34ec3243d4e8ae3ea85e8290ad71f51e9d56437f80e6055ac0d3a59ffa25c741

Observation dfd9860a-aa8c-4ed6-96b7-e8bd12f2190f · inbound

Compiling Agentic Workflows into LLM Weights: Near-Frontier Quality at Two Orders of Magnitude Less Cost cites this paper.

Compiling Agentic Workflows into LLM Weights: Near-Frontier Quality at Two Orders of Magnitude Less Cost LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-05-22T06:31:10.413126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-05-22T06:26:31.841138Z digest=sha256:05d3dfa88d41d461c61e932c7ce3bc607a6746921d7242485ad76d5fcbe761dd