Paper Citation Record · LEDGER

BlendServe: Optimizing Offline Inference for Auto-regressive Large Models with Resource-aware Batching

As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2411.16102.

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

pith.paper-citation-record.v1
2411.16102 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T10:07:06.141581Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-06-29T10:13:17.818185Z

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 262562b0-1ae9-4f6c-96be-77925df35f73 · inbound

PipeMax: Enhancing Offline LLM Inference on Commodity GPU Servers cites this paper.

PipeMax: Enhancing Offline LLM Inference on Commodity GPU Servers BlendServe: Optimizing Offline Inference for Auto-regressive Large Models with Resource-aware Batching

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-06-09T02:05:18.167025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation bd5afe29-54ed-49c5-91cc-713705a915aa · inbound

MoE-Prefill: Zero Redundancy Overheads in MoE Prefill Serving cites this paper.

MoE-Prefill: Zero Redundancy Overheads in MoE Prefill Serving BlendServe: Optimizing Offline Inference for Auto-regressive Large Models with Resource-aware Batching

Reference 70

Resolution: verified exact; arxiv_id, observed 2026-06-09T02:05:18.167025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 40da5d97-e34b-4bd6-bb78-be2975760002 · inbound

MoE-Prefill: Zero Redundancy Overheads in MoE Prefill Serving cites this paper.

MoE-Prefill: Zero Redundancy Overheads in MoE Prefill Serving BlendServe: Optimizing Offline Inference for Auto-regressive Large Models with Resource-aware Batching

Reference 70

Resolution: verified exact; arxiv_id, observed 2026-06-09T02:05:18.167025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 95354377-b877-4ed4-ac49-9fc37cf7a73e · inbound

SiDP: Memory-Efficient Data Parallelism for Offline LLM Inference cites this paper.

SiDP: Memory-Efficient Data Parallelism for Offline LLM Inference BlendServe: Optimizing Offline Inference for Auto-regressive Large Models with Resource-aware Batching

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-06-29T10:13:17.819382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.