Paper Citation Record · LEDGER

Seesaw: High-throughput LLM Inference via Model Re-sharding

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2503.06433.

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

pith.paper-citation-record.v1
2503.06433 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:32:32.805859Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T19:25:31.176024Z

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 97826bf4-382f-47e4-a23f-1c48ba6253a6 · inbound

Hardware-Efficient Attention for Fast Decoding cites this paper.

Hardware-Efficient Attention for Fast Decoding Seesaw: High-throughput LLM Inference via Model Re-sharding

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T13:32:32.805859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2843cd6-6086-4013-a65e-50a35eefc970 · inbound

Military AI Cyber Agents (MAICAs) Constitute a Global Threat to Critical Infrastructure cites this paper.

Military AI Cyber Agents (MAICAs) Constitute a Global Threat to Critical Infrastructure Seesaw: High-throughput LLM Inference via Model Re-sharding

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T04:27:40.300419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f90f5743-a83d-4467-9673-c90090343477 · inbound

Learning to Shard: RL for Co-optimizing the Parallelism Degrees and Per-operator Sharding Dimensions in Distributed LLM Inference cites this paper.

Learning to Shard: RL for Co-optimizing the Parallelism Degrees and Per-operator Sharding Dimensions in Distributed LLM Inference Seesaw: High-throughput LLM Inference via Model Re-sharding

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T13:52:30.186309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa051f31-3a6f-4b06-b44a-918fcd2191a2 · inbound

Amoeba: Runtime Tensor Parallel Transformation for LLM Inference Services cites this paper.

Amoeba: Runtime Tensor Parallel Transformation for LLM Inference Services Seesaw: High-throughput LLM Inference via Model Re-sharding

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-18T15:01:31.471942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3032c9cd-70e1-4ccb-ad37-ff338f069620 · inbound

Understanding and Improving Communication Performance in Multi-node LLM Inference cites this paper.

Understanding and Improving Communication Performance in Multi-node LLM Inference Seesaw: High-throughput LLM Inference via Model Re-sharding

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-21T19:25:31.178045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 477b74ee-b1b7-40a1-81df-105cc8c4381c · inbound

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

PipeMax: Enhancing Offline LLM Inference on Commodity GPU Servers Seesaw: High-throughput LLM Inference via Model Re-sharding

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T06:10:41.559447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d03c1a45-79c5-4cc0-aa89-63acdc06e022 · inbound

Requests of a Feather Must Flock Together: Batch Size vs. Prefix Homogeneity in LLM Inference cites this paper.

Requests of a Feather Must Flock Together: Batch Size vs. Prefix Homogeneity in LLM Inference Seesaw: High-throughput LLM Inference via Model Re-sharding

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:46:09.132527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d5ce4034-caa2-4db7-b85a-033e718f27c3 · inbound

Attention to Detail: Evaluating Energy, Performance, and Accuracy Trade-offs Across vLLM Configurations cites this paper.

Attention to Detail: Evaluating Energy, Performance, and Accuracy Trade-offs Across vLLM Configurations Seesaw: High-throughput LLM Inference via Model Re-sharding

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-13T04:56:20.002453Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c21354b9-9887-4bd3-b3e7-198b286803d2 · inbound

Attention to Detail: Evaluating Energy, Performance, and Accuracy Trade-offs Across vLLM Configurations cites this paper.

Attention to Detail: Evaluating Energy, Performance, and Accuracy Trade-offs Across vLLM Configurations Seesaw: High-throughput LLM Inference via Model Re-sharding

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-02T07:43:18.447867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.