Paper Citation Record · LEDGER

Reducing Activation Recomputation in Large Transformer Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 32 inbound Pith citation observations for arXiv:2205.05198.

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

pith.paper-citation-record.v1
2205.05198 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 32 of 32 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:30:56.938309Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

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External citation measurements

55
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 23efadb5-39a3-4564-96fa-05b7a3431662 · inbound

BloombergGPT: A Large Language Model for Finance cites this paper.

BloombergGPT: A Large Language Model for Finance Reducing Activation Recomputation in Large Transformer Models

Reference 53

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T23:19:46.710952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5a993fea-6aa6-42ef-ab50-9fbbc7c9c94a · inbound

A Survey of Large Language Models cites this paper.

A Survey of Large Language Models Reducing Activation Recomputation in Large Transformer Models

Reference 79

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T22:46:40.812840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e3169a5d-bf93-40d0-b0f4-2af1cd38ed7b · inbound

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel cites this paper.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Reducing Activation Recomputation in Large Transformer Models

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:15:20.079382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cba71a44-9ef7-4c93-85f8-b271d07509da · inbound

Ring Attention with Blockwise Transformers for Near-Infinite Context cites this paper.

Ring Attention with Blockwise Transformers for Near-Infinite Context Reducing Activation Recomputation in Large Transformer Models

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T19:28:28.258495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 691d0b08-e397-4417-a090-0b2b67fe70d9 · inbound

An Empirical Study of Mamba-based Language Models cites this paper.

An Empirical Study of Mamba-based Language Models Reducing Activation Recomputation in Large Transformer Models

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T10:31:03.874186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6c13164e-123b-4bca-9aec-6c6c21527958 · inbound

Wan: Open and Advanced Large-Scale Video Generative Models cites this paper.

Wan: Open and Advanced Large-Scale Video Generative Models Reducing Activation Recomputation in Large Transformer Models

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-22T23:07:14.441789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c7c516d8-0213-4b7b-924e-62402ec17b46 · inbound

MegaScale-Data: Scaling Dataloader for Multisource Large Foundation Model Training cites this paper.

MegaScale-Data: Scaling Dataloader for Multisource Large Foundation Model Training Reducing Activation Recomputation in Large Transformer Models

Reference 40

Resolution: metadata mismatch; arxiv_id, observed 2026-05-22T21:15:09.565107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6cf91668-8f56-47a2-9b89-8c4066a1a1d8 · inbound

Training LLMs on HPC Systems: Best Practices from the OpenGPT-X Project cites this paper.

Training LLMs on HPC Systems: Best Practices from the OpenGPT-X Project Reducing Activation Recomputation in Large Transformer Models

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-22T21:05:09.524204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 05cf9cdb-0e18-47b3-8b67-145d5b4307f4 · inbound

Arctic Long Sequence Training: Scalable And Efficient Training For Multi-Million Token Sequences cites this paper.

Arctic Long Sequence Training: Scalable And Efficient Training For Multi-Million Token Sequences Reducing Activation Recomputation in Large Transformer Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T00:30:56.938309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56cb0289-22aa-40af-8685-0e92e1d25712 · inbound

RoboBrain 2.0 Technical Report cites this paper.

RoboBrain 2.0 Technical Report Reducing Activation Recomputation in Large Transformer Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:28.846985Z

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Unavailable: canonical work link unavailable.

Observation ab079cb3-4ffd-4be9-96f4-13dc456ad6b2 · inbound

Photonic Fabric Platform for AI Accelerators cites this paper.

Photonic Fabric Platform for AI Accelerators Reducing Activation Recomputation in Large Transformer Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T16:20:28.750375Z

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Observation 29feca7a-4ba7-4d1c-870a-f553e4fdb36f · inbound

Unlock the Potential of Fine-grained LLM Serving via Dynamic Module Scaling cites this paper.

Unlock the Potential of Fine-grained LLM Serving via Dynamic Module Scaling Reducing Activation Recomputation in Large Transformer Models

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-06T14:45:20.758677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2310f253-5f7a-4c4f-b35c-c2b3c75cacbb · inbound

Chameleon: Adaptive Fault Tolerance for Distributed Training via Real-time Policy Selection cites this paper.

Chameleon: Adaptive Fault Tolerance for Distributed Training via Real-time Policy Selection Reducing Activation Recomputation in Large Transformer Models

Reference 28

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T20:41:50.661629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1599cf5d-4ed1-448c-9376-f64d6d84fb65 · inbound

SpikingBrain: Spiking Brain-inspired Large Models cites this paper.

SpikingBrain: Spiking Brain-inspired Large Models Reducing Activation Recomputation in Large Transformer Models

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T18:51:45.802088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e994574e-f6b7-406f-9d3a-6140912e951d · inbound

InfiniPipe: Elastic Pipeline Parallelism for Efficient Variable-Length Long-Context LLM Training cites this paper.

InfiniPipe: Elastic Pipeline Parallelism for Efficient Variable-Length Long-Context LLM Training Reducing Activation Recomputation in Large Transformer Models

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-18T14:06:27.277349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e0fd7fba-7f23-4a1d-9063-d8b7f7787471 · inbound

Scalable Synthesis of distributed LLM workloads through Symbolic Tensor Graphs cites this paper.

Scalable Synthesis of distributed LLM workloads through Symbolic Tensor Graphs Reducing Activation Recomputation in Large Transformer Models

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-03T22:28:46.339471Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12782da5-748a-4f8c-b556-b6116eb53347 · inbound

NVIDIA Nemotron 3: Efficient and Open Intelligence cites this paper.

NVIDIA Nemotron 3: Efficient and Open Intelligence Reducing Activation Recomputation in Large Transformer Models

Reference 128

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T01:40:42.615219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 25597e34-7a25-4688-a37f-d0c9ac86d4fa · inbound

Untied Ulysses: Memory-Efficient Context Parallelism via Headwise Chunking cites this paper.

Untied Ulysses: Memory-Efficient Context Parallelism via Headwise Chunking Reducing Activation Recomputation in Large Transformer Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T21:11:49.812534Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44a5279e-e3ff-4c8f-aa1e-c570a6561d63 · inbound

Efficient Scaling of LLM Training with Flexible Context Parallelism cites this paper.

Efficient Scaling of LLM Training with Flexible Context Parallelism Reducing Activation Recomputation in Large Transformer Models

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-02T20:59:24.724128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2543f4e-0d6d-47cd-9f31-2669ecf6a87b · inbound

CoCoDiff: Optimizing Collective Communications for Distributed Diffusion Transformer Inference Under Ulysses Sequence Parallelism cites this paper.

CoCoDiff: Optimizing Collective Communications for Distributed Diffusion Transformer Inference Under Ulysses Sequence Parallelism Reducing Activation Recomputation in Large Transformer Models

Reference 54

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T10:24:20.560853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 570aaedd-d074-469c-ab98-e26078be9db5 · inbound

Decoupled DiLoCo for Resilient Distributed Pre-training cites this paper.

Decoupled DiLoCo for Resilient Distributed Pre-training Reducing Activation Recomputation in Large Transformer Models

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-09T22:49:15.683024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 01b389ca-2465-4751-aeb1-731fb8ab9bfa · inbound

Efficient Training on Multiple Consumer GPUs with RoundPipe cites this paper.

Efficient Training on Multiple Consumer GPUs with RoundPipe Reducing Activation Recomputation in Large Transformer Models

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:31:26.808957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 626393f0-cf06-4885-8f1f-e18a1ba5e337 · inbound

Piper: Efficient Large-Scale MoE Training via Resource Modeling and Pipelined Hybrid Parallelism cites this paper.

Piper: Efficient Large-Scale MoE Training via Resource Modeling and Pipelined Hybrid Parallelism Reducing Activation Recomputation in Large Transformer Models

Reference 40

Resolution: malformed identifier; arxiv_id, observed 2026-05-11T17:51:07.974000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8b587fa3-48b1-4433-8d58-456f31566783 · inbound

MegaScale-Omni: A Hyper-Scale, Workload-Resilient System for MultiModal LLM Training in Production cites this paper.

MegaScale-Omni: A Hyper-Scale, Workload-Resilient System for MultiModal LLM Training in Production Reducing Activation Recomputation in Large Transformer Models

Reference 28

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T02:06:15.053308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 99be7701-2fdb-4847-b917-fbca9bf0467d · inbound

Instant GPU Efficiency Visibility at Fleet Scale cites this paper.

Instant GPU Efficiency Visibility at Fleet Scale Reducing Activation Recomputation in Large Transformer Models

Reference 37

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T02:43:54.741443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f8d65840-193a-47fe-8a6c-495610f149ab · inbound

Explaining Data Mixing Scaling Laws cites this paper.

Explaining Data Mixing Scaling Laws Reducing Activation Recomputation in Large Transformer Models

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T20:37:22.529353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0c493078-284b-448d-af6a-03d51e9a3e57 · inbound

Explaining Data Mixing Scaling Laws cites this paper.

Explaining Data Mixing Scaling Laws Reducing Activation Recomputation in Large Transformer Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-15T10:54:16.434702Z

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Unavailable: canonical work link unavailable.

Observation 8d91bf9b-23e5-4d5a-98c0-f507144fa1b9 · inbound

Explaining Data Mixing Scaling Laws cites this paper.

Explaining Data Mixing Scaling Laws Reducing Activation Recomputation in Large Transformer Models

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-02T12:13:46.609563Z

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Unavailable: canonical work link unavailable.

Observation faa07337-c6ca-41dc-a16f-b1657d9e5bd8 · inbound

The Cost and Network Limits of Space-Based AI Compute cites this paper.

The Cost and Network Limits of Space-Based AI Compute Reducing Activation Recomputation in Large Transformer Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-02T04:42:46.028286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a31fe6a3-500d-467f-9f96-095c7d2e8fba · inbound

LongStraw: Long-Context RL Beyond 2M Tokens under a Fixed GPU Budget cites this paper.

LongStraw: Long-Context RL Beyond 2M Tokens under a Fixed GPU Budget Reducing Activation Recomputation in Large Transformer Models

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-02T00:41:45.462772Z

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Unavailable: canonical work link unavailable.

Observation 58bacf8a-3c44-477c-ad9a-a7ebad505fcb · inbound

A Training-Memory Regression in MLA Sequence Parallelism: Why Megatron-Core Forbids Absorption, and LAGA -- a Communication-Efficient Fix cites this paper.

A Training-Memory Regression in MLA Sequence Parallelism: Why Megatron-Core Forbids Absorption, and LAGA -- a Communication-Efficient Fix Reducing Activation Recomputation in Large Transformer Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T17:32:37.263658Z

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Unavailable: canonical work link unavailable.

Observation 2b777e7d-da11-4b27-8c90-c084795671c0 · inbound

QLPO: Quadrant-weighted Sampling for Length-aware Policy Optimization cites this paper.

QLPO: Quadrant-weighted Sampling for Length-aware Policy Optimization Reducing Activation Recomputation in Large Transformer Models

Reference 194

Resolution: unresolved; no resolver link, observed 2026-08-01T06:48:44.020572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.