Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
ByteScale: Efficient Scaling of LLM Training with a 2048K Context Length on More Than 12,000 GPUs
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2502.21231.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-02T00:41:45.459582Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T23:27:28.048568Z
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 8017b579-2402-4f6b-95f9-c90fcc2523bd · inbound
MegaScale-Data: Scaling Dataloader for Multisource Large Foundation Model Training cites this paper.
MegaScale-Data: Scaling Dataloader for Multisource Large Foundation Model Training ByteScale: Efficient Scaling of LLM Training with a 2048K Context Length on More Than 12,000 GPUs
Reference 27
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T21:15:09.582899Z
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 d062ed69-fb33-4f4d-9da4-7b47739030ca · inbound
MAGI-1: Autoregressive Video Generation at Scale cites this paper.
MAGI-1: Autoregressive Video Generation at Scale ByteScale: Efficient Scaling of LLM Training with a 2048K Context Length on More Than 12,000 GPUs
Reference 12
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T20:31:15.775808Z
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 033a95d0-8d15-4b90-991a-67655237a820 · 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 ByteScale: Efficient Scaling of LLM Training with a 2048K Context Length on More Than 12,000 GPUs
Reference 15
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T14:06:27.261784Z
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 a492123d-5b69-401e-b410-6bbfaff96408 · inbound
MTraining: Distributed Dynamic Sparse Attention for Efficient Ultra-Long Context Training cites this paper.
MTraining: Distributed Dynamic Sparse Attention for Efficient Ultra-Long Context Training ByteScale: Efficient Scaling of LLM Training with a 2048K Context Length on More Than 12,000 GPUs
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T19:44:19.608821Z
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 dc9975fe-da9b-44d6-a3bd-52eb37215dba · inbound
GLM-5: from Vibe Coding to Agentic Engineering cites this paper.
GLM-5: from Vibe Coding to Agentic Engineering ByteScale: Efficient Scaling of LLM Training with a 2048K Context Length on More Than 12,000 GPUs
Reference 12
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T05:46:40.953528Z
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 e5ff70bb-1d1b-4c95-87ef-838c8574ecd0 · inbound
MCAP: Deployment-Time Layer Profiling for Memory-Constrained LLM Inference cites this paper.
MCAP: Deployment-Time Layer Profiling for Memory-Constrained LLM Inference ByteScale: Efficient Scaling of LLM Training with a 2048K Context Length on More Than 12,000 GPUs
Reference 14
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-10T00:54:48.395862Z
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 c1b504b3-a5c5-42c3-8af3-6e14e81c1f74 · 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 ByteScale: Efficient Scaling of LLM Training with a 2048K Context Length on More Than 12,000 GPUs
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T02:06:14.982178Z
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 e25cb165-2ed7-4dd4-b56c-c6e1d6cf8167 · inbound
FlashCP: Load-Balanced Communication-Efficient Context Parallelism for LLM Training cites this paper.
FlashCP: Load-Balanced Communication-Efficient Context Parallelism for LLM Training ByteScale: Efficient Scaling of LLM Training with a 2048K Context Length on More Than 12,000 GPUs
Reference 6
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T23:27:28.050061Z
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 4e602654-14e9-497d-ab0b-7d5d49199b30 · 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 ByteScale: Efficient Scaling of LLM Training with a 2048K Context Length on More Than 12,000 GPUs
Reference 2025
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T00:41:45.459582Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.