Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2505.01043.
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 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-31T05:04:00.337750Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T04:57:38.354280Z
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 59bbba49-12f8-46e9-84de-abfb8f8bae93 · inbound
Reliable Evaluation Protocol for Low-Precision Retrieval cites this paper.
Reliable Evaluation Protocol for Low-Precision Retrieval Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities
Reference 3
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-30T01:18:51.459998Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 51ef9cb5-1de1-4065-b39d-0102ff8bb07f · inbound
Why Low-Precision Transformer Training Fails: An Analysis on Flash Attention cites this paper.
Why Low-Precision Transformer Training Fails: An Analysis on Flash Attention Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-07-30T01:18:51.459998Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 39dce1fe-0db1-4a7d-9f44-71a1270f71df · inbound
StoSignSGD: Unbiased Structural Stochasticity Fixes SignSGD for Training Large Language Models cites this paper.
StoSignSGD: Unbiased Structural Stochasticity Fixes SignSGD for Training Large Language Models Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities
Reference 15
- Resolution
- verified exact
- arxiv_id, observed 2026-07-30T01:18:51.459998Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 36ec92df-01cd-4af2-b355-d84a91ac7ecf · inbound
Decomposing MXFP4 quantization error for LLM reinforcement learning: reducible bias, recoverable deadzone, and an irreducible floor cites this paper.
Decomposing MXFP4 quantization error for LLM reinforcement learning: reducible bias, recoverable deadzone, and an irreducible floor Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities
Reference 20
- Resolution
- verified exact
- arxiv_id, observed 2026-07-30T01:18:51.459998Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation abc6f417-a4f1-4143-805a-dffdfe2e8334 · inbound
Decomposing MXFP4 quantization error for LLM reinforcement learning: reducible bias, recoverable deadzone, and an irreducible floor cites this paper.
Decomposing MXFP4 quantization error for LLM reinforcement learning: reducible bias, recoverable deadzone, and an irreducible floor Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities
Reference 20
- Resolution
- verified exact
- arxiv_id, observed 2026-07-30T01:18:51.459998Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 9c233b99-177d-4c2d-b369-b680d2585e10 · inbound
Decomposing MXFP4 quantization error for LLM reinforcement learning: reducible bias, recoverable deadzone, and an irreducible floor cites this paper.
Decomposing MXFP4 quantization error for LLM reinforcement learning: reducible bias, recoverable deadzone, and an irreducible floor Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities
Reference 20
- Resolution
- verified exact
- arxiv_id, observed 2026-07-30T01:18:51.459998Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation c77a3e84-30d4-4fc4-a870-dbab44402c3f · inbound
PowLU: An Activation Function for Stable Pre-Training of LLMs cites this paper.
PowLU: An Activation Function for Stable Pre-Training of LLMs Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-07-30T01:18:51.459998Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation f575022c-f983-4bbd-a8ee-d7a055aee18d · inbound
LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization cites this paper.
LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-07-30T01:18:51.459998Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation e8f262cc-5885-442e-bd3d-e59dd40bb7d3 · inbound
LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization cites this paper.
LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-07-30T01:18:51.459998Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 8351f110-4e6f-40bb-b4c0-54b4054b48ed · inbound
Stable FP4 Training via Transposition-Invariant Block Quantization cites this paper.
Stable FP4 Training via Transposition-Invariant Block Quantization Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-07-31T05:04:00.337750Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.