Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Dissecting the Runtime Performance of the Training, Fine-tuning, and Inference of Large Language Models
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2311.03687.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T15:31:50.377932Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-30T16:35:12.203073Z
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 7f35b27e-8ed2-4e43-a349-594e6a9a3798 · inbound
The Role of Visualization in LLM-Assisted Knowledge Graph Systems: Effects on User Trust, Exploration, and Workflows cites this paper.
The Role of Visualization in LLM-Assisted Knowledge Graph Systems: Effects on User Trust, Exploration, and Workflows Dissecting the Runtime Performance of the Training, Fine-tuning, and Inference of Large Language Models
Reference 77
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:31:50.377932Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3e686972-1574-4ea8-b1f7-8c2c52cb941c · inbound
Position: LLM Inference Should Be Evaluated as Energy-to-Token Production cites this paper.
Position: LLM Inference Should Be Evaluated as Energy-to-Token Production Dissecting the Runtime Performance of the Training, Fine-tuning, and Inference of Large Language Models
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T05:27:19.391042Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 6ec29d38-9c15-4e6a-8675-ac199cbab98c · inbound
MeMo: Memory as a Model cites this paper.
MeMo: Memory as a Model Dissecting the Runtime Performance of the Training, Fine-tuning, and Inference of Large Language Models
Reference 50
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-15T03:19:43.596727Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 51bb6484-2eea-47ca-94c3-9aeb058e299d · inbound
MeMo: Memory as a Model cites this paper.
MeMo: Memory as a Model Dissecting the Runtime Performance of the Training, Fine-tuning, and Inference of Large Language Models
Reference 50
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-21T08:39:53.652711Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 80dfdf6f-448e-4589-a243-7e5c5201c91f · inbound
The Efficiency Frontier: A Unified Framework for Cost-Performance Optimization in LLM Context Management cites this paper.
The Efficiency Frontier: A Unified Framework for Cost-Performance Optimization in LLM Context Management Dissecting the Runtime Performance of the Training, Fine-tuning, and Inference of Large Language Models
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T05:25:23.645950Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 7c5c0321-a30f-496b-8edb-3ee1fc97c5b8 · inbound
The Efficiency Frontier: A Unified Framework for Cost-Performance Optimization in LLM Context Management cites this paper.
The Efficiency Frontier: A Unified Framework for Cost-Performance Optimization in LLM Context Management Dissecting the Runtime Performance of the Training, Fine-tuning, and Inference of Large Language Models
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T16:35:12.205676Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.