Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
N2G: A Scalable Approach for Quantifying Interpretable Neuron Representations in Large Language Models
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2304.12918.
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 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T04:43:45.537514Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
0
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 afa62a3b-cc74-48e5-80be-c3e7ec6863a5 · inbound
Neurons Speak in Ranges: Breaking Free from Discrete Neuronal Attribution cites this paper.
Neurons Speak in Ranges: Breaking Free from Discrete Neuronal Attribution N2G: A Scalable Approach for Quantifying Interpretable Neuron Representations in Large Language Models
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T04:12:30.722056Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 3655251c-b0ab-4b08-81bb-5cec5437e60f · inbound
Self-Ablating Transformers: More Interpretability, Less Sparsity cites this paper.
Self-Ablating Transformers: More Interpretability, Less Sparsity N2G: A Scalable Approach for Quantifying Interpretable Neuron Representations in Large Language Models
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T04:43:45.537514Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 93607f0e-b860-4e98-854b-d61691cd1217 · inbound
Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces cites this paper.
Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces N2G: A Scalable Approach for Quantifying Interpretable Neuron Representations in Large Language Models
Reference 147
- Resolution
- verified exact
- arxiv_id, observed 2026-05-14T20:17:54.354334Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.