Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Toward universal steering and monitoring of AI models
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2502.03708.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T04:45:42.691479Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T16:07:08.191129Z
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 a07c02d3-86f8-4ddc-a234-f26edc18ed46 · inbound
Resa: Transparent Reasoning Models via SAEs cites this paper.
Resa: Transparent Reasoning Models via SAEs Toward universal steering and monitoring of AI models
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:45:42.691479Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 72763c8e-e5cf-4e8f-9e15-48f13c44e7d0 · inbound
The Features at Convergence Theorem: a first-principles alternative to the Neural Feature Ansatz for how networks learn representations cites this paper.
The Features at Convergence Theorem: a first-principles alternative to the Neural Feature Ansatz for how networks learn representations Toward universal steering and monitoring of AI models
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T19:31:18.912515Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 375ec137-643e-4a7e-8fc0-1e0298d8a21a · inbound
xRFM: Accurate, scalable, and interpretable feature learning models for tabular data cites this paper.
xRFM: Accurate, scalable, and interpretable feature learning models for tabular data Toward universal steering and monitoring of AI models
Reference 6
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T23:11:53.937668Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation bd418a8b-dd7b-449d-a04d-b71529e8a7f6 · inbound
Can LLMs Lie? Investigation beyond Hallucination cites this paper.
Can LLMs Lie? Investigation beyond Hallucination Toward universal steering and monitoring of AI models
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T10:55:31.196148Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1f94177a-cf88-476d-8e00-537d3b51ac7e · inbound
From Actions to Understanding: Conformal Interpretability of Temporal Concepts in LLM Agents cites this paper.
From Actions to Understanding: Conformal Interpretability of Temporal Concepts in LLM Agents Toward universal steering and monitoring of AI models
Reference 6
- Resolution
- verified exact
- arxiv_id, observed 2026-05-14T22:33:10.418094Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation c333f1d8-e01b-441b-a5aa-b6301ee9fbb5 · inbound
Activation Steering for Synthetic Data Generation: The Role of Diversity in Downstream Safety Detection cites this paper.
Activation Steering for Synthetic Data Generation: The Role of Diversity in Downstream Safety Detection Toward universal steering and monitoring of AI models
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T14:03:29.910475Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 04d420b4-6eaa-4b4d-852c-80852e9f932d · inbound
K-Inverse-RFM: A Modified RFM that Bridges the Gap to Neural Networks for Data-Corrupted Mathematical Tasks cites this paper.
K-Inverse-RFM: A Modified RFM that Bridges the Gap to Neural Networks for Data-Corrupted Mathematical Tasks Toward universal steering and monitoring of AI models
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T16:07:08.192626Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.