Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Probing Latent Subspaces in LLM for AI Security: Identifying and Manipulating Adversarial States
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2503.09066.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T23:49:13.842722Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-29T18:13:48.598286Z
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 8db9b532-ee68-48e0-95c5-0aa7879e17d3 · inbound
Probing the Robustness of Large Language Models Safety to Latent Perturbations cites this paper.
Probing the Robustness of Large Language Models Safety to Latent Perturbations Probing Latent Subspaces in LLM for AI Security: Identifying and Manipulating Adversarial States
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:49:13.842722Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4a910ff5-58da-47f2-b6aa-899e2175f7ca · inbound
Zero-Direction Probing: A Linear-Algebraic Framework for Deep Analysis of Large-Language-Model Drift cites this paper.
Zero-Direction Probing: A Linear-Algebraic Framework for Deep Analysis of Large-Language-Model Drift Probing Latent Subspaces in LLM for AI Security: Identifying and Manipulating Adversarial States
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T22:40:30.528845Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 97a09046-37cd-4e96-9cbb-4103b8d4b15a · inbound
Cell-Based Representation of Relational Binding in Language Models cites this paper.
Cell-Based Representation of Relational Binding in Language Models Probing Latent Subspaces in LLM for AI Security: Identifying and Manipulating Adversarial States
Reference 66
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T13:06:04.660349Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation f77aeff6-5bd2-445d-a6fb-56d599260efd · inbound
HyperLens: Quantifying Cognitive Effort in LLMs with Fine-grained Confidence Trajectory cites this paper.
HyperLens: Quantifying Cognitive Effort in LLMs with Fine-grained Confidence Trajectory Probing Latent Subspaces in LLM for AI Security: Identifying and Manipulating Adversarial States
Reference 25
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T19:36:10.825882Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 0f10d6d0-5d77-4f50-8305-ae81b27460a7 · inbound
TRACES: Proactive Safety Auditing for Multi-Turn LLM Agents via Trajectory-State Modeling cites this paper.
TRACES: Proactive Safety Auditing for Multi-Turn LLM Agents via Trajectory-State Modeling Probing Latent Subspaces in LLM for AI Security: Identifying and Manipulating Adversarial States
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T18:13:48.600085Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.