Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T19:11:23.895739Z
Paper Citation Record · LEDGER
Agentic End-to-End De Novo Protein Design for Tailored Dynamics Using a Language Diffusion Model
As of 8 August 2026, this Paper Citation Record lists 3 of 3 outbound references and 0 inbound Pith citation observations for arXiv:2502.10173.
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
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
3 of 3 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved3
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 31767f65-d503-4ec9-9c1d-b4743f92c31b · outbound
This paper cites High-Resolution Image Synthesis with Latent Diffusion Models.
Agentic End-to-End De Novo Protein Design for Tailored Dynamics Using a Language Diffusion Model High-Resolution Image Synthesis with Latent Diffusion Models
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T19:11:23.888479Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1c69cb28-2426-4b9f-a193-8419a6eb1086 · outbound
This paper cites GPT-4 Technical Report.
Agentic End-to-End De Novo Protein Design for Tailored Dynamics Using a Language Diffusion Model GPT-4 Technical Report
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T19:11:23.892556Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 034b2609-4fe2-45cd-8e23-2a9cf833672b · outbound
This paper cites SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning.
Agentic End-to-End De Novo Protein Design for Tailored Dynamics Using a Language Diffusion Model SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning
Reference 74
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T19:11:23.895739Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.