Pith. sign in

Paper Citation Record · LEDGER

Rapid and Automated Alloy Design with Graph Neural Network-Powered LLM-Driven Multi-Agent Systems

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2410.13768.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2410.13768 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:02:32.193525Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-05T22:56:21.439883Z

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 b8bf6174-49ac-443d-a958-672d6678fb26 · inbound

TopoMAS: Large Language Model Driven Topological Materials Multiagent System cites this paper.

TopoMAS: Large Language Model Driven Topological Materials Multiagent System Rapid and Automated Alloy Design with Graph Neural Network-Powered LLM-Driven Multi-Agent Systems

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-06T20:02:32.193525Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T20:02:32.193525Z digest=sha256:8c2165f339b963053dc08c6ec2cd7882442619f0da0e5e9e845e1e4ee576b6ee

Observation f5ccb9ed-f0b7-4806-a346-5dc0d53da2f9 · inbound

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials cites this paper.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials Rapid and Automated Alloy Design with Graph Neural Network-Powered LLM-Driven Multi-Agent Systems

Reference 29

Resolution
verified exact
local_arxiv, observed 2026-08-05T22:56:21.492690Z

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.

source=arxiv_source observed=2026-08-05T22:56:19.116624Z digest=sha256:2e8ac3f58767c5a8698550863b70599869288ff6fe564291d25a68b86a693db6