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Paper Citation Record · LEDGER

Pseudocode-Injection Magic: Enabling LLMs to Tackle Graph Computational Tasks

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2501.13731.

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

pith.paper-citation-record.v1
2501.13731 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 2026-08-06T18:43:57.462689Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T17:07:12.215404Z

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 2524e37f-3834-445b-ad3f-2766ed9f426a · inbound

Teaching LLM to Reason: Reinforcement Learning from Algorithmic Problems without Code cites this paper.

Teaching LLM to Reason: Reinforcement Learning from Algorithmic Problems without Code Pseudocode-Injection Magic: Enabling LLMs to Tackle Graph Computational Tasks

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-06T18:43:57.462689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:43:57.462689Z digest=sha256:4167e246d8ed9ac38447a8cc195f20e29d7901444b5bac92303738bb9ec25cc4

Observation 8614ef63-779a-47b6-942a-3cc55fd57ac3 · inbound

Are Large Language Models Suitable for Graph Computation? Progress and Prospects cites this paper.

Are Large Language Models Suitable for Graph Computation? Progress and Prospects Pseudocode-Injection Magic: Enabling LLMs to Tackle Graph Computational Tasks

Reference 182

Resolution
verified exact
arxiv_id, observed 2026-07-02T17:07:12.216872Z

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=2026-06-27T22:15:03.223540Z digest=sha256:459416ee829b7fa4b9e6b5772c638bf56c7822f1cd1c1d1fed1b77a908ceaa7e