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Paper Citation Record · LEDGER

Investigating Symbolic Capabilities of Large Language Models

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2405.13209.

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

pith.paper-citation-record.v1
2405.13209 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 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:30:10.046467Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T23:10:52.585381Z

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 f69e4fd5-e182-403a-a396-193db6ba8449 · inbound

ReaLM: Reflection-Enhanced Autonomous Reasoning with Small Language Models cites this paper.

ReaLM: Reflection-Enhanced Autonomous Reasoning with Small Language Models Investigating Symbolic Capabilities of Large Language Models

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-15T17:30:10.046467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T17:30:10.046467Z digest=sha256:93ff1db5f24cbd45f68cf9db11f691ba5283144084a9b7ae0784dfa49b504718

Observation 230402ce-f5e9-4abf-8a69-a37670a9d3d5 · inbound

Assessing Large Language Models for Stabilizing Numerical Expressions in Scientific Software cites this paper.

Assessing Large Language Models for Stabilizing Numerical Expressions in Scientific Software Investigating Symbolic Capabilities of Large Language Models

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-10T23:10:52.594209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-10T19:18:06.308941Z digest=sha256:39e7376cc1ca383d5fc1f3372945488e8e33cbf0ac985555f4e6e60698039813