Paper Citation Record · LEDGER

What Can Neural Networks Reason About?

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1905.13211.

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

pith.paper-citation-record.v1
1905.13211 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T17:37:40.806200Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T02:39:30.031619Z

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 a7ab293e-260c-4d8b-b740-afd8e7b4ffe7 · inbound

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges cites this paper.

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges What Can Neural Networks Reason About?

Reference 101

Resolution: verified exact; arxiv_id, observed 2026-05-13T02:39:30.034728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8e536755-9a40-4a33-93ff-19d5b89a3da3 · inbound

Metastable Dynamics of Chain-of-Thought Reasoning: Provable Benefits of Search, RL and Distillation cites this paper.

Metastable Dynamics of Chain-of-Thought Reasoning: Provable Benefits of Search, RL and Distillation What Can Neural Networks Reason About?

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-09T17:37:40.806200Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6501c605-6238-42e5-933f-e393935a1407 · inbound

When More is Less: Understanding Chain-of-Thought Length in LLMs cites this paper.

When More is Less: Understanding Chain-of-Thought Length in LLMs What Can Neural Networks Reason About?

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-08T13:23:22.149796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2e5e071-65b0-4489-a9b0-9a3feb2804ff · inbound

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning cites this paper.

G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning What Can Neural Networks Reason About?

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T14:34:06.300801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 780e10e8-565b-4654-b999-e6a8fe6f3084 · inbound

Distance-Preserving Embeddings in Inhomogeneous Random Graphs cites this paper.

Distance-Preserving Embeddings in Inhomogeneous Random Graphs What Can Neural Networks Reason About?

Reference 187

Resolution: unresolved; no resolver link, observed 2026-07-14T00:37:04.989965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.