Paper Citation Record · LEDGER

Large Language Models Are Not Strong Abstract Reasoners

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2305.19555.

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

pith.paper-citation-record.v1
2305.19555 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:20:43.028897Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T13:09:50.072171Z

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 b5d5559f-a62b-42a0-9ce0-880d5263b7bd · inbound

Efficient Causal Graph Discovery Using Large Language Models cites this paper.

Efficient Causal Graph Discovery Using Large Language Models Large Language Models Are Not Strong Abstract Reasoners

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-09T04:25:28.683305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a3fb7fdb-ef50-4083-b3b4-67b221cc8d9f · inbound

EXP-Bench: Can AI Conduct AI Research Experiments? cites this paper.

EXP-Bench: Can AI Conduct AI Research Experiments? Large Language Models Are Not Strong Abstract Reasoners

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:43.028897Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6ace9b7-614f-4344-a2af-50d24872b524 · inbound

Adaptive Multi-Agent Reasoning via Automated Workflow Generation cites this paper.

Adaptive Multi-Agent Reasoning via Automated Workflow Generation Large Language Models Are Not Strong Abstract Reasoners

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T16:10:28.200105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a465cd9-7fe6-402c-bdf2-9d091861f7c3 · inbound

Large Language Models Show Signs of Alignment with Human Neurocognition During Abstract Reasoning cites this paper.

Large Language Models Show Signs of Alignment with Human Neurocognition During Abstract Reasoning Large Language Models Are Not Strong Abstract Reasoners

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T21:10:30.905697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c048c413-1338-4c46-8ad1-e97401805e32 · inbound

Analysis of Error Sources in LLM-based Hypothesis Search for Few-Shot Rule Induction cites this paper.

Analysis of Error Sources in LLM-based Hypothesis Search for Few-Shot Rule Induction Large Language Models Are Not Strong Abstract Reasoners

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-05T13:02:25.658020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aecfdd32-f831-4261-9a74-6e6c44aa3e31 · inbound

Gradient-Based Program Synthesis with Neurally Interpreted Languages cites this paper.

Gradient-Based Program Synthesis with Neurally Interpreted Languages Large Language Models Are Not Strong Abstract Reasoners

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-11T11:56:08.328649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5812a36e-d6aa-4ddd-b97e-396c810e9ddf · inbound

Why We Need World Models for AGI: Where LLMs Fail and How World Models May Outperform cites this paper.

Why We Need World Models for AGI: Where LLMs Fail and How World Models May Outperform Large Language Models Are Not Strong Abstract Reasoners

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-07-01T14:25:46.626588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2fed1131-5412-4b61-b831-7c61c8caf7b2 · inbound

The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes cites this paper.

The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes Large Language Models Are Not Strong Abstract Reasoners

Reference 73

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T05:57:41.563473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d1d5af42-3690-4818-9150-77adace763c3 · inbound

DiARC: Distinguishing Positive and Negative Samples Helps Improving ARC-like Reasoning Ability of Large Language Models cites this paper.

DiARC: Distinguishing Positive and Negative Samples Helps Improving ARC-like Reasoning Ability of Large Language Models Large Language Models Are Not Strong Abstract Reasoners

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-07-04T13:09:50.074083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a77aed3c-ae71-4185-b50f-f8008370f086 · inbound

DiARC: Distinguishing Positive and Negative Samples Helps Improving ARC-like Reasoning Ability of Large Language Models cites this paper.

DiARC: Distinguishing Positive and Negative Samples Helps Improving ARC-like Reasoning Ability of Large Language Models Large Language Models Are Not Strong Abstract Reasoners

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-06-29T17:33:45.602884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.