Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Large Language Model (LLM) as a System of Multiple Expert Agents: An Approach to solve the Abstraction and Reasoning Corpus (ARC) Challenge
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2310.05146.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T18:50:17.659685Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T12:59:53.248058Z
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 29c1a0a4-5ac4-4a40-a913-5fa605ded169 · inbound
Polymath: A Challenging Multi-modal Mathematical Reasoning Benchmark cites this paper.
Polymath: A Challenging Multi-modal Mathematical Reasoning Benchmark Large Language Model (LLM) as a System of Multiple Expert Agents: An Approach to solve the Abstraction and Reasoning Corpus (ARC) Challenge
Reference 39
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T20:05:47.681975Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 9c905bae-8f0e-43b5-9b04-ea96da15ecb1 · inbound
Capturing Sparks of Abstraction for the ARC Challenge cites this paper.
Capturing Sparks of Abstraction for the ARC Challenge Large Language Model (LLM) as a System of Multiple Expert Agents: An Approach to solve the Abstraction and Reasoning Corpus (ARC) Challenge
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T18:50:17.659685Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4ea4f125-d88e-45e5-acf1-3ed8e327e9f0 · inbound
The Stochastic Parrot on LLM's Shoulder: A Summative Assessment of Physical Concept Understanding cites this paper.
The Stochastic Parrot on LLM's Shoulder: A Summative Assessment of Physical Concept Understanding Large Language Model (LLM) as a System of Multiple Expert Agents: An Approach to solve the Abstraction and Reasoning Corpus (ARC) Challenge
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T23:12:23.473446Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 089465cd-ed0d-41a0-aa23-402761fdad06 · inbound
From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems cites this paper.
From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems Large Language Model (LLM) as a System of Multiple Expert Agents: An Approach to solve the Abstraction and Reasoning Corpus (ARC) Challenge
Reference 170
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T11:52:16.263240Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation f899a631-26ea-4e18-836c-0d580d4efc09 · 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 Model (LLM) as a System of Multiple Expert Agents: An Approach to solve the Abstraction and Reasoning Corpus (ARC) Challenge
Reference 58
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-04T12:59:53.249386Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 34f5153a-eafe-4b25-9f28-7771fa85961a · 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 Model (LLM) as a System of Multiple Expert Agents: An Approach to solve the Abstraction and Reasoning Corpus (ARC) Challenge
Reference 58
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-29T17:23:45.526642Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.