Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2304.14979.
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 2026-08-15T19:28:27.173463Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
8
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 6fed811c-2c35-4fd5-86b5-7715295ca769 · inbound
C3oT: Generating Shorter Chain-of-Thought without Compromising Effectiveness cites this paper.
C3oT: Generating Shorter Chain-of-Thought without Compromising Effectiveness MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks
Reference 43
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T14:47:04.038473Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9f5d2308-4b7a-4ee2-94cf-b04818d82cb0 · inbound
Dolphin: Moving Towards Closed-loop Auto-research through Thinking, Practice, and Feedback cites this paper.
Dolphin: Moving Towards Closed-loop Auto-research through Thinking, Practice, and Feedback MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks
Reference 49
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T21:48:57.225697Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3ce32ebc-da14-4aa1-b31b-82766d965d51 · inbound
GPT-HTree: A Decision Tree Framework Integrating Hierarchical Clustering and Large Language Models for Explainable Classification cites this paper.
GPT-HTree: A Decision Tree Framework Integrating Hierarchical Clustering and Large Language Models for Explainable Classification MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T15:43:24.872974Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 31c0de53-121a-49ae-bbb0-42a42f0dd9ad · inbound
ML-Master: Towards AI-for-AI via Integration of Exploration and Reasoning cites this paper.
ML-Master: Towards AI-for-AI via Integration of Exploration and Reasoning MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T19:28:27.173463Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 11e63751-21be-4b26-b881-d77bb69b2d79 · inbound
AgentGA: Evolving Code Solutions in Agent-Seed Space cites this paper.
AgentGA: Evolving Code Solutions in Agent-Seed Space MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks
Reference 31
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T12:00:20.958605Z
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 aa2efba9-2efc-46e6-8643-6e369cf27e81 · inbound
AgentGA: Evolving Code Solutions in Agent-Seed Space cites this paper.
AgentGA: Evolving Code Solutions in Agent-Seed Space MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks
Reference 31
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T04:16:20.148470Z
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 64599db6-34c9-4500-875c-3dc845ee1898 · inbound
ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows cites this paper.
ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T05:57:22.481141Z
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 c7776a47-a5b7-4316-aae1-6e7125a1c779 · inbound
ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows cites this paper.
ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks
Reference 47
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T14:05:46.718615Z
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 0a7eeb0d-568c-4bf9-a676-3fc6263f7d64 · inbound
Is Inter-Seed Cross-Play Enough? Evaluating the Robustness of Zero-Shot Coordination Algorithms to Implementation Details cites this paper.
Is Inter-Seed Cross-Play Enough? Evaluating the Robustness of Zero-Shot Coordination Algorithms to Implementation Details MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks
Reference 177
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T15:25:40.103926Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.