Paper Citation Record · LEDGER

AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 29 inbound Pith citation observations for arXiv:2410.02958.

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

pith.paper-citation-record.v1
2410.02958 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 29 of 29 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:43:38.727199Z

measured 1 of 1 external citation measurements

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

5
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 dcb334b6-cf8b-4cde-85d3-83bec4798401 · inbound

LLM-based Multi-Agent Systems: Techniques and Business Perspectives cites this paper.

LLM-based Multi-Agent Systems: Techniques and Business Perspectives AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-12T15:40:35.928675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e061537a-bd39-4d31-ad0e-3ed019efe6f4 · inbound

TeLL-Drive: Enhancing Autonomous Driving with Teacher LLM-Guided Deep Reinforcement Learning cites this paper.

TeLL-Drive: Enhancing Autonomous Driving with Teacher LLM-Guided Deep Reinforcement Learning AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-09T15:32:47.489140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4297f404-f0e3-491c-99b5-0e8251de4533 · inbound

ADL: A Declarative Language for Agent-Based Chatbots cites this paper.

ADL: A Declarative Language for Agent-Based Chatbots AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-16T11:43:38.727199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9624a48-2f08-4541-a4d5-a2af687342f7 · inbound

Can AI Agents Design and Implement Drug Discovery Pipelines? cites this paper.

Can AI Agents Design and Implement Drug Discovery Pipelines? AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T05:44:03.768963Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a73bfd8-0702-4af8-8c7c-be72bc4e4681 · inbound

Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows cites this paper.

Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T23:34:30.217268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e94b0674-39b4-43c2-baee-255a781de66d · inbound

MLE-Dojo: Interactive Environments for Empowering LLM Agents in Machine Learning Engineering cites this paper.

MLE-Dojo: Interactive Environments for Empowering LLM Agents in Machine Learning Engineering AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T22:11:54.214542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0be14125-a3e0-4dac-8c26-21e7e8e5c690 · inbound

MLZero: A Multi-Agent System for End-to-end Machine Learning Automation cites this paper.

MLZero: A Multi-Agent System for End-to-end Machine Learning Automation AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-15T20:12:31.117990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a246d982-bfd5-438f-aea2-346df077fccd · inbound

Large Language Model-Empowered Interactive Load Forecasting cites this paper.

Large Language Model-Empowered Interactive Load Forecasting AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T15:03:06.441899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa6e9eed-72ef-419c-a719-81667f724bcd · inbound

CoNav: Collaborative Cross-Modal Reasoning for Embodied Navigation cites this paper.

CoNav: Collaborative Cross-Modal Reasoning for Embodied Navigation AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T15:02:55.975100Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b77e4347-4f33-4d0c-860e-3c3352ebc8d9 · inbound

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

EXP-Bench: Can AI Conduct AI Research Experiments? AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-07T12:20:47.109938Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0102d6d7-e266-49d4-b53f-414e4a58121f · inbound

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance cites this paper.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T21:51:50.747347Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8fb82867-9c9d-404d-80f9-2edc778aa2c6 · inbound

Evaluation of Large Language Model-Driven AutoML in Data and Model Management from Human-Centered Perspective cites this paper.

Evaluation of Large Language Model-Driven AutoML in Data and Model Management from Human-Centered Perspective AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T19:19:00.239289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5aa9c77a-a8f0-4a99-a995-be1e58387bae · inbound

KompeteAI: Accelerated Autonomous Multi-Agent System for End-to-End Pipeline Generation for Machine Learning Problems cites this paper.

KompeteAI: Accelerated Autonomous Multi-Agent System for End-to-End Pipeline Generation for Machine Learning Problems AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-18T22:22:51.743847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e9ddd13c-81ff-4d55-b7d2-bf74a930fef4 · inbound

Aleks: AI powered Multi Agent System for Autonomous Scientific Discovery via Data-Driven Approaches in Plant Science cites this paper.

Aleks: AI powered Multi Agent System for Autonomous Scientific Discovery via Data-Driven Approaches in Plant Science AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T15:52:16.533994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60bc4674-b765-4019-b04a-1f5d1fc0f955 · inbound

TusoAI: Agentic Optimization for Scientific Methods cites this paper.

TusoAI: Agentic Optimization for Scientific Methods AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-21T22:34:23.801120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8b9e0d3f-4b29-4110-b230-bef0162de918 · inbound

Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory cites this paper.

Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 76

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T23:13:16.371001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 58d6112b-ca48-4840-a1f9-bdc22d945486 · inbound

Cost and Accuracy of Long-Term Memory in Distributed Multi-Agent Systems Based on Large Language Models cites this paper.

Cost and Accuracy of Long-Term Memory in Distributed Multi-Agent Systems Based on Large Language Models AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T11:01:43.454147Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 400a824e-8298-49fe-a5ce-b148ce96543e · inbound

iML: Executable, Problem-Grounded, and Broadly Exploratory Code-Driven AutoML cites this paper.

iML: Executable, Problem-Grounded, and Broadly Exploratory Code-Driven AutoML AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-02T23:25:33.124946Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8745d686-93c2-4c3c-b335-2ebd26f55239 · inbound

Scaling Multi-agent Systems: A Smart Middleware for Improving Agent Interactions cites this paper.

Scaling Multi-agent Systems: A Smart Middleware for Improving Agent Interactions AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-13T17:53:05.186867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f4a77986-eb29-4481-84eb-914c7692d656 · inbound

Scaling Multi-agent Systems: A Smart Middleware for Improving Agent Interactions cites this paper.

Scaling Multi-agent Systems: A Smart Middleware for Improving Agent Interactions AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-13T13:23:41.432041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd02c1b1-0231-4c5b-81dc-ec7c0de5beaa · inbound

AgentGA: Evolving Code Solutions in Agent-Seed Space cites this paper.

AgentGA: Evolving Code Solutions in Agent-Seed Space AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-10T12:00:20.963697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0e0c7732-5804-4e24-825d-23daeb80b0a3 · inbound

AgentGA: Evolving Code Solutions in Agent-Seed Space cites this paper.

AgentGA: Evolving Code Solutions in Agent-Seed Space AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:16:20.140713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 412e21bd-a250-410e-8380-3956db309f80 · inbound

Multi-Agent Systems: From Classical Paradigms to Large Foundation Model-Enabled Futures cites this paper.

Multi-Agent Systems: From Classical Paradigms to Large Foundation Model-Enabled Futures AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 85

Resolution: verified exact; arxiv_id, observed 2026-05-11T11:51:04.071865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 95b39de6-3822-443c-8d39-0b01f853a356 · inbound

Memory-Augmented LLM-based Multi-Agent System for Automated Feature Generation on Tabular Data cites this paper.

Memory-Augmented LLM-based Multi-Agent System for Automated Feature Generation on Tabular Data AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 84

Resolution: verified exact; arxiv_id, observed 2026-05-10T00:39:48.443851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f13c40a5-347a-4719-85a5-d81eb8199a46 · inbound

ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows cites this paper.

ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:57:22.521447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3e7f2052-123b-4f24-a1a1-a6c7c91df8db · inbound

ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows cites this paper.

ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-07-01T14:05:46.740334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a363d693-4d11-40e5-88b6-7e143d32da18 · inbound

Trustworthy Self-Composable Big-Data-as-a-Service: An LLM-Orchestrated Multi-Agent Framework for Automated Data Engineering, AutoML, MLOps Deployment, and Drift-Aware Lifecycle Optimization cites this paper.

Trustworthy Self-Composable Big-Data-as-a-Service: An LLM-Orchestrated Multi-Agent Framework for Automated Data Engineering, AutoML, MLOps Deployment, and Drift-Aware Lifecycle Optimization AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-07-03T23:29:02.938044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 79ab9d7b-e659-403f-8e05-9197e6e0a342 · inbound

Agentic AutoResearch forSpace Autonomy: An Auditable, LLM-Driven Research Agent for Aerospace Control Problems cites this paper.

Agentic AutoResearch forSpace Autonomy: An Auditable, LLM-Driven Research Agent for Aerospace Control Problems AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-07-04T03:59:32.687813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fcc342bc-6520-482a-a143-7f21e0e93e28 · inbound

VTOS: Learning to Orchestrate Vision Tools by Co-Searching Solutions and Observers cites this paper.

VTOS: Learning to Orchestrate Vision Tools by Co-Searching Solutions and Observers AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 40

Resolution: metadata mismatch; arxiv_id, observed 2026-06-26T21:40:08.282139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.