Paper Citation Record · LEDGER

AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2410.20424.

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

pith.paper-citation-record.v1
2410.20424 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 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 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:01:08.574502Z

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

1
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 6e6963ea-3b6d-40a8-8432-5d5d3e7f93a9 · inbound

Qwen2.5-Coder Technical Report cites this paper.

Qwen2.5-Coder Technical Report AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T12:33:39.068779Z

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 ed2366e0-d98a-4cdc-b79a-f632305c3275 · inbound

A Survey of Scaling in Large Language Model Reasoning cites this paper.

A Survey of Scaling in Large Language Model Reasoning AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions

Reference 105

Resolution: verified exact; arxiv_id, observed 2026-05-22T21:22:09.364756Z

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 58b5e258-c888-4157-aed7-ce976b3b6e6d · inbound

KG-HTC: Integrating Knowledge Graphs into LLMs for Effective Zero-shot Hierarchical Text Classification cites this paper.

KG-HTC: Integrating Knowledge Graphs into LLMs for Effective Zero-shot Hierarchical Text Classification AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-22T15:36:44.949381Z

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 8e9ec177-bc41-4190-965b-3f7ff071d622 · inbound

SWE-Flow: Synthesizing Software Engineering Data in a Test-Driven Manner cites this paper.

SWE-Flow: Synthesizing Software Engineering Data in a Test-Driven Manner AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T05:01:08.574502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5b4ceed-068e-43fb-ad23-bbc3f8c4f042 · inbound

Coding Triangle: How Does Large Language Model Understand Code? cites this paper.

Coding Triangle: How Does Large Language Model Understand Code? AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T19:15:33.042830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4ec9551-eb9f-42f8-8a87-a18bd4ec5166 · inbound

IFEvalCode: Controlled Code Generation cites this paper.

IFEvalCode: Controlled Code Generation AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T11:44:34.002594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8fce9f46-61dc-49bb-91c3-c42ff7159270 · 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 AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T22:22:51.760303Z

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 bbc19c93-e2ff-42b8-b25e-cd50ec355bb9 · inbound

Reinforcement Learning for Machine Learning Engineering Agents cites this paper.

Reinforcement Learning for Machine Learning Engineering Agents AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.281057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a50618b7-6b2f-4032-b833-b7a27fdbf557 · inbound

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

AgentGA: Evolving Code Solutions in Agent-Seed Space AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions

Reference 15

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T12:00:20.933734Z

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 ed451777-01d0-42c4-9cea-ea873c61bc03 · inbound

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

AgentGA: Evolving Code Solutions in Agent-Seed Space AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions

Reference 15

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T04:16:20.144693Z

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 3f331a04-f9b1-4cf3-894c-49e6f8ff1ee7 · inbound

Agentic Discovery of Neural Architectures: AIRA-Compose and AIRA-Design cites this paper.

Agentic Discovery of Neural Architectures: AIRA-Compose and AIRA-Design AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-20T18:58:53.850837Z

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 b400c09b-c38e-4762-94cb-65e875961f6e · inbound

Towards Persistent Case-Based Memory for Autonomous Data Science: A CBR-Augmented R&D-Agent with a Locally Deployable Small Language Model cites this paper.

Towards Persistent Case-Based Memory for Autonomous Data Science: A CBR-Augmented R&D-Agent with a Locally Deployable Small Language Model AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions

Reference 32

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T10:06:51.682263Z

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 2d7b6cde-1905-46d1-bbbc-5b2c5ba507b3 · inbound

LATTEArena: An Evaluation Framework for LLM-powered Tabular Feature Engineering (Extended Version) cites this paper.

LATTEArena: An Evaluation Framework for LLM-powered Tabular Feature Engineering (Extended Version) AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions

Reference 30

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T00:57:30.254401Z

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 e1654d27-9de8-471c-b6db-1ba5e75bf16b · inbound

Matryoshka Agent: Unfolding Sub-Agents for Long-Horizon Machine Learning Engineering cites this paper.

Matryoshka Agent: Unfolding Sub-Agents for Long-Horizon Machine Learning Engineering AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-31T01:39:47.689559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.