Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models
As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2410.07331.
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 2026-08-12T06:34:41.77262+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T06:00:19.491104Z
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
0
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 d121ce82-086d-4d30-a89e-e59964d33560 · inbound
Large Language Model Agent: A Survey on Methodology, Applications and Challenges cites this paper.
Large Language Model Agent: A Survey on Methodology, Applications and Challenges DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models
Reference 140
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T21:52:10.421754Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 3b944511-a60a-467d-87f4-4285fa1271aa · inbound
Evolutionary Perspectives on the Evaluation of LLM-Based AI Agents: A Comprehensive Survey cites this paper.
Evolutionary Perspectives on the Evaluation of LLM-Based AI Agents: A Comprehensive Survey DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models
Reference 100
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T06:00:19.491104Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5e2d337a-746b-4917-ab7f-3eb5b5e4049b · inbound
LLM-Based Multi-Agent Systems for Code Generation: A Multi-Vocal Literature Review cites this paper.
LLM-Based Multi-Agent Systems for Code Generation: A Multi-Vocal Literature Review DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models
Reference 53
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T19:56:33.898593Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 84ac1730-5e0c-46e9-8020-3906a0f8732a · inbound
Business Utility of Large Language Models as Exploratory Data Analysis Agents cites this paper.
Business Utility of Large Language Models as Exploratory Data Analysis Agents DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models
Reference 11
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-30T23:35:06.967514Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation ade5c9a6-3b8d-4267-aefb-aeb31df3c3c0 · inbound
Auto-Configuring Scientific Simulators with Lightweight Coding-Agent Adapters cites this paper.
Auto-Configuring Scientific Simulators with Lightweight Coding-Agent Adapters DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models
Reference 13
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T01:47:32.165108Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 042e4f50-b82f-4ff9-aa66-73d617b5b81c · inbound
Auto-Configuring Scientific Simulators with Lightweight Coding-Agent Adapters cites this paper.
Auto-Configuring Scientific Simulators with Lightweight Coding-Agent Adapters DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T15:13:32.320321Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation a1f33716-ad98-44dd-9bd5-32a47f59ed69 · inbound
Matching Matters: A Fair Quality-Efficiency Benchmark for Command-Line Agents cites this paper.
Matching Matters: A Fair Quality-Efficiency Benchmark for Command-Line Agents DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models
Reference 38
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T06:59:37.157500Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.