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Paper Citation Record · LEDGER

AgentQuest: A Modular Benchmark Framework to Measure Progress and Improve LLM Agents

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2404.06411.

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

pith.paper-citation-record.v1
2404.06411 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T12:44:21.580085Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-29T20:13:58.831517Z

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 1ad5e1cc-4025-4189-834a-920f3b668c5c · inbound

MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering cites this paper.

MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering AgentQuest: A Modular Benchmark Framework to Measure Progress and Improve LLM Agents

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-23T19:13:21.517261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-05-23T19:11:20.600633Z digest=sha256:0470117e062a7b65ee78c2d69e2dad2be1513429cfe87e4566a87f2b6b30dff5

Observation 59328938-7cca-42cf-9dbe-815a015015de · 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 AgentQuest: A Modular Benchmark Framework to Measure Progress and Improve LLM Agents

Reference 50

Resolution
verified exact
arxiv_id, observed 2026-05-19T11:52:16.435588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-19T11:49:36.574471Z digest=sha256:18bc37588a206fa2af91c9919f683e0e34738a255a92cc84d940ef7c76bb7d7b

Observation c0640dad-e5f3-42c0-9f55-e0a2bed5c23e · inbound

Evaluation and Benchmarking of LLM Agents: A Survey cites this paper.

Evaluation and Benchmarking of LLM Agents: A Survey AgentQuest: A Modular Benchmark Framework to Measure Progress and Improve LLM Agents

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-06T12:44:21.580085Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T12:44:21.580085Z digest=sha256:2977c7613892bd952e9c2da55389005655719aade11c122a5e41d2d444adba3b

Observation c571ecb9-e8e1-4c25-aee7-055d9eaa395d · inbound

Latent Action Reparameterization for Efficient Agent Inference cites this paper.

Latent Action Reparameterization for Efficient Agent Inference AgentQuest: A Modular Benchmark Framework to Measure Progress and Improve LLM Agents

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-20T10:48:12.949593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-20T10:45:20.306945Z digest=sha256:f63a5c107611b852e64a89fe3efce83daa3fe834cafbf3ef7b1803c15d0726a1

Observation e89e0d70-0a17-4f7f-bf31-566562d13eff · inbound

Agentic AI Workload Characteristics cites this paper.

Agentic AI Workload Characteristics AgentQuest: A Modular Benchmark Framework to Measure Progress and Improve LLM Agents

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-06-29T20:13:58.832980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-29T20:11:50.722787Z digest=sha256:dce5c7f4968e1fa55e1670b594ba1920ba0062532c154477a0d082e601e4bcc6