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Paper Citation Record · LEDGER

A Vision for Auto Research with LLM Agents

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2504.18765.

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

pith.paper-citation-record.v1
2504.18765 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:57:10.171022Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T17:28:44.375697Z

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 f4af4632-4396-4fa2-b0c9-125301d9e1e2 · inbound

SGSimEval: A Comprehensive Multifaceted and Similarity-Enhanced Benchmark for Automatic Survey Generation Systems cites this paper.

SGSimEval: A Comprehensive Multifaceted and Similarity-Enhanced Benchmark for Automatic Survey Generation Systems A Vision for Auto Research with LLM Agents

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-05T20:02:56.127542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:02:56.127542Z digest=sha256:b68f2ad5d7922b9fc3aad67571901b1e5ac6367e82ebcdd8c3a0dad7774b7d5d

Observation 885f60bb-2392-4423-8320-8445e7f15ad1 · inbound

GenCellAgent: Generalizable, Training-Free Cellular Image Segmentation via Large Language Model Agents cites this paper.

GenCellAgent: Generalizable, Training-Free Cellular Image Segmentation via Large Language Model Agents A Vision for Auto Research with LLM Agents

Reference 41

Resolution
metadata mismatch
arxiv_id, observed 2026-05-18T07:26:03.460175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-18T07:22:58.072356Z digest=sha256:30668b4544b8de113b0466d9e530df880a6d92b50237e839b5b6909829223a20

Observation 4b49e341-d1c3-4214-965a-d77f01228435 · inbound

Agentic Inequality cites this paper.

Agentic Inequality A Vision for Auto Research with LLM Agents

Reference 33

Resolution
verified exact
arxiv_id, observed 2026-05-18T06:15:57.990039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-18T06:15:12.105326Z digest=sha256:fb6387503c6e38ee437421eb0d3f5c584e4078ae93675ab6a19866a68a2ac1ff

Observation 918a9e2d-46c6-46d7-bec4-feaef352b433 · inbound

BadScientist: Can a Research Agent Write Convincing but Unsound Papers that Fool LLM Reviewers? cites this paper.

BadScientist: Can a Research Agent Write Convincing but Unsound Papers that Fool LLM Reviewers? A Vision for Auto Research with LLM Agents

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-04T08:59:47.937717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T08:59:47.937717Z digest=sha256:834186377cf8c922df64b9b87f2cfde20284ea3594bf23d6c9b783f8590e5775

Observation 94f1d899-7cc2-43b7-952f-954c9b066987 · inbound

Frontier-Eng: Benchmarking Self-Evolving Agents on Real-World Engineering Tasks with Generative Optimization cites this paper.

Frontier-Eng: Benchmarking Self-Evolving Agents on Real-World Engineering Tasks with Generative Optimization A Vision for Auto Research with LLM Agents

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-11T09:05:57.728575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-10T16:17:32.290531Z digest=sha256:c39a579ad3dc2422af874c7a033e3be9aad20a7e1855d0473112286a20a3f0f0

Observation 35efda3c-229d-4078-aaa1-78a591dc0360 · inbound

EngiAgent: Fully Connected Coordination of LLM Agents for Solving Open-ended Engineering Problems with Feasible Solutions cites this paper.

EngiAgent: Fully Connected Coordination of LLM Agents for Solving Open-ended Engineering Problems with Feasible Solutions A Vision for Auto Research with LLM Agents

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-11T16:31:07.238547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-09T16:32:51.749853Z digest=sha256:674d4779d7980caca3069329fffeb453d26a0b3d0ef2c56abbb29af4a33cf469

Observation c343cb5b-aeca-4859-8c61-6275a2010705 · inbound

AutoResearch AI: Towards AI-Powered Research Automation for Scientific Discovery cites this paper.

AutoResearch AI: Towards AI-Powered Research Automation for Scientific Discovery A Vision for Auto Research with LLM Agents

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-25T04:50:21.542369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-25T04:46:43.679185Z digest=sha256:7930c38247dda20fb9f57374b42777616ba4e6d32c80dffca9fc686f471ac5c0

Observation 9cc9cff8-35c6-401a-8857-cf7db0c0f97d · inbound

AutoScientists: Self-Organizing Agent Teams for Long-Running Scientific Experimentation cites this paper.

AutoScientists: Self-Organizing Agent Teams for Long-Running Scientific Experimentation A Vision for Auto Research with LLM Agents

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-06-29T12:43:25.740462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-06-29T12:39:00.460842Z digest=sha256:c0fa61a47d4bb3c2bb6934f474c4454988a04e633ce156ba269abe95c740e084

Observation 640f68be-ae30-405b-9619-143f49e4e31b · inbound

Thinking Like a Scientist? A Structural Study of LLM-Generated Research Methods cites this paper.

Thinking Like a Scientist? A Structural Study of LLM-Generated Research Methods A Vision for Auto Research with LLM Agents

Reference 16

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T17:28:44.377361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-06-27T04:09:11.258587Z digest=sha256:34010e2043248b5bde4e2bfda4a53ec4683fe1f4644725fb9bcbda456bcd4f48

Observation d00fe041-3bda-434a-bfeb-456a26bef455 · inbound

Clarus: Coordinating Autonomous Research Agents toward Web-Scale Scientific Collaboration cites this paper.

Clarus: Coordinating Autonomous Research Agents toward Web-Scale Scientific Collaboration A Vision for Auto Research with LLM Agents

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-06-30T07:34:22.161079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-06-30T06:15:29.120280Z digest=sha256:710234de6b0e8e6b1e6e8437e26aa54a74f56906b2eef1cd5cc5f2350c3095d9

Observation 1e88b67e-5c2c-4c90-9a4b-a062554e6766 · inbound

ResearchStudio-Idea: An Evidence-Grounded Research-Ideation Skill Suite from ML Conference Outcomes cites this paper.

ResearchStudio-Idea: An Evidence-Grounded Research-Ideation Skill Suite from ML Conference Outcomes A Vision for Auto Research with LLM Agents

Reference 26

Resolution
unresolved
no resolver link, observed 2026-07-11T19:11:37.739920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-11T19:11:37.739920Z digest=sha256:2d565f2174df238912276dfae38a413422b0a3c5666e0d707d4561ff612b117f

Observation 3e9bbbdd-9722-4d45-a290-a061280c9721 · inbound

Internalizing Academic Writing Workflows for Introduction Generation via Struct-Aware Policy Learning cites this paper.

Internalizing Academic Writing Workflows for Introduction Generation via Struct-Aware Policy Learning A Vision for Auto Research with LLM Agents

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-06T00:57:10.171022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:57:10.171022Z digest=sha256:6f1c1bdc5d06113a5f8cf0139c3847a0ae69d03013dcbe28a74740771a983c99