Paper Citation Record · LEDGER

MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2311.08166.

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

pith.paper-citation-record.v1
2311.08166 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:56:19.053872Z

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

3
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 48b270ea-f5c4-4882-910a-ee2326a826f3 · inbound

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials cites this paper.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-05T22:56:19.053872Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b61819e-7312-4d80-90ec-a59c28674322 · inbound

ALL-FEM: Agentic Large Language models Fine-tuned for Finite Element Methods cites this paper.

ALL-FEM: Agentic Large Language models Fine-tuned for Finite Element Methods MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T15:51:05.260945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ab5d95f8-a4de-4084-9294-b56af5cd19f1 · inbound

SCICONVBENCH: Benchmarking LLMs on Multi-Turn Clarification for Task Formulation in Computational Science cites this paper.

SCICONVBENCH: Benchmarking LLMs on Multi-Turn Clarification for Task Formulation in Computational Science MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-20T10:38:12.195249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8f379924-2b8e-4633-bf1e-1425b8209faf · inbound

TO-Agents: A Multi-Agent AI Pipeline for Preference-Guided Topology Optimization cites this paper.

TO-Agents: A Multi-Agent AI Pipeline for Preference-Guided Topology Optimization MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-22T09:34:46.914178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 39107686-cfa7-4df6-9f44-179e36e9085a · inbound

Auto-Configuring Scientific Simulators with Lightweight Coding-Agent Adapters cites this paper.

Auto-Configuring Scientific Simulators with Lightweight Coding-Agent Adapters MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-03T01:47:32.159933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5c121d08-aace-4b8a-a8a6-bd71f23dc658 · inbound

Auto-Configuring Scientific Simulators with Lightweight Coding-Agent Adapters cites this paper.

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-06-29T15:23:33.004589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.