Paper Citation Record · LEDGER

REALM-Bench: A Benchmark for Evaluating Multi-Agent Systems on Real-world, Dynamic Planning and Scheduling Tasks

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2502.18836.

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

pith.paper-citation-record.v1
2502.18836 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T06:00:19.575152Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T14:06:37.869211Z

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 109daa63-659a-4af3-840f-7d4c5d1cf243 · inbound

LLM-Powered AI Agent Systems and Their Applications in Industry cites this paper.

LLM-Powered AI Agent Systems and Their Applications in Industry REALM-Bench: A Benchmark for Evaluating Multi-Agent Systems on Real-world, Dynamic Planning and Scheduling Tasks

Reference 112

Resolution: verified exact; arxiv_id, observed 2026-05-22T14:06:37.872768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4cb51ecc-fc3c-4026-9251-3baf8bb50f17 · 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 REALM-Bench: A Benchmark for Evaluating Multi-Agent Systems on Real-world, Dynamic Planning and Scheduling Tasks

Reference 122

Resolution: unresolved; no resolver link, observed 2026-08-07T06:00:19.575152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 936492cb-53a7-4681-8eb0-fe29ffe4d9f2 · inbound

Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities cites this paper.

Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities REALM-Bench: A Benchmark for Evaluating Multi-Agent Systems on Real-world, Dynamic Planning and Scheduling Tasks

Reference 217

Resolution: unresolved; no resolver link, observed 2026-08-06T23:26:54.504075Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a58901e-2fd1-4e76-b9f1-3f512b7b46ab · inbound

MCPEval: Automatic MCP-based Deep Evaluation for AI Agent Models cites this paper.

MCPEval: Automatic MCP-based Deep Evaluation for AI Agent Models REALM-Bench: A Benchmark for Evaluating Multi-Agent Systems on Real-world, Dynamic Planning and Scheduling Tasks

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T16:45:59.600438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56f0b705-b7e5-40db-8ed0-6a3d368801ce · inbound

Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory cites this paper.

Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory REALM-Bench: A Benchmark for Evaluating Multi-Agent Systems on Real-world, Dynamic Planning and Scheduling Tasks

Reference 82

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T23:13:16.417856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6674bf51-d1f7-4ab4-b7b8-e4a3d81caff0 · inbound

AGI Requires a Coordination Layer on Top of Pattern Repositories cites this paper.

AGI Requires a Coordination Layer on Top of Pattern Repositories REALM-Bench: A Benchmark for Evaluating Multi-Agent Systems on Real-world, Dynamic Planning and Scheduling Tasks

Reference 2026

Resolution: unresolved; no resolver link, observed 2026-08-03T18:22:03.249593Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aaae33ec-9c1f-48d7-a4bf-3ab9e178c53c · inbound

Do Agent Societies Develop Intellectual Elites? The Hidden Power Laws of Collective Cognition in LLM Multi-Agent Systems cites this paper.

Do Agent Societies Develop Intellectual Elites? The Hidden Power Laws of Collective Cognition in LLM Multi-Agent Systems REALM-Bench: A Benchmark for Evaluating Multi-Agent Systems on Real-world, Dynamic Planning and Scheduling Tasks

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-13T19:13:09.682986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3e0ee32c-57a2-48fa-9aab-f73ff19b04ce · inbound

Robust Agent Compensation (RAC): Teaching AI Agents to Compensate cites this paper.

Robust Agent Compensation (RAC): Teaching AI Agents to Compensate REALM-Bench: A Benchmark for Evaluating Multi-Agent Systems on Real-world, Dynamic Planning and Scheduling Tasks

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-12T11:01:30.437550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 38e7b35b-63a5-4033-97f9-5f3ab4a792fb · inbound

Robust Agent Compensation (RAC): Teaching AI Agents to Compensate cites this paper.

Robust Agent Compensation (RAC): Teaching AI Agents to Compensate REALM-Bench: A Benchmark for Evaluating Multi-Agent Systems on Real-world, Dynamic Planning and Scheduling Tasks

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-21T00:23:52.442212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.