Paper Citation Record · LEDGER

MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 25 inbound Pith citation observations for arXiv:2508.14704.

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

pith.paper-citation-record.v1
2508.14704 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 25 of 25 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T05:55:44.720516Z

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

2
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 69125550-6611-4428-b14e-e6d3a9f12807 · inbound

DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models cites this paper.

DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-10T13:05:26.757169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9d92ba12-d334-4ef6-82be-45e464a741d0 · inbound

MCP-Atlas: A Large-Scale Benchmark for Tool-Use Competency with Real MCP Servers cites this paper.

MCP-Atlas: A Large-Scale Benchmark for Tool-Use Competency with Real MCP Servers MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-16T08:30:45.635713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation dfa94d0d-b9c8-43b9-ae2d-edaf6eb15fa6 · inbound

MCP-Atlas: A Large-Scale Benchmark for Tool-Use Competency with Real MCP Servers cites this paper.

MCP-Atlas: A Large-Scale Benchmark for Tool-Use Competency with Real MCP Servers MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-21T15:10:16.406044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 593d568d-b3df-4616-9c2b-7c9f65680ce3 · inbound

Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning cites this paper.

Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Reference 2025

Resolution: malformed identifier; no resolver link, observed 2026-08-03T01:21:07.423753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce949294-6236-4f31-a63f-0081bf149ec0 · inbound

Agent-Diff: Benchmarking LLM Agents on Enterprise API Tasks via Code Execution with State-Diff-Based Evaluation cites this paper.

Agent-Diff: Benchmarking LLM Agents on Enterprise API Tasks via Code Execution with State-Diff-Based Evaluation MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-16T03:07:11.521829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8e779399-18b9-4c3a-9840-75d953e16790 · inbound

Model Context Protocol (MCP) Tool Descriptions Are Smelly! Towards Improving AI Agent Efficiency with Augmented MCP Tool Descriptions cites this paper.

Model Context Protocol (MCP) Tool Descriptions Are Smelly! Towards Improving AI Agent Efficiency with Augmented MCP Tool Descriptions MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-02T23:04:32.736348Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22552c04-e76f-4e2f-8c91-db610cfafe7e · inbound

Real Faults in Model Context Protocol (MCP) Software: a Comprehensive Taxonomy cites this paper.

Real Faults in Model Context Protocol (MCP) Software: a Comprehensive Taxonomy MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Reference 105

Resolution: unresolved; no resolver link, observed 2026-08-04T05:55:44.720516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6a86117-703e-4b9c-8590-b5f47a97040b · inbound

PHMForge: Evaluating LLM Agents on Industrial Prognostics through MCP-Native, Algorithm-Grounded Tools cites this paper.

PHMForge: Evaluating LLM Agents on Industrial Prognostics through MCP-Native, Algorithm-Grounded Tools MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-13T22:08:20.734278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2e7f9f2f-92a2-47ba-8be8-9724141b9a85 · inbound

ANX: Protocol-First Design for AI Agent Interaction with a Supporting 3EX Decoupled Architecture cites this paper.

ANX: Protocol-First Design for AI Agent Interaction with a Supporting 3EX Decoupled Architecture MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:05:49.940609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e42fd763-d473-4809-ba00-dbce8ed4a7b7 · inbound

From Language to Action: Enhancing LLM Task Efficiency with Task-Aware MCP Server Recommendation cites this paper.

From Language to Action: Enhancing LLM Task Efficiency with Task-Aware MCP Server Recommendation MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Reference 71

Resolution: verified exact; arxiv_id, observed 2026-05-10T06:21:26.755658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 21447ab9-9ac1-43d2-b5e5-bbb40ea66f89 · inbound

Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence cites this paper.

Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Reference 62

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T05:25:54.737759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 655a4ae8-3cce-4af4-8345-63cbf2fcafaa · inbound

Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization cites this paper.

Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T14:01:07.105970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1d10706c-2cf3-4669-9e20-90ea824c2b23 · inbound

MCP-Cosmos: World Model-Augmented Agents for Complex Task Execution in MCP Environments cites this paper.

MCP-Cosmos: World Model-Augmented Agents for Complex Task Execution in MCP Environments MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T02:11:15.317013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 71709c90-878f-40d4-b904-428615cab0ec · inbound

PREPING: Building Agent Memory without Tasks cites this paper.

PREPING: Building Agent Memory without Tasks MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T06:15:06.160195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a3ed0ffe-3c32-4572-9ab3-7a7969a97fe5 · inbound

From Text to Voice: A Reproducible and Verifiable Framework for Evaluating Tool Calling LLM Agents cites this paper.

From Text to Voice: A Reproducible and Verifiable Framework for Evaluating Tool Calling LLM Agents MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Reference 271

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T08:49:53.738775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 59c9c8a8-3d89-4314-a560-1749b756683e · inbound

TOBench: A Task-Oriented Omni-Modal Benchmark for Real-World Tool-Using Agents cites this paper.

TOBench: A Task-Oriented Omni-Modal Benchmark for Real-World Tool-Using Agents MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T20:47:45.985164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 09363262-59e4-48f3-8ff4-47c02fbe7bcf · inbound

Notation Matters: A Benchmark Study of Token-Optimized Formats in Agentic AI Systems cites this paper.

Notation Matters: A Benchmark Study of Token-Optimized Formats in Agentic AI Systems MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T07:13:16.038067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 434f5c42-a5a1-4f85-aeb5-c896307475a6 · inbound

Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application cites this paper.

Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Reference 135

Resolution: metadata mismatch; arxiv_id, observed 2026-06-27T09:50:48.301730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 64d89991-898c-4f9d-83ab-65751ace8828 · inbound

SENTINEL: Failure-Driven Reinforcement Learning for Training Tool-Using Language Model Agents cites this paper.

SENTINEL: Failure-Driven Reinforcement Learning for Training Tool-Using Language Model Agents MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T14:58:33.191600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b432a545-0c98-410b-9ef8-55c4d090ae68 · inbound

DynAMO:Dynamic Asset Management Orchestration via Topological Multi-Agent Scheduling cites this paper.

DynAMO:Dynamic Asset Management Orchestration via Topological Multi-Agent Scheduling MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-07-03T17:28:44.727542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 22815be2-c235-482a-8200-94c1d5d31fbe · inbound

Beyond Static Leaderboards: Predictive Validity for the Evaluation of LLM Agents cites this paper.

Beyond Static Leaderboards: Predictive Validity for the Evaluation of LLM Agents MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T03:29:30.310286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9c54a24a-1415-432f-aabe-98facabdc82b · inbound

Metis: Bridging Text and Code Memory for Self-Evolving Agents cites this paper.

Metis: Bridging Text and Code Memory for Self-Evolving Agents MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T16:29:56.693386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 64bf4e58-c472-490b-9086-b2cd84c4b7e1 · inbound

TUA-Bench: A Benchmark for General-Purpose Terminal-Use Agents cites this paper.

TUA-Bench: A Benchmark for General-Purpose Terminal-Use Agents MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Reference 75

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T15:35:48.601770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9faf4ce9-fe33-44b6-8e92-0c9a08061033 · inbound

E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios cites this paper.

E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.485800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e53b575-3a82-4944-b637-80a33b86ca9b · inbound

The Grokked Illusion: True Equilibrium Mitigates Catastrophic Forgetting cites this paper.

The Grokked Illusion: True Equilibrium Mitigates Catastrophic Forgetting MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T05:43:20.572273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.