Evidence map›Paper›PMID 42211174›Full record

ArticleFrontiers in artificial intelligence2026

Building MCP-native hierarchical AI scientist ecosystems: a perspective on scaling multi-agent scientific discovery.

Ling Yue, Ching-Yun Ko, Pin-Yu Chen, Shimin Di, Shaowu Pan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Ling YueDepartment of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, United States.
Ching-Yun KoIBM Research, Yorktown Heights, NY, United States.
Pin-Yu ChenIBM Research, Yorktown Heights, NY, United States.
Shimin DiSchool of Computer Science and Engineering, Southeast University, Nanjing, China.
Shaowu PanDepartment of Mechanical, Aerospace, and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Large language models (LLMs) are evolving from chatbots with limited tool-using capabilities to agentic AI systems that can perform deep research, assist in proposing hypotheses, help design experiments, automate data analysis, and draft scientific reports. However, there are currently two bottlenecks limiting LLMs' real-world impact on the broader scientific research community beyond academic demonstrations:

Indexed as

agent ecosystemsAI for Scienceautonomous scientific discoveryHierarchical coordinationModel Context Protocolmulti-agent systemsorganizational designtool interoperability

Identifiers

PMID42211174
PMCPMC13212480

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.