Evidence map›Paper›PMID 40395432›Full record

ArticlePNAS nexus2025

Neurosymbolic AI as an antithesis to scaling laws.

Alvaro Velasquez, Neel Bhatt, Ufuk Topcu, Zhangyang Wang, Katia Sycara, Simon Stepputtis, Sandeep Neema, Gautam Vallabha

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PNAS nexus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. The Era of Large-Scale Data in Biological Sciences.Advances in experimental medicine and biology · 2026
    Review
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

8 authors.

Alvaro VelasquezDepartment of Computer Science, University of Colorado Boulder, 430 UCB, 1111 Engineering Dr, Boulder, CO 80309, USA.ORCID https://orcid.org/0000-0001-6757-105X
Neel BhattOden Institute for Computational Engineering and Sciences, University of Texas at Austin, 201 E 24th St, Austin, TX 78712, USA.ORCID https://orcid.org/0000-0002-1807-9672
Ufuk TopcuOden Institute for Computational Engineering and Sciences, University of Texas at Austin, 201 E 24th St, Austin, TX 78712, USA.ORCID https://orcid.org/0000-0003-0819-9985
Zhangyang WangOden Institute for Computational Engineering and Sciences, University of Texas at Austin, 201 E 24th St, Austin, TX 78712, USA.ORCID https://orcid.org/0000-0002-2050-5693
Katia SycaraDepartment of Machine Learning, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA.
Simon StepputtisDepartment of Machine Learning, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA.
Sandeep NeemaDepartment of Computer Science, Vanderbilt University, 1400 18th Ave S, Nashville, TN 37212, USA.ORCID https://orcid.org/0000-0002-9781-3619
Gautam VallabhaResearch and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd, Laurel, MD 20723, USA.ORCID https://orcid.org/0000-0001-6856-7968

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The recent progress in machine learning has shifted the trends in artificial intelligence (AI) toward an overreliance on increasing amounts of data, computing power, and model parameters. These trends have resulted in success, but have also created a monolithic perspective for AI, increased the barriers to entry outside of large tech companies, and raised concerns about computational sustainability. Neurosymbolic AI is a growing area that promotes methodological heterogeneity and aims to push the frontiers of AI through affordable data and computing power.

Indexed as

bitter lessonfoundation modelslarge language modelsneurosymbolic AIscaling laws

Identifiers

PMID40395432
PMCPMC12084822

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.