From tanja.gutenbrunner at risc.jku.at  Tue Jul  8 13:46:39 2025
From: tanja.gutenbrunner at risc.jku.at (Tanja Gutenbrunner)
Date: Tue, 8 Jul 2025 13:46:39 +0200
Subject: [Publicity] Symbolic Computation and Machine Learning (Call for
	Papers)
In-Reply-To: <344a136c-218b-4a3f-8a28-85376565bd40@risc.jku.at>
References: <344a136c-218b-4a3f-8a28-85376565bd40@risc.jku.at>
Message-ID: <869aaa4e-11ed-42be-ab0e-b92ce10c3d4c@risc.jku.at>

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SCML: A PUBLISHING FORUM FOR SYMBOLIC COMPUTATION AND MACHINE LEARNING
An initiative of the Research Institute for Symbolic Computation (RISC)
https://scml.risc.jku.at
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CONTINUOUS CALL FOR PAPERS

The SCML publishing forum is dedicated to all research that strives to
combine Symbolic Computation (SC) and Machine Learning (ML) as two major
approaches to "Artificial Intelligence", in particular the application
of ML to SC, the application of SC to ML, and the hybrid combination of
SC and ML to solving problems. We consider submissions that explore the
interaction between the two fields - not standalone works on either SC
or ML.

Examples of topics in the scope of SCML are (this list is not
exhaustive, we expect that it will grow rapidly with the evolution of
the field):

* Applying ML to computer mathematics, algebra, geometry;
integrating ML into mathematical software systems.
* Applying ML to automated reasoning, theorem proving, satisfiability
solving; integrating ML into interactive and automated provers.
* Applying ML to program synthesis; integrating ML into program
verification systems.
* Applying SC to analyzing ML models ("explainable AI"),
deriving error bounds, ensuring robustness, interpreting answers.
* Applying SC to verifying ML models ("verified AI"), preventing errors
and hallucinations.
* Applying SC to synthesizing ML models with guaranteed error bounds,
robustness, correctness properties.
* Integrating SC capabilities (such as computer algebra and automated
reasoning) into ML models.
* Applying LLMs to the automatic formalization of mathematical/logical
texts.
* Applying LLMs as natural language interfaces to SC systems,
integrating co-pilots into SC systems.
* Combining linguistic reasoning (LLMs) and formal reasoning
(theorem provers).
* Combining LLMs and SC systems for education.
* Teaching (for example, in mathematics) using a combination
of SC and ML systems.
* Software and system descriptions, datasets, benchmarks, and
metrics related to the interplay of SC and ML.

SCML primarily solicits papers that present original research results
but also accepts survey and position papers that add a new perspective
to the interplay of SC and ML.

SUBMISSION

SCML papers can be *continuously submitted* (see the link below)
and enter the reviewing process immediately after their submission.
The final versions of accepted papers are published in the electronic

* RISC Proceedings on Symbolic Computation and Machine Learning.

They are archived with a DOI and are freely available for download from
the SCML web page under a Creative Commons License. Authors of accepted
papers are expected to present them at a subsequent SCML workshop. These

* SCML workshops

take place in semi-regular intervals in purely online form (via Zoom),
typically in half a day. Authors of accepted SCML papers that present
original research may be invited to submit extended versions of their
papers to the

* SCML Track of the Journal of Symbolic Computation.

CONTACT

* Web Page & Submission: https://scml.risc.jku.at
* SCML Managing Editors: scml at risc.jku.at
* Steering Committee, Editorial Board, Scientific Committee:
see the web page.


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