Call for Presentations
The SCML-2026 conference 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 to the application of ML to SC, the application of SC to ML, and the hybrid combination of SC and ML to solving problems. SCML-2026 provides ample space to exchange ideas and discuss recent approaches this newly emerging research field.
SCML-2026 is a "presentation-oriented" conference that solicits submissions in the form of extended abstracts (1-2 pages) which are only briefly reviewed with respect to their relevance to the topics of the conference. The abstracts of accepted presentations are collectively published as a "conference booklet" in the frame of the SCML publication forum. At least one author of an accepted abstract is required to register as a presenter at the conference.
Furthermore, we explicitly encourage the SCML-2026 authors to also submit full papers related to their presentations to the SCML publishing forum, where they are refereed according to the rules of the forum and, if accepted, published there. According to the "continuous call for papers" of the SCML publishing forum, papers can be submitted at any time before or after the conference (without deadline). However, the acceptance of a presentation at the SCML-2026 conference does not depend on the acceptance of a paper at the SCML publishing forum.
Examples of topics in the scope of SCML-2026 are (this list is not exhaustive):
- 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.