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From the 1 of 13 linked papers with an AI index.

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cs.AI2026

Neurosymbolic Discovery of Algebraic Graph Constructions

David Seka, Stefan Szeider

There are several methods for searching for graphs with prescribed properties, such as SAT solvers and specialized generators. These methods return the result as raw data: an adjac…

cs.AI2026

LeanCSP: A Framework for Certifying Constraint Reformulation and Solving in Lean

Pablo Manrique, Stefan Szeider

The paper presents LeanCSP, a framework built in the Lean theorem prover that can formally verify constraint reformulations and certify solver results for constraint problems, prov…

cs.AI2026

Algorithm Selection with Zero Domain Knowledge via Text Embeddings

Stefan Szeider

We propose a feature-free approach to algorithm selection: instead of hand-crafted instance features, we use pretrained text embeddings. Our method, ZeroFolio, proceeds in three st…

cs.AI2026

Agentic Neurosymbolic Collaboration for Mathematical Discovery: A Case Study in Combinatorial Design

Hai Xia, Carla P. Gomes, Bart Selman +1

We study mathematical discovery through the lens of neurosymbolic reasoning, where an AI agent powered by a large language model (LLM), coupled with symbolic computation tools, and…

cs.AI2026

CP-Agent: Agentic Constraint Programming

Stefan Szeider

The translation of natural language to formal constraint models requires expertise in the problem domain and modeling frameworks. To explore the effectiveness of agentic workflows,…

cs.AI2025

What Do LLM Agents Do When Left Alone? Evidence of Spontaneous Meta-Cognitive Patterns

Stefan Szeider

We introduce an architecture for studying the behavior of large language model (LLM) agents in the absence of externally imposed tasks. Our continuous reason and act framework, usi…