From the 1 of 7 linked papers with an AI index.
7 papers
Solver-Aware Decompositions for Programming-by-Example: When Dividing Requires Knowing how to Conquer
Janis Zenkner, Tobias Sesterhenn, Tim Grams +1
Decomposition-based Programming-by-example (PBE) scales performance by splitting tasks into subtasks that a learned synthesizer solves: a decomposer predicts intermediate subgoals,…
Revisiting Predictive Process Monitoring in the Age of Foundation Models: A Comparative Study of Sequence, Tabular, and LLM Approaches
Lennart Fertig, Lukas Kirchdorfer, Tobias Sesterhenn
The paper benchmarks predictive process monitoring using three approaches—sequence models, tabular foundation models, and large language models—across several datasets and tasks, f…
BIM-Edit: Benchmarking Large Language Models for IFC-Based Building Information Modeling
Bharathi Kannan Nithyanantham, Clemens Kujat, Tobias Sesterhenn +5
Large language models (LLMs) are increasingly applied to computer-aided design (CAD) to generate design artifacts from textual instructions. In engineering practice, this requires…
Beyond Either-Or Reasoning: Transduction and Induction as Cooperative Problem-Solving Paradigms
Janis Zenkner, Tobias Sesterhenn, Christian Bartelt
Traditionally, in Programming-by-example (PBE) the goal is to synthesize a program from a small set of input-output examples. Lately, PBE has gained traction as a few-shot reasonin…
MCP4IFC: IFC-Based Building Design Using Large Language Models
Bharathi Kannan Nithyanantham, Tobias Sesterhenn, Ashwin Nedungadi +4
Bringing generative AI into the architecture, engineering and construction (AEC) field requires systems that can translate natural language instructions into actions on standardize…
A Compute-Matched Re-Evaluation of TroVE on MATH
Tobias Sesterhenn, Ian Berlot-Attwell, Janis Zenkner +1
Reusing established theorems and formulas is central to mathematical problem solving, serving as essential building blocks for tackling increasingly complex challenges. Recent work…