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David Berghaus

Multi-Modal Vision vs. Text-Based Parsing: Benchmarking LLM Strategies for Invoice Processing
2025 · David Berghaus, Armin Berger, et al. · IEEE International Conference on Big Data (BigData) · 3
Foundation Inference Models for Markov Jump Processes
2024 · David Berghaus, Kostadin Cvejoski, et al. · neural information processing systems · 1
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Spherical Fixed-Point Reasoners: Adaptive Loop Halting on the Hypersphere
2026 · Zela, Arber, Maranjyan, Artavazd, et al. · arXiv (Cornell University)
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Have an LLM Write Your Anomaly Detector: Autonomous Discovery of Compact, Interpretable Detectors for Time Series
2026 · Berghaus, David · arXiv (Cornell University)
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LLM Parameters for Math Across Languages: Shared or Separate?
2026 · Shomali, Behzad, Victor, Luisa, et al. · arXiv (Cornell University)
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EVIL Baseline: LLM-Discovered Heuristics can be strong Baselines for Scientific Inference
2026 · Berghaus, David, :unav · Fraunhofer-Publica (Fraunhofer-Gesellschaft)
Adaptive Loops and Memory in Transformers: Think Harder or Know More?
2026 · Frey, Markus, Shomali, Behzad, et al. · arXiv (Cornell University)
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Foundation Inference Models for Ordinary Differential Equations
2026 · Mauel, Maximilian, Hübers, Johannes R., et al. · arXiv (Cornell University)
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Domain-Adaptation through Synthetic Data: Fine-Tuning Large Language Models for German Law
2026 · Bashir, Ali Hamza, Khalid, Muhammad Rehan, et al. · arXiv (Cornell University)
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Beyond Reinforcement Learning: Fast and Scalable Quantum Circuit Synthesis
2026 · Theißinger, Lukas, Gerlach, Thore, et al. · arXiv (Cornell University)
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Is continuous CoT better suited for multi-lingual reasoning?
2026 · Bashir, Ali Hamza, Shomali, Behzad, et al. · arXiv (Cornell University)
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EVIL: Evolving Interpretable Algorithms for Zero-Shot Inference on Event Sequences and Time Series with LLMs
2026 · Berghaus, David, :unav · arXiv (Cornell University)
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LLM Parameters for Math Across Languages: Shared or Separate?
2026 · Behzad Shomali, Luisa Victor, et al. · Proceedings of the conference - Association for Computational Linguistics. Meeting
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Foundation Inference Models for Ordinary Differential Equations
2026 · Hübers, Johannes Radu, Mauel, Maximilian, et al. · Fraunhofer-Publica (Fraunhofer-Gesellschaft)
Towards Fast Coarse-graining and Equation Discovery with Foundation Inference Models
2025 · Hinz, Manuel, Mauel, Maximilian, et al. · arXiv (Cornell University)
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Towards Foundation Inference Models that Learn ODEs In-Context
2025 · Mauel, Maximilian, Hinz, Manuel, et al. · arXiv (Cornell University)
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On Foundation Models for Temporal Point Processes to Accelerate Scientific Discovery
2025 · Berghaus, David, Seifner, Patrick, et al. · arXiv (Cornell University)
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In-Context Learning of Temporal Point Processes with Foundation Inference Models
2025 · Berghaus, David, Seifner, Patrick, et al. · arXiv (Cornell University)
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Not constructing Ramsey Graphs using Deep Reinforcement Learning
2025 · Berghaus, David, :unav · Fraunhofer-Publica (Fraunhofer-Gesellschaft)
In-Context Learning of Stochastic Differential Equations with Foundation Inference Models
2025 · Patrick Seifner, Kostadin Cvejoski, et al. · neural information processing systems
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Works count: 20
Citation count: 4
H-index: 1
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