2 citations · 7 across the 24 of their papers we have counts for
26 papers
Neural Networks with Differentiable Belief-Function Layers
Antonin Sulc
Yager's 1987 rule of combination was proposed on epistemic grounds: under high conflict, mass should be redistributed to ignorance rather than renormalised onto the clas…
Event Embedding of Protein Networks : Compositional Learning of Biological Function
Antonin Sulc
In this work, we study whether enforcing strict compositional structure in sequence embeddings yields meaningful geometric organization when applied to protein-protein interaction…
FastLSQ: Solving PDEs in One Shot via Fourier Features with Exact Analytical Derivatives
Antonin Sulc
We present FastLSQ, a framework for PDE solving and inverse problems built on trigonometric random Fourier features with exact analytical derivatives. Trigonometric features admit…
Modal Logical Neural Networks for Financial AI
Antonin Sulc
The financial industry faces a critical dichotomy in AI adoption: deep learning often delivers strong empirical performance, while symbolic logic offers interpretability and rule a…
Continuous Modal Logical Neural Networks: Modal Reasoning via Stochastic Accessibility
Antonin Sulc
We propose Fluid Logic, a paradigm in which modal logical reasoning, temporal, epistemic, doxastic, deontic, is lifted from discrete Kripke structures to continuous manifolds via N…
Differentiable Modal Logic for Multi-Agent Diagnosis, Orchestration and Communication
Antonin Sulc
As multi-agent AI systems evolve from simple chatbots to autonomous swarms, debugging semantic failures requires reasoning about knowledge, belief, causality, and obligation, preci…
Differentiable Logical Programming for Quantum Circuit Discovery and Optimization
Antonin Sulc
Designing high-fidelity quantum circuits remains challenging, and current paradigms often depend on heuristic, fixed-ansatz structures or rule-based compilers that can be suboptima…
From Natural Language to Control Signals: A Conceptual Framework for Semantic Channel Finding in Complex Experimental Infrastructure
Thorsten Hellert, Nikolay Agladze, Alex Giovannone +5
Modern experimental platforms such as particle accelerators, fusion devices, telescopes, and industrial process control systems expose tens to hundreds of thousands of control and…
Modal Logic Neural Networks
Antonin Sulc, Noor Naddour
Neural Networks are indispensable to natural sciences and society. Their impact extends from applications in public health to workforce productivity. Here, we introduce Modal Logic…
QCD in Language Models: What do they really know about QCD?
Antonin Sulc, Patrick L. S. Connor
This study presents an analysis of modern open-source large language models (LLMs) -- including Llama, Qwen, and Gemma -- to evaluate their encoded knowledge of Quantum Chromodynam…
Agentic System with Modal Logic for Autonomous Diagnostics
Antonin Sulc, Thorsten Hellert
The development of intelligent agents, particularly those powered by language models (LMs), has shown a critical role in various environments that require intelligent and autonomou…
Unsupervised Anomaly Detection in ALS EPICS Event Logs
Antonin Sulc, Thorsten Hellert, Steven Hunt
This paper introduces an automated fault analysis framework for the Advanced Light Source (ALS) that processes real-time event logs from its EPICS control system. By treating log e…
Quantum Noise Tomography with Physics-Informed Neural Networks
Antonin Sulc
Characterizing the environmental interactions of quantum systems is a critical bottleneck in the development of robust quantum technologies. Traditional tomographic methods are oft…
Agentic Artificial Intelligence for Multistage Physics Experiments at a Large-Scale User Facility Particle Accelerator
Thorsten Hellert, Drew Bertwistle, Simon C. Leemann +2
We present the first language-model-driven agentic artificial intelligence (AI) system to autonomously execute multi-stage physics experiments on a production synchrotron light sou…
Event2Vec: A Geometric Approach to Learning Composable Representations of Event Sequences
Antonin Sulc
The study of neural representations, both in biological and artificial systems, is increasingly revealing the importance of geometric and topological structures. Inspired by this,…
A Straightforward Pipeline for Targeted Entailment and Contradiction Detection
Antonin Sulc
Finding the relationships between sentences in a document is crucial for tasks like fact-checking, argument mining, and text summarization. A key challenge is to identify which sen…
Osprey: Production-Ready Agentic AI for Safety-Critical Control Systems
Thorsten Hellert, João Montenegro, Antonin Sulc
Operating large-scale scientific facilities requires coordinating diverse subsystems, translating operator intent into precise hardware actions, and maintaining strict safety overs…
eLog analysis for accelerators: status and future outlook
Antonin Sulc, Thorsten Hellert, Aaron Reed +11
This work demonstrates electronic logbook (eLog) systems leveraging modern AI-driven information retrieval capabilities at the accelerator facilities of Fermilab, Jefferson Lab, La…
ChatQCD: Let Large Language Models Explore QCD
Antonin Sulc, Patrick L. S. Connor
Quantum chromodynamics (QCD) has yielded a vast literature spanning distinct phenomena. We construct a corpus of papers and build a generative model. This model holds promise for a…
Towards Agentic AI on Particle Accelerators
Antonin Sulc, Thorsten Hellert, Raimund Kammering +2
As particle accelerators grow in complexity, traditional control methods face increasing challenges in achieving optimal performance. This paper envisions a paradigm shift: a decen…
Towards Unlocking Insights from Logbooks Using AI
Antonin Sulc, Alex Bien, Annika Eichler +15
Electronic logbooks contain valuable information about activities and events concerning their associated particle accelerator facilities. However, the highly technical nature of lo…
Automated Anomaly Detection on European XFEL Klystrons
Antonin Sulc, Annika Eichler, Tim Wilksen
High-power multi-beam klystrons represent a key component to amplify RF to generate the accelerating field of the superconducting radio frequency (SRF) cavities at European XFEL. E…
PACuna: Automated Fine-Tuning of Language Models for Particle Accelerators
Antonin Sulc, Raimund Kammering, Annika Eichler +1
Navigating the landscape of particle accelerators has become increasingly challenging with recent surges in contributions. These intricate devices challenge comprehension, even wit…
Textual Analysis of ICALEPCS and IPAC Conference Proceedings: Revealing Research Trends, Topics, and Collaborations for Future Insights and Advanced Search
Antonin Sulc, Annika Eichler, Tim Wilksen
In this paper, we show a textual analysis of past ICALEPCS and IPAC conference proceedings to gain insights into the research trends and topics discussed in the field. We use natur…