12 papers
This paper introduces a genetic programming-based method for the symbolic discovery of stochastic differential equations that jointly optimizes drift and diffusion functions via maximum likelihood estimation, enabling the accurate, scalable, and interpretable modeling of noisy dynamical systems.
This paper introduces a hybrid algebraic verification technique implemented in TalisMan2.0 that utilizes parallel multimodular reasoning to efficiently verify arithmetic circuits with large operands while avoiding the computational overhead of big integer arithmetic.
This paper introduces the Randomized Distributed Function Computation (RDFC) framework, a semantic communication approach that achieves local differential privacy and significantly reduces transmission rates compared to lossless methods, even in scenarios without shared randomness, by leveraging strong coordination metrics and randomized function generation.
This paper presents "Agent Hunt," a decentralized autoformalization system for algebraic topology that utilizes a simulated bounty-based marketplace to coordinate multiple LLM agents in dynamically proposing, competing to prove, and iteratively refining formal statements within an Interactive Theorem Proving environment.
This habilitation thesis investigates polynomial algorithmics under space constraints, presenting time- and space-efficient algorithms for fundamental operations and introducing the first quasi-linear algorithm for sparse polynomial interpolation while addressing hard problems in sparse factorization and divisibility.
This paper proposes a reinforcement learning framework with a probabilistic reward function and a Gaussian baseline to discover power-flow network configurations that yield a significantly higher number of equilibrium points than current computational algebra methods can identify.
This paper presents a neural network implementation of Fuzzy Cognitive Maps (FHM) that utilizes Langevin differential dynamics to learn causality patterns, derive inverse solutions for output modification, and evaluates its performance across multiple datasets.
This paper proposes a novel framework that distills the geometric semantics of Signal Temporal Logic into a Transformer encoder via kernel alignment, enabling efficient, invertible, and semantically faithful neural representations that overcome the computational limitations of symbolic kernels and the structural deficiencies of syntax-based embeddings.
This paper presents an efficient algorithm that utilizes partial Gröbner basis computation and sparse interpolation to find simple generating sets for subfields of rational function fields, demonstrating improved performance and practical utility in applications like structural parameter identifiability.
This paper introduces Structured Kolmogorov-Arnold Neural ODEs (SKANODEs), a framework that combines structured state-space modeling with Kolmogorov-Arnold Networks to accurately recover interpretable physical latent states and discover compact symbolic governing equations for nonlinear dynamical systems, outperforming black-box neural ODEs and classical identification methods across synthetic and real-world datasets.
This paper introduces a physics-constrained machine learning framework using genetic programming to automatically discover interpretable, closed-form equations for multimodal water retention curves directly from experimental data, overcoming the limitations of traditional superposition-based models.
The paper introduces R1-Code-Interpreter, a general-purpose LLM trained via multi-stage reinforcement learning and supervised fine-tuning on a diverse dataset of 144 tasks, which significantly improves reasoning accuracy and enables emergent self-checking behaviors, outperforming both text-only and tool-augmented GPT-4o models.