Tag
A research paper analyzing what iterated self-feeding probes of language models actually measure, distinguishing model-dependent signals from construction artifacts using a ring of resampled token cells and common random numbers coupling.
The paper presents a controlled matched-integrator evaluation of Hamiltonian Neural Networks against a parameter-matched baseline on pendulum and Kepler dynamics, showing significant reductions in energy drift and trajectory error. It also examines the behavior of Störmer–Verlet-style rollouts with learned non-separable Hamiltonians.
This paper studies adaptive symmetry discovery for dynamical system identification, showing that known symmetries reduce the trajectory length needed for identification and proposing a method to learn unknown symmetry groups from a single trajectory to achieve the same optimal length.
This paper proposes a provable two-stage pipeline that distills nonlinear dynamical systems into compact linear state-space models using convex optimization, with theoretical guarantees and experiments on LDS benchmarks and MuJoCo.
This paper introduces a bidirectional latent diffusion model that steps dynamical systems forward or backward in time, using round-trip consistency as a self-supervised test-time error signal to predict rollout errors without ground truth or ensembles.
Introduces a Lindblad-inspired multi-timescale reservoir architecture that separates rotation and dissipation for independent control of mixing, memory, and stability, achieving competitive results on benchmarks like NARMA-20 and Lorenz-63.
This paper introduces a verifier-guided workflow around ODEFormer, a pretrained symbolic transformer, to discover interpretable equations for physical dynamical systems. It demonstrates transfer to vortex shedding and other systems using dynamical and physical-admissibility criteria to select equations from a candidate pool.
Introduces HyperODE, a zero-shot surrogate that maps ODE structures to hypergraphs, enabling simulation and parameter inference across entire families of dynamical systems without retraining.
A research paper introduces a recurrent neural operator to forecast tipping points in non-stationary dynamical systems, applied to climate and aerodynamics, with uncertainty quantification using conformal prediction. The tweet highlights the paper's utility for early detection of abrupt changes.
This paper provides theoretical guarantees for distinguishing LLM responses by modeling token embeddings as trajectories of a dynamical system, proving exponential decay of misclassification probability and characterizing cross-embedding generalization.
This paper evaluates whether large language models can function as interpretable controllers for dynamical systems, specifically a thermal environment. It finds that high-complexity models like Qwen-3 14B and GPT-4o achieve accurate control and coherent reasoning, while smaller models struggle, highlighting opportunities for hybrid model-based and language-driven control strategies.
This paper shows that the smallest eigenvalue of the invariant-measure moment matrix determines the identifiability ceiling for system discovery methods like SINDy and PySR, based on attractor geometry. It introduces a framework that predicts discovery limits from a short reference trajectory without refitting.
A new formal theorem, verified in Lean, shows that for thresholds tending to infinity, almost every positive integer falls below the threshold within 436 log N Collatz steps, strengthening Terence Tao's earlier result with explicit bounds and natural density.
This paper proposes NeoST, the first spatio-temporal foundation model pre-trained solely on procedurally generated synthetic data. It introduces a latent-space reasoning architecture that generates and iteratively refines multiple future trajectories, outperforming existing STFMs on real-world benchmarks.
This paper studies oversmoothing in hypergraph neural networks from a dynamical-systems perspective, proposing a reaction-diffusion framework (HNRD) that preserves node-discriminative variation and achieves depth-robust propagation. Experiments show consistent improvement over baselines.
Introduces Cluster-Weighted EDMD, a data-driven method that jointly learns a partition and per-cluster Koopman operators via expectation-maximization, improving prediction accuracy over standard EDMD on classical dynamical systems.
This paper applies dynamical systems analysis to interpret latent chain-of-thought reasoning in models like CODI and COCONUT, revealing structured dynamics with stable and unstable classes.
Introduces Weak-form Kernel Ridge Regression (WKRR) for learning dynamical systems from noisy measurements, combining a weak formulation with kernel ridge regression to filter noise and improve accuracy. The method outperforms baseline methods on chaotic benchmarks up to 64 dimensions and 15,000-dimensional real-world fluid data.
Proposes a hierarchical Bayesian framework for meta-learning in dynamical systems from multiple sparse, noisy datasets, using gradient-based MCMC with an embedded ODE solver for efficient posterior inference of shared and dataset-specific parameters.
This paper analyzes why machine learning, particularly neural networks, remains opaque in its learning process by framing it as a complex dynamical system, identifying three key properties that contribute to learning opacity, and arguing that some sources may be irreducible.