arXiv research

A novel controller for wheeled robots handles joystick inputs for smooth steering.

Improved driver identification accuracy using steering wheel data.

This paper explores the capability of deep neural networks to capture key characteristics of vehicle dynamics, and their ability to perform coupled longitudinal and lateral control of a vehicle. To this extent, two different artificial neural networks are trained to compute vehicle controls corresponding to a reference…

Motivated by the problem of deformation quantization we introduce and study directed graph complexes with oriented loops and wheels. We develop some technique for computing cohomology of such graph complexes and apply it to several concrete examples such as wheeled completion of the operad of strongly homotopy Lie alge…

New PROP structure defined for automorphism group cohomology.

New model for visual cortex border completion using bicycle wheel motions.

Unique CaTherine wheel found for LQG geodesic tree.

CaTherine wheels map between diverse geometric fields.

Geometric models for Lie algebras from simple singularities.

The paper computes groups and modules for wheel graphs using Fibonacci and Chebyshev polynomials.

The purpose of this research paper it is to present a new approach in the framework of a biased roulette wheel. It is used the approach of a quantitative trading strategy, commonly used in quantitative finance, in order to assess the profitability of the strategy in the short term. The tools of backtesting and walk-for…

Paper introduces exact credible sets for classification problems.

The model of a bicycle is a unit segment AB that can move in the plane so that it remains tangent to the trajectory of point A (the rear wheel is fixed on the bicycle frame); the same model describes the hatchet planimeter. The trajectory of the front wheel and the initial position of the bicycle uniquely determine its…

In a previous paper, we generalized the definition of the framed Kontsevich integral initially presented by Le and Murakami. We also defined an isotopy invariant $\widetilde{Z}_f$ that is well-behaved under band sum moves. Using this invariant we study the construction of the LMO invariant, the Wheeling Theorem, and th…

Using elementary equalities between various cables of the unknot and the Hopf link, we prove the Wheels and Wheeling conjectures of [Bar-Natan, Garoufalidis, Rozansky and Thurston, arXiv:q-alg/9703025] and [Deligne, letter to Bar-Natan, January 1996, http://www.ma.huji.ac.il/~drorbn/Deligne/], which give, respectively,…

We study the unwheeled rational Kontsevich integral of torus knots. We give a precise formula for these invariants up to loop degree 3 and show that they appear as colorings of simple diagrams. We show that they behave under cyclic branched coverings in a very simple way. Our proof is combinatorial: it uses the results…

Using technique of wheeled props we establish a correspondence between the homotopy theory of unimodular Lie 1-bialgebras and the famous Batalin-Vilkovisky formalism. Solutions of the so called quantum master equation satisfying certain boundary conditions are proven to be in 1-1 correspondence with representations of …

We study the rational Kontsevich integral of torus knots. We construct explicitely a series of diagrams made of circles joined together in a tree-like fashion and colored by some special rational functions. We show that this series codes exactly the unwheeled rational Kontsevich integral of torus knots, and that it beh…

We express characteristic numbers of compact hyperkähler manifolds in graph-theoretical form, considering them as a special case of the curvature invariants introduced by Rozansky and Witten. The appropriate graphs are generated by ``wheels'' and we use the recently proved Wheeling Theorem to give a formula for the L2 …

Predict steering angles of self-driving cars from images.

New analysis shows interpretability doesn't guarantee steering utility in LLMs.

New method identifies drivers from car logs without reverse-engineering CAN protocol.

New method steers protein design towards desired properties.

Study designs incentives for adapting multi-agent systems without knowing their learning dynamics.

We show LLMs can be locally linear, enabling better control of activations.

Study designs steering rewards for MFGs with unknown dynamics and model uncertainty.

New model shows most users on Q&A site Stack Overflow ignore badges.

The complement of a non-separating planar graph contains a K_n minor.

The paper explores how AI systems use information geometry to encode semantic structure.

We write a formula for the LMO invariant of a rational homology sphere presented as a rational surgery on a link in S^3. Our main tool is a careful use of the Aarhus integral and the (now proven) "Wheels" and "Wheeling" conjectures of B-N, Garoufalidis, Rozansky and Thurston. As steps, side benefits and asides we give …

New approach extracts AI model representations for steering and monitoring.

Painless Activation Steering automates post-training for LMs without manual intervention.

This research tackles balancing exploration and exploitation in deep RL for partially observable systems.

Solves steering problem with continuous time, Hilbert-Schmidt cost, and matrix ODEs.

A new method steers Gaussian distributions with minimal effort.

Mobile robot navigation in complex and dynamic environments is a challenging but important problem. Reinforcement learning approaches fail to solve these tasks efficiently due to reward sparsities, temporal complexities and high-dimensionality of sensorimotor spaces which are inherent in such problems. We present a nov…

Interactive steering improves hierarchical clustering for diverse user needs.

We recall the construction of the Kontsevich graph orientation morphism $γ\mapsto {\rm O\vec{r}}(γ)$ which maps cocycles $γ$ in the non-oriented graph complex to infinitesimal symmetries $\dot{\mathcal{P}} = {\rm O\vec{r}}(γ)(\mathcal{P})$ of Poisson bi-vectors on affine manifolds. We reveal in particular why there alw…

This paper improves model training by using a reference model to guide target model training.

A new method for steering large agent populations efficiently.

Model-free reinforcement learning has recently been shown to successfully learn navigation policies from raw sensor data. In this work, we address the problem of learning driving policies for an autonomous agent in a high-fidelity simulator. Building upon recent research that applies deep reinforcement learning to navi…

Unified Bayesian model explains in-context learning and activation steering in LLMs.

MFMs enable efficient reward alignment for generative models.

New method prevents RLHF alignment collapse by accounting for policy's influence on reward model updates.

Bayesian Power Steering fine-tunes large diffusion models for domain adaptation.

Hybrid model uses LLM to build transparent Bayesian networks for trading decisions.

A spiral unibike track emerges from a mathematical construction.

ELS framework improves safety alignment by dynamically steering LLMs towards helpful responses.