Tootfinder

Typicality with Feedback
Thomas Sturma, Mich\`ele Wigger
https://arxiv.org/abs/2507.16384 https://arxiv.org/pdf/2507.16384…

Typicality with Feedback
The main objective of this paper is to analyze a closed-loop feedback system where a transmitter probes a discrete memoryless channel (DMC) and can adapt its inputs based on the previous channel outputs. We prove that, regardless of the transmitter's strategy, the conditional type of the outputs given the inputs remains close to the DMC transition law $P_{Y|X}$. This general result enables the study of fundamental limits in certain adaptive systems. As an application, we establish a conv…

Discrete-Time LQ Stochastic Two Person Nonzero Sum Difference Games With Random Coefficients:~Closed-Loop Nash Equilibrium
Qingxin Meng, Yiwei Wu
https://arxiv.org/abs/2507.16412 …

Discrete-Time LQ Stochastic Two Person Nonzero Sum Difference Games With Random Coefficients:~Closed-Loop Nash Equilibrium
This paper investigates closed-loop Nash equilibria for discrete-time linear-quadratic (LQ) stochastic nonzero-sum difference games with random coefficients. Unlike existing works, we consider randomness in both state dynamics and cost functionals, leading to a complex structure of fully coupled cross-coupled stochastic Riccati equations (CCREs). The key contributions lie in characterizing the equilibrium via state-feedback strategies derived by decoupling stochastic Hamiltonian systems governe…

Apology of Green Digitalization in the Context of Information and Climate Feedback Theory
Eldar Knar
https://arxiv.org/abs/2507.14162 https://

Apology of Green Digitalization in the Context of Information and Climate Feedback Theory
Amid accelerated digitalization, not only is the scale of data processing and storage increasing, but so too is the associated infrastructure load on the climate. Current climate models and environmental protocols almost entirely overlook the impact of information and communication technologies on the thermal and energy balance of the biosphere. This paper proposes the theory of information and climate feedback (ICF) as a new nonlinear model describing the loop of digitalization, energy consu…

Static Analysis as a Feedback Loop: Enhancing LLM-Generated Code Beyond Correctness
Scott Blyth, Sherlock A. Licorish, Christoph Treude, Markus Wagner
https://arxiv.org/abs/2508.14419

Static Analysis as a Feedback Loop: Enhancing LLM-Generated Code Beyond Correctness
Large language models (LLMs) have demonstrated impressive capabilities in code generation, achieving high scores on benchmarks such as HumanEval and MBPP. However, these benchmarks primarily assess functional correctness and neglect broader dimensions of code quality, including security, reliability, readability, and maintainability. In this work, we systematically evaluate the ability of LLMs to generate high-quality code across multiple dimensions using the PythonSecurityEval benchmark. We in…

Iterative Youla-Kucera Loop Shaping For Precision Motion Control
Xiaohai Hu, Jason Laks, Guoxiao Guo, Xu Chen
https://arxiv.org/abs/2508.14309 https://arxi…

Iterative Youla-Kucera Loop Shaping For Precision Motion Control
This paper presents a numerically robust approach to multi-band disturbance rejection using an iterative Youla-Kucera parameterization technique. The proposed method offers precise control over shaping the frequency response of a feedback loop while maintaining numerical stability through a systematic design process. By implementing an iterative approach, we overcome a critical numerical issue in rejecting vibrations with multiple frequency bands. Meanwhile, our proposed modification of the all…

Human-in-the-Loop Systems for Adaptive Learning Using Generative AI
Bhavishya Tarun, Haoze Du, Dinesh Kannan, Edward F. Gehringer
https://arxiv.org/abs/2508.11062 https://

Human-in-the-Loop Systems for Adaptive Learning Using Generative AI
A Human-in-the-Loop (HITL) approach leverages generative AI to enhance personalized learning by directly integrating student feedback into AI-generated solutions. Students critique and modify AI responses using predefined feedback tags, fostering deeper engagement and understanding. This empowers students to actively shape their learning, with AI serving as an adaptive partner. The system uses a tagging technique and prompt engineering to personalize content, informing a Retrieval-Augmented Gen…

Boundary Feedback and Observer Synthesis for a Class of Nonlinear Parabolic--Elliptic PDE Systems
Kamal Fenza, Moussa Labbadi, Mohamed Ouzahra
https://arxiv.org/abs/2507.12615

Boundary Feedback and Observer Synthesis for a Class of Nonlinear Parabolic--Elliptic PDE Systems
This paper investigates the stabilization of a coupled system comprising a parabolic PDE and an elliptic PDE with nonlinear terms. A rigorous backstepping design provides an explicit boundary control law and exponentially convergent observers from partial boundary measurements. Several theorems ensure exponential stability and well-posedness of the nonlinear closed-loop system.

Bandwidth enhanced current noise suppression with a SERF magnetometer
Tobias Menold, Arianna Bertoluzza, Patrick Hildebrand, Ann-Kathrin Gottschalk, Daniel Braun, J\'ozsef Fort\'agh, Andreas G\"unther
https://arxiv.org/abs/2506.11923

Bandwidth enhanced current noise suppression with a SERF magnetometer
This work investigates the behavior of a spin-exchange relaxation-free (SERF) magnetometer integrated into the feedback branch of a closed-loop control circuit, designed to actively suppress noise from a current source. In this configuration, the magnetometer bandwidth is enhanced by almost two orders of magnitude as compared to the open-loop setup. Incorporating an injection transformer, the proposed system effectively reduces current noise to below 3 nA/sqrt(Hz) over a broad frequency bandwid…

A Robust Controller based on Gaussian Processes for Robotic Manipulators with Unknown Uncertainty
Giulio Giacomuzzo, Mohamed Abdelwahab, Marco Cal\`i, Alberto Dalla Libera, Ruggero Carli
https://arxiv.org/abs/2507.11170

A Robust Controller based on Gaussian Processes for Robotic Manipulators with Unknown Uncertainty
In this paper, we propose a novel learning-based robust feedback linearization strategy to ensure precise trajectory tracking for an important family of Lagrangian systems. We assume a nominal knowledge of the dynamics is given but no a-priori bounds on the model mismatch are available. In our approach, the key ingredient is the adoption of a regression framework based on Gaussian Processes (GPR) to estimate the model mismatch. This estimate is added to the outer loop of a classical feedback li…

LearnLens: LLM-Enabled Personalised, Curriculum-Grounded Feedback with Educators in the Loop
Runcong Zhao, Artem Borov, Jiazheng Li, Yulan He
https://arxiv.org/abs/2507.04295

LearnLens: LLM-Enabled Personalised, Curriculum-Grounded Feedback with Educators in the Loop
Effective feedback is essential for student learning but is time-intensive for teachers. We present LearnLens, a modular, LLM-based system that generates personalised, curriculum-aligned feedback in science education. LearnLens comprises three components: (1) an error-aware assessment module that captures nuanced reasoning errors; (2) a curriculum-grounded generation module that uses a structured, topic-linked memory chain rather than traditional similarity-based retrieval, improving relevance …

The Lock-in Hypothesis: Stagnation by Algorithm
Tianyi Alex Qiu, Zhonghao He, Tejasveer Chugh, Max Kleiman-Weiner
https://arxiv.org/abs/2506.06166 https://…

The Lock-in Hypothesis: Stagnation by Algorithm
The training and deployment of large language models (LLMs) create a feedback loop with human users: models learn human beliefs from data, reinforce these beliefs with generated content, reabsorb the reinforced beliefs, and feed them back to users again and again. This dynamic resembles an echo chamber. We hypothesize that this feedback loop entrenches the existing values and beliefs of users, leading to a loss of diversity and potentially the lock-in of false beliefs. We formalize this hypothe…

🔄 Agent loop architecture: user input − model inference − tool execution − result feedback − continuous conversation flow
💻 String replacement editing approach works effectively with current #AI models for precise code modifications

Disruption of parkinsonian brain oscillations
C\'edric Join, Jakub Or{\l}owski, Antoine Chaillet, Madeleine Lowery, Hugues Mounier, Michel Fliess
https://arxiv.org/abs/2506.15384

Disruption of parkinsonian brain oscillations
Deep brain stimulation (DBS) is an advanced surgical treatment for the symptoms of Parkinson's disease (PD), involving electrical stimulation of neurons within the basal ganglia region of the brain. DBS is traditionally delivered in an open-loop manner using fixed stimulation parameters, which may lead to suboptimal results. In an effort to overcome these limitations, closed loop DBS, using pathological subthalamic beta (13--30 Hz) activity as a feedback signal, offers the potential to adapt DB…

Building Blocks of a User Experience Research Point of View
Patricia Diaz
https://arxiv.org/abs/2506.15332 https://arxiv.org/pdf/2506…

Building Blocks of a User Experience Research Point of View
This paper presents three User Experience Research (UXR) perspectives based on data, evidence and insights - known as Point of View (POV) - showcasing how the strategies and methods of building a POV work in an enterprise setting. The POV are: 1. Smart Visuals: Use AI to extract and translate text from visuals in videos (2019). 2. Assessable Code Editor: Focus on direct AI-feedback to the learner as it is the loop that requires the least effort for the highest impact(2023). 3. Opportunity Lands…

CLONE: Closed-Loop Whole-Body Humanoid Teleoperation for Long-Horizon Tasks
Yixuan Li, Yutang Lin, Jieming Cui, Tengyu Liu, Wei Liang, Yixin Zhu, Siyuan Huang
https://arxiv.org/abs/2506.08931

CLONE: Closed-Loop Whole-Body Humanoid Teleoperation for Long-Horizon Tasks
Humanoid teleoperation plays a vital role in demonstrating and collecting data for complex humanoid-scene interactions. However, current teleoperation systems face critical limitations: they decouple upper- and lower-body control to maintain stability, restricting natural coordination, and operate open-loop without real-time position feedback, leading to accumulated drift. The fundamental challenge is achieving precise, coordinated whole-body teleoperation over extended durations while maintain…

Gentrification feedback loop. As SF displaces its working class, Area Median Income rises, raising the rent levels for “affordable” housing, which further displaces and excludes working-class households, so AMI rises again… and a 1-bedroom for $2,900 is now defined as low-income housing.
#sfpol #housing

"The most worrying possibility behind Earth’s [accelerating warming] is how the general nature of clouds may be changing in response to climbing temperatures. It’s a feedback loop that could potentially exacerbate warming and is one of the single biggest uncertainties in predicting future climate."

Global Warming Is Speeding Up and the World Is Feeling the Effects
Human-caused global warming has been increasing faster and faster since the 1970s.

Graphs With Polarities
John C. Baez, Adittya Chaudhuri
https://arxiv.org/abs/2506.23375 https://arxiv.org/pdf/2506.23375

Graphs With Polarities
In fields ranging from business to systems biology, directed graphs with edges labeled by signs are used to model systems in a simple way: the nodes represent entities of some sort, and an edge indicates that one entity directly affects another either positively or negatively. Multiplying the signs along a directed path of edges lets us determine indirect positive or negative effects, and if the path is a loop we call this a positive or negative feedback loop. Here we generalize this to graphs …

Dynamic Output-Feedback Controller Synthesis for Dissipativity from Noisy Input-State Data
Pietro Kristovi\'c, Andrej Joki\'c, Mircea Lazar
https://arxiv.org/abs/2507.06788

Dynamic Output-Feedback Controller Synthesis for Dissipativity from Noisy Input-State Data
In this paper we propose a dynamic output-feedback controller synthesis method for discrete-time linear time-invariant systems. The synthesis goal is to render closed-loop system dissipative with respect to a given generic unstructured quadratic supply rate, while the system dynamics is partially represented by input-state data corrupted by a bounded disturbance. The controller synthesis is performed with respect to all systems which are consistent with the available data, and it is formulated …

Emission enhanced exciton-polariton condensates with optical feedback
R. Mirek, M. Furman, A. Opala, M. Kr\'ol, W. Pacuski, J. Szczytko, H. Sigur{\dh}sson, B. Pi\k{e}tka
https://arxiv.org/abs/2507.20235

Emission enhanced exciton-polariton condensates with optical feedback
Optical feedback is a well-known method of controlling laser dynamics, which has been widely studied in photonic systems to induce complex behaviors such as chaos or enhanced coherence. However, its application to systems in the strong light-matter coupling regime remains unexplored. In this work, we introduce a delayed optical feedback loop into a nonresonantly pumped polariton condensate. By feeding part of the emission back into the cavity to seed the next condensate, we observe a strong inc…

This https://arxiv.org/abs/2505.15914 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSD_…

A Novel Deep Learning Framework for Efficient Multichannel Acoustic Feedback Control
This study presents a deep-learning framework for controlling multichannel acoustic feedback in audio devices. Traditional digital signal processing methods struggle with convergence when dealing with highly correlated noise such as feedback. We introduce a Convolutional Recurrent Network that efficiently combines spatial and temporal processing, significantly enhancing speech enhancement capabilities with lower computational demands. Our approach utilizes three training methods: In-a-Loop Trai…

ReFuzzer: Feedback-Driven Approach to Enhance Validity of LLM-Generated Test Programs
Iti Shree, Karine Even-Mendoz, Tomasz Radzik
https://arxiv.org/abs/2508.03603 https://

ReFuzzer: Feedback-Driven Approach to Enhance Validity of LLM-Generated Test Programs
Existing LLM-based compiler fuzzers often produce syntactically or semantically invalid test programs, limiting their effectiveness in exercising compiler optimizations and backend components. We introduce ReFuzzer, a framework for refining LLM-generated test programs by systematically detecting and correcting compilation and runtime violations (e.g. division by zero or array out-of-bounds accesses). ReFuzzer employs a feedback loop with a local LLM to validate and filter erroneous programs bef…

High-gain model-following control for trajectory tracking
Nicals Tietze, Kai Wulff, Johann Reger
https://arxiv.org/abs/2506.13463 https://

High-gain model-following control for trajectory tracking
We consider trajectory tracking for minimum-phase nonlinear systems in Byrnes-Isidori form using the model-following control (MFC) architecture. The tracking problem is motivated by a hierarchical control concept where a higher-level instance provides the reference trajectory at run-time. We present a computational efficient implementation of the feedback linearisation MFC design, and apply high-gain feedback in the process control loop (PCL) to achieve practical tracking in presence of Lipschi…

Civil Society in the Loop: Feedback-Driven Adaptation of (L)LM-Assisted Classification in an Open-Source Telegram Monitoring Tool
Milena Pustet, Elisabeth Steffen, Helena Mihaljevi\'c, Grischa Stanjek, Yannis Illies
https://arxiv.org/abs/2507.06734

Civil Society in the Loop: Feedback-Driven Adaptation of (L)LM-Assisted Classification in an Open-Source Telegram Monitoring Tool
The role of civil society organizations (CSOs) in monitoring harmful online content is increasingly crucial, especially as platform providers reduce their investment in content moderation. AI tools can assist in detecting and monitoring harmful content at scale. However, few open-source tools offer seamless integration of AI models and social media monitoring infrastructures. Given their thematic expertise and contextual understanding of harmful content, CSOs should be active partners in co-dev…

Enhancing Serendipity Recommendation System by Constructing Dynamic User Knowledge Graphs with Large Language Models
Qian Yong, Yanhui Li, Jialiang Shi, Yaguang Dou, Tian Qi
https://arxiv.org/abs/2508.04032

Enhancing Serendipity Recommendation System by Constructing Dynamic User Knowledge Graphs with Large Language Models
The feedback loop in industrial recommendation systems reinforces homogeneous content, creates filter bubble effects, and diminishes user satisfaction. Recently, large language models(LLMs) have demonstrated potential in serendipity recommendation, thanks to their extensive world knowledge and superior reasoning capabilities. However, these models still face challenges in ensuring the rationality of the reasoning process, the usefulness of the reasoning results, and meeting the latency requirem…

Load-Dependent Power-Law Exponent in Creep Rupture of Heterogeneous Materials
Chlo\'e Braux, Antoine B\'erut, Lo\"ic Vanel
https://arxiv.org/abs/2506.06145

Load-Dependent Power-Law Exponent in Creep Rupture of Heterogeneous Materials
Creep tests on heterogeneous materials under subcritical loading typically show a power-law decaying strain rate before failure, with the exponent often considered material-dependent but independent of applied stress. By imposing successive small stress relaxations through a displacement feedback loop, we probe creep dynamics and show experimentally that this exponent varies with both applied load and loading direction. Simulations of a disordered fiber bundle model reproduce this load dependen…

Thermal fingers in cold channels: Thermo-viscous instability in dispersive flow
Federico Lanza, Gaute Linga, Fabian Barras, Eirik Grude Flekk{\o}y
https://arxiv.org/abs/2507.06135

Thermal fingers in cold channels: Thermo-viscous instability in dispersive flow
An instability may occur when a hot viscous fluid, such as magma, flows into a thin gap and cools due to heat transfer to the surrounding environment, such as the host rock. Fluids that exhibit a drastic increase in viscosity as they cool give rise to a feedback loop where hotter regions flow faster, cooling more slowly, thus forming ``fingers'' of hot fluid. To investigate this phenomenon, we employ a model system in which hot fluid is injected into a Hele-Shaw cell where the top and bottom pl…

Human-in-the-loop: Real-time Preference Optimization
Wenbin Wang, Wenjie Xu, Colin N. Jones
https://arxiv.org/abs/2506.02225 https://…

Human-in-the-loop: Real-time Preference Optimization
Human-aware controllers play an important role in engineering systems for improving productivity, efficiency, and sustainability. It is essential to design such a controller that optimizes user utility while adhering to plant dynamics. While most online optimization algorithms rely on first-order or zeroth-order oracles, human feedback often appears as pairwise comparisons. In this work, we propose an online feedback optimization algorithm that leverages such preference feedback. We design a co…

E.A.R.T.H.: Structuring Creative Evolution through Model Error in Generative AI
Yusen Peng, Shuhua Mao
https://arxiv.org/abs/2507.18004 https://arxiv.org/p…

E.A.R.T.H.: Structuring Creative Evolution through Model Error in Generative AI
How can AI move beyond imitation toward genuine creativity? This paper proposes the E.A.R.T.H. framework, a five-stage generative pipeline that transforms model-generated errors into creative assets through Error generation, Amplification, Refine selection, Transform, and Harness feedback. Drawing on cognitive science and generative modeling, we posit that "creative potential hides in failure" and operationalize this via structured prompts, semantic scoring, and human-in-the-loop evaluation. Im…

A Unified Framework for Probabilistic Dynamic-, Trajectory- and Vision-based Virtual Fixtures
Maximilian M\"uhlbauer, Freek Stulp, Sylvain Calinon, Alin Albu-Sch\"affer, Jo\~ao Silv\'erio
https://arxiv.org/abs/2506.10239

A Unified Framework for Probabilistic Dynamic-, Trajectory- and Vision-based Virtual Fixtures
Probabilistic Virtual Fixtures (VFs) enable the adaptive selection of the most suitable haptic feedback for each phase of a task, based on learned or perceived uncertainty. While keeping the human in the loop remains essential, for instance, to ensure high precision, partial automation of certain task phases is critical for productivity. We present a unified framework for probabilistic VFs that seamlessly switches between manual fixtures, semi-automated fixtures (with the human handling precise…

Phase stabilization and phase tuning of an optical lattice with a variable period
P. A. Aksentsev, V. A. Khlebnikov, I. S. Cojocaru, A. E. Rudnev, I. A. Pyrkh, D. A. Kumpilov, P. V. Trofimova, A. M. Ibrahimov, O. I. Blokhin, K. O. Frolov, S. A. Kuzmin, A. K. Zykova, D. A. Pershin, V. V. Tsyganok, A. V. Akimov
https://arxiv.org/a…

Phase stabilization and phase tuning of an optical lattice with a variable period
Optical lattices play a significant role in the field of cold atom physics, particularly in quantum simulations. Varying the lattice period is often a useful feature, but it presents the challenge of maintaining lattice phase stability in both stationary and varying-period regimes. Here, we report the realization of a feedback loop for a tunable optical lattice. Our scheme employs a CCD camera, a computer, and a piezoelectric actuator mounted on a mirror. Using this setup, we significantly impr…

Controlling Topological Defects in Polar Fluids via Reinforcement Learning
Abhinav Singh, Petros Koumoutsakos
https://arxiv.org/abs/2507.19298 https://arxi…

Controlling Topological Defects in Polar Fluids via Reinforcement Learning
Topological defects in active polar fluids exhibit complex dynamics driven by internally generated stresses, reflecting the deep interplay between topology, flow, and non-equilibrium hydrodynamics. Feedback control offers a powerful means to guide such systems, enabling transitions between dynamic states. We investigated closed-loop steering of integer-charged defects in a confined active fluid by modulating the spatial profile of activity. Using a continuum hydrodynamic model, we show that loc…

Innovative Adaptive Imaged Based Visual Servoing Control of 6 DoFs Industrial Robot Manipulators
Rongfei Li, Francis Assadian
https://arxiv.org/abs/2506.10240

Innovative Adaptive Imaged Based Visual Servoing Control of 6 DoFs Industrial Robot Manipulators
Image-based visual servoing (IBVS) methods have been well developed and used in many applications, especially in pose (position and orientation) alignment. However, most research papers focused on developing control solutions when 3D point features can be detected inside the field of view. This work proposes an innovative feedforward-feedback adaptive control algorithm structure with the Youla Parameterization method. A designed feature estimation loop ensures stable and fast motion control whe…

Stabilization of Quasilinear Parabolic Equations by Cubic Feedback at Boundary with Estimated Region of Attraction
Mohamed Camil Belhadjoudja (GIPSA-INFINITY), M Maghenem (GIPSA-INFINITY), E Witrant (GIPSA-INFINITY), M Krstic (UC San Diego)
https://arxiv.org/abs/2506.18634

Stabilization of Quasilinear Parabolic Equations by Cubic Feedback at Boundary with Estimated Region of Attraction
For quasilinear parabolic partial differential equations (PDEs) that exhibit finite-time blow up in open loop, i.e., under null boundary conditions, we provide an estimate of the region of attraction under cubic feedback laws applied at the boundary, using boundary measurements. We guarantee: 1-L 2 and H 1 exponential stability of the origin with an estimate of the region of attraction. 2-Convergence of the H 2 and the C 1 norms of the solutions to zero. 3-Existence and uniqueness of complete c…

Fundamental limitations of monotonic tracking systems
Hamed Taghavian
https://arxiv.org/abs/2508.08844 https://arxiv.org/pdf/2508.08844

Fundamental limitations of monotonic tracking systems
We consider the monotonic tracking control problem for continuous-time single-input single-output linear systems using output-feedback linear controllers in this paper. We provide the necessary and sufficient conditions for this problem to be solvable and expose its fundamental limitations: the exact feasible locations of the plant zeros, the minimum controller order possible, and the maximum decay rate achievable for the closed-loop system. The relationship between these bounds is explained by…

Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving
Haochen Liu, Tianyu Li, Haohan Yang, Li Chen, Caojun Wang, Ke Guo, Haochen Tian, Hongchen Li, Hongyang Li, Chen Lv
https://arxiv.org/abs/2506.09800

Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving
End-to-end autonomous driving has emerged as a promising paradigm for directly mapping sensor inputs to planning maneuvers using learning-based modular integrations. However, existing imitation learning (IL)-based models suffer from generalization to hard cases, and a lack of corrective feedback loop under post-deployment. While reinforcement learning (RL) offers a potential solution to tackle hard cases with optimality, it is often hindered by overfitting to specific driving cases, resulting i…

This https://arxiv.org/abs/2505.23387 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization
Large Language Models (LLMs) generate functionally correct solutions but often fall short in code efficiency, a critical bottleneck for real-world deployment. In this paper, we introduce a novel test-time iterative optimization framework to address this, employing a closed-loop system where LLMs iteratively refine code based on empirical performance feedback from an execution sandbox. We explore three training strategies: Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and G…

React to Surprises: Stable-by-Design Neural Feedback Control and the Youla-REN
Nicholas H. Barbara, Ruigang Wang, Alexandre Megretski, Ian R. Manchester
https://arxiv.org/abs/2506.01226

React to Surprises: Stable-by-Design Neural Feedback Control and the Youla-REN
We study parameterizations of stabilizing nonlinear policies for learning-based control. We propose a structure based on a nonlinear version of the Youla-Kučera parameterization combined with robust neural networks such as the recurrent equilibrium network (REN). The resulting parameterizations are unconstrained, and hence can be searched over with first-order optimization methods, while always ensuring closed-loop stability by construction. We study the combination of (a) nonlinear dynamics, …

Controller Design for Bilinear Neural Feedback Loops
Dhruv Shah, Jorge Cort\'es
https://arxiv.org/abs/2505.24106 https://arxiv.or…

Controller Design for Bilinear Neural Feedback Loops
This paper considers a class of bilinear systems with a neural network in the loop. These arise naturally when employing machine learning techniques to approximate general, non-affine in the input, control systems. We propose a controller design framework that combines linear fractional representations and tools from linear parameter varying control to guarantee local exponential stability of a desired equilibrium. The controller is obtained from the solution of linear matrix inequalities, whic…

Shot-Noise-Limited Laser Frequency Stabilization Using a High-Resolution Wavelength Meter
Mengde Gan, Haoyi Zhang, Xiaodong Tan, Jiaming Li, Le Luo
https://arxiv.org/abs/2506.20894

Shot-Noise-Limited Laser Frequency Stabilization Using a High-Resolution Wavelength Meter
We present a compact laser frequency stabilization method by locking a 556 nm laser to a high-precision wavelength meter. Unlike traditional schemes that rely on optical cavities or atomic references, we stabilize the laser frequency via a closed-loop feedback system referenced to a wavelength meter. This configuration effectively suppresses long-term frequency drifts, achieving frequency stability to the wavelength meter's shot-noise-limited resolution. The system enables sub-hundred-kilohertz…

Recurrent neural network-based robust control systems with closed-loop regional incremental ISS and application to MPC design
Daniele Ravasio, Marcello Farina, Alessio La Bella, Andrea Ballarino
https://arxiv.org/abs/2506.20334

Recurrent neural network-based robust control systems with closed-loop regional incremental ISS and application to MPC design
This paper investigates the design of output-feedback schemes for systems described by a class of recurrent neural networks. We propose a procedure based on linear matrix inequalities for designing an observer and a static state-feedback controller. The algorithm leverages global and regional incremental input-to-state stability (incremental ISS) and enables the tracking of constant setpoints, ensuring robustness to disturbances and state estimation uncertainty. To address the potential limitat…

Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization
Mingzhe Du, Luu Tuan Tuan, Yue Liu, Yuhao Qing, Dong Huang, Xinyi He, Qian Liu, Zejun Ma, See-kiong Ng
https://arxiv.org/abs/2505.23387

Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization
Large Language Models (LLMs) generate functionally correct solutions but often fall short in code efficiency, a critical bottleneck for real-world deployment. In this paper, we introduce a novel test-time iterative optimization framework to address this, employing a closed-loop system where LLMs iteratively refine code based on empirical performance feedback from an execution sandbox. We explore three training strategies: Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and G…

Sliding Mode Control for Uncertain Systems with Time-Varying Delays via Predictor Feedback and Super-Twisting Observer
Hardy Pinto, Tiago Roux Oliveira, Liu Hsu
https://arxiv.org/abs/2507.21281

Sliding Mode Control for Uncertain Systems with Time-Varying Delays via Predictor Feedback and Super-Twisting Observer
This paper introduces a novel stabilization control strategy for linear time-invariant systems affected by known time-varying measurement delays and matched unknown nonlinear disturbances, which may encompass actuator faults. It is considered that part of the state vector is not available for real-time measurement. To address this, the proposed approach combines an open-loop predictor with a state observer designed using the Super-Twisting Algorithm, aiming to compensate for the delays and esti…

The Bias of Subspace-based Data-Driven Predictive Control
Keith Moffat, Florian D\"orfler, Alessandro Chiuso
https://arxiv.org/abs/2507.02468 https://…

The Bias of Subspace-based Data-Driven Predictive Control
This paper quantifies and addresses the bias of subspace-based Data-Driven Predictive Control (DDPC) for linear, time-invariant (LTI) systems. The primary focus is the bias that arises when the training data is gathered with a feedback controller in closed-loop with the system. First, the closed-loop bias of Subspace Predictive Control is quantified using the training data innovations. Next, the bias of direct, subspace-based DDPC methods DeePC and $γ$-DDPC is shown to consist of two parts--th…

Data-driven nonlinear output regulation via data-enforced incremental passivity
Yixuan Liu, Meichen Guo
https://arxiv.org/abs/2506.06079 https://

Data-driven nonlinear output regulation via data-enforced incremental passivity
This work proposes a data-driven regulator design that drives the output of a nonlinear system asymptotically to a time-varying reference and rejects time-varying disturbances. The key idea is to design a data-driven feedback controller such that the closed-loop system is incrementally passive with respect to the regulation error and a virtual input. By carefully designing the virtual input, we solve the data-driven nonlinear output regulation problem where the reference and disturbances are ge…

Passive nonlinear FIR filters for data-driven control
Zixing Wang, Fulvio Forni
https://arxiv.org/abs/2508.05279 https://arxiv.org/pdf/2508.05279

Passive nonlinear FIR filters for data-driven control
We propose a new class of passive nonlinear finite impulse response operators. This class is constructed by the action of finite impulse response filters in a lifted space. This allows for efficient control synthesis through constrained optimization. Closed-loop performance is taken into account through least-squares fitting, based on the theory of virtual reference feedback tuning. Passivity is established through efficient linear constraints, based on sampling in the frequency domain. Because…

This https://arxiv.org/abs/2506.01226 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

React to Surprises: Stable-by-Design Neural Feedback Control and the Youla-REN
We study parameterizations of stabilizing nonlinear policies for learning-based control. We propose a structure based on a nonlinear version of the Youla-Kučera parameterization combined with robust neural networks such as the recurrent equilibrium network (REN). The resulting parameterizations are unconstrained, and hence can be searched over with first-order optimization methods, while always ensuring closed-loop stability by construction. We study the combination of (a) nonlinear dynamics, …

Adversarial Observability and Performance Tradeoffs in Optimal Control
Filippos Fotiadis, Ufuk Topcu
https://arxiv.org/abs/2506.19829 https://

Adversarial Observability and Performance Tradeoffs in Optimal Control
We develop a feedback controller that minimizes the observability of a set of adversarial sensors of a linear system, while adhering to strict closed-loop performance constraints. We quantify the effectiveness of adversarial sensors using the trace of their observability Gramian and its inverse, capturing both average observability and the least observable state directions of the system. We derive theoretical lower bounds on these metrics under performance constraints, characterizing the fundam…