Tootfinder

On Secrecy Capacity of Binary Beampointing Channels with Block Memory and Feedback
Siyao Li, Mingzhe Chen, Shuangyang Li, Giuseppe Caire
https://arxiv.org/abs/2508.20980 https:/…

On Secrecy Capacity of Binary Beampointing Channels with Block Memory and Feedback
This paper investigates the secrecy capacity of the binary beampointing (BBP) channel with block memory and feedback, a simplified yet insightful model for millimeter-wave (mmWave) systems with beamformed transmissions and backscatter feedback. We consider a system where a legitimate receiver and a passive eavesdropper experience independent and uniformly distributed angular directions over transmission blocks, with the base station receiving noiseless, unit-delayed feedback from both, under th…

Demonstrating Interoperable Channel State Feedback Compression with Machine Learning
Dani Korpi, Rachel Wang, Jerry Wang, Abdelrahman Ibrahim, Carl Nuzman, Runxin Wang, Kursat Rasim Mestav, Dustin Zhang, Iraj Saniee, Shawn Winston, Gordana Pavlovic, Wei Ding, William J. Hillery, Chenxi Hao, Ram Thirunagari, Jung Chang, Jeehyun Kim, Bartek Kozicki, Dragan Samardzija, Taesang Yoo, Andreas Maeder, Tingfang Ji, Harish Viswanathan

Demonstrating Interoperable Channel State Feedback Compression with Machine Learning
Neural network-based compression and decompression of channel state feedback has been one of the most widely studied applications of machine learning (ML) in wireless networks. Various simulation-based studies have shown that ML-based feedback compression can result in reduced overhead and more accurate channel information. However, to the best of our knowledge, there are no real-life proofs of concepts demonstrating the benefits of ML-based channel feedback compression in a practical setting, …

DxHF: Providing High-Quality Human Feedback for LLM Alignment via Interactive Decomposition
Danqing Shi, Furui Cheng, Tino Weinkauf, Antti Oulasvirta, Mennatallah El-Assady
https://arxiv.org/abs/2507.18802

DxHF: Providing High-Quality Human Feedback for LLM Alignment via Interactive Decomposition
Human preferences are widely used to align large language models (LLMs) through methods such as reinforcement learning from human feedback (RLHF). However, the current user interfaces require annotators to compare text paragraphs, which is cognitively challenging when the texts are long or unfamiliar. This paper contributes by studying the decomposition principle as an approach to improving the quality of human feedback for LLM alignment. This approach breaks down the text into individual claim…

A Practical Guide for Supporting Formative Assessment and Feedback Using Generative AI
Sapolnach Prompiengchai, Charith Narreddy, Steve Joordens
https://arxiv.org/abs/2505.23405

A Practical Guide for Supporting Formative Assessment and Feedback Using Generative AI
Formative assessment is a cornerstone of effective teaching and learning, providing students with feedback to guide their learning. While there has been an exponential growth in the application of generative AI in scaling various aspects of formative assessment, ranging from automatic question generation to intelligent tutoring systems and personalized feedback, few have directly addressed the core pedagogical principles of formative assessment. Here, we critically examined how generative AI, e…

Cooling of an optically levitated nanoparticle via measurement-free coherent feedback
Bruno Melo, Daniel Veldhuizen, Gregoire F. M. Tomassi, Nadine Meyer, Romain Quidant
https://arxiv.org/abs/2506.21341

Cooling of an optically levitated nanoparticle via measurement-free coherent feedback
We demonstrate coherent, measurement-free optical feedback control of a levitated nanoparticle, achieving phonon occupations down to a few hundred phonons. Unlike measurement-based feedback, this all-optical scheme preserves the correlations between mechanical motion and the feedback signal. Adjustment of the feedback phase and delay provides precise and tunable control over the system dynamics. The ultimate cooling performance is currently limited by phase noise, which we analyze within a theo…

Nonconvex Optimization Framework for Group-Sparse Feedback Linear-Quadratic Optimal Control. II: Non-Penalty Approach
Lechen Feng, Xun Li, Yuan-Hua Ni
https://arxiv.org/abs/2507.19895

Nonconvex Optimization Framework for Group-Sparse Feedback Linear-Quadratic Optimal Control. II: Non-Penalty Approach
This work is a companion paper of [8], where the distributed linear-quadratic problem with fixed communication topology (DFT-LQ) and the sparse feedback LQ problem (SF-LQ) are formulated into a nonsmooth and nonconvex optimization problem with affine constraints. Moreover, a penalty approach is considered in \cite{feng-part1}, and the PALM (proximal alternating linearized minimization) algorithm is studied with convergence and complexity analysis. In this paper, we aim to address the inherent d…

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…

Local Stability and Region of Attraction Analysis for Neural Network Feedback Systems under Positivity Constraints
Hamidreza Montazeri Hedesh, Moh Kamalul Wafi, Milad Siami
https://arxiv.org/abs/2505.22889

Local Stability and Region of Attraction Analysis for Neural Network Feedback Systems under Positivity Constraints
We study the local stability of nonlinear systems in the Lur'e form with static nonlinear feedback realized by feedforward neural networks (FFNNs). By leveraging positivity system constraints, we employ a localized variant of the Aizerman conjecture, which provides sufficient conditions for exponential stability of trajectories confined to a compact set. Using this foundation, we develop two distinct methods for estimating the Region of Attraction (ROA): (i) a less conservative Lyapunov-based a…

Hmm :thaenkin:

inploid: Inploid: an online social Q&A platform
Inploid is a social question & answer website in Turkish. Users can follow others and see their questions and answers on the main page. Each user is associated with a reputability score which is influenced by feedback of others about questions and answers of the user. Each user can also specify interest in topics. The data is crawled in June 2017 and consist of 39,749 nodes and 57,276 directed links between them. In addition, for …

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

Just fixed a bunch of DPI scaling issues in ngscopeclient.
Very interested in feedback especially if you are on a Mac and/or have a high-DPI display.

CTR-Driven Ad Text Generation via Online Feedback Preference Optimization
Yanda Chen, Zihui Ren, Qixiang Gao, Jiale Chen, Si Chen, Xubin Li, Tiezheng Ge, Bo Zheng
https://arxiv.org/abs/2507.20227

CTR-Driven Ad Text Generation via Online Feedback Preference Optimization
Advertising text plays a critical role in determining click-through rates (CTR) in online advertising. Large Language Models (LLMs) offer significant efficiency advantages over manual ad text creation. However, LLM-generated ad texts do not guarantee higher CTR performance compared to human-crafted texts, revealing a gap between generation quality and online performance of ad texts. In this work, we propose a novel ad text generation method which optimizes for CTR through preference optimizatio…

Some modernization planning for Duende IdentityServer. If you are in the #dotnet space, please leave your thoughts. https://github.com/orgs/DuendeSoftware/discussions/285

RFC: Modernizing CancellationToken Usage in Async APIs · DuendeSoftware · Discussion #285
Hey everyone, We're considering a significant change to how IdentityServer handles asynchronous operations, and we need your feedback before moving forward. TL;DR: We want to add CancellationToken ...

A Way to Get More Useful Performance Feedback?
#MastodonMusician #MusicianTips

A Way to Get More Useful Performance Feedback?
Want to get more useful feedback from colleagues? Turns out you don’t want to ask for “feedback.” Research suggests there’s a better word.

On the Sensing Capacity of Gaussian "Beam-Pointing" Channels with Block Memory and Feedback
Siyao Li, Shuangyang Li, Giuseppe Caire
https://arxiv.org/abs/2508.20997 ht…

On the Sensing Capacity of Gaussian "Beam-Pointing" Channels with Block Memory and Feedback
Driven by the demands of high-frequency wireless communications in 5G and 6G systems (e.g., mmWave, sub-THz), we explore a state-dependent {\em Gaussian beam-pointing} (GBP) channel. In this model, the channel state defines an unknown angle of departure (AoD), which remains constant within each coherence block of $Q$ time slots but changes independently across blocks. The transmitter receives strictly causal feedback which may originate from a radar detection system or explicit feedback from th…

Information-Preserving CSI Feedback: Invertible Networks with Endogenous Quantization and Channel Error Mitigation
Haotian Tian, Lixiang Lian, Jiaqi Cao, Sijie Ji
https://arxiv.org/abs/2507.20283

Information-Preserving CSI Feedback: Invertible Networks with Endogenous Quantization and Channel Error Mitigation
Deep learning has emerged as a promising so- lution for efficient channel state information (CSI) feedback in frequency division duplex (FDD) massive MIMO systems. Conventional deep learning-based methods typically rely on a deep autoencoder to compress the CSI, which leads to irre- versible information loss and degrades reconstruction accuracy. This paper introduces InvCSINet, an information-preserving CSI feedback framework based on invertible neural networks (INNs). By leveraging the bijecti…

Feedback from the printer on my pamphlet layout: "that's not how people normally do things"
But I *was* taking into account the fold sample they gave me...

Observed Timescales of Stellar Feedback in Star-Forming, Low-Mass Galaxies
Laura C. Hunter, Liese van Zee, Roger E. Cohen, Kristen B. McQuinn, Madison Markham, Justin A. Kader, Lexi N. Gault, Andrew E. Dolphin
https://arxiv.org/abs/2507.19573

Observed Timescales of Stellar Feedback in Star-Forming, Low-Mass Galaxies
Understanding the timescales of atomic gas turbulence is crucial to understanding the interplay between star formation and the interstellar medium (ISM). To investigate the timescales of turbulence low-mass galaxies ($10^{6.8}

A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach for UAV-Assisted Vehicular Networks with Delayed CSI Feedback
Zhang Liu (Sherman), Lianfen Huang (Sherman), Zhibin Gao (Sherman), Xianbin Wang (Sherman), Dusit Niyato (Sherman), Xuemin (Sherman), Shen
https://arxiv.org/abs/2507.20524…

A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach for UAV-Assisted Vehicular Networks with Delayed CSI Feedback
Low altitude uncrewed aerial vehicles (UAVs) are expected to facilitate the development of aerial-ground integrated intelligent transportation systems and unlocking the potential of the emerging low-altitude economy. However, several critical challenges persist, including the dynamic optimization of network resources and UAV trajectories, limited UAV endurance, and imperfect channel state information (CSI). In this paper, we offer new insights into low-altitude economy networking by exploring i…

Make your voices heard.
I also added something to a few bugs. @… really needs to go back to the drawing board and think about whose problems they are trying to solve. Because who is this proposal for? The only beneficiaries are AI companies.

Nach Alpha-Feedback: Bungie verschiebt "Marathon" auf unbestimmte Zeit
Für das Studio Bungie sind es schwierige Monate: Nach dem Kunstklau-Ärger um "Marathon" fiel auch das Alpha-Feedback schlecht aus. Der Shooter wird verschoben.

Sequential Decision Problems with Missing Feedback
Filippo Palomba
https://arxiv.org/abs/2507.19596 https://arxiv.org/pdf/2507.19596

Sequential Decision Problems with Missing Feedback
This paper investigates the challenges of optimal online policy learning under missing data. State-of-the-art algorithms implicitly assume that rewards are always observable. I show that when rewards are missing at random, the Upper Confidence Bound (UCB) algorithm maintains optimal regret bounds; however, it selects suboptimal policies with high probability as soon as this assumption is relaxed. To overcome this limitation, I introduce a fully nonparametric algorithm-Doubly-Robust Upper Confid…

Memory-aware feedback enhances power in active information engines
Sehoon Bahng, Jae Sung Lee, Cheol-Min Ghim
https://arxiv.org/abs/2508.16866 https://arxi…

Memory-aware feedback enhances power in active information engines
We study an information engine operating in an active bath, where a Brownian particle confined in a harmonic trap undergoes feedback-driven displacement cycles. Unlike thermal environments, active baths exhibit temporally correlated fluctuations, introducing memory effects that challenge conventional feedback strategies. Extending the framework of stochastic thermodynamics to account for such memory, we analyze a feedback protocol that periodically shifts the potential minimum based on noisy me…

Closing the Loop: Integrating Material Needs of Energy Technologies into Energy System Models
C\'elia Burghardt, Mirko Sch\"afer, Anke Weidlich
https://arxiv.org/abs/2508.19950

Closing the Loop: Integrating Material Needs of Energy Technologies into Energy System Models
The transition to a climate-neutral energy system demands large-scale renewable generation expansion, which requires substantial amounts of bulk materials like steel, cement, and polymers. The production of these materials represents an additional energy demand for the system, creating an energy-material feedback loop. Current energy system models lack a complete representation of this feedback loop. Material requirements of energy system transformation have been studied in a retrospective appr…

#phpugmrn @… introducing us to his ideas what feedback culture should look like

#Vancouver
Share your feedback on the 2025 Gastown summer pilot and next steps
Share your feedback on the pilot
http://list.vancouver.ca/t/613733/693347/20369/2/

Medium's CEO explains how the platform's AI policy has evolved as it adopts some AI tools while prioritizing human curation and inviting community feedback (Tony Stubblebine/The Medium Blog)
https://medium.com/blog/we-want-your-f

We want your feedback: How can writers use AI to tell human stories?
How we are navigating the role AI does and doesn’t play in our mission to deepen understanding.

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/4]:
- Semi-autonomous Prosthesis Control Using Minimal Depth Information and Vibrotactile Feedback
Miguel Nobre Castro, Strahinja Dosen

Inference-Time Alignment Control for Diffusion Models with Reinforcement Learning Guidance
Luozhijie Jin, Zijie Qiu, Jie Liu, Zijie Diao, Lifeng Qiao, Ning Ding, Alex Lamb, Xipeng Qiu
https://arxiv.org/abs/2508.21016

Inference-Time Alignment Control for Diffusion Models with Reinforcement Learning Guidance
Denoising-based generative models, particularly diffusion and flow matching algorithms, have achieved remarkable success. However, aligning their output distributions with complex downstream objectives, such as human preferences, compositional accuracy, or data compressibility, remains challenging. While reinforcement learning (RL) fine-tuning methods, inspired by advances in RL from human feedback (RLHF) for large language models, have been adapted to these generative frameworks, current RL ap…

May the Feedback Be with You! Unlocking the Power of Feedback-Driven Deep Learning Framework Fuzzing via LLMs
Shaoyu Yang, Chunrong Fang, Haifeng Lin, Xiang Chen, Zhenyu Chen
https://arxiv.org/abs/2506.17642

May the Feedback Be with You! Unlocking the Power of Feedback-Driven Deep Learning Framework Fuzzing via LLMs
Artificial Intelligence (AI) Infrastructures, represented by Deep Learning (DL) frameworks, have served as fundamental DL systems over the last decade. However, the bugs in DL frameworks could lead to catastrophic consequences in some critical scenarios (e.g., healthcare and autonomous driving). A simple yet effective way to find bugs in DL frameworks is fuzz testing (Fuzzing). Unfortunately, existing fuzzing techniques have not comprehensively considered multiple types of feedback. Additionall…

Decoupling Periodic Systems: An Algebraic Approach
Vladim\'ir Ku\v{c}era
https://arxiv.org/abs/2505.23616 https://arxiv.org/pdf/2…

Decoupling Periodic Systems: An Algebraic Approach
This paper addresses the problem of row-by-row (or diagonal) decoupling of discrete-time linear multi-input multi-output systems with periodic time-varying coefficients using periodic state feedback. Previous solutions have tackled row-by-row decoupling using dynamic compensation for square systems and block-decoupling through regular state feedback for nonsquare systems with more outputs than inputs. While it appears likely that a row-by-row state feedback solution for square systems can be de…

Thanks for the detailed feedback, arts council england #thirdtimeunlucky

inploid: Inploid: an online social Q&A platform
Inploid is a social question & answer website in Turkish. Users can follow others and see their questions and answers on the main page. Each user is associated with a reputability score which is influenced by feedback of others about questions and answers of the user. Each user can also specify interest in topics. The data is crawled in June 2017 and consist of 39,749 nodes and 57,276 directed links between them. In addition, for …

Lightweight Fingernail Haptic Device: Unobstructed Fingerpad Force and Vibration Feedback for Enhanced Virtual Dexterous Manipulation
Yunxiu Xu, Siyu Wang, Shoichi Hasegawa
https://arxiv.org/abs/2506.21417

Lightweight Fingernail Haptic Device: Unobstructed Fingerpad Force and Vibration Feedback for Enhanced Virtual Dexterous Manipulation
This study presents a lightweight, wearable fingertip haptic device that provides physics-based haptic feedback for dexterous manipulation in virtual environments without hindering real-world interactions. The device, designed with thin strings and actuators attached to the fingernails, ensures minimal weight (1.55 g per finger) and preserves finger flexibility. Integrating the software with a physics engine renders multiple types of haptic feedback (grip force, collision, and sliding vibration…

LLMs-guided adaptive compensator: Bringing Adaptivity to Automatic Control Systems with Large Language Models
Zhongchao Zhou, Yuxi Lu, Yaonan Zhu, Yifan Zhao, Bin He, Liang He, Wenwen Yu, Yusuke Iwasawa
https://arxiv.org/abs/2507.20509

LLMs-guided adaptive compensator: Bringing Adaptivity to Automatic Control Systems with Large Language Models
With rapid advances in code generation, reasoning, and problem-solving, Large Language Models (LLMs) are increasingly applied in robotics. Most existing work focuses on high-level tasks such as task decomposition. A few studies have explored the use of LLMs in feedback controller design; however, these efforts are restricted to overly simplified systems, fixed-structure gain tuning, and lack real-world validation. To further investigate LLMs in automatic control, this work targets a key subfiel…

That's Not the Feedback I Need! -- Student Engagement with GenAI Feedback in the Tutor Kai
Sven Jacobs, Maurice Kempf, Natalie Kiesler
https://arxiv.org/abs/2506.20433

That's Not the Feedback I Need! -- Student Engagement with GenAI Feedback in the Tutor Kai
The potential of Generative AI (GenAI) for generating feedback in computing education has been the subject of numerous studies. However, there is still limited research on how computing students engage with this feedback and to what extent it supports their problem-solving. For this reason, we built a custom web application providing students with Python programming tasks, a code editor, GenAI feedback, and compiler feedback. Via a think-aloud protocol including eye-tracking and a post-intervie…

Discounted LQR: stabilizing (near-)optimal state-feedback laws
Jonathan de Brusse, Jamal Daafouz, Mathieu Granzotto, Romain Postoyan, Dragan Nesic
https://arxiv.org/abs/2508.19599

Discounted LQR: stabilizing (near-)optimal state-feedback laws
We study deterministic, discrete linear time-invariant systems with infinite-horizon discounted quadratic cost. It is well-known that standard stabilizability and detectability properties are not enough in general to conclude stability properties for the system in closed-loop with the optimal controller when the discount factor is small. In this context, we first review some of the stability conditions based on the optimal value function found in the learning and control literature and highligh…

#Wikipedia Editors Reject Founder's AI Review Proposal After ChatGPT Fails Basic Policy Test
https://slashdot.org/story/446032

Wikipedia Editors Reject Founder's AI Review Proposal After ChatGPT Fails Basic Policy Test - Slashdot
Wikipedia's volunteer editors have rejected founder Jimmy Wales' proposal to use ChatGPT for article review guidance after the AI tool produced error-filled feedback when Wales tested it on a draft submission. The ChatGPT response misidentified Wikipedia policies, suggested citing non-existent sourc...

Dynamic Delegation with Reputation Feedback
Georgy Lukyanov, Anna Vlasova
https://arxiv.org/abs/2508.19676 https://arxiv.org/pdf/2508.19676

Dynamic Delegation with Reputation Feedback
We study dynamic delegation with reputation feedback: a long-lived expert advises a sequence of implementers whose effort responds to current reputation, altering outcome informativeness and belief updates. We solve for a recursive, belief-based equilibrium and show that advice is a reputation-dependent cutoff in the expert's signal. A diagnosticity condition - failures at least as informative as successes - implies reputational conservatism: the cutoff (weakly) rises with reputation. Comparati…

Medium's CEO explains how the platform's AI policy has evolved as it adopts some AI tools while prioritizing human curation and seeking community feedback (Tony Stubblebine/The Medium Blog)
https://medium.com/blog/we-want-your-f…

We want your feedback: How can writers use AI to tell human stories?
How we are navigating the role AI does and doesn’t play in our mission to deepen understanding.

Replaced article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- Fully Energy-Efficient Randomized Backoff: Slow Feedback Loops Yield Fast Contention Resolution
Michael A. Bender, Jeremy T. Fineman, Seth Gilbert, John Kuszmaul, Maxwell Young

Deep Learning-Based CSI Feedback for Wi-Fi Systems With Temporal Correlation
Junyong Shin, Eunsung Jeon, Inhyoung Kim, Yo-Seb Jeon
https://arxiv.org/abs/2505.23198

Deep Learning-Based CSI Feedback for Wi-Fi Systems With Temporal Correlation
To achieve higher throughput in next-generation Wi-Fi systems, a station (STA) needs to efficiently compress channel state information (CSI) and feed it back to an access point (AP). In this paper, we propose a novel deep learning (DL)-based CSI feedback framework tailored for next-generation Wi-Fi systems. Our framework incorporates a pair of encoder and decoder neural networks to compress and reconstruct the angle parameters of the CSI. To enable an efficient finite-bit representation of the …

Bengals training camp surprise: Defense outshines Joe Burrow on Day 1 after getting valuable feedback from QB

https://www.cbssports.com/nfl/news/bengals…

Technological folie \`a deux: Feedback Loops Between AI Chatbots and Mental Illness
Sebastian Dohn\'any, Zeb Kurth-Nelson, Eleanor Spens, Lennart Luettgau, Alastair Reid, Christopher Summerfield, Murray Shanahan, Matthew M Nour
https://arxiv.org/abs/2507.19218

Technological folie à deux: Feedback Loops Between AI Chatbots and Mental Illness
Artificial intelligence chatbots have achieved unprecedented adoption, with millions now using these systems for emotional support and companionship in contexts of widespread social isolation and capacity-constrained mental health services. While some users report psychological benefits, concerning edge cases are emerging, including reports of suicide, violence, and delusional thinking linked to perceived emotional relationships with chatbots. To understand this new risk profile we need to cons…

»Was sich hinter dem Link verbirgt – Firefox bringt Seitenvorschau fürs ganze Web:
KI-Features sind auch für Firefox die große Zukunft. Mozilla will dabei konstant auf das Feedback der Nutzer bauen und gleichzeitig deren Daten schützen.«
Da bin ich mal gespannt, wie dies sich nützlich nutzen lässt und was es einem wirklich bringt…?
🦊

Was sich hinter dem Link verbirgt: Firefox bringt Seitenvorschau fürs ganze Web
KI-Features sind auch für Firefox die große Zukunft. Mozilla will dabei konstant auf das Feedback der Nutzer bauen und gleichzeitig deren Daten schützen.

Feedback Indicators: The Alignment between Llama and a Teacher in Language Learning
Sylvio R\"udian, Yassin Elsir, Marvin Kretschmer, Sabine Cayrou, Niels Pinkwart
https://arxiv.org/abs/2508.11364

Feedback Indicators: The Alignment between Llama and a Teacher in Language Learning
Automated feedback generation has the potential to enhance students' learning progress by providing timely and targeted feedback. Moreover, it can assist teachers in optimizing their time, allowing them to focus on more strategic and personalized aspects of teaching. To generate high-quality, information-rich formative feedback, it is essential first to extract relevant indicators, as these serve as the foundation upon which the feedback is constructed. Teachers often employ feedback criteria g…

I have extended thoughts on a few nuances of burnout, resilience, and employment
Before taking time off for burnout, my skip manager reminded me to read the strongly positive 360 feedback from my reports. That's both a shallow and a deep reinforcement of resilience, first and foremost by rebuilding and grounding self-confidence. Reading positive feedback provides evidence that I'm capable and effective at my job.
Beyond self-confidence, I have other needs in a workplace, …

diveXplore at the Video Browser Showdown 2024
Klaus Schoeffmann, Sahar Nasirihaghighi
https://arxiv.org/abs/2508.20560 https://arxiv.org/pdf/2508.20560

diveXplore at the Video Browser Showdown 2024
According to our experience from VBS2023 and the feedback from the IVR4B special session at CBMI2023, we have largely revised the diveXplore system for VBS2024. It now integrates OpenCLIP trained on the LAION-2B dataset for image/text embeddings that are used for free-text and visual similarity search, a query server that is able to distribute different queries and merge the results, a user interface optimized for fast browsing, as well as an exploration view for large clusters of similar video…

"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.

Reflective Verbal Reward Design for Pluralistic Alignment
Carter Blair, Kate Larson, Edith Law
https://arxiv.org/abs/2506.17834 https://

Reflective Verbal Reward Design for Pluralistic Alignment
AI agents are commonly aligned with "human values" through reinforcement learning from human feedback (RLHF), where a single reward model is learned from aggregated human feedback and used to align an agent's behavior. However, human values are not homogeneous--different people hold distinct and sometimes conflicting values. Aggregating feedback into a single reward model risks disproportionately suppressing minority preferences. To address this, we present a novel reward modeling approach for …

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…

A Hybrid Particle-Continuum Method for Simulating Fast Ice via Subgrid Iceberg Interaction
Carolin Mehlmann, Saskia Kahl
https://arxiv.org/abs/2507.20306 https://

A Hybrid Particle-Continuum Method for Simulating Fast Ice via Subgrid Iceberg Interaction
A significant fraction (4%-13%) of Antarctic sea ice remains stationary as landfast sea-ice ("fast ice"), typically anchored by grounded icebergs. Current global climate models do not represent fast-ice formation due to iceberg grounding, as iceberg-sea-ice interaction mostly occurs at subgrid scales. We propose a novel subgrid-scale coupling mechanism between Lagrangian iceberg particles and an Eulerian sea-ice continuum model. This hybrid particle-continuum approach integrates feedback from i…

Towards trustworthy AI in materials mechanics through domain-guided attention
Jesco Talies, Eric Breitbarth, David Melching
https://arxiv.org/abs/2507.20658 https://

Towards trustworthy AI in materials mechanics through domain-guided attention
Ensuring the trustworthiness and robustness of deep learning models remains a fundamental challenge, particularly in high-stakes scientific applications. In this study, we present a framework called attention-guided training that combines explainable artificial intelligence techniques with quantitative evaluation and domain-specific priors to guide model attention. We demonstrate that domain specific feedback on model explanations during training can enhance the model's generalization capabilit…

The Co-Evolution of Stellar Wind-blown Bubbles and Photoionized Gas I: Physical Principles and a Semi-Analytic Model
Lachlan Lancaster, Jeong-Gyu Kim, Greg L. Bryan, Shyam H. Menon, Eve C. Ostriker, Chang-Goo Kim
https://arxiv.org/abs/2505.22730

The Co-Evolution of Stellar Wind-blown Bubbles and Photoionized Gas I: Physical Principles and a Semi-Analytic Model
We propose a new framework for the simultaneous feedback of stellar winds and photo-ionizing radiation from massive stars, distinguishing the locations where forces are applied, and consequences for internal spatio-temporal evolution of the whole feedback bubble (FB). We quantify the relative dynamical importance of wind-blown bubbles (WBB) versus the photoionized region (PIR) by the ratio of the radius at which the WBB is in pressure equilibrium with the PIR, $R_{\rm eq}$, to the Strömgren ra…

An Efficient Data-Driven Framework for Linear Quadratic Output Feedback Control
Jun Xie, Yuan-hua Ni, Yiqin Yang, Feng Duan, Bo Xu
https://arxiv.org/abs/2508.20748 https://

An Efficient Data-Driven Framework for Linear Quadratic Output Feedback Control
Linear quadratic regulator with unmeasurable states and unknown system matrix parameters better aligns with practical scenarios. However, for this problem, balancing the optimality of the resulting controller and the leniency of the algorithm's feasibility conditions remains a non-trivial challenge, as no well-established general method has yet been developed to address this trade-off. To address this gap, this study first develops a comprehensive theoretical framework for state parameterizatio…

Jet-mode Feedback in NGC 5972 - Insights from Resolved MUSE, GMRT, and VLA Observations: #Voorwerp Galaxy: https://aasnova.org/2025/07/18/written-in-the-gas-jet-feedback-in-a-voorwerp-galaxy/

Ensemble Control of Stochastic Oscillators via Periodic and Feedback Control
Kaito Ito, Haruhiro Kume, Hideaki Ishii
https://arxiv.org/abs/2507.21441 https://

Ensemble Control of Stochastic Oscillators via Periodic and Feedback Control
We address the problem of steering the phase distribution of oscillators all receiving the same control input to a given target distribution. In a large population limit, the distribution of oscillators can be described by a probability density. Then, our problem can be seen as that of ensemble control with a constraint on the steady-state density. In particular, we consider the case where oscillators are subject to stochastic noise, for which the theoretical understanding is still lacking. Fir…

Effect of Haptic Feedback on Avoidance Behavior and Visual Exploration in Dynamic VR Pedestrian Environment
Kyosuke Ishibashi, Atsushi Saito, Zin Y. Tun, Lucas Ray, Megan C. Coram, Akihiro Sakurai, Allison M. Okamura, Ko Yamamoto
https://arxiv.org/abs/2506.20952

Effect of Haptic Feedback on Avoidance Behavior and Visual Exploration in Dynamic VR Pedestrian Environment
Human crowd simulation in virtual reality (VR) is a powerful tool with potential applications including emergency evacuation training and assessment of building layout. While haptic feedback in VR enhances immersive experience, its effect on walking behavior in dense and dynamic pedestrian flows is unknown. Through a user study, we investigated how haptic feedback changes user walking motion in crowded pedestrian flows in VR. The results indicate that haptic feedback changed users' collision av…

Agentic Program Repair from Test Failures at Scale: A Neuro-symbolic approach with static analysis and test execution feedback
Chandra Maddila, Adam Tait, Claire Chang, Daniel Cheng, Nauman Ahmad, Vijayaraghavan Murali, Marshall Roch, Arnaud Avondet, Aaron Meltzer, Victor Montalvao, Michael Hopko, Chris Waterson, Parth Thakkar, Renuka Fernandez, Kristian Kristensen, Sivan Barzily, Sherry Chen, Rui Abreu, Nachiappan Nagappan, Payam Shodjai, Killian Murphy, James Everingham, Aparna Raman…

Agentic Program Repair from Test Failures at Scale: A Neuro-symbolic approach with static analysis and test execution feedback
Aim: With the advent of LLMs, sophisticated agentic program repair has become viable at large organizations with large codebases. In this work, we develop an Engineering Agent that fixes the source code based on test failures at scale across diverse software offerings internally. Method: Using Llama as the base, we employ the ReAct harness to develop an agent. We start with a test failure that was triaged by a rule-based test failure bot. We then set up an agentic harness and allow the agent …

Two-flow Feedback Multi-scale Progressive Generative Adversarial Network
Sun Weikai, Song Shijie, Chi Wenjie
https://arxiv.org/abs/2508.16089 https://arxiv…

Two-flow Feedback Multi-scale Progressive Generative Adversarial Network
Although diffusion model has made good progress in the field of image generation, GAN\cite{huang2023adaptive} still has a large development space due to its unique advantages, such as WGAN\cite{liu2021comparing}, SSGAN\cite{guibas2021adaptive} \cite{zhang2022vsa} \cite{zhou2024adapt} and so on. In this paper, we propose a novel two-flow feedback multi-scale progressive generative adversarial network (MSPG-SEN) for GAN models. This paper has four contributions: 1) : We propose a two-flow feedbac…

Task-Oriented Edge-Assisted Cross-System Design for Real-Time Human-Robot Interaction in Industrial Metaverse
Kan Chen, Zhen Meng, Xiangmin Xu, Jiaming Yang, Emma Li, Philip G. Zhao
https://arxiv.org/abs/2508.20664

Task-Oriented Edge-Assisted Cross-System Design for Real-Time Human-Robot Interaction in Industrial Metaverse
Real-time human-device interaction in industrial Metaverse faces challenges such as high computational load, limited bandwidth, and strict latency. This paper proposes a task-oriented edge-assisted cross-system framework using digital twins (DTs) to enable responsive interactions. By predicting operator motions, the system supports: 1) proactive Metaverse rendering for visual feedback, and 2) preemptive control of remote devices. The DTs are decoupled into two virtual functions-visual display a…

Efficient Feedback Design for Unsourced Random Access with Integrated Sensing and Communication
Mohammad Javad Ahmadi, Mohammad Kazemi, Rafael F. Schaefer
https://arxiv.org/abs/2506.20262

Efficient Feedback Design for Unsourced Random Access with Integrated Sensing and Communication
We consider an unsourced random access (URA) system enhanced with a feedback mechanism that serves both communication and sensing tasks. While traditional URA systems do not incorporate feedback, we propose a novel feedback signal design that announces the decoding status of users and simultaneously enables target sensing. To design this dual-purpose feedback, we introduce a modified projected gradient descent algorithm that minimizes a weighted combination of communication and sensing errors. …

Multi-mode feedback cooling of the collective modes of a Bose-Einstein condensate
Ryan J. Thomas, Jordan A. McMahon, Zain Mehdi, Stuart S. Szigeti, Simon A. Haine, Samuel Legge, John D. Close, Joseph J. Hope
https://arxiv.org/abs/2506.19739

Multi-mode feedback cooling of the collective modes of a Bose-Einstein condensate
We experimentally demonstrate cavity-free feedback cooling of the three lowest-lying collective modes of a Bose-Einstein condensate in a prolate harmonic trap. Using shadowgraph imaging as an \textit{in situ} probe of the atomic density, we measure the time-dependent centers-of-mass and widths of the condensate in two dimensions and use these measurements to damp oscillations in the two visible dipole modes and the low-frequency quadrupole mode. By inducing oscillations in the condensate, we sh…

RecCoT: Enhancing Recommendation via Chain-of-Thought
Shuo Yang, Jiangxia Cao, Haipeng Li, Yuqi Mao, Shuchao Pang
https://arxiv.org/abs/2506.21032 https://…

RecCoT: Enhancing Recommendation via Chain-of-Thought
In real-world applications, users always interact with items in multiple aspects, such as through implicit binary feedback (e.g., clicks, dislikes, long views) and explicit feedback (e.g., comments, reviews). Modern recommendation systems (RecSys) learn user-item collaborative signals from these implicit feedback signals as a large-scale binary data-streaming, subsequently recommending other highly similar items based on users' personalized historical interactions. However, from this collaborat…

Closed-Form Input Design for Identification under Output Feedback with Perturbation Constraints
Jingwei Hu, Dave Zachariah, Torbj\"orn Wigren, Petre Stoica
https://arxiv.org/abs/2508.18813

Closed-Form Input Design for Identification under Output Feedback with Perturbation Constraints
In many applications, system identification experiments must be performed under output feedback to ensure safety or to maintain system operation. In this paper, we consider the online design of informative experiments for ARMAX models by applying a bounded perturbation to the input signal generated by a fixed output feedback controller. Specifically, the design constrains the resulting output perturbation within user-specified limits and can be efficiently computed in closed form. We demonstrat…

XMAGNET : Kinetic, Thermal and Magnetic AGN Feedback in Massive Galaxies at Halo Masses $\sim 10^{13.5}$ M$_\odot$
Deovrat Prasad, Philipp Grete, Brian O'Shea, Forrest Glines, Mark Voit, Freeke van de Voort, Martin Fournier, Benjamin Wibking
https://arxiv.org/abs/2508.17508

XMAGNET : Kinetic, Thermal and Magnetic AGN Feedback in Massive Galaxies at Halo Masses $\sim 10^{13.5}$ M$_\odot$
The interplay between radiative cooling of the circumgalactic medium (CGM) and feedback heating governs the evolution of the universe's most massive galaxies. This paper presents simulations of feedback processes in massive galaxies showing how kinetic, thermal, and magnetic active galactic nuclei (AGN) feedback interacts with the CGM under different environmental conditions. We find that in massive galaxies with shallower central gravitational potential and higher CGM pressure (multiphase gala…

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…

TransLink seeks feedback on Metrotown-North Shore Bus Rapid Transit
Input will help decide between two route options
TransLink is launching public engagement on the future Bus Rapid Transit (BRT) route between Metrotown and the North Shore. Members of the public are being asked to provide feedback on the project, rank features that are most important to them, and pick which of the two routes they prefer.

Granular feedback merits sophisticated aggregation
Anmol Kagrecha, Henrik Marklund, Potsawee Manakul, Richard Zeckhauser, Benjamin Van Roy
https://arxiv.org/abs/2507.12041

Granular feedback merits sophisticated aggregation
Human feedback is increasingly used across diverse applications like training AI models, developing recommender systems, and measuring public opinion -- with granular feedback often being preferred over binary feedback for its greater informativeness. While it is easy to accurately estimate a population's distribution of feedback given feedback from a large number of individuals, cost constraints typically necessitate using smaller groups. A simple method to approximate the population distribut…

Phase transitions in voting simulated by an intelligent Ising model
Guanyu Xu, Jiahang Chen, Xin Zhou, Yanting Wang
https://arxiv.org/abs/2507.19161 https://

Phase transitions in voting simulated by an intelligent Ising model
Voting is an important social activity for expressing public opinions. By conceptually considering a group of voting agents to be intelligent matter, the impact of real-time information on voting results is quantitatively studied by an intelligent Ising model, which is formed by adding nonlinear instantaneous feedback of the overall magnetization to the conventional Ising model. In the new model, the interaction strength becomes a variable depending on the total magnetization rather than a cons…

What About Emotions? Guiding Fine-Grained Emotion Extraction from Mobile App Reviews
Quim Motger, Marc Oriol, Max Tiessler, Xavier Franch, Jordi Marco
https://arxiv.org/abs/2505.23452

What About Emotions? Guiding Fine-Grained Emotion Extraction from Mobile App Reviews
Opinion mining plays a vital role in analysing user feedback and extracting insights from textual data. While most research focuses on sentiment polarity (e.g., positive, negative, neutral), fine-grained emotion classification in app reviews remains underexplored. This paper addresses this gap by identifying and addressing the challenges and limitations in fine-grained emotion analysis in the context of app reviews. Our study adapts Plutchik's emotion taxonomy to app reviews by developing a str…

Adaptive network dynamics and behavioral contagion in multi-state drug use propagation
Hsuan-Wei Lee, Yi-Hsuan Huang, Nishant Malik
https://arxiv.org/abs/2506.21766

Adaptive network dynamics and behavioral contagion in multi-state drug use propagation
Addictive behavior spreads through social networks via feedback among choice, peer pressure, and shifting ties, a process that eludes standard epidemic models. We present a comprehensive multi-state network model that integrates utility-based behavioral transitions with adaptive network rewiring, capturing the co-evolutionary dynamics between drug use patterns and social structure. Our framework distinguishes four distinct individual states by combining drug use behavior with addiction status, …

Early Assessment of Artificial Lower Extremity Sensory Response Times and Proprioceptive Acuity via Sensory Cortex Electrical Stimulation
Won Joon Sohn, Jeffrey Lim, Po T. Wang, Susan J. Shaw, Michelle Armacost, Hui Gong, Brian Lee, Darrin Lee, Payam Heydari, Richard A. Andersen, Charles Y. Liu, Zoran Nenadic, An H. Do
https://a…

Early Assessment of Artificial Lower Extremity Sensory Response Times and Proprioceptive Acuity via Sensory Cortex Electrical Stimulation
Bi-directional brain computer interfaces (BD-BCIs) may restore brain-controlled walking and artificial leg sensation after spinal cord injury. Current BD-BCIs provide only simplistic "tingling" feedback, which lacks proprioceptive information to perceive critical gait events (leg swing, double support). This information must also be perceived adequately fast to facilitate timely motor responses. Here, we investigated utilizing primary sensory cortex (S1) direct cortical electrical stimulation (…

Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation
Tianjun Wei, Huizhong Guo, Yingpeng Du, Zhu Sun, Chen Huang, Dongxia Wang, Jie Zhang
https://arxiv.org/abs/2508.18142

Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation
User simulation is increasingly vital to develop and evaluate recommender systems (RSs). While Large Language Models (LLMs) offer promising avenues to simulate user behavior, they often struggle with the absence of specific domain alignment required for RSs and the efficiency demands of large-scale simulation. A vast yet underutilized resource for enhancing this alignment is the extensive user feedback inherent in RSs. However, directly leveraging such feedback presents two significant challeng…

TDR: Task-Decoupled Retrieval with Fine-Grained LLM Feedback for In-Context Learning
Yifu Chen, Bingchen Huang, Zhiling Wang, Yuanchao Du, Junfeng Luo, Lei Shen, Zhineng chen
https://arxiv.org/abs/2507.18340

TDR: Task-Decoupled Retrieval with Fine-Grained LLM Feedback for In-Context Learning
In-context learning (ICL) has become a classic approach for enabling LLMs to handle various tasks based on a few input-output examples. The effectiveness of ICL heavily relies on the quality of these examples, and previous works which focused on enhancing example retrieval capabilities have achieved impressive performances. However, two challenges remain in retrieving high-quality examples: (1) Difficulty in distinguishing cross-task data distributions, (2) Difficulty in making the fine-grained…

A Modified Adaptive Data-Enabled Policy Optimization Control to Resolve State Perturbations
Mojtaba Kaheni, Niklas Persson, Vittorio De Iuliis, Costanzo Manes, Alessandro V. Papadopoulos
https://arxiv.org/abs/2507.20580

A Modified Adaptive Data-Enabled Policy Optimization Control to Resolve State Perturbations
This paper proposes modifications to the data-enabled policy optimization (DeePO) algorithm to mitigate state perturbations. DeePO is an adaptive, data-driven approach designed to iteratively compute a feedback gain equivalent to the certainty-equivalence LQR gain. Like other data-driven approaches based on Willems' fundamental lemma, DeePO requires persistently exciting input signals. However, linear state-feedback gains from LQR designs cannot inherently produce such inputs. To address this, …

AI, Expert or Peer? -- Examining the Impact of Perceived Feedback Source on Pre-Service Teachers Feedback Perception and Uptake
Lucas Jasper Jacobsen, Ute Mertens, Thorben Jansen, Kira Elena Weber
https://arxiv.org/abs/2507.16013

AI, Expert or Peer? -- Examining the Impact of Perceived Feedback Source on Pre-Service Teachers Feedback Perception and Uptake
Feedback plays a central role in learning, yet pre-service teachers' engagement with feedback depends not only on its quality but also on their perception of the feedback content and source. Large Language Models (LLMs) are increasingly used to provide educational feedback; however, negative perceptions may limit their practical use, and little is known about how pre-service teachers' perceptions and behavioral responses differ by feedback source. This study investigates how the perceived sourc…

Retrieval Enhanced Feedback via In-context Neural Error-book
Jongyeop Hyun, Bumsoo Kim
https://arxiv.org/abs/2508.16313 https://arxiv.org/pdf/2508.16313

Retrieval Enhanced Feedback via In-context Neural Error-book
Recent advancements in Large Language Models (LLMs) have significantly improved reasoning capabilities, with in-context learning (ICL) emerging as a key technique for adaptation without retraining. While previous works have focused on leveraging correct examples, recent research highlights the importance of learning from errors to enhance performance. However, existing methods lack a structured framework for analyzing and mitigating errors, particularly in Multimodal Large Language Models (MLLM…

Auto-grader Feedback Utilization and Its Impacts: An Observational Study Across Five Community Colleges
Adam Zhang, Heather Burte, Jaromir Savelka, Christopher Bogart, Majd Sakr
https://arxiv.org/abs/2507.14235

Auto-grader Feedback Utilization and Its Impacts: An Observational Study Across Five Community Colleges
Automated grading systems, or auto-graders, have become ubiquitous in programming education, and the way they generate feedback has become increasingly automated as well. However, there is insufficient evidence regarding auto-grader feedback's effectiveness in improving student learning outcomes, in a way that differentiates students who utilized the feedback and students who did not. In this study, we fill this critical gap. Specifically, we analyze students' interactions with auto-graders in …

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…

Retrieval Feedback Memory Enhancement Large Model Retrieval Generation Method
Leqian Li, Dianxi Shi, Jialu Zhou, Xinyu Wei, Mingyue Yang, Songchang Jin, Shaowu Yang
https://arxiv.org/abs/2508.17862

Retrieval Feedback Memory Enhancement Large Model Retrieval Generation Method
Large Language Models (LLMs) have shown remarkable capabilities across diverse tasks, yet they face inherent limitations such as constrained parametric knowledge and high retraining costs. Retrieval-Augmented Generation (RAG) augments the generation process by retrieving externally stored knowledge absent from the models internal parameters. However, RAG methods face challenges such as information loss and redundant retrievals during multi-round queries, accompanying the difficulties in precise…

RIS Codebook Index Assignment under Imperfect Control Links Using TSP-Inspired Optimization
Liangshun Wu, Wen Chen, Qingqing Wu, Xudong Bai, Kunlun Wang
https://arxiv.org/abs/2507.18727

RIS Codebook Index Assignment under Imperfect Control Links Using TSP-Inspired Optimization
Reconfigurable Intelligent Surfaces (RIS) promise transformative gains in wireless communications by enabling programmable control of the propagation environment through discrete phase configurations. In practical deployments, the control of RIS phase states is typically managed using finite codebooks, with configuration indices transmitted over low latency, yet imperfect, wireless feedback channels. Even rare feedback bit errors can lead to significant mismatches between intended and applied R…

Chapter 11 Students' interaction with and appreciation of automated informative tutoring feedback
Gerben van der Hoek, Bastiaan Heeren, Rogier Bos, Paul Drijvers, Johan Jeuring
https://arxiv.org/abs/2507.15650

Chapter 11 Students' interaction with and appreciation of automated informative tutoring feedback
Computer aided formative assessment can be used to enhance a learning process, for instance by providing feedback. There are many design choices for delivering feedback, that lead to a feedback strategy. In an informative feedback strategy, students do not immediately receive information about the correct response, but are offered the opportunity to retry a task to apply feedback information. In this small-scale qualitative study, we explore an informative feedback strategy designed to offer a …

A direct approach of the existence of the solution to a Riccati equation
Gabriela Marinoschi
https://arxiv.org/abs/2507.20171 https://arxiv.org/pdf/2507.20…

A direct approach of the existence of the solution to a Riccati equation
Finding the state feedback control in an $% H^{\infty }$-optimal control problem involves a challenging approach of the associated algebraic Riccati equation of the generic form $A^{\ast }P+PA+PΓP=F$. In view of this objective, we explore first in this paper the existence of the solution to this algebraic Riccati equation by a direct operatorial approach in the space of Hilbert-Schmidt operators. The proofs are provided, under certain assumptions on the operators $Γ$ and $F,$ for the cases wi…

Blowing star formation away in AGN Hosts (BAH) -- III: Feeding and feedback in 3C\:293 observed with JWST NIRSpec
Mait\^e S. Z. de Mellos, Rogemar A. Riffel, Gabriel L. Souza-Oliveira, Nadia L. Zakamska, Marina Bianchin, Thaisa Storchi-Bergmann, Rog\'erio Riffel, Jos\'e Henrique Costa-Souza
https://arxiv.org/abs/2506.208…

Blowing star formation away in AGN Hosts (BAH) -- III: Feeding and feedback in 3C\:293 observed with JWST NIRSpec
We use JWST/NIRSpec observations of the radio galaxy 3C 293 to map the emission, extinction, and kinematics of hot molecular and ionized gas, as well as stellar kinematics, within the inner ~ 2 kpc. The stellar velocity field is well described by a rotating disk model, with its kinematical center offset by ~ 0.5 arcsec from the continuum peak. The hot molecular gas is traced by the H$_2$2.12$μ$m emission line, and the ionized gas by [Fe II]1.64$μ$m and Pa$α$. The gas presents three main kine…

Benefiting from Negative yet Informative Feedback by Contrasting Opposing Sequential Patterns
Veronika Ivanova, Evgeny Frolov, Alexey Vasilev
https://arxiv.org/abs/2508.14786 ht…

Benefiting from Negative yet Informative Feedback by Contrasting Opposing Sequential Patterns
We consider the task of learning from both positive and negative feedback in a sequential recommendation scenario, as both types of feedback are often present in user interactions. Meanwhile, conventional sequential learning models usually focus on considering and predicting positive interactions, ignoring that reducing items with negative feedback in recommendations improves user satisfaction with the service. Moreover, the negative feedback can potentially provide a useful signal for more acc…

Convergent Weight and Activation Dynamics in Memristor Neural Networks
Mauro Di Marco, Mauro Forti, Luca Pancioni, Giacomo Innocenti, Alberto Tesi
https://arxiv.org/abs/2507.20634

Convergent Weight and Activation Dynamics in Memristor Neural Networks
Convergence of dynamic feedback neural networks (NNs), as the Cohen-Grossberg, Hopfield and cellular NNs, has been for a long time a workhorse of NN theory. Indeed, convergence in the presence of multiple stable equilibrium points (EPs) is crucial to implement content addressable memories and solve several other signal processing tasks in real time. There are two typical ways to use a convergent NN, i.e.: a) let the activations evolve while maintaining fixed weights and inputs (activation dynam…

OnGoal: Tracking and Visualizing Conversational Goals in Multi-Turn Dialogue with Large Language Models
Adam Coscia, Shunan Guo, Eunyee Koh, Alex Endert
https://arxiv.org/abs/2508.21061

OnGoal: Tracking and Visualizing Conversational Goals in Multi-Turn Dialogue with Large Language Models
As multi-turn dialogues with large language models (LLMs) grow longer and more complex, how can users better evaluate and review progress on their conversational goals? We present OnGoal, an LLM chat interface that helps users better manage goal progress. OnGoal provides real-time feedback on goal alignment through LLM-assisted evaluation, explanations for evaluation results with examples, and overviews of goal progression over time, enabling users to navigate complex dialogues more effectively…

Training Language Model Agents to Find Vulnerabilities with CTF-Dojo
Terry Yue Zhuo, Dingmin Wang, Hantian Ding, Varun Kumar, Zijian Wang
https://arxiv.org/abs/2508.18370 https:…

Training Language Model Agents to Find Vulnerabilities with CTF-Dojo
Large language models (LLMs) have demonstrated exceptional capabilities when trained within executable runtime environments, notably excelling at software engineering tasks through verified feedback loops. Yet, scalable and generalizable execution-grounded environments remain scarce, limiting progress in training more capable ML agents. We introduce CTF-Dojo, the first large-scale executable runtime tailored for training LLMs with verifiable feedback, featuring 658 fully functional Capture-The-…

The Co-Evolution of Stellar Wind-blown Bubbles and Photoionized Gas II: 3D RMHD Simulations and Tests of Semi-Analytic Models
Lachlan Lancaster, Chang-Goo Kim, Jeong-Gyu Kim, Eve C. Ostriker, Greg L. Bryan
https://arxiv.org/abs/2505.22733

The Co-Evolution of Stellar Wind-blown Bubbles and Photoionized Gas II: 3D RMHD Simulations and Tests of Semi-Analytic Models
In a companion paper (Paper I) we presented a Co-Evolution Model (CEM) in which to consider the evolution of feedback bubbles driven by massive stars through both stellar winds and ionizing radiation, outlining when either of these effects is dominant and providing a model for how they evolve together. Here we present results from three-dimensional radiation magneto-hydrodynamical (RMHD) simulations of this scenario for parameters typical of massive star-forming clouds in the Milky Way: precise…

Nonconvex Optimization Framework for Group-Sparse Feedback Linear-Quadratic Optimal Control I: Penalty Approach
Lechen Feng, Xun Li, Yuan-Hua Ni
https://arxiv.org/abs/2507.18114

Nonconvex Optimization Framework for Group-Sparse Feedback Linear-Quadratic Optimal Control I: Penalty Approach
This paper develops a unified nonconvex optimization framework for the design of group-sparse feedback controllers in infinite-horizon linear-quadratic (LQ) problems. We address two prominent extensions of the classical LQ problem: the distributed LQ problem with fixed communication topology (DFT-LQ) and the sparse feedback LQ problem (SF-LQ), both of which are motivated by the need for scalable and structure-aware control in large-scale systems. Unlike existing approaches that rely on convex r…

RhythmTA: A Visual-Aided Interactive System for ESL Rhythm Training via Dubbing Practice
Chang Chen, Sicheng Song, Shuchang Xu, Zhicheng Li, Huamin Qu, Yanna Lin
https://arxiv.org/abs/2507.19026

RhythmTA: A Visual-Aided Interactive System for ESL Rhythm Training via Dubbing Practice
English speech rhythm, the temporal patterns of stressed syllables, is essential for English as a second language (ESL) learners to produce natural-sounding and comprehensible speech. Rhythm training is generally based on imitation of native speech. However, it relies heavily on external instructor feedback, preventing ESL learners from independent practice. To address this gap, we present RhythmTA, an interactive system for ESL learners to practice speech rhythm independently via dubbing, an i…

Delay-adaptive Control of Nonlinear Systems with Approximate Neural Operator Predictors
Luke Bhan, Miroslav Krstic, Yuanyuan Shi
https://arxiv.org/abs/2508.20367 https://…

Delay-adaptive Control of Nonlinear Systems with Approximate Neural Operator Predictors
In this work, we propose a rigorous method for implementing predictor feedback controllers in nonlinear systems with unknown and arbitrarily long actuator delays. To address the analytically intractable nature of the predictor, we approximate it using a learned neural operator mapping. This mapping is trained once, offline, and then deployed online, leveraging the fast inference capabilities of neural networks. We provide a theoretical stability analysis based on the universal approximation the…

Focus on the Experts: Co-designing an Augmented Reality Eye-Gaze Tracking System with Surgical Trainees to Improve Endoscopic Instruction
Jumanh Atoum, Jinkyung Park, Mamtaj Akter, Nicholas Kavoussi, Pamela Wisniewski, Jie Ying Wu
https://arxiv.org/abs/2506.21896

Focus on the Experts: Co-designing an Augmented Reality Eye-Gaze Tracking System with Surgical Trainees to Improve Endoscopic Instruction
The current apprenticeship model for surgical training requires a high level of supervision, which does not scale well to meet the growing need for more surgeons. Many endoscopic procedures are directly taught in the operating room (OR) while the attending surgeon and trainee operate on patients. The need to prioritize patient care limits the trainees' opportunities to experiment and receive feedback on their performance. Augmented reality (AR) has the potential to increase efficiency in endosc…

Optimal Feedback Control in Social Networks in a McKean-Vlasov-Friedkin-Johnsen System
Paramahansa Pramanik
https://arxiv.org/abs/2508.17138 https://arxiv.…

Optimal Feedback Control in Social Networks in a McKean-Vlasov-Friedkin-Johnsen System
This paper presents a comprehensive analytical formulation for deriving a closed-form optimal strategy for agents operating within a social network, modeled through a McKean-Vlasov stochastic differential equation (SDE). Each agent aims to minimize a personal dynamic cost functional that accounts for deviations from the collective opinions of others, their own past beliefs, and is influenced by randomness and inherent opinion rigidity, often described as stubbornness. To tackle this, we develop…

Linear-Quadratic Discrete-Time Dynamic Games with Unknown Dynamics
Shengyuan Huang, Xiaoguang Yang, Zhigang Cao, Wenjun Mei
https://arxiv.org/abs/2506.22073

Linear-Quadratic Discrete-Time Dynamic Games with Unknown Dynamics
Considering linear-quadratic discrete-time games with unknown input/output/state (i/o/s) dynamics and state, we provide necessary and sufficient conditions for the existence and uniqueness of feedback Nash equilibria (FNE) in the finite-horizon game, based entirely on offline input/output data. We prove that the finite-horizon unknown-dynamics game and its corresponding known-dynamics game have the same FNEs, and provide detailed relationships between their respective FNE matrices. To simplify …

Bootstrap Policy Iteration for Stochastic LQ Tracking with Multiplicative Noise
Jiayu Chen, Zhenhui Xu, Xinghu Wang
https://arxiv.org/abs/2508.20394 https://

Bootstrap Policy Iteration for Stochastic LQ Tracking with Multiplicative Noise
This paper studies the optimal tracking control problem for continuous-time stochastic linear systems with multiplicative noise. The solution framework involves solving a stochastic algebraic Riccati equation for the feedback gain and a Sylvester equation for the feedforward gain. To enable model-free optimal tracking, we first develop a two-phase bootstrap policy iteration (B-PI) algorithm, which bootstraps a stabilizing control gain from the trivially initialized zero-value start and proceeds…

Online Feedback Optimization for Monotone Systems without Timescale Separation
Mattia Bianchi, Florian D\"orfler
https://arxiv.org/abs/2506.16564 http…

Online Feedback Optimization for Monotone Systems without Timescale Separation
Online Feedback Optimization (OFO) steers a dynamical plant to a cost-efficient steady-state, only relying on input-output sensitivity information, rather than on a full plant model. Unlike traditional feedforward approaches, OFO leverages real-time measurements from the plant, thereby inheriting the robustness and adaptability of feedback control. Unfortunately, existing theoretical guarantees for OFO assumes that the controller operates on a slower timescale than the plant, which can affect r…

Output Feedback Design for Parameter Varying Systems subject to Persistent Disturbances and Control Rate Constraints
Jackson G. Ernesto, Eugenio B. Castelan, Walter Lucia
https://arxiv.org/abs/2507.17475

Output Feedback Design for Parameter Varying Systems subject to Persistent Disturbances and Control Rate Constraints
This paper presents a technique for designing output feedback controllers for constrained linear parameter-varying systems that are subject to persistent disturbances. Specifically, we develop an incremental parameter-varying output feedback control law to address control rate constraints, as well as state and control amplitude constraints. The proposal is based on the concept of robust positively invariant sets and applies the extended Farkas' lemma to derive a set of algebraic conditions that…