Tootfinder

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…

Listening with Language Models: Using LLMs to Collect and Interpret Classroom Feedback
Sai Siddartha Maram, Ulia Zaman, Magy Seif El-Nasr
https://arxiv.org/abs/2508.11707 https:…

Listening with Language Models: Using LLMs to Collect and Interpret Classroom Feedback
Traditional end-of-quarter surveys often fail to provide instructors with timely, detailed, and actionable feedback about their teaching. In this paper, we explore how Large Language Model (LLM)-powered chatbots can reimagine the classroom feedback process by engaging students in reflective, conversational dialogues. Through the design and deployment of a three-part system-PromptDesigner, FeedbackCollector, and FeedbackAnalyzer-we conducted a pilot study across two graduate courses at UC Santa …

Wisdom of the Crowd: Reinforcement Learning from Coevolutionary Collective Feedback
Wenzhen Yuan, Shengji Tang, Weihao Lin, Jiacheng Ruan, Ganqu Cui, Bo Zhang, Tao Chen, Ting Liu, Yuzhuo Fu, Peng Ye, Lei Bai
https://arxiv.org/abs/2508.12338

Wisdom of the Crowd: Reinforcement Learning from Coevolutionary Collective Feedback
Reinforcement learning (RL) has significantly enhanced the reasoning capabilities of large language models (LLMs), but its reliance on expensive human-labeled data or complex reward models severely limits scalability. While existing self-feedback methods aim to address this problem, they are constrained by the capabilities of a single model, which can lead to overconfidence in incorrect answers, reward hacking, and even training collapse. To this end, we propose Reinforcement Learning from Coev…

Was mir bei den "KI"-Didaktik Ansätzen, die "KI"-Feedback in den Vordergrund stellen ein Rätsel bleibt, ist: Wieso nicht Peer-Feedback stattdessen? Es ist in Form der anderen Schüler*innen sowieso verfügbar, kostet keinen Strom und hat v.A. keine Wahrscheinlichkeitshypothek. #FediLZ

Factored Output Feedback Controller Synthesis with Locality Constraints for Spatially-Invariant Systems
Walden Marshall
https://arxiv.org/abs/2509.14168 https://

Factored Output Feedback Controller Synthesis with Locality Constraints for Spatially-Invariant Systems
We consider H2 output feedback controller synthesis with pre-specified constraints on spatial communication distance (locality) for spatially-invariant systems using two factored controller frameworks: the system-level parameterization and the input-output parameterization. In our main result, we show that in both frameworks, output feedback controller synthesis with locality constraints can be formulated as a convex problem in finitely many transfer function variables, admitting the use of sta…

Precision and cost of feedback cooling
Andreas Dechant, Jakob H\"upfl, Shuta Kobayashi, Sosuke Ito, Stefan Rotter
https://arxiv.org/abs/2508.12875 https://

Precision and cost of feedback cooling
We investigate the consequences of information exchange between a system and a measurement-feedback apparatus that cools the system below the environmental temperature. A quantitative relationship between entropy pumping and information acquired about the system is derived, showing that, independent of the concrete realization of the feedback, the latter exceeds the former by a positive amount of excess information flow. This excess information flow satisfies a trade-off relation with the preci…

From Feedback to Failure: Automated Android Performance Issue Reproduction
Zhengquan Li, Zhenhao Li, Zishuo Ding
https://arxiv.org/abs/2508.11147 https://a…

From Feedback to Failure: Automated Android Performance Issue Reproduction
Mobile application performance is a vital factor for user experience. Yet, performance issues are notoriously difficult to detect within development environments, where their manifestations are often less conspicuous and diagnosis proves more challenging. To address this limitation, we propose RevPerf, an advanced performance issue reproduction tool that leverages app reviews from Google Play to acquire pertinent information. RevPerf employs relevant reviews and prompt engineering to enrich the…

Toward Practical Equilibrium Propagation: Brain-inspired Recurrent Neural Network with Feedback Regulation and Residual Connections
Zhuo Liu, Tao Chen
https://arxiv.org/abs/2508.11659

Toward Practical Equilibrium Propagation: Brain-inspired Recurrent Neural Network with Feedback Regulation and Residual Connections
Brain-like intelligent systems need brain-like learning methods. Equilibrium Propagation (EP) is a biologically plausible learning framework with strong potential for brain-inspired computing hardware. However, existing im-plementations of EP suffer from instability and prohibi-tively high computational costs. Inspired by the structure and dynamics of the brain, we propose a biologically plau-sible Feedback-regulated REsidual recurrent neural network (FRE-RNN) and study its learning performance…

EvoPSF: Online Evolution of Autonomous Driving Models via Planning-State Feedback
Jiayue Jin, Lang Qian, Jingyu Zhang, Chuanyu Ju, Liang Song
https://arxiv.org/abs/2508.11453 ht…

EvoPSF: Online Evolution of Autonomous Driving Models via Planning-State Feedback
Recent years have witnessed remarkable progress in autonomous driving, with systems evolving from modular pipelines to end-to-end architectures. However, most existing methods are trained offline and lack mechanisms to adapt to new environments during deployment. As a result, their generalization ability diminishes when faced with unseen variations in real-world driving scenarios. In this paper, we break away from the conventional "train once, deploy forever" paradigm and propose EvoPSF, a nove…

VeriRel: Verification Feedback to Enhance Document Retrieval for Scientific Fact Checking
Xingyu Deng, Xi Wang, Mark Stevenson
https://arxiv.org/abs/2508.11122 https://

+VeriRel: Verification Feedback to Enhance Document Retrieval for Scientific Fact Checking
Identification of appropriate supporting evidence is critical to the success of scientific fact checking. However, existing approaches rely on off-the-shelf Information Retrieval algorithms that rank documents based on relevance rather than the evidence they provide to support or refute the claim being checked. This paper proposes +VeriRel which includes verification success in the document ranking. Experimental results on three scientific fact checking datasets (SciFact, SciFact-Open and Check…

Eliminating Tokamak Disruptions with Feedback
H. R. Strauss
https://arxiv.org/abs/2508.13105 https://arxiv.org/pdf/2508.13105

Eliminating Tokamak Disruptions with Feedback
Many disruptions are caused by resistive wall tearing modes (RWTM). A database of DIII-D locked mode disruptions provides two main disruption criteria, which are shown to be signatures of RWTMs. The first is that the q = 2 rational surface must be sufficiently close the resistive wall surrounding the plasma to interact with it. If active feedback is used, this implies that RWTMs can be prevented from causing major disruptions. This is demonstrated in simulations. The second criterion is that th…

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…

Demonstrated today that even professors can benefit from feedback early and often:
I scheduled myself a practice talk with a group of students in @…, and I hadn't had time to finish more than a partial script and even fewer actual slides.
I didn't want to cancel so I plugged my laptop into the projector and asked if anyone had a problem with…

Integrating Feedback Loss from Bi-modal Sarcasm Detector for Sarcastic Speech Synthesis
Zhu Li, Yuqing Zhang, Xiyuan Gao, Devraj Raghuvanshi, Nagendra Kumar, Shekhar Nayak, Matt Coler
https://arxiv.org/abs/2508.13028

Integrating Feedback Loss from Bi-modal Sarcasm Detector for Sarcastic Speech Synthesis
Sarcastic speech synthesis, which involves generating speech that effectively conveys sarcasm, is essential for enhancing natural interactions in applications such as entertainment and human-computer interaction. However, synthesizing sarcastic speech remains a challenge due to the nuanced prosody that characterizes sarcasm, as well as the limited availability of annotated sarcastic speech data. To address these challenges, this study introduces a novel approach that integrates feedback loss fr…

#DH2025 thanks @flochiff.bsky.social for sharing this link as I wanted to follow up on Pandore! 'Pandore: automating text-processing workflows for humanities researchers' from Sorbonne Université and ObTIC - Observatoire des textes, des idées et des corpus

NEFT: A Unified Transformer Framework for Efficient Near-Field CSI Feedback in XL-MIMO Systems
Haiyang Li, Tianqi Mao, Pengyu Wang, Ruiqi Liu, Shunyu Li, Zhaocheng Wang
https://arxiv.org/abs/2509.12748

NEFT: A Unified Transformer Framework for Efficient Near-Field CSI Feedback in XL-MIMO Systems
Extremely large-scale multiple-input multiple-output (XL-MIMO) systems, operating in the near-field region due to their massive antenna arrays, are a key enabler of next-generation wireless communications but face significant challenges in channel state information (CSI) feedback. Deep learning has emerged as a powerful tool by learning compact CSI representations for feedback. However, existing methods struggle to capture the intricate structure of near-field CSI while incurring prohibitive co…

Was vielleicht sehr wenige Leute wissen, ist, dass man im Programm der FrOSCon die Vorträge bewerten kann.
Macht das bitte, es ist eine tolle Möglichkeit, Feedback zu geben und für die Vortragenden eine Möglichkeit besser zu werden. Die können die Bewertungen anonym im Frab sehen.
https://programm.froscon.org/fro…

Mapping feedback signatures in 3C 297: A quasar-host merger at Cosmic Noon
Chetna Duggal, Christopher P. O'Dea, Stefi A. Baum, John Jiwa, Grant Tremblay, Marco Chiaberge, George Miley, Carlo Stanghellini, William Sparks
https://arxiv.org/abs/2508.11926

Mapping feedback signatures in 3C 297: A quasar-host merger at Cosmic Noon
We present a study of quasar host galaxy 3C 297 which is home to a powerful bent-jet radio source suggesting vigorous interaction with a dense ISM and/or jet precession. Archival HST imaging showed interestingly perturbed morphology of the host with a bright ~30 kpc arc feature, extended filamentary structure of line-emitting gas and clumpy blue excess emission co-spatial with the radio hotspots. Our VLT/SINFONI integral-field observations reveal complex, spatially-resolved Hα+[NII] emission i…

The PHANGS-MUSE/HST-Halpha Nebulae Catalogue: #PHANGS

Two-point Random Gradient-free Methods for Model-free Feedback Optimization
Amir Mehrnoosh, Gianluca Bianchin
https://arxiv.org/abs/2509.11666 https://arxi…

Two-point Random Gradient-free Methods for Model-free Feedback Optimization
Feedback optimization has emerged as a promising approach for optimizing the steady-state operation of dynamical systems while requiring minimal modeling efforts. Unfortunately, most existing feedback optimization methods rely on knowledge of the plant dynamics, which may be difficult to obtain or estimate in practice. In this paper, we introduce a novel randomized two-point gradient-free feedback optimization method, inspired by zeroth-order optimization techniques. Our method relies on functi…

Fusing Rewards and Preferences in Reinforcement Learning
Sadegh Khorasani, Saber Salehkaleybar, Negar Kiyavash, Matthias Grossglauser
https://arxiv.org/abs/2508.11363 https://…

Fusing Rewards and Preferences in Reinforcement Learning
We present Dual-Feedback Actor (DFA), a reinforcement learning algorithm that fuses both individual rewards and pairwise preferences (if available) into a single update rule. DFA uses the policy's log-probabilities directly to model the preference probability, avoiding a separate reward-modeling step. Preferences can be provided by human-annotators (at state-level or trajectory-level) or be synthesized online from Q-values stored in an off-policy replay buffer. Under a Bradley-Terry model, we p…

Humanizing Automated Programming Feedback: Fine-Tuning Generative Models with Student-Written Feedback
Victor-Alexandru P\u{a}durean, Tung Phung, Nachiket Kotalwar, Michael Liut, Juho Leinonen, Paul Denny, Adish Singla
https://arxiv.org/abs/2509.10647

Humanizing Automated Programming Feedback: Fine-Tuning Generative Models with Student-Written Feedback
The growing need for automated and personalized feedback in programming education has led to recent interest in leveraging generative AI for feedback generation. However, current approaches tend to rely on prompt engineering techniques in which predefined prompts guide the AI to generate feedback. This can result in rigid and constrained responses that fail to accommodate the diverse needs of students and do not reflect the style of human-written feedback from tutors or peers. In this study, we…

Feedback Linearization for Replicator Dynamics: A Control Framework for Evolutionary Game Convergence
Adil Faisal
https://arxiv.org/abs/2508.12583 https://…

Feedback Linearization for Replicator Dynamics: A Control Framework for Evolutionary Game Convergence
This paper demonstrates the first application of feedback linearization to replicator dynamics, driving the evolution of non-convergent evolutionary games to systems with guaranteed global asymptotic stability.

(PDF) Feedback signals can explain spike-silent working memory traces in visual cortex https://2025.ccneuro.org/abstract_pdf/Krause_2025_Feedback_signals_explain_spike-silent_working_memory.pdf

RadGame: An AI-Powered Platform for Radiology Education
Mohammed Baharoon, Siavash Raissi, John S. Jun, Thibault Heintz, Mahmoud Alabbad, Ali Alburkani, Sung Eun Kim, Kent Kleinschmidt, Abdulrahman O. Alhumaydhi, Mohannad Mohammed G. Alghamdi, Jeremy Francis Palacio, Mohammed Bukhaytan, Noah Michael Prudlo, Rithvik Akula, Brady Chrisler, Benjamin Galligos, Mohammed O. Almutairi, Mazeen Mohammed Alanazi, Nasser M. Alrashdi, Joel Jihwan Hwang, Sri Sai Dinesh Jaliparthi, Luke David Nelson…

RadGame: An AI-Powered Platform for Radiology Education
We introduce RadGame, an AI-powered gamified platform for radiology education that targets two core skills: localizing findings and generating reports. Traditional radiology training is based on passive exposure to cases or active practice with real-time input from supervising radiologists, limiting opportunities for immediate and scalable feedback. RadGame addresses this gap by combining gamification with large-scale public datasets and automated, AI-driven feedback that provides clear, struct…

Optical Interferometric Readout of a Magnetically Levitated Superconducting Microsphere
J. J. Hansen, S. Minniberger, D. Ilk, P. Asenbaum, G. Higgins, R. G. Povey, P. Schmidt, J. Hofer, R. Claessen, M. Aspelmeyer, M. Trupke
https://arxiv.org/abs/2508.11731

Optical Interferometric Readout of a Magnetically Levitated Superconducting Microsphere
We probe the motion of a 6 $μg$ magnetically levitated superconducting microsphere using optical interferometry at 3 K, achieving a resolution better than 1 $nm/ \sqrt{Hz}$, and use the measured signal to feedback-cool its motion. The resolution exceeds the shot-noise limit of 11 $pm/ \sqrt{Hz}$ primarily due to technical noise arising from the roughness of the particle. Combined with established techniques of cavity optomechanics, the high degree of isolation from environmental noise afforded…

from my link log —
Use the force (feedback) to solder small things.
https://www.bunniestudios.com/blog/2025/use-the-force-feedback-to-solder-small-things/
saved 2025-09-27

Ask-to-Clarify: Resolving Instruction Ambiguity through Multi-turn Dialogue
Xingyao Lin, Xinghao Zhu, Tianyi Lu, Sicheng Xie, Hui Zhang, Xipeng Qiu, Zuxuan Wu, Yu-Gang Jiang
https://arxiv.org/abs/2509.15061

Ask-to-Clarify: Resolving Instruction Ambiguity through Multi-turn Dialogue
The ultimate goal of embodied agents is to create collaborators that can interact with humans, not mere executors that passively follow instructions. This requires agents to communicate, coordinate, and adapt their actions based on human feedback. Recently, advances in VLAs have offered a path toward this goal. However, most current VLA-based embodied agents operate in a one-way mode: they receive an instruction and execute it without feedback. This approach fails in real-world scenarios where …

Frag mich ob die Analogie zu "KI"-Lernen funkioniert:
Whats the point of "AI" Feedback, if I still have to pay a teacher to fix it?
Wenn wir annehmen, dass Lernen ein ähnlich komplexer Vorgang wie programmieren ist, dann können wir das übertragen. Vielleicht ist ja "KI"-Lernen wenig mehr als ein Rollenspiel für Leute, die sich wie Lehrende fühlen wollen, ohne die eigentliche Arbeit zu machen.
Bringt uns der Begriff vibe learning weiter?

tante (@tante@tldr.nettime.org)
Angehängt: 1 Bild I think the Vibecoding reddit has accidentally stumbled on the best description of vibecoding: It's "roleplay for guys [it is always guys] who want to feel like hackers without doing the hard part". (Source: https://www.reddit.com/r/vibecoding/comments/1mu6t8z/whats_the_point_of_vibe_coding_if_i_still_have_to/ ) #ai #vibecoding

Eine neue Weisheit! Es geht um Feedback! Herzensschwestern! Wasser! Lottogewinne!
UND: Eine beachtliche Flachwitzsammlung von euch, liebe Hörer*innen, an der sich @… labt und @…, @…

Major wine regions could disappear from global warming, Europe to be hit especially hard - Science Feedback
https://science.feedback.org/major-wine-regions-could-disappear-from-global-warming-europe-to-be-hit-especially-hard/

Updating the Complex Systems Keyword Diagram Using Collective Feedback and Latest Literature Data
Hiroki Sayama
https://arxiv.org/abs/2509.11997 https://ar…

Updating the Complex Systems Keyword Diagram Using Collective Feedback and Latest Literature Data
The complex systems keyword diagram generated by the author in 2010 has been used widely in a variety of educational and outreach purposes, but it definitely needs a major update and reorganization. This short paper reports our recent attempt to update the keyword diagram using information collected from the following multiple sources: (a) collective feedback posted on social media, (b) recent reference books on complex systems and network science, (c) online resources on complex systems, and (…

Behavioral-feedback SIR epidemic model: analysis and control
Martina Alutto, Leonardo Cianfanelli, Giacomo Como, Fabio Fagnani, Francesca Parise
https://arxiv.org/abs/2509.12257

Behavioral-feedback SIR epidemic model: analysis and control
This paper investigates a behavioral-feedback SIR model in which the infection rate adapts dynamically based on the fractions of susceptible and infected individuals. We introduce an invariant of motion and we characterize the peak of infection. We further examine the system under a threshold constraint on the infection level. Based on this analysis, we formulate an optimal control problem to keep the infection curve below a healthcare capacity threshold while minimizing the economic cost. For …

Feedback on this talk included „it was very ADHD friendly“ 😁
https://hachyderm.io/@swiftserverconf/115378314888816891

Crosslisted article(s) found for q-bio.NC. https://arxiv.org/list/q-bio.NC/new
[1/1]:
- Toward Practical Equilibrium Propagation: Brain-inspired Recurrent Neural Network with Feedback R...
Zhuo Liu, Tao Chen

Folks creating free and open software for the common good: here’s a checklist of everything else you should be doing so corporations can make the best use of your free labour.
I mean, sure, some good security tips here and worth reading anyway but do fuck off with holding free software developers to account for supply chain attacks. It’s your fucking supply chain, not ours, you fucking corporation.

RLNVR: Reinforcement Learning from Non-Verified Real-World Rewards
Rohit Krishnan, Jon Evans
https://arxiv.org/abs/2508.12165 https://arxiv.org/pdf/2508.12…

RLNVR: Reinforcement Learning from Non-Verified Real-World Rewards
This paper introduces RLNVR (Reinforcement Learning from Non-Verified Rewards), a framework for training language models using noisy, real-world feedback signals without requiring explicit human verification. Traditional RLHF requires expensive, verified reward signals that are impractical in many real-world domains. RLNVR addresses this challenge through baseline normalization and semantic similarity-based reward transfer. We demonstrate RLNVR through Walter, a prototype system that optimizes …

Sentiment Feedback in Equity Markets: Asymmetries, Retail Heterogeneity, and Structural Calibration
Lucas Marques Sneller
https://arxiv.org/abs/2509.11970 https://

Sentiment Feedback in Equity Markets: Asymmetries, Retail Heterogeneity, and Structural Calibration
We study how sentiment shocks propagate through equity returns and investor clientele using four independent proxies with sign-aligned kappa-rho parameters. A structural calibration links a one standard deviation innovation in sentiment to a pricing impact of 1.06 basis points with persistence parameter rho = 0.940, yielding a half-life of 11.2 months. The impulse response peaks around the 12-month horizon, indicating slow-moving amplification. Cross-sectionally, a simple D10-D1 portfolio earns…

Deutschland-Stack: So soll die nationale souveräne Technologieplattform aussehen
Das Digitalministerium hat eine "Landkarte" und Skizze für ein Gesamtbild des geplanten Deutschland-Stack veröffentlicht. Interessierte können Feedback geben.

If you send a bulk mail to a retired email address (former staff member, for example) and your unsubscribe feedback page doesn't have "address no longer used", I *will* click the "this is spam" feedback item.

Dust Attenuation of Lyman-Werner Feedback: Reassessing Early Super Massive Black Holes Seed Formation
Bisweswar Sen
https://arxiv.org/abs/2509.11111 https://

Dust Attenuation of Lyman-Werner Feedback: Reassessing Early Super Massive Black Holes Seed Formation
We investigate the impact of dust shielding on Lyman-Werner (LW) radiation fields and its implications for supermassive black hole (SMBH) seed formation at high redshift. Using an updated version of the GAMETE/QSOdust semi-analytical model, we implement a simple dust shielding prescription that accounts for the absorption of LW photons by dust grains. We find that even modest dust enrichment can significantly reduce the effective LW radiation field, allowing H2 cooling to persist in regions pre…

Characterizing the interplay between Galactic star formation and ionization feedback with PRIMA
Annie Zavagno, Delphine Russeil, Paolo Suin, Siju Zhang, Ram Kesh Yadav, Miguel Figueira, Loris Berthelot, Doris Arzoumanian, Manash Ranjan Samal, Vineet Rawat, Philippe Andr\'e, Michael Mattern, Hong-Li Liu, Sarah Sadavoy, Parisa Nozari, Beno\^it Epinat
https://…

Characterizing the interplay between Galactic star formation and ionization feedback with PRIMA
Recent results from the James Webb Space Telescope show that nearby spiral galaxies are dominated by the presence of H I and H II bubbles that strongly shape their surrounding medium. These bubbles result from the feedback of high-mass stars at different stages of their life cycle. However, early (pre-supernova) feedback from high-mass stars is still poorly quantified. Recent results from numerical simulations suggest that the impact of high-mass star early feedback (photoionization, wind) on s…

A Nonlinear Scaling-based Design of Control Lyapunov-barrier Function for Relative Degree 2 Case and its Application to Safe Feedback Linearization
Haechan Pyon, Gyunghoon Park
https://arxiv.org/abs/2509.15071

A Nonlinear Scaling-based Design of Control Lyapunov-barrier Function for Relative Degree 2 Case and its Application to Safe Feedback Linearization
In this paper we address the problem of control Lyapunov-barrier function (CLBF)-based safe stabilization for a class of nonlinear control-affine systems. A difficulty may arise for the case when a constraint has the relative degree larger than 1, at which computing a proper CLBF is not straightforward. Instead of adding an (possibly non-existent) control barrier function (CBF) to a control Lyapunov function (CLF), our key idea is to simply scale the value of the CLF on the unsafe set, by utili…

Anyone who follows me has probably gathered that I've become a fan of Discourse after running a few Discourse instances. They are currently requesting feedback.
I was pleased to see that they used a survey vendor that appears to have meaningful a11y — I didn't look for aria labels so I won't speak to that, but what I did notice is that there is explicitly-labeled support for keyboard-only interaction on every page, and I really appreciated that thoughtfulness.

ViScratch: Using Large Language Models and Gameplay Videos for Automated Feedback in Scratch
Yuan Si, Daming Li, Hanyuan Shi, Jialu Zhang
https://arxiv.org/abs/2509.11065 https:…

ViScratch: Using Large Language Models and Gameplay Videos for Automated Feedback in Scratch
Block-based programming environments such as Scratch are increasingly popular in programming education, in particular for young learners. While the use of blocks helps prevent syntax errors, semantic bugs remain common and difficult to debug. Existing tools for Scratch debugging rely heavily on predefined rules or user manual inputs, and crucially, they ignore the platform's inherently visual nature. We introduce ViScratch, the first multimodal feedback generation system for Scratch that leve…

I gave a talk at SCD today, thanks for attending and for the good feedback - if you have more questions, keep them coming.
Until then, here are the slides and a link to the repository with the config.
https://winkelwagen.de/2025/09/16/deploying-shopwa…

A Phase Synthesizer for Decorrelation to Improve Acoustic Feedback Cancellation
Klaus Linhard, Philipp Bulling
https://arxiv.org/abs/2510.12377 https://arx…

A Phase Synthesizer for Decorrelation to Improve Acoustic Feedback Cancellation
Undesired acoustic feedback is a known issue in communication systems, such as speech in-car communication, public address systems, or hearing aids. Without additional precautions, there is a high risk that the adaptive filter - intended to cancel the feedback path - also suppresses parts of the desired signal. One solution is to decorrelate the loudspeaker and microphone signals. In this work, we combine the two decorrelation approaches frequency shifting and phase modulation in a unified fram…

When motivation can be more than a message: designing agents to boost physical activity
Alessandro Silacci, Maurizio Caon, Mauro Cherubini
https://arxiv.org/abs/2508.12388 https…

When motivation can be more than a message: designing agents to boost physical activity
Virtual agents are commonly used in physical activity interventions to support behavior change, often taking the role of coaches that deliver encouragement and feedback. While effective for compliance, this role typically lacks relational depth. This pilot study explores how such agents might be perceived not just as instructors, but as co-participants: entities that appear to exert effort alongside users. Drawing on thematic analysis of semi-structured interviews with 12 participants from a pr…

Global Mean-Amplitude Enhanced Spiking Neural Network Coherent Ising Machine
Yan Chen Jiang, Lu Ma, Chuan Wang, Tie Jun Wang
https://arxiv.org/abs/2509.13917 https://

Global Mean-Amplitude Enhanced Spiking Neural Network Coherent Ising Machine
The coherent Ising machine (CIM) is a quantum-inspired computing platform that leverages optical parametric oscillation dynamics to solve combinatorial optimization problems by searching for the ground state of an Ising Hamiltonian. Conventional CIM implementations face challenges in handling non-uniform coupling strengths and maintaining amplitude stability during computation. In this paper, a new global mean-amplitude feedback-enhanced spiking neural network CIM (GFSNN-CIM) is introduced with…

Pref-GUIDE: Continual Policy Learning from Real-Time Human Feedback via Preference-Based Learning
Zhengran Ji, Boyuan Chen
https://arxiv.org/abs/2508.07126 https://

Pref-GUIDE: Continual Policy Learning from Real-Time Human Feedback via Preference-Based Learning
Training reinforcement learning agents with human feedback is crucial when task objectives are difficult to specify through dense reward functions. While prior methods rely on offline trajectory comparisons to elicit human preferences, such data is unavailable in online learning scenarios where agents must adapt on the fly. Recent approaches address this by collecting real-time scalar feedback to guide agent behavior and train reward models for continued learning after human feedback becomes un…

User eXperience Perception Insights Dataset (UXPID): Synthetic User Feedback from Public Industrial Forums
Mikhail Kulyabin, Jan Joosten, Choro Ulan uulu, Nuno Miguel Martins Pacheco, Fabian Ries, Filippos Petridis, Jan Bosch, Helena Holmstr\"om Olsson
https://arxiv.org/abs/2509.11777

User eXperience Perception Insights Dataset (UXPID): Synthetic User Feedback from Public Industrial Forums
Customer feedback in industrial forums reflect a rich but underexplored source of insight into real-world product experience. These publicly shared discussions offer an organic view of user expectations, frustrations, and success stories shaped by the specific contexts of use. Yet, harnessing this information for systematic analysis remains challenging due to the unstructured and domain-specific nature of the content. The lack of structure and specialized vocabulary makes it difficult for tradi…

Diagnostic-Guided Dynamic Profile Optimization for LLM-based User Simulators in Sequential Recommendation
Hongyang Liu, Zhu Sun, Tianjun Wei, Yan Wang, Jiajie Zhu, Xinghua Qu
https://arxiv.org/abs/2508.12645

Diagnostic-Guided Dynamic Profile Optimization for LLM-based User Simulators in Sequential Recommendation
Recent advances in large language models (LLMs) have enabled realistic user simulators for developing and evaluating recommender systems (RSs). However, existing LLM-based simulators for RSs face two major limitations: (1) static and single-step prompt-based inference that leads to inaccurate and incomplete user profile construction; (2) unrealistic and single-round recommendation-feedback interaction pattern that fails to capture real-world scenarios. To address these limitations, we propose D…

Digital Post-Distortion Architectures for Nonlinear Power Amplifiers: Volterra and Kernel Methods
Daniel Sch\"aufele, Jochen Fink, Renato L. G. Cavalcante, S{\l}awomir Sta\'nczak
https://arxiv.org/abs/2508.11792

Digital Post-Distortion Architectures for Nonlinear Power Amplifiers: Volterra and Kernel Methods
In modern 5G user equipments (UEs), the power amplifier (PA) contributes significantly to power consumption during uplink transmissions, especially in cell-edge scenarios. While reducing power backoff can enhance PA efficiency, it introduces nonlinear distortions that degrade signal quality. Existing solutions, such as digital pre-distortion, require complex feedback mechanisms for optimal performance, leading to increased UE complexity and power consumption. Instead, in this study we explore d…

Calibrated Generative AI as Meta-Reviewer: A Systemic Functional Linguistics Discourse Analysis of Reviews of Peer Reviews
Gabriela C. Zapata, Bill Cope, Mary Kalantzis, Duane Searsmith
https://arxiv.org/abs/2509.15035

Calibrated Generative AI as Meta-Reviewer: A Systemic Functional Linguistics Discourse Analysis of Reviews of Peer Reviews
This study investigates the use of generative AI to support formative assessment through machine generated reviews of peer reviews in graduate online courses in a public university in the United States. Drawing on Systemic Functional Linguistics and Appraisal Theory, we analyzed 120 metareviews to explore how generative AI feedback constructs meaning across ideational, interpersonal, and textual dimensions. The findings suggest that generative AI can approximate key rhetorical and relational fe…

Design of MIMO Lur'e oscillators via dominant system theory with application in multi-agent rhythm synchronization
Yu Kawano, Fulvio Forni
https://arxiv.org/abs/2508.12141 h…

Design of MIMO Lur'e oscillators via dominant system theory with application in multi-agent rhythm synchronization
This paper presents a new design framework for dynamic output-feedback controllers for Lur'e oscillation in a multiple-input multiple-output setting. We first revisit and extend dominant system theory to state-dependent rates, with the goal of deriving conditions based on linear matrix inequalities. Then, we introduce a separation principle for Lur'e oscillator design, which allows for the independent design of a state-feedback oscillator and an observer. Our proposed control synthesis is demon…

Glovity: Learning Dexterous Contact-Rich Manipulation via Spatial Wrench Feedback Teleoperation System
Yuyang Gao, Haofei Ma, Pai Zheng
https://arxiv.org/abs/2510.09229 https://…

Glovity: Learning Dexterous Contact-Rich Manipulation via Spatial Wrench Feedback Teleoperation System
We present Glovity, a novel, low-cost wearable teleoperation system that integrates a spatial wrench (force-torque) feedback device with a haptic glove featuring fingertip Hall sensor calibration, enabling feedback-rich dexterous manipulation. Glovity addresses key challenges in contact-rich tasks by providing intuitive wrench and tactile feedback, while overcoming embodiment gaps through precise retargeting. User studies demonstrate significant improvements: wrench feedback boosts success rate…

Personalized and Constructive Feedback for Computer Science Students Using the Large Language Model (LLM)
Javed Ali Khan, Muhammad Yaqoob, Mamoona Tasadduq, Hafsa Shareef Dar, Aitezaz Ahsan
https://arxiv.org/abs/2510.11556

Personalized and Constructive Feedback for Computer Science Students Using the Large Language Model (LLM)
The evolving pedagogy paradigms are leading toward educational transformations. One fundamental aspect of effective learning is relevant, immediate, and constructive feedback to students. Providing constructive feedback to large cohorts in academia is an ongoing challenge. Therefore, academics are moving towards automated assessment to provide immediate feedback. However, current approaches are often limited in scope, offering simplistic responses that do not provide students with personalized …

WIP: Leveraging LLMs for Enforcing Design Principles in Student Code: Analysis of Prompting Strategies and RAG
Dhruv Kolhatkar, Soubhagya Akkena, Edward F. Gehringer
https://arxiv.org/abs/2508.11717

WIP: Leveraging LLMs for Enforcing Design Principles in Student Code: Analysis of Prompting Strategies and RAG
This work-in-progress research-to-practice paper explores the integration of Large Language Models (LLMs) into the code-review process for open-source software projects developed in computer science and software engineering courses. The focus is on developing an automated feedback tool that evaluates student code for adherence to key object-oriented design principles, addressing the need for more effective and scalable methods to teach software design best practices. The innovative practice inv…

Searching for Stellar-Feedback-Driven Outflow Signatures: A Deep Dive into NGC 3741
Lexi N. Gault, Liese van Zee, Elizabeth A. K. Adams, James M. Wells, Laura Congreve Hunter, Kristen B. W. McQuinn, Roger E. Cohen, O. Grace Telford
https://arxiv.org/abs/2510.12645

Searching for Stellar-Feedback-Driven Outflow Signatures: A Deep Dive into NGC 3741
Stellar feedback drives winds and outflows critical to the baryon cycles of low-mass galaxies whose shallow gravitational potential wells make them particularly susceptible to mass and metal loss through outflows. However, spatially resolved observations of stellar-feedback-driven outflows are limited due to their low-surface brightness and transient nature. We present the pilot of a larger multi-wavelength study searching for and quantifying stellar-feedback-driven winds and outflows on both s…

Image Realness Assessment and Localization with Multimodal Features
Lovish Kaushik, Agnij Biswas, Somdyuti Paul
https://arxiv.org/abs/2509.13289 https://ar…

Image Realness Assessment and Localization with Multimodal Features
A reliable method of quantifying the perceptual realness of AI-generated images and identifying visually inconsistent regions is crucial for practical use of AI-generated images and for improving photorealism of generative AI via realness feedback during training. This paper introduces a framework that accomplishes both overall objective realness assessment and local inconsistency identification of AI-generated images using textual descriptions of visual inconsistencies generated by vision-lang…

Learning Constraints from Stochastic Partially-Observed Closed-Loop Demonstrations
Chih-Yuan Chiu, Zhouyu Zhang, Glen Chou
https://arxiv.org/abs/2509.15109 https://

Learning Constraints from Stochastic Partially-Observed Closed-Loop Demonstrations
We present an algorithm for learning unknown parametric constraints from locally-optimal input-output trajectory data. We assume that the given data is generated by demonstrators with stochastic nonlinear dynamics who execute a state or output feedback law to robustly satisfy the constraints despite worst-case dynamics and output noise. We encode the Karush-Kuhn-Tucker (KKT) conditions of this robust optimal output feedback control problem within a feasibility problem to recover constraints con…

From customer survey feedback to software improvements: Leveraging the full potential of data
Erik Bertram, Nina Hollender, Sebastian Juhl, Sandra Loop, Martin Schrepp
https://arxiv.org/abs/2509.10064 …

From customer survey feedback to software improvements: Leveraging the full potential of data
Converting customer survey feedback data into usable insights has always been a great challenge for large software enterprises. Despite the improvements on this field, a major obstacle often remains when drawing the right conclusions out of the data and channeling them into the software development process. In this paper we present a practical end-to-end approach of how to extract useful information out of a data set and leverage the information to drive change. We describe how to choose the ri…

Improving Detection of Watermarked Language Models
Dara Bahri, John Wieting
https://arxiv.org/abs/2508.13131 https://arxiv.org/pdf/2508.13131

Improving Detection of Watermarked Language Models
Watermarking has recently emerged as an effective strategy for detecting the generations of large language models (LLMs). The strength of a watermark typically depends strongly on the entropy afforded by the language model and the set of input prompts. However, entropy can be quite limited in practice, especially for models that are post-trained, for example via instruction tuning or reinforcement learning from human feedback (RLHF), which makes detection based on watermarking alone challenging…

exUMI: Extensible Robot Teaching System with Action-aware Task-agnostic Tactile Representation
Yue Xu, Litao Wei, Pengyu An, Qingyu Zhang, Yong-Lu Li
https://arxiv.org/abs/2509.14688

exUMI: Extensible Robot Teaching System with Action-aware Task-agnostic Tactile Representation
Tactile-aware robot learning faces critical challenges in data collection and representation due to data scarcity and sparsity, and the absence of force feedback in existing systems. To address these limitations, we introduce a tactile robot learning system with both hardware and algorithm innovations. We present exUMI, an extensible data collection device that enhances the vanilla UMI with robust proprioception (via AR MoCap and rotary encoder), modular visuo-tactile sensing, and automated cal…

Sensory robustness through top-down feedback and neural stochasticity in recurrent vision models
Antonino Greco, Marco D'Alessandro, Karl J. Friston, Giovanni Pezzulo, Markus Siegel
https://arxiv.org/abs/2508.07115

Sensory robustness through top-down feedback and neural stochasticity in recurrent vision models
Biological systems leverage top-down feedback for visual processing, yet most artificial vision models succeed in image classification using purely feedforward or recurrent architectures, calling into question the functional significance of descending cortical pathways. Here, we trained convolutional recurrent neural networks (ConvRNN) on image classification in the presence or absence of top-down feedback projections to elucidate the specific computational contributions of those feedback pathw…

Discovery of Jet–Bubble–Disk Interaction - Jet Feedback on a #ProtoplanetaryDisk Via an Expanding Bubble in WSB 52: https://iopscience.iop.org/article/10.3847/1538-4357/add47e -> Baby Star Sets off Explosion, Gets Caught in Blast: https://www.nao.ac.jp/en/news/science/2025/20250805-alma.html

CrossDenoise: Denoising Implicit Feedback via a Lightweight Entity-Aware Synergistic Framework
Ze Liu, Xianquan Wang, Shuochen Liu, Jie Ma, Huibo Xu, Yupeng Han, Zhe Yang, Kai Zhang, Longfei Li, Jun Zhou
https://arxiv.org/abs/2508.10851

CrossDenoise: Denoising Implicit Feedback via a Lightweight Entity-Aware Synergistic Framework
Recommender systems heavily rely on implicit feedback, which is inherently noisy due to false positives and negatives, severely degrading recommendation accuracy. Existing denoising strategies often overlook entity-aware modeling, suffer from high computational overhead, or demand excessive hyperparameter tuning, limiting their real-world applicability. We propose CrossDenoise, a novel and lightweight framework that addresses these challenges by disentangling noise estimation into user-, item-,…

How well can LLMs provide planning feedback in grounded environments?
Yuxuan Li, Victor Zhong
https://arxiv.org/abs/2509.09790 https://arxiv.org/pdf/2509.0…

How well can LLMs provide planning feedback in grounded environments?
Learning to plan in grounded environments typically requires carefully designed reward functions or high-quality annotated demonstrations. Recent works show that pretrained foundation models, such as large language models (LLMs) and vision language models (VLMs), capture background knowledge helpful for planning, which reduces the amount of reward design and demonstrations needed for policy learning. We evaluate how well LLMs and VLMs provide feedback across symbolic, language, and continuous c…

AI Agentic Programming: A Survey of Techniques, Challenges, and Opportunities
Huanting Wang, Jingzhi Gong, Huawei Zhang, Zheng Wang
https://arxiv.org/abs/2508.11126 https://

AI Agentic Programming: A Survey of Techniques, Challenges, and Opportunities
AI agentic programming is an emerging paradigm in which large language models (LLMs) autonomously plan, execute, and interact with external tools like compilers, debuggers, and version control systems to iteratively perform complex software development tasks. Unlike conventional code generation tools, agentic systems are capable of decomposing high-level goals, coordinating multi-step processes, and adapting their behavior based on intermediate feedback. These capabilities are transforming the …

Optimizing Peer Grading: A Systematic Literature Review of Reviewer Assignment Strategies and Quantity of Reviewers
Uchswas Paul, Shail Shah, Sri Vaishnavi Mylavarapu, M. Parvez Rashid, Edward Gehringer
https://arxiv.org/abs/2508.11678

Optimizing Peer Grading: A Systematic Literature Review of Reviewer Assignment Strategies and Quantity of Reviewers
Peer assessment has established itself as a critical pedagogical tool in academic settings, offering students timely, high-quality feedback to enhance learning outcomes. However, the efficacy of this approach depends on two factors: (1) the strategic allocation of reviewers and (2) the number of reviews per artifact. This paper presents a systematic literature review of 87 studies (2010--2024) to investigate how reviewer-assignment strategies and the number of reviews per submission impact the …

The Language of Approval: Identifying the Drivers of Positive Feedback Online
Agam Goyal, Charlotte Lambert, Eshwar Chandrasekharan
https://arxiv.org/abs/2509.10370 https://

The Language of Approval: Identifying the Drivers of Positive Feedback Online
Positive feedback via likes and awards is central to online governance, yet which attributes of users' posts elicit rewards -- and how these vary across authors and communities -- remains unclear. To examine this, we combine quasi-experimental causal inference with predictive modeling on 11M posts from 100 subreddits. We identify linguistic patterns and stylistic attributes causally linked to rewards, controlling for author reputation, timing, and community context. For example, overtly complic…

GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning
Kelin Yu, Sheng Zhang, Harshit Soora, Furong Huang, Heng Huang, Pratap Tokekar, Ruohan Gao
https://arxiv.org/abs/2508.11049

GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning
Recent advances have shown that video generation models can enhance robot learning by deriving effective robot actions through inverse dynamics. However, these methods heavily depend on the quality of generated data and struggle with fine-grained manipulation due to the lack of environment feedback. While video-based reinforcement learning improves policy robustness, it remains constrained by the uncertainty of video generation and the challenges of collecting large-scale robot datasets for tra…

An Intelligent Mobile Application to Monitor and Correct Sitting Posture Using Raspberry Pi and MediaPipe Pose Detection
Yung-Chen (Hailey), Hsieh, Yu Sun
https://arxiv.org/abs/2508.11683

An Intelligent Mobile Application to Monitor and Correct Sitting Posture Using Raspberry Pi and MediaPipe Pose Detection
Poor posture has become an increasingly prevalent concern due to students and workers spending extended amounts of time sitting at a desk. To address this issue, we developed PoseTrack, a mobile application that uses a Raspberry Pi Camera and Mediapipe Pose landmarks to monitor the userś posture and provide real time feedback. The system detects poor posture, including forward lean, slouching, hunched shoulders, crossed legs, etc. Some challenges we faced were obtaining posture data, transferr…

Discriminative Feature Feedback with General Teacher Classes
Omri Bar Oz, Tosca Lechner, Sivan Sabato
https://arxiv.org/abs/2510.07245 https://arxiv.org/pd…

Discriminative Feature Feedback with General Teacher Classes
We study the theoretical properties of the interactive learning protocol Discriminative Feature Feedback (DFF) (Dasgupta et al., 2018). The DFF learning protocol uses feedback in the form of discriminative feature explanations. We provide the first systematic study of DFF in a general framework that is comparable to that of classical protocols such as supervised learning and online learning. We study the optimal mistake bound of DFF in the realizable and the non-realizable settings, and obtain …

Modeling Cosmological Evolution of Jetted Seyfert Galaxies for z<10
Julianne Goddard (Department of Physics and Astronomy, University of Kentucky, KY, USA), Isaac Shlosman (Department of Physics and Astronomy, University of Kentucky, KY, USA), Emilio Romano-Diaz (Argelander-Institut f\"ur Astronomie, Bonn, Germany)
https://arxiv.or…

Modeling Cosmological Evolution of Jetted Seyfert Galaxies for z<10
We use high-resolution cosmological zoom-in simulations to model mechanical and thermal feedback from AGN onto the evolution of Seyfert-type galaxies, studying the morphology of central galaxies growing within dark matter (DM) halos with masses logM/Mo ~11.8 at z=0. In Paper I, we focused on the end products at z=0, here we analyze evolution for z<10. Black holes (SMBHs) of ~10^6 Mo were seeded at z~9.1 and z~3.7, producing jets along their spin axes. Obtained SMBH accretion rates vary in the r…

Cyclic Variational Quantum Eigensolver: Escaping Barren Plateaus through Staircase Descent
Hao Zhang, Ayush Asthana
https://arxiv.org/abs/2509.13096 https://

Cyclic Variational Quantum Eigensolver: Escaping Barren Plateaus through Staircase Descent
We introduce the Cyclic Variational Quantum Eigensolver (CVQE), a hardware-efficient framework for accurate ground-state quantum simulation on noisy intermediate-scale quantum (NISQ) devices. CVQE departs from conventional VQE by incorporating a measurement-driven feedback cycle: Slater determinants with significant sampling probability are iteratively added to the reference superposition, while a fixed entangler (e.g., single-layer UCCSD) is reused throughout. This adaptive reference growth sy…

G-UBS: Towards Robust Understanding of Implicit Feedback via Group-Aware User Behavior Simulation
Boyu Chen, Siran Chen, Zhengrong Yue, Kainan Yan, Chenyun Yu, Beibei Kong, Cheng Lei, Chengxiang Zhuo, Zang Li, Yali Wang
https://arxiv.org/abs/2508.05709

G-UBS: Towards Robust Understanding of Implicit Feedback via Group-Aware User Behavior Simulation
User feedback is critical for refining recommendation systems, yet explicit feedback (e.g., likes or dislikes) remains scarce in practice. As a more feasible alternative, inferring user preferences from massive implicit feedback has shown great potential (e.g., a user quickly skipping a recommended video usually indicates disinterest). Unfortunately, implicit feedback is often noisy: a user might skip a video due to accidental clicks or other reasons, rather than disliking it. Such noise can ea…

SIGN: Safety-Aware Image-Goal Navigation for Autonomous Drones via Reinforcement Learning
Zichen Yan, Rui Huang, Lei He, Shao Guo, Lin Zhao
https://arxiv.org/abs/2508.12394 http…

SIGN: Safety-Aware Image-Goal Navigation for Autonomous Drones via Reinforcement Learning
Image-goal navigation (ImageNav) tasks a robot with autonomously exploring an unknown environment and reaching a location that visually matches a given target image. While prior works primarily study ImageNav for ground robots, enabling this capability for autonomous drones is substantially more challenging due to their need for high-frequency feedback control and global localization for stable flight. In this paper, we propose a novel sim-to-real framework that leverages visual reinforcement l…

A Longitudinal Study on Different Annotator Feedback Loops in Complex RAG Tasks
Sara Rosenthal, Maeda Hanafi, Yannis Katsis, Lucian Popa, Marina Danilevsky
https://arxiv.org/abs/2510.11897

A Longitudinal Study on Different Annotator Feedback Loops in Complex RAG Tasks
Grounding conversations in existing passages, known as Retrieval-Augmented Generation (RAG), is an important aspect of Chat-Based Assistants powered by Large Language Models (LLMs) to ensure they are faithful and don't provide misinformation. Several benchmarks have been created to measure the performance of LLMs on this task. We present a longitudinal study comparing the feedback loop of an internal and external human annotator group for the complex annotation task of creating multi-turn RAG c…

Dean of LLM Tutors: Exploring Comprehensive and Automated Evaluation of LLM-generated Educational Feedback via LLM Feedback Evaluators
Keyang Qian, Yixin Cheng, Rui Guan, Wei Dai, Flora Jin, Kaixun Yang, Sadia Nawaz, Zachari Swiecki, Guanliang Chen, Lixiang Yan, Dragan Ga\v{s}evi\'c
https://arxiv.org/abs/2508.05952

Dean of LLM Tutors: Exploring Comprehensive and Automated Evaluation of LLM-generated Educational Feedback via LLM Feedback Evaluators
The use of LLM tutors to provide automated educational feedback to students on student assignment submissions has received much attention in the AI in Education field. However, the stochastic nature and tendency for hallucinations in LLMs can undermine both quality of learning experience and adherence to ethical standards. To address this concern, we propose a method that uses LLM feedback evaluators (DeanLLMs) to automatically and comprehensively evaluate feedback generated by LLM tutor for su…

Cosmic-ray impact on optical and mid-infrared emission line diagnostics in NGC 5728
E. Koutsoumpou, J. A. Fern\'andez-Ontiveros, K. M. Dasyra, L. Spinoglio
https://arxiv.org/abs/2509.14328

Cosmic-ray impact on optical and mid-infrared emission line diagnostics in NGC 5728
Cosmic rays (CRs), from active galactic nuclei (AGN) jets and supernovae (SNe), serve as a significant feedback mechanism influencing emission lines in narrow line region (NLR) clouds. These highly energetic particles, propelled by shocks, heat the interstellar medium (ISM) and modify its chemical composition. This study investigates the role of CRs, particularly in their ability to excite gas and align with observed line ratios across UV and optical diagnostics. We employ CLOUDY to explore CR …

Investigating Sensors and Methods in Grasp State Classification in Agricultural Manipulation
Benjamin Walt, Jordan Westphal, Girish Krishnan
https://arxiv.org/abs/2508.11588 htt…

Investigating Sensors and Methods in Grasp State Classification in Agricultural Manipulation
Effective and efficient agricultural manipulation and harvesting depend on accurately understanding the current state of the grasp. The agricultural environment presents unique challenges due to its complexity, clutter, and occlusion. Additionally, fruit is physically attached to the plant, requiring precise separation during harvesting. Selecting appropriate sensors and modeling techniques is critical for obtaining reliable feedback and correctly identifying grasp states. This work investigate…

Learning from Natural Language Feedback for Personalized Question Answering
Alireza Salemi, Hamed Zamani
https://arxiv.org/abs/2508.10695 https://arxiv.org…

Learning from Natural Language Feedback for Personalized Question Answering
Personalization is crucial for enhancing both the effectiveness and user satisfaction of language technologies, particularly in information-seeking tasks like question answering. Current approaches for personalizing large language models (LLMs) often rely on retrieval-augmented generation (RAG), followed by reinforcement learning with scalar reward signals to teach models how to use retrieved personal context. We believe that these scalar rewards sometimes provide weak, non-instructive feedback…

Zero-sum turn games using Q-learning: finite computation with security guarantees
Sean Anderson, Chris Darken, Jo\~ao Hespanha
https://arxiv.org/abs/2509.13585 https://

Zero-sum turn games using Q-learning: finite computation with security guarantees
This paper addresses zero-sum ``turn'' games, in which only one player can make decisions at each state. We show that pure saddle-point state-feedback policies for turn games can be constructed from dynamic programming fixed-point equations for a single value function or Q-function. These fixed-points can be constructed using a suitable form of Q-learning. For discounted costs, convergence of this form of Q-learning can be established using classical techniques. For undiscounted costs, we provi…

The Anatomy of Alignment: Decomposing Preference Optimization by Steering Sparse Features
Jeremias Ferrao, Matthijs van der Lende, Ilija Lichkovski, Clement Neo
https://arxiv.org/abs/2509.12934

The Anatomy of Alignment: Decomposing Preference Optimization by Steering Sparse Features
Aligning large language models is critical for their usability and safety. However, the prevailing approach of Reinforcement Learning from Human Feedback (RLHF) induces diffuse, opaque parameter changes, making it difficult to discern what the model has internalized. Hence, we introduce Feature Steering with Reinforcement Learning (FSRL), a transparent alignment framework that trains a lightweight adapter to steer behavior by modulating interpretable features from a Sparse Autoencoder (SAE). Fi…

The Role of Touch: Towards Optimal Tactile Sensing Distribution in Anthropomorphic Hands for Dexterous In-Hand Manipulation
Jo\~ao Dami\~ao Almeida, Egidio Falotico, Cecilia Laschi, Jos\'e Santos-Victor
https://arxiv.org/abs/2509.14984

The Role of Touch: Towards Optimal Tactile Sensing Distribution in Anthropomorphic Hands for Dexterous In-Hand Manipulation
In-hand manipulation tasks, particularly in human-inspired robotic systems, must rely on distributed tactile sensing to achieve precise control across a wide variety of tasks. However, the optimal configuration of this network of sensors is a complex problem, and while the fingertips are a common choice for placing sensors, the contribution of tactile information from other regions of the hand is often overlooked. This work investigates the impact of tactile feedback from various regions of the…

Ellipsoidal partitions for improved multi-stage robust model predictive control
Moritz Heinlein, Florian Messerer, Moritz Diehl, Sergio Lucia
https://arxiv.org/abs/2509.12792 ht…

Ellipsoidal partitions for improved multi-stage robust model predictive control
Ellipsoidal tube-based model predictive control methods effectively account for the propagation of the reachable set, typically employing linear feedback policies. In contrast, scenario-based approaches offer more flexibility in the feedback structure by considering different control actions for different branches of a scenario tree. However, they face challenges in ensuring rigorous guarantees. This work aims to integrate the strengths of both methodologies by enhancing ellipsoidal tube-based …

Generative AI-Enabled Adaptive Learning Platform: How I Can Help You Pass Your Driving Test?
Riya Gill, Ievgeniia Kuzminykh, Maher Salem, Bogdan Ghita
https://arxiv.org/abs/2509.11438

Generative AI-Enabled Adaptive Learning Platform: How I Can Help You Pass Your Driving Test?
This study aims to develop an adaptive learning platform that leverages generative AI to automate assessment creation and feedback delivery. The platform provides self-correcting tests and personalised feedback that adapts to each learners progress and history, ensuring a tailored learning experience. The study involves the development and evaluation of a web-based application for revision for the UK Driving Theory Test. The platform generates dynamic, non-repetitive question sets and offers ad…

How many stars form in compact clusters in the local Milky Way?
Alexis L. Quintana, Emily L. Hunt, Hanna Parul
https://arxiv.org/abs/2508.12788 https://arx…

How many stars form in compact clusters in the local Milky Way?
Two main models coexist for the environment in which stars form. The clustered model stipulates that the bulk of star formation occurs within dense embedded clusters, but only a minority of them survive the residual gas expulsion phase caused by massive stellar feedback unbinding the clusters. On the other hand, the hierarchical model predicts that star formation happens at a range of scales and densities, where open clusters (OCs) only emerge from the densest regions. We aim to exploit a recen…

Beyond "Not Novel Enough": Enriching Scholarly Critique with LLM-Assisted Feedback
Osama Mohammed Afzal, Preslav Nakov, Tom Hope, Iryna Gurevych
https://arxiv.org/abs/2508.10795

Beyond "Not Novel Enough": Enriching Scholarly Critique with LLM-Assisted Feedback
Novelty assessment is a central yet understudied aspect of peer review, particularly in high volume fields like NLP where reviewer capacity is increasingly strained. We present a structured approach for automated novelty evaluation that models expert reviewer behavior through three stages: content extraction from submissions, retrieval and synthesis of related work, and structured comparison for evidence based assessment. Our method is informed by a large scale analysis of human written novelty…

On Finite- and Fixed-Time Stabilization of Abstract Nonlinear Systems with Well-Posedness Guarantees
Kamal Fenza, Moussa Labbadi, Mohamed Ouzahra
https://arxiv.org/abs/2509.14376

On Finite- and Fixed-Time Stabilization of Abstract Nonlinear Systems with Well-Posedness Guarantees
This paper addresses the problem of stabilization for infinite-dimensional systems. In particular, we design nonlinear stabilizers for both linear and nonlinear abstract systems. We focus on two classes of systems: the first class comprises linear abstract systems subject to matched perturbations, while the second class encompasses fully nonlinear abstract systems. Our main objective is to synthesize state-feedback controllers that guarantee finite- or fixed-time stability of the closed-loop sy…

Aeos is Mixing it Up: The (In)homogeneity of Metal Mixing Following Population III Star Formation
Jennifer Mead, Kaley Brauer, Greg L. Bryan, Mordecai-Mark Mac Low, Alexander P. Ji, John H. Wise, Eric P. Andersson, Anna Frebel, Andrew Emerick, Benoit C\^ot\'e
https://arxiv.org/abs/2509.13580

Aeos is Mixing it Up: The (In)homogeneity of Metal Mixing Following Population III Star Formation
Stellar surface abundances are records of the state of the gas from which stars formed, and thus trace how individual elements have mixed into the surrounding medium following their ejection from stars. In this work, we test the common assumption of instantaneous and homogeneous metal mixing during the formation of the first Population II stars by characterizing the chemical homogeneity of the gas in simulated star-forming environments enriched by Population III stellar feedback. Testing the ho…

LLM-as-a-Supervisor: Mistaken Therapeutic Behaviors Trigger Targeted Supervisory Feedback
Chen Xu, Zhenyu Lv, Tian Lan, Xianyang Wang, Luyao Ji, Leyang Cui, Minqiang Yang, Jian Shen, Qunxi Dong, Xiuling Liu, Juan Wang, Bin Hu
https://arxiv.org/abs/2508.09042

LLM-as-a-Supervisor: Mistaken Therapeutic Behaviors Trigger Targeted Supervisory Feedback
Although large language models (LLMs) hold significant promise in psychotherapy, their direct application in patient-facing scenarios raises ethical and safety concerns. Therefore, this work shifts towards developing an LLM as a supervisor to train real therapists. In addition to the privacy of clinical therapist training data, a fundamental contradiction complicates the training of therapeutic behaviors: clear feedback standards are necessary to ensure a controlled training system, yet there i…

Smart Device Development for Gait Monitoring: Multimodal Feedback in an Interactive Foot Orthosis, Walking Aid, and Mobile Application
Stefan Resch, Andr\'e Kousha, Anna Carroll, Noah Severinghaus, Felix Rehberg, Marco Zatschker, Yunus S\"oyleyici, Daniel Sanchez-Morillo
https://arxiv.org/abs/2509.09359

Smart Device Development for Gait Monitoring: Multimodal Feedback in an Interactive Foot Orthosis, Walking Aid, and Mobile Application
Smart assistive technologies such as sensor-based footwear and walking aids offer promising opportunities to support rehabilitation through real-time feedback and patient-centered monitoring. However, most orthotic devices remain passive and lack integrated sensing or feedback functionalities, while existing research often focuses on isolated prototypes rather than cohesive, interactive systems. In this work, we present the design and implementation of a novel modular sensor system that combine…

Feedback stabilization of a nanoparticle at the intensity minimum of an optical double-well potential
Vojt\v{e}ch Mlyn\'a\v{r} (Automation and Control Institute, TU Wien, Vienna, Austria), Salamb\^o Dago (University of Vienna, Faculty of Physics, Vienna Center for Quantum Science and Technology, Vienna, Austria), Jakob Rieser (University of Vienna, Faculty of Physics, Vienna Center for Quantum Science and Technology, Vienna, Austria), Mario A. Ciampini (University of Vienna, Facult…

Feedback stabilization of a nanoparticle at the intensity minimum of an optical double-well potential
In this work, we develop and analyze adaptive feedback control strategies to stabilize and confine a nanoparticle at the unstable intensity minimum of an optical double-well potential. The resulting stochastic optimal control problem for a noise-driven mechanical particle in a nonlinear optical potential must account for unavoidable experimental imperfections such as measurement nonlinearities and slow drifts of the optical setup. To address these issues, we simplify the model in the vicinity o…

Feed-O-Meter: Fostering Design Feedback Skills through Role-playing Interactions with AI Mentee
Hyunseung Lim, Dasom Choi, DaEun Choi, Sooyohn Nam, Hwajung Hong
https://arxiv.org/abs/2509.07424

Feed-O-Meter: Fostering Design Feedback Skills through Role-playing Interactions with AI Mentee
Effective feedback, including critique and evaluation, helps designers develop design concepts and refine their ideas, supporting informed decision-making throughout the iterative design process. However, in studio-based design courses, students often struggle to provide feedback due to a lack of confidence and fear of being judged, which limits their ability to develop essential feedback-giving skills. Recent advances in large language models (LLMs) suggest that role-playing with AI agents can…

SitLLM: Large Language Models for Sitting Posture Health Understanding via Pressure Sensor Data
Jian Gao, Fufangchen Zhao, Yiyang Zhang, Danfeng Yan
https://arxiv.org/abs/2509.12994

SitLLM: Large Language Models for Sitting Posture Health Understanding via Pressure Sensor Data
Poor sitting posture is a critical yet often overlooked factor contributing to long-term musculoskeletal disorders and physiological dysfunctions. Existing sitting posture monitoring systems, although leveraging visual, IMU, or pressure-based modalities, often suffer from coarse-grained recognition and lack the semantic expressiveness necessary for personalized feedback. In this paper, we propose \textbf{SitLLM}, a lightweight multimodal framework that integrates flexible pressure sensing with …

Feedback Matters: Augmenting Autonomous Dissection with Visual and Topological Feedback
Chung-Pang Wang, Changwei Chen, Xiao Liang, Soofiyan Atar, Florian Richter, Michael Yip
https://arxiv.org/abs/2510.04074

Feedback Matters: Augmenting Autonomous Dissection with Visual and Topological Feedback
Autonomous surgical systems must adapt to highly dynamic environments where tissue properties and visual cues evolve rapidly. Central to such adaptability is feedback: the ability to sense, interpret, and respond to changes during execution. While feedback mechanisms have been explored in surgical robotics, ranging from tool and tissue tracking to error detection, existing methods remain limited in handling the topological and perceptual challenges of tissue dissection. In this work, we propose…

Input-gated Bilateral Teleoperation: An Easy-to-implement Force Feedback Teleoperation Method for Low-cost Hardware
Yoshiki Kanai, Akira Kanazawa, Hideyuki Ichiwara, Hiroshi Ito, Naoaki Noguchi, Tetsuya Ogata
https://arxiv.org/abs/2509.08226

Input-gated Bilateral Teleoperation: An Easy-to-implement Force Feedback Teleoperation Method for Low-cost Hardware
Effective data collection in contact-rich manipulation requires force feedback during teleoperation, as accurate perception of contact is crucial for stable control. However, such technology remains uncommon, largely because bilateral teleoperation systems are complex and difficult to implement. To overcome this, we propose a bilateral teleoperation method that relies only on a simple feedback controller and does not require force sensors. The approach is designed for leader-follower setups usi…

Prespecified-Performance Kinematic Tracking Control for Aerial Manipulation
Hauzi Cao, Jiahao Shen, Zhengzhen Li, Qinquan Ren, Shiyu Zhao
https://arxiv.org/abs/2509.10065 https:…

Prespecified-Performance Kinematic Tracking Control for Aerial Manipulation
This paper studies the kinematic tracking control problem for aerial manipulators. Existing kinematic tracking control methods, which typically employ proportional-derivative feedback or tracking-error-based feedback strategies, may fail to achieve tracking objectives within specified time constraints. To address this limitation, we propose a novel control framework comprising two key components: end-effector tracking control based on a user-defined preset trajectory and quadratic programming-b…