Tootfinder

Unlock the gateway to supplemental income with our dynamic online business model – jsubstantial commissions await! No technical skills are needed, just your ambition and enthusiasm. Enjoy flexibility, unparalleled mentorship, and the chance to redefine your future. Are you ready? 🚀 #OpportunityAwaits

On Dynamic Programming Theory for Leader-Follower Stochastic Games
Jilles Steeve Dibangoye, Thibaut Le Marre, Ocan Sankur, Fran\c{c}ois Schwarzentruber
https://arxiv.org/abs/2512.05667 https://arxiv.org/pdf/2512.05667 https://arxiv.org/html/2512.05667
arXiv:2512.05667v1 Announce Type: new
Abstract: Leader-follower general-sum stochastic games (LF-GSSGs) model sequential decision-making under asymmetric commitment, where a leader commits to a policy and a follower best responds, yielding a strong Stackelberg equilibrium (SSE) with leader-favourable tie-breaking. This paper introduces a dynamic programming (DP) framework that applies Bellman recursion over credible sets-state abstractions formally representing all rational follower best responses under partial leader commitments-to compute SSEs. We first prove that any LF-GSSG admits a lossless reduction to a Markov decision process (MDP) over credible sets. We further establish that synthesising an optimal memoryless deterministic leader policy is NP-hard, motivating the development of {\epsilon}-optimal DP algorithms with provable guarantees on leader exploitability. Experiments on standard mixed-motive benchmarks-including security games, resource allocation, and adversarial planning-demonstrate empirical gains in leader value and runtime scalability over state-of-the-art methods.
toXiv_bot_toot

Model-Based Lookahead Reinforcement Learning for in-hand manipulation
Alexandre Lopes, Catarina Barata, Plinio Moreno
https://arxiv.org/abs/2510.08884 https://

Model-Based Lookahead Reinforcement Learning for in-hand manipulation
In-Hand Manipulation, as many other dexterous tasks, remains a difficult challenge in robotics by combining complex dynamic systems with the capability to control and manoeuvre various objects using its actuators. This work presents the application of a previously developed hybrid Reinforcement Learning (RL) Framework to In-Hand Manipulation task, verifying that it is capable of improving the performance of the task. The model combines concepts of both Model-Free and Model-Based Reinforcement L…

Inflated Excellence or True Performance? Rethinking Medical Diagnostic Benchmarks with Dynamic Evaluation
Xiangxu Zhang, Lei Li, Yanyun Zhou, Xiao Zhou, Yingying Zhang, Xian Wu
https://arxiv.org/abs/2510.09275

Inflated Excellence or True Performance? Rethinking Medical Diagnostic Benchmarks with Dynamic Evaluation
Medical diagnostics is a high-stakes and complex domain that is critical to patient care. However, current evaluations of large language models (LLMs) are fundamentally misaligned with real-world clinical practice. Most of them rely on static benchmarks derived from public medical exam items, which tend to overestimate model performance and ignore the difference between textbook cases and the ambiguous, varying conditions in the real world. Recent efforts toward dynamic evaluation offer a promi…

ViCO: A Training Strategy towards Semantic Aware Dynamic High-Resolution
Long Cui, Weiyun Wang, Jie Shao, Zichen Wen, Gen Luo, Linfeng Zhang, Yanting Zhang, Yu Qiao, Wenhai Wang
https://arxiv.org/abs/2510.12793

ViCO: A Training Strategy towards Semantic Aware Dynamic High-Resolution
Existing Multimodal Large Language Models (MLLMs) suffer from increased inference costs due to the additional vision tokens introduced by image inputs. In this work, we propose Visual Consistency Learning (ViCO), a novel training algorithm that enables the model to represent images of varying semantic complexities using different numbers of vision tokens. The key idea behind our method is to employ multiple MLP connectors, each with a different image compression ratio, to downsample the vision …

Localist LLMs -- A Mathematical Framework for Dynamic Locality Control
Joachim Diederich
https://arxiv.org/abs/2510.09338 https://arxiv.org/pdf/2510.09338

Localist LLMs -- A Mathematical Framework for Dynamic Locality Control
We present a novel framework for training large language models with continuously adjustable internal representations that span the full spectrum from localist (interpretable, rule-based) to distributed (generalizable, efficient) encodings. The key innovation is a locality dial, a tunable parameter that dynamically controls the degree of localization during both training and inference without requiring model retraining. This is achieved through group sparsity penalties on attention mechanisms, …

DyKnow-RAG: Dynamic Knowledge Utilization Reinforcement Framework for Noisy Retrieval-Augmented Generation in E-commerce Search Relevance
Tingqiao Xu, Shaowei Yao, Chenhe Dong, Yiming Jin, Zerui Huang, Dan Ou, Haihong Tang
https://arxiv.org/abs/2510.11122

DyKnow-RAG: Dynamic Knowledge Utilization Reinforcement Framework for Noisy Retrieval-Augmented Generation in E-commerce Search Relevance
Accurately modeling query-item relevance drives e-commerce ranking, yet long-tail, knowledge-heavy, and fast-evolving queries exceed parametric LLM coverage. External context (reviews, attribute encyclopedias, UGC) can help but is noisy, and single-pass latency and cost forbid any clean-then-summarize step. The model must, per query, judge relevance and decide whether to use, partially use, or ignore the context. DyKnow-RAG is a dynamic noisy-RAG framework built on Group Relative Policy Optimiz…

PrediQL: Automated Testing of GraphQL APIs with LLMs
Shaolun Liu, Sina Marefat, Omar Tsai, Yu Chen, Zecheng Deng, Jia Wang, Mohammad A. Tayebi
https://arxiv.org/abs/2510.10407 h…

PrediQL: Automated Testing of GraphQL APIs with LLMs
GraphQL's flexible query model and nested data dependencies expose APIs to complex, context-dependent vulnerabilities that are difficult to uncover using conventional testing tools. Existing fuzzers either rely on random payload generation or rigid mutation heuristics, failing to adapt to the dynamic structures of GraphQL schemas and responses. We present PrediQL, the first retrieval-augmented, LLM-guided fuzzer for GraphQL APIs. PrediQL combines large language model reasoning with adaptive fee…

Combining Blotto Networks and Voter Models to Simulate Voter Behavior in Response to Competitive Election Spending
Renee Jerome
https://arxiv.org/abs/2510.09697 https://

Combining Blotto Networks and Voter Models to Simulate Voter Behavior in Response to Competitive Election Spending
In the past, the Voter Model has been explicitly used to model the impact of propaganda on a dynamic, interconnected population, and certain factors have been identified that influence the behavior of voters when under outside influence. The Blotto Game has also been explicitly used to study information wars between two opposing parties, whether in regards to a political issue or advertising war. Both the graph theory behind the Voter Model and the game theory aspects of the Blotto Game are rel…

Frenkel anomaly on co-ordination numbers in liquid CO2 at 100 and 1000 bar studied by Monte Carlo simulation using Kihara potential model
Koji Kobashi
https://arxiv.org/abs/2510.11029

Frenkel anomaly on co-ordination numbers in liquid CO2 at 100 and 1000 bar studied by Monte Carlo simulation using Kihara potential model
An issue concerning the Frenkel line of liquid CO2 is that its location has not been unequivocally determined. So far, reliable Frenkel lines were identified from velocity autocorrelation functions (VAFs) computed by Molecular Dynamic simulations; however VAFs cannot be directly verified experimentally. By contrast, the co-ordination numbers (CNs) can be measured experimentally by X-ray and neutron scattering or computed by Monte Carlo (MC) simulation, and thus provide an alternative means of d…

Experimental Characterization and Dynamic Modeling of THz Channels Under Fog Conditions
Jiaobiao Zhao, Kefeng Huang, Xiaoxiang Li, Mingxia Zhang, Peian Li, Jie Yang, Wenbo Liu, Yiming Zhao, Weidong Hu, Jianjun Ma
https://arxiv.org/abs/2510.09906

Experimental Characterization and Dynamic Modeling of THz Channels Under Fog Conditions
The terahertz (THz) band is a promising candidate for sixth-generation wireless networks, but its deploymen in outdoor environments is challenged by meteorological phenomena, particularly fog, which imposes variable and difficult-to-predict channel degradation. This article introduces dynamic channel model for the THz band explicitly driven by the time-evolving droplet size distribution (DSD) of fog, integrating real-time microphysical sensing to capture variations in the fog microstructure. Ex…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/5]:
- Look-Ahead Reasoning on Learning Platforms
Haiqing Zhu, Tijana Zrnic, Celestine Mendler-D\"unner
https://arxiv.org/abs/2511.14745 https://mastoxiv.page/@arXiv_csLG_bot/115575981129228810
- Deep Gaussian Process Proximal Policy Optimization
Matthijs van der Lende, Juan Cardenas-Cartagena
https://arxiv.org/abs/2511.18214 https://mastoxiv.page/@arXiv_csLG_bot/115610315210502140
- Spectral Concentration at the Edge of Stability: Information Geometry of Kernel Associative Memory
Akira Tamamori
https://arxiv.org/abs/2511.23083 https://mastoxiv.page/@arXiv_csLG_bot/115644325602130493
- xGR: Efficient Generative Recommendation Serving at Scale
Sun, Liu, Zhang, Wu, Yang, Liang, Li, Ma, Liang, Ren, Zhang, Liu, Zhang, Qian, Yang
https://arxiv.org/abs/2512.11529 https://mastoxiv.page/@arXiv_csLG_bot/115723008170311172
- Credit Risk Estimation with Non-Financial Features: Evidence from a Synthetic Istanbul Dataset
Atalay Denknalbant, Emre Sezdi, Zeki Furkan Kutlu, Polat Goktas
https://arxiv.org/abs/2512.12783 https://mastoxiv.page/@arXiv_csLG_bot/115729287232895097
- The Semantic Illusion: Certified Limits of Embedding-Based Hallucination Detection in RAG Systems
Debu Sinha
https://arxiv.org/abs/2512.15068 https://mastoxiv.page/@arXiv_csLG_bot/115740048142898391
- Towards Reproducibility in Predictive Process Mining: SPICE -- A Deep Learning Library
Stritzel, H\"uhnerbein, Rauch, Zarate, Fleischmann, Buck, Lischka, Frey
https://arxiv.org/abs/2512.16715 https://mastoxiv.page/@arXiv_csLG_bot/115745910810427061
- Differentially private Bayesian tests
Abhisek Chakraborty, Saptati Datta
https://arxiv.org/abs/2401.15502 https://mastoxiv.page/@arXiv_statML_bot/111843467510507382
- SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning
Paul Mangold, Sergey Samsonov, Safwan Labbi, Ilya Levin, Reda Alami, Alexey Naumov, Eric Moulines
https://arxiv.org/abs/2402.04114
- Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?
Guiomar Pescador-Barrios, Sarah Filippi, Mark van der Wilk
https://arxiv.org/abs/2408.07588 https://mastoxiv.page/@arXiv_statML_bot/112965266196097314
- Non-Perturbative Trivializing Flows for Lattice Gauge Theories
Mathis Gerdes, Pim de Haan, Roberto Bondesan, Miranda C. N. Cheng
https://arxiv.org/abs/2410.13161 https://mastoxiv.page/@arXiv_heplat_bot/113327593338897860
- Dynamic PET Image Prediction Using a Network Combining Reversible and Irreversible Modules
Sun, Zhang, Xia, Sun, Chen, Yang, Liu, Zhu, Liu
https://arxiv.org/abs/2410.22674 https://mastoxiv.page/@arXiv_eessIV_bot/113401026110345647
- Targeted Learning for Variable Importance
Xiaohan Wang, Yunzhe Zhou, Giles Hooker
https://arxiv.org/abs/2411.02221 https://mastoxiv.page/@arXiv_statML_bot/113429912435819479
- Refined Analysis of Federated Averaging and Federated Richardson-Romberg
Paul Mangold, Alain Durmus, Aymeric Dieuleveut, Sergey Samsonov, Eric Moulines
https://arxiv.org/abs/2412.01389 https://mastoxiv.page/@arXiv_statML_bot/113588027268311334
- Embedding-Driven Data Distillation for 360-Degree IQA With Residual-Aware Refinement
Abderrezzaq Sendjasni, Seif-Eddine Benkabou, Mohamed-Chaker Larabi
https://arxiv.org/abs/2412.12667 https://mastoxiv.page/@arXiv_csCV_bot/113672538318570349
- 3D Cell Oversegmentation Correction via Geo-Wasserstein Divergence
Peter Chen, Bryan Chang, Olivia A Creasey, Julie Beth Sneddon, Zev J Gartner, Yining Liu
https://arxiv.org/abs/2502.01890 https://mastoxiv.page/@arXiv_csCV_bot/113949981686723660
- DHP: Discrete Hierarchical Planning for Hierarchical Reinforcement Learning Agents
Shashank Sharma, Janina Hoffmann, Vinay Namboodiri
https://arxiv.org/abs/2502.01956 https://mastoxiv.page/@arXiv_csRO_bot/113949997485625086
- Foundation for unbiased cross-validation of spatio-temporal models for species distribution modeling
Diana Koldasbayeva, Alexey Zaytsev
https://arxiv.org/abs/2502.03480
- GraphCompNet: A Position-Aware Model for Predicting and Compensating Shape Deviations in 3D Printing
Juheon Lee (Rachel), Lei (Rachel), Chen, Juan Carlos Catana, Hui Wang, Jun Zeng
https://arxiv.org/abs/2502.09652 https://mastoxiv.page/@arXiv_csCV_bot/114017924551186136
- LookAhead Tuning: Safer Language Models via Partial Answer Previews
Liu, Wang, Luo, Yuan, Sun, Liang, Zhang, Zhou, Hooi, Deng
https://arxiv.org/abs/2503.19041 https://mastoxiv.page/@arXiv_csCL_bot/114227502448008352
- Constraint-based causal discovery with tiered background knowledge and latent variables in single...
Christine W. Bang, Vanessa Didelez
https://arxiv.org/abs/2503.21526 https://mastoxiv.page/@arXiv_statML_bot/114238919468512990
toXiv_bot_toot

Dynamic Stress Detection: A Study of Temporal Progression Modelling of Stress in Speech
Vishakha Lall, Yisi Liu
https://arxiv.org/abs/2510.08586 https://ar…

Dynamic Stress Detection: A Study of Temporal Progression Modelling of Stress in Speech
Detecting psychological stress from speech is critical in high-pressure settings. While prior work has leveraged acoustic features for stress detection, most treat stress as a static label. In this work, we model stress as a temporally evolving phenomenon influenced by historical emotional state. We propose a dynamic labelling strategy that derives fine-grained stress annotations from emotional labels and introduce cross-attention-based sequential models, a Unidirectional LSTM and a Transformer…

PRoH: Dynamic Planning and Reasoning over Knowledge Hypergraphs for Retrieval-Augmented Generation
Xiangjun Zai, Xingyu Tan, Xiaoyang Wang, Qing Liu, Xiwei Xu, Wenjie Zhang
https://arxiv.org/abs/2510.12434

PRoH: Dynamic Planning and Reasoning over Knowledge Hypergraphs for Retrieval-Augmented Generation
Knowledge Hypergraphs (KHs) have recently emerged as a knowledge representation for retrieval-augmented generation (RAG), offering a paradigm to model multi-entity relations into a structured form. However, existing KH-based RAG methods suffer from three major limitations: static retrieval planning, non-adaptive retrieval execution, and superficial use of KH structure and semantics, which constrain their ability to perform effective multi-hop question answering. To overcome these limitations, w…

Boosting Multi-modal Keyphrase Prediction with Dynamic Chain-of-Thought in Vision-Language Models
Qihang Ma, Shengyu Li, Jie Tang, Dingkang Yang, Shaodong Chen, Yingyi Zhang, Chao Feng, Jiao Ran
https://arxiv.org/abs/2510.09358

Boosting Multi-modal Keyphrase Prediction with Dynamic Chain-of-Thought in Vision-Language Models
Multi-modal keyphrase prediction (MMKP) aims to advance beyond text-only methods by incorporating multiple modalities of input information to produce a set of conclusive phrases. Traditional multi-modal approaches have been proven to have significant limitations in handling the challenging absence and unseen scenarios. Additionally, we identify shortcomings in existing benchmarks that overestimate model capability due to significant overlap in training tests. In this work, we propose leveraging…

Hierarchical Semantic RL: Tackling the Problem of Dynamic Action Space for RL-based Recommendations
Minmao Wang, Xingchen Liu, Shijie Yi, Likang Wu, Hongke Zhao, Fei Pan, Qingpeng Cai, Peng Jiang
https://arxiv.org/abs/2510.09167

Hierarchical Semantic RL: Tackling the Problem of Dynamic Action Space for RL-based Recommendations
Recommender Systems (RS) are fundamental to modern online services. While most existing approaches optimize for short-term engagement, recent work has begun to explore reinforcement learning (RL) to model long-term user value. However, these efforts face significant challenges due to the vast, dynamic action spaces inherent in recommendation, which hinder stable policy learning. To resolve this bottleneck, we introduce Hierarchical Semantic RL (HSRL), which reframes RL-based recommendation over…

Whole Body Model Predictive Control for Spin-Aware Quadrupedal Table Tennis
David Nguyen, Zulfiqar Zaidi, Kevin Karol, Jessica Hodgins, Zhaoming Xie
https://arxiv.org/abs/2510.08754

Whole Body Model Predictive Control for Spin-Aware Quadrupedal Table Tennis
Developing table tennis robots that mirror human speed, accuracy, and ability to predict and respond to the full range of ball spins remains a significant challenge for legged robots. To demonstrate these capabilities we present a system to play dynamic table tennis for quadrupedal robots that integrates high speed perception, trajectory prediction, and agile control. Our system uses external cameras for high-speed ball localization, physical models with learned residuals to infer spin and pred…

Study of the stability of the fractional Stokes system from nonlinear optics around the zero equlibrium state
Mihai Ivan
https://arxiv.org/abs/2510.10130 https://

Study of the stability of the fractional Stokes system from nonlinear optics around the zero equlibrium state
The main purpose of this paper is to study the fractional-order system with Caputo derivative associated to single Stokes pulse. The dynamic behavior for this fractional model (called the fractional Stokes system) is investigated, including: the asymptotic stability around zero equilibrium state, the stabilization problem using appropriate linear controls and the numerical integration based on fractional Euler method.

Replaced article(s) found for physics.plasm-ph. https://arxiv.org/list/physics.plasm-ph/new
[1/1]:
- A Comprehensive Analytical Model of the Dynamic Z-Pinch
Alejandro Mesa Dame, Eric S. Lavine, David A. Hammer

A Vision for Access Control in LLM-based Agent Systems
Xinfeng Li, Dong Huang, Jie Li, Hongyi Cai, Zhenhong Zhou, Wei Dong, XiaoFeng Wang, Yang Liu
https://arxiv.org/abs/2510.11108

A Vision for Access Control in LLM-based Agent Systems
The autonomy and contextual complexity of LLM-based agents render traditional access control (AC) mechanisms insufficient. Static, rule-based systems designed for predictable environments are fundamentally ill-equipped to manage the dynamic information flows inherent in agentic interactions. This position paper argues for a paradigm shift from binary access control to a more sophisticated model of information governance, positing that the core challenge is not merely about permission, but about…

Travel Bans vs. Social Distancing: A Mathematical Analysis
Christian Borgs, Karissa Huang, Geng Zhao
https://arxiv.org/abs/2510.08895 https://arxiv.org/pdf…

Travel Bans vs. Social Distancing: A Mathematical Analysis
As the world grows increasingly connected, infectious disease transmission and outbreaks become a pressing global concern for public health officials and policymakers. While policy interventions to contain and prevent the spread of disease have been proposed and implemented, there has been little rigorous quantitative analysis of the effectiveness of such interventions. In this paper, we study the susceptible-infected-recovered (SIR) infection process on a dynamic network model that models two …

Benders Decomposition for Passenger-Oriented Train Timetabling with Hybrid Periodicity
Zhiyuan Yao, Anita Sch\"obel, Lei Nie, Sven J\"ager
https://arxiv.org/abs/2511.09892 https://arxiv.org/pdf/2511.09892 https://arxiv.org/html/2511.09892
arXiv:2511.09892v1 Announce Type: new
Abstract: Periodic timetables are widely adopted in passenger railway operations due to their regular service patterns and well-coordinated train connections. However, fluctuations in passenger demand require varying train services across different periods, necessitating adjustments to the periodic timetable. This study addresses a hybrid periodic train timetabling problem, which enhances the flexibility and demand responsiveness of a given periodic timetable through schedule adjustments and aperiodic train insertions, taking into account the rolling stock circulation. Since timetable modifications may affect initial passenger routes, passenger routing is incorporated into the problem to guide planning decisions towards a passenger-oriented objective. Using a time-space network representation, the problem is formulated as a dynamic railway service network design model with resource constraints. To handle the complexity of real-world instances, we propose a decomposition-based algorithm integrating Benders decomposition and column generation, enhanced with multiple preprocessing and accelerating techniques. Numerical experiments demonstrate the effectiveness of the algorithm and highlight the advantage of hybrid periodic timetables in reducing passenger travel costs.
toXiv_bot_toot

TC-LoRA: Temporally Modulated Conditional LoRA for Adaptive Diffusion Control
Minkyoung Cho, Ruben Ohana, Christian Jacobsen, Adityan Jothi, Min-Hung Chen, Z. Morley Mao, Ethem Can
https://arxiv.org/abs/2510.09561

TC-LoRA: Temporally Modulated Conditional LoRA for Adaptive Diffusion Control
Current controllable diffusion models typically rely on fixed architectures that modify intermediate activations to inject guidance conditioned on a new modality. This approach uses a static conditioning strategy for a dynamic, multi-stage denoising process, limiting the model's ability to adapt its response as the generation evolves from coarse structure to fine detail. We introduce TC-LoRA (Temporally Modulated Conditional LoRA), a new paradigm that enables dynamic, context-aware control by c…

HackWorld: Evaluating Computer-Use Agents on Exploiting Web Application Vulnerabilities
Xiaoxue Ren, Penghao Jiang, Kaixin Li, Zhiyong Huang, Xiaoning Du, Jiaojiao Jiang, Zhenchang Xing, Jiamou Sun, Terry Yue Zhuo
https://arxiv.org/abs/2510.12200

HackWorld: Evaluating Computer-Use Agents on Exploiting Web Application Vulnerabilities
Web applications are prime targets for cyberattacks as gateways to critical services and sensitive data. Traditional penetration testing is costly and expertise-intensive, making it difficult to scale with the growing web ecosystem. While language model agents show promise in cybersecurity, modern web applications demand visual understanding, dynamic content handling, and multi-step interactions that only computer-use agents (CUAs) can perform. Yet, their ability to discover and exploit vulnera…

Unlocking the initial neutron density distribution from the two-pion HBT correlation function in heavy-ion collisions
Pengcheng Li, Manzi Nan, Haojie Zhang, Junhuai Xu, Xilong Xiang, Yijie Wang, Yongjia Wang, Gaochan Yong, Tadaaki Isobe, Zhigang Xiao, Qingfeng Li
https://arxiv.org/abs/2510.12226

Unlocking the initial neutron density distribution from the two-pion HBT correlation function in heavy-ion collisions
Revealing the neutron density distribution in the nucleus is one of the crucial tasks of nuclear physics. Within the framework of the ultrarelativistic quantum molecular dynamic model followed by a correlation afterburner program, we investigate the effects of the initial neutron density distribution on the charged-pion yield ratio $π^{-}/π^{+}$, the two-pion momentum correlation function, and the emission source dimension. It is found that the $π^{-}/π^{+}$ ratio is sensitive to the initia…

The Data Enclave Advantage: A New Paradigm for Least-Privileged Data Access in a Zero-Trust World
Nico Bistolfi, Andreea Georgescu, Dave Hodson
https://arxiv.org/abs/2510.09494 …

The Data Enclave Advantage: A New Paradigm for Least-Privileged Data Access in a Zero-Trust World
As cloud infrastructure evolves to support dynamic and distributed workflows, accelerated now by AI-driven processes, the outdated model of standing permissions has become a critical vulnerability. Based on the Cloud Security Alliance (CSA) Top Threats to Cloud Computing Deep Dive 2025 Report, our analysis details how standing permissions cause catastrophic cloud breaches. While current security tools are addressing network and API security, the challenge of securing granular data access remain…

Simultaneous Calibration of Noise Covariance and Kinematics for State Estimation of Legged Robots via Bi-level Optimization
Denglin Cheng, Jiarong Kang, Xiaobin Xiong
https://arxiv.org/abs/2510.11539

Simultaneous Calibration of Noise Covariance and Kinematics for State Estimation of Legged Robots via Bi-level Optimization
Accurate state estimation is critical for legged and aerial robots operating in dynamic, uncertain environments. A key challenge lies in specifying process and measurement noise covariances, which are typically unknown or manually tuned. In this work, we introduce a bi-level optimization framework that jointly calibrates covariance matrices and kinematic parameters in an estimator-in-the-loop manner. The upper level treats noise covariances and model parameters as optimization variables, while …

Direct Data-Driven Predictive Control for a Three-dimensional Cable-Driven Soft Robotic Arm
Cheng Ouyang, Moeen Ul Islam, Dong Chen, Kaixiang Zhang, Zhaojian Li, Xiaobo Tan
https://arxiv.org/abs/2510.08953

Direct Data-Driven Predictive Control for a Three-dimensional Cable-Driven Soft Robotic Arm
Soft robots offer significant advantages in safety and adaptability, yet achieving precise and dynamic control remains a major challenge due to their inherently complex and nonlinear dynamics. Recently, Data-enabled Predictive Control (DeePC) has emerged as a promising model-free approach that bypasses explicit system identification by directly leveraging input-output data. While DeePC has shown success in other domains, its application to soft robots remains underexplored, particularly for thr…

FOGMACHINE -- Leveraging Discrete-Event Simulation and Scene Graphs for Modeling Hierarchical, Interconnected Environments under Partial Observations from Mobile Agents
Lars Ohnemus, Nils Hantke, Max Wei{\ss}er, Kai Furmans
https://arxiv.org/abs/2510.09483

FOGMACHINE -- Leveraging Discrete-Event Simulation and Scene Graphs for Modeling Hierarchical, Interconnected Environments under Partial Observations from Mobile Agents
Dynamic Scene Graphs (DSGs) provide a structured representation of hierarchical, interconnected environments, but current approaches struggle to capture stochastic dynamics, partial observability, and multi-agent activity. These aspects are critical for embodied AI, where agents must act under uncertainty and delayed perception. We introduce FOGMACHINE , an open-source framework that fuses DSGs with discrete-event simulation to model object dynamics, agent observations, and interactions at scal…