Tootfinder

baseball: Baseball steroid use (2008)
Two networks representing steroid use among baseball players. First, a bipartite network of players and their steroid providers (of illegal performance-enhancing substances). Second, a one-mode projection of players, which are linked if they have a common supplier.
This network has 84 nodes and 84 edges.
Tags: Social, Offline, Weighted, Projection

Test-Time Learning and Inference-Time Deliberation for Efficiency-First Offline Reinforcement Learning in Care Coordination and Population Health Management
Sanjay Basu, Sadiq Y. Patel, Parth Sheth, Bhairavi Muralidharan, Namrata Elamaran, Aakriti Kinra, Rajaie Batniji
https://arxiv.org/abs/2509.16291

Test-Time Learning and Inference-Time Deliberation for Efficiency-First Offline Reinforcement Learning in Care Coordination and Population Health Management
Care coordination and population health management programs serve large Medicaid and safety-net populations and must be auditable, efficient, and adaptable. While clinical risk for outreach modalities is typically low, time and opportunity costs differ substantially across text, phone, video, and in-person visits. We propose a lightweight offline reinforcement learning (RL) approach that augments trained policies with (i) test-time learning via local neighborhood calibration, and (ii) inference…

baseball: Baseball steroid use (2008)
Two networks representing steroid use among baseball players. First, a bipartite network of players and their steroid providers (of illegal performance-enhancing substances). Second, a one-mode projection of players, which are linked if they have a common supplier.
This network has 72 nodes and 1089 edges.
Tags: Social, Offline, Weighted, Projection

On the System Theoretic Offline Learning of Continuous-Time LQR with Exogenous Disturbances
Sayak Mukherjee, Ramij R. Hossain, Mahantesh Halappanavar
https://arxiv.org/abs/2509.16746

On the System Theoretic Offline Learning of Continuous-Time LQR with Exogenous Disturbances
We analyze offline designs of linear quadratic regulator (LQR) strategies with uncertain disturbances. First, we consider the scenario where the exogenous variable can be estimated in a controlled environment, and subsequently, consider a more practical and challenging scenario where it is unknown in a stochastic setting. Our approach builds on the fundamental learning-based framework of adaptive dynamic programming (ADP), combined with a Lyapunov-based analytical methodology to design the algo…

Offline Imitation Learning upon Arbitrary Demonstrations by Pre-Training Dynamics Representations
Haitong Ma, Bo Dai, Zhaolin Ren, Yebin Wang, Na Li
https://arxiv.org/abs/2508.14383

Offline Imitation Learning upon Arbitrary Demonstrations by Pre-Training Dynamics Representations
Limited data has become a major bottleneck in scaling up offline imitation learning (IL). In this paper, we propose enhancing IL performance under limited expert data by introducing a pre-training stage that learns dynamics representations, derived from factorizations of the transition dynamics. We first theoretically justify that the optimal decision variable of offline IL lies in the representation space, significantly reducing the parameters to learn in the downstream IL. Moreover, the dynam…

Temporal Logic-Based Multi-Vehicle Backdoor Attacks against Offline RL Agents in End-to-end Autonomous Driving
Xuan Chen, Shiwei Feng, Zikang Xiong, Shengwei An, Yunshu Mao, Lu Yan, Guanhong Tao, Wenbo Guo, Xiangyu Zhang
https://arxiv.org/abs/2509.16950

Temporal Logic-Based Multi-Vehicle Backdoor Attacks against Offline RL Agents in End-to-end Autonomous Driving
Assessing the safety of autonomous driving (AD) systems against security threats, particularly backdoor attacks, is a stepping stone for real-world deployment. However, existing works mainly focus on pixel-level triggers that are impractical to deploy in the real world. We address this gap by introducing a novel backdoor attack against the end-to-end AD systems that leverage one or more other vehicles' trajectories as triggers. To generate precise trigger trajectories, we first use temporal log…

baseball: Baseball steroid use (2008)
Two networks representing steroid use among baseball players. First, a bipartite network of players and their steroid providers (of illegal performance-enhancing substances). Second, a one-mode projection of players, which are linked if they have a common supplier.
This network has 72 nodes and 1089 edges.
Tags: Social, Offline, Weighted, Projection

linrax: A JAX Compatible, Simplex Method Linear Program Solver
Brendan Gould, Akash Harapanahalli, Samuel Coogan
https://arxiv.org/abs/2509.19484 https://a…

linrax: A JAX Compatible, Simplex Method Linear Program Solver
We present linrax, the first simplex based linear program (LP) solver compatible with the JAX ecosystem. In many control algorithms, LPs are often automatically generated and frequently solved either offline or online in the control loop. This motivates the design of linrax, which is especially suited for compilation into a complex JAX-based pipeline as a subroutine. We discuss the challenges associated with implementing a general purpose LP solver under strict design requirements from JAX. Not…

End-to-End 4D Heart Mesh Recovery Across Full-Stack and Sparse Cardiac MRI
Yihong Chen, Jiancheng Yang, Deniz Sayin Mercadier, Hieu Le, Juerg Schwitter, Pascal Fua
https://arxiv.org/abs/2509.12090

End-to-End 4D Heart Mesh Recovery Across Full-Stack and Sparse Cardiac MRI
Reconstructing cardiac motion from cine CMR sequences is critical for diagnosis, prediction, and intervention. Existing methods rely on complete CMR stacks to infer full heart motion, limiting their utility in intra-procedural scenarios where only sparse observations are available. We present TetHeart, the first end-to-end framework that unifies full 4D multi-structure heart mesh recovery from both offline full-stack acquisitions and intra-procedural sparse-slice observations. Our method levera…

First Experience with Real-Time Control Using Simulated VQC-Based Quantum Policies
Yize Sun, Mohamad Hagog, Marc Weber, Daniel Hein, Steffen Udluft, Volker Tresp, Yunpu Ma
https://arxiv.org/abs/2508.01690

First Experience with Real-Time Control Using Simulated VQC-Based Quantum Policies
This paper investigates the integration of quantum computing into offline reinforcement learning and the deployment of the resulting quantum policy in a real-time control hardware realization of the cart-pole system. Variational Quantum Circuits (VQCs) are used to represent the policy. Classical model-based offline policy search was applied, in which a pure VQC with trainable input-output weights is used as a policy network instead of a classical multilayer perceptron. The goal is to evaluate t…

The local-first paradigm provides transformative advantages, such as user-owned data, seamless offline capabilities, and instant interactions. But how can you get started? At Berlin Buzzwords, Miloš Sutanovac discussed the core concepts and demonstrated how to begin your local-first journey.
Watch the full session: https://

baseball: Baseball steroid use (2008)
Two networks representing steroid use among baseball players. First, a bipartite network of players and their steroid providers (of illegal performance-enhancing substances). Second, a one-mode projection of players, which are linked if they have a common supplier.
This network has 84 nodes and 84 edges.
Tags: Social, Offline, Weighted, Projection

This morning I was pissed to discover the manufacturer of my cleaning robot went bankrupt, shut down their cloud services and rendered their mobile app unusable, nearly bricking the whole device.
Fortunately there's another app that still does the job.
God dammit tho, why can't we have nice offline-first things any more.

Streaming Sequence-to-Sequence Learning with Delayed Streams Modeling
Neil Zeghidour, Eugene Kharitonov, Manu Orsini, V\'aclav Volhejn, Gabriel de Marmiesse, Edouard Grave, Patrick P\'erez, Laurent Mazar\'e, Alexandre D\'efossez
https://arxiv.org/abs/2509.08753

Streaming Sequence-to-Sequence Learning with Delayed Streams Modeling
We introduce Delayed Streams Modeling (DSM), a flexible formulation for streaming, multimodal sequence-to-sequence learning. Sequence-to-sequence generation is often cast in an offline manner, where the model consumes the complete input sequence before generating the first output timestep. Alternatively, streaming sequence-to-sequence rely on learning a policy for choosing when to advance on the input stream, or write to the output stream. DSM instead models already time-aligned streams with a …

IP-Basis PINNs: Efficient Multi-Query Inverse Parameter Estimation
Shalev Manor, Mohammad Kohandel
https://arxiv.org/abs/2509.07245 https://arxiv.org/pdf/2…

IP-Basis PINNs: Efficient Multi-Query Inverse Parameter Estimation
Solving inverse problems with Physics-Informed Neural Networks (PINNs) is computationally expensive for multi-query scenarios, as each new set of observed data requires a new, expensive training procedure. We present Inverse-Parameter Basis PINNs (IP-Basis PINNs), a meta-learning framework that extends the foundational work of Desai et al. (2022) to enable rapid and efficient inference for inverse problems. Our method employs an offline-online decomposition: a deep network is first trained offl…

Fine-tuning Behavioral Cloning Policies with Preference-Based Reinforcement Learning
Ma\"el Macuglia, Paul Friedrich, Giorgia Ramponi
https://arxiv.org/abs/2509.26605 https…

Fine-tuning Behavioral Cloning Policies with Preference-Based Reinforcement Learning
Deploying reinforcement learning (RL) in robotics, industry, and health care is blocked by two obstacles: the difficulty of specifying accurate rewards and the risk of unsafe, data-hungry exploration. We address this by proposing a two-stage framework that first learns a safe initial policy from a reward-free dataset of expert demonstrations, then fine-tunes it online using preference-based human feedback. We provide the first principled analysis of this offline-to-online approach and introduce…

baseball: Baseball steroid use (2008)
Two networks representing steroid use among baseball players. First, a bipartite network of players and their steroid providers (of illegal performance-enhancing substances). Second, a one-mode projection of players, which are linked if they have a common supplier.
This network has 84 nodes and 84 edges.
Tags: Social, Offline, Weighted, Projection

baseball: Baseball steroid use (2008)
Two networks representing steroid use among baseball players. First, a bipartite network of players and their steroid providers (of illegal performance-enhancing substances). Second, a one-mode projection of players, which are linked if they have a common supplier.
This network has 84 nodes and 84 edges.
Tags: Social, Offline, Weighted, Projection

When Truth Does Not Take on Its Shoes: How Misinformation Spreads in Chatrooms
Shuige Liu
https://arxiv.org/abs/2510.08658 https://arxiv.org/pdf/2510.08658…

When Truth Does Not Take on Its Shoes: How Misinformation Spreads in Chatrooms
We examine how misinformation spreads in social networks composed of individuals with long-term offline relationships. Especially, we focus on why misinformation persists and diffuses despite being recognized by most as false. In our psychological game theoretical model, each agent who receives a piece of (mis)information must first decide how to react -- by openly endorsing it, remaining silent, or openly challenging it. After observing the reactions of her neighbors who also received the mess…

Multi-Agent Path Finding via Offline RL and LLM Collaboration
Merve Atasever, Matthew Hong, Mihir Nitin Kulkarni, Qingpei Li, Jyotirmoy V. Deshmukh
https://arxiv.org/abs/2509.22130

Multi-Agent Path Finding via Offline RL and LLM Collaboration
Multi-Agent Path Finding (MAPF) poses a significant and challenging problem critical for applications in robotics and logistics, particularly due to its combinatorial complexity and the partial observability inherent in realistic environments. Decentralized reinforcement learning methods commonly encounter two substantial difficulties: first, they often yield self-centered behaviors among agents, resulting in frequent collisions, and second, their reliance on complex communication modules leads…

Efficient Training-Free Online Routing for High-Volume Multi-LLM Serving
Fangzhou Wu, Sandeep Silwal
https://arxiv.org/abs/2509.02718 https://arxiv.org/pdf…

Efficient Training-Free Online Routing for High-Volume Multi-LLM Serving
Increasing demand for Large Language Models (LLMs) services imposes substantial deployment and computation costs on providers. LLM routing offers a cost-efficient solution by directing queries to the optimal LLM based on model and query features. However, existing works primarily focus on offline scenarios and struggle to adapt to online settings with high query volume and constrained token budgets. In this work, we introduce the first training-free algorithm for online routing scenarios. Our a…

Learning Relationships Between Separate Audio Tracks for Creative Applications
Balthazar Bujard (IRCAM, SU), J\'er\^ome Nika (IRCAM), F\'ed\'eric Bevilacqua (IRCAM), Nicolas Obin
https://arxiv.org/abs/2509.25296

Learning Relationships Between Separate Audio Tracks for Creative Applications
This paper presents the first step in a research project situated within the field of musical agents. The objective is to achieve, through training, the tuning of the desired musical relationship between a live musical input and a real-time generated musical output, through the curation of a database of separated tracks. We propose an architecture integrating a symbolic decision module capable of learning and exploiting musical relationships from such musical corpus. We detail an offline implem…

Distributional Inverse Reinforcement Learning
Feiyang Wu, Ye Zhao, Anqi Wu
https://arxiv.org/abs/2510.03013 https://arxiv.org/pdf/2510.03013

Distributional Inverse Reinforcement Learning
We propose a distributional framework for offline Inverse Reinforcement Learning (IRL) that jointly models uncertainty over reward functions and full distributions of returns. Unlike conventional IRL approaches that recover a deterministic reward estimate or match only expected returns, our method captures richer structure in expert behavior, particularly in learning the reward distribution, by minimizing first-order stochastic dominance (FSD) violations and thus integrating distortion risk mea…

Self-consistent clustering analysis for homogenisation of heterogeneous plates
Menglei Li, Haolin Li, Bing Wang, Bing Wang
https://arxiv.org/abs/2508.20446 https://

Self-consistent clustering analysis for homogenisation of heterogeneous plates
This work introduces a reduced-order model for plate structures with periodic micro-structures by coupling self-consistent clustering analysis (SCA) with the Lippmann-Schwinger equation, enabling rapid multiscale homogenisation of heterogeneous plates. A plate-specific SCA scheme is derived for the first time and features two key elements: (i) an offline-online strategy that combines Green's functions with k-means data compression, and (ii) an online self-consistent update that exploits the wea…

baseball: Baseball steroid use (2008)
Two networks representing steroid use among baseball players. First, a bipartite network of players and their steroid providers (of illegal performance-enhancing substances). Second, a one-mode projection of players, which are linked if they have a common supplier.
This network has 84 nodes and 84 edges.
Tags: Social, Offline, Weighted, Projection

baseball: Baseball steroid use (2008)
Two networks representing steroid use among baseball players. First, a bipartite network of players and their steroid providers (of illegal performance-enhancing substances). Second, a one-mode projection of players, which are linked if they have a common supplier.
This network has 84 nodes and 84 edges.
Tags: Social, Offline, Weighted, Projection

baseball: Baseball steroid use (2008)
Two networks representing steroid use among baseball players. First, a bipartite network of players and their steroid providers (of illegal performance-enhancing substances). Second, a one-mode projection of players, which are linked if they have a common supplier.
This network has 84 nodes and 84 edges.
Tags: Social, Offline, Weighted, Projection

baseball: Baseball steroid use (2008)
Two networks representing steroid use among baseball players. First, a bipartite network of players and their steroid providers (of illegal performance-enhancing substances). Second, a one-mode projection of players, which are linked if they have a common supplier.
This network has 72 nodes and 1089 edges.
Tags: Social, Offline, Weighted, Projection

baseball: Baseball steroid use (2008)
Two networks representing steroid use among baseball players. First, a bipartite network of players and their steroid providers (of illegal performance-enhancing substances). Second, a one-mode projection of players, which are linked if they have a common supplier.
This network has 72 nodes and 1089 edges.
Tags: Social, Offline, Weighted, Projection

baseball: Baseball steroid use (2008)
Two networks representing steroid use among baseball players. First, a bipartite network of players and their steroid providers (of illegal performance-enhancing substances). Second, a one-mode projection of players, which are linked if they have a common supplier.
This network has 84 nodes and 84 edges.
Tags: Social, Offline, Weighted, Projection