Tootfinder

ActorDB: A Unified Database Model Integrating Single-Writer Actors, Incremental View Maintenance, and Zero-Trust Messaging
Jun Kawasaki
https://arxiv.org/abs/2509.25285 https://…

ActorDB: A Unified Database Model Integrating Single-Writer Actors, Incremental View Maintenance, and Zero-Trust Messaging
This paper presents ActorDB ( Dekigoto ) , a novel database architecture that tightly integrates a single-writer actor model for writes, Incremental View Maintenance (IVM), and a zero-trust security model as a core component. The primary contribution of this work is the unification of these powerful but complex concepts into a single, cohesive system designed to reduce architectural complexity for developers of modern, data-intensive applications. We argue that by providing these capabilities o…

ProRe: A Proactive Reward System for GUI Agents via Reasoner-Actor Collaboration
Gaole Dai, Shiqi Jiang, Ting Cao, Yuqing Yang, Yuanchun Li, Rui Tan, Mo Li, Lili Qiu
https://arxiv.org/abs/2509.21823

ProRe: A Proactive Reward System for GUI Agents via Reasoner-Actor Collaboration
Reward is critical to the evaluation and training of large language models (LLMs). However, existing rule-based or model-based reward methods struggle to generalize to GUI agents, where access to ground-truth trajectories or application databases is often unavailable, and static trajectory-based LLM-as-a-Judge approaches suffer from limited accuracy. To address these challenges, we propose ProRe, a proactive reward system that leverages a general-purpose reasoner and domain-specific evaluator a…

Quantum Reinforcement Learning-Guided Diffusion Model for Image Synthesis via Hybrid Quantum-Classical Generative Model Architectures
Chi-Sheng Chen, En-Jui Kuo
https://arxiv.org/abs/2509.14163

Quantum Reinforcement Learning-Guided Diffusion Model for Image Synthesis via Hybrid Quantum-Classical Generative Model Architectures
Diffusion models typically employ static or heuristic classifier-free guidance (CFG) schedules, which often fail to adapt across timesteps and noise conditions. In this work, we introduce a quantum reinforcement learning (QRL) controller that dynamically adjusts CFG at each denoising step. The controller adopts a hybrid quantum--classical actor--critic architecture: a shallow variational quantum circuit (VQC) with ring entanglement generates policy features, which are mapped by a compact multil…

MPC-based Deep Reinforcement Learning Method for Space Robotic Control with Fuel Sloshing Mitigation
Mahya Ramezani, M. Amin Alandihallaj, Bar{\i}\c{s} Can Yal\c{c}{\i}n, Miguel Angel Olivares Mendez, Holger Voos
https://arxiv.org/abs/2509.21045

MPC-based Deep Reinforcement Learning Method for Space Robotic Control with Fuel Sloshing Mitigation
This paper presents an integrated Reinforcement Learning (RL) and Model Predictive Control (MPC) framework for autonomous satellite docking with a partially filled fuel tank. Traditional docking control faces challenges due to fuel sloshing in microgravity, which induces unpredictable forces affecting stability. To address this, we integrate Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) RL algorithms with MPC, leveraging MPC's predictive capabilities to accelerate RL training a…

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…

Dual Actor DDPG for Airborne STAR-RIS Assisted Communications
Danish Rizvi, David Boyle
https://arxiv.org/abs/2509.13328 https://arxiv.org/pdf/2509.13328…

Dual Actor DDPG for Airborne STAR-RIS Assisted Communications
This study departs from the prevailing assumption of independent Transmission and Reflection Coefficients (TRC) in Airborne Simultaneous Transmit and Reflect Reconfigurable Intelligent Surface (STAR-RIS) research. Instead, we explore a novel multi-user downlink communication system that leverages a UAV-mounted STAR-RIS (Aerial-STAR) incorporating a coupled TRC phase shift model. Our key contributions include the joint optimization of UAV trajectory, active beamforming vectors at the base statio…

People CEO Neil Vogel calls Google a "bad actor" for refusing to pay publishers for AI content and says Google Search drives 25%-30% of visits to People sites (Jeff John Roberts/Fortune)
https://fortune.com/2025/09/11/media-in…

Google’s AI is the ‘worst’ for stealing content, says People CEO
But Google-owned YouTube could provide a model for media and AI to coexist and thrive, says internet industry pioneer Bill Gross.

A Hybrid APIM-CFGM Model for Longitudinal Non-Exchangeable Dyads: Demonstrating and Comparing Estimation Approaches Using Multilevel Modeling
Liu Liu
https://arxiv.org/abs/2509.00993

A Hybrid APIM-CFGM Model for Longitudinal Non-Exchangeable Dyads: Demonstrating and Comparing Estimation Approaches Using Multilevel Modeling
Understanding change over time within dyads, such as mentor-mentee or therapist-client pairs, poses unique challenges, particularly in studies with small samples and distinguishable roles. This paper introduces a flexible hybrid longitudinal modeling that integrates features of the Actor-Partner Interdependence Model (APIM) and the Common Fate Growth Model (CFGM) to simultaneously estimate individual-level and shared dyad-level effects. Using a hypothetical peer-mentoring example (novices paire…

Learning to Crawl: Latent Model-Based Reinforcement Learning for Soft Robotic Adaptive Locomotion
Vaughn Gzenda, Robin Chhabra
https://arxiv.org/abs/2510.05957 https://

Learning to Crawl: Latent Model-Based Reinforcement Learning for Soft Robotic Adaptive Locomotion
Soft robotic crawlers are mobile robots that utilize soft body deformability and compliance to achieve locomotion through surface contact. Designing control strategies for such systems is challenging due to model inaccuracies, sensor noise, and the need to discover locomotor gaits. In this work, we present a model-based reinforcement learning (MB-RL) framework in which latent dynamics inferred from onboard sensors serve as a predictive model that guides an actor-critic algorithm to optimize loc…

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models
Runpeng Dai, Linfeng Song, Haolin Liu, Zhenwen Liang, Dian Yu, Haitao Mi, Zhaopeng Tu, Rui Liu, Tong Zheng, Hongtu Zhu, Dong Yu
https://arxiv.org/abs/2509.09675

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models
Reinforcement Learning with Verifiable Rewards (RLVR) is a powerful paradigm for enhancing the reasoning ability of Large Language Models (LLMs). Yet current RLVR methods often explore poorly, leading to premature convergence and entropy collapse. To address this challenge, we introduce Curiosity-Driven Exploration (CDE), a framework that leverages the model's own intrinsic sense of curiosity to guide exploration. We formalize curiosity with signals from both the actor and the critic: for the a…

Mapping Innovation Networks: A Network-Based Approach to Actor Heterogeneity in National Innovation Systems
Dawoon Jeong, Taewon Kang, Saerom Si, Sangnam Lee, Wonsub Eum
https://arxiv.org/abs/2508.03498

Mapping Innovation Networks: A Network-Based Approach to Actor Heterogeneity in National Innovation Systems
The Triple Helix model has provided a foundational framework for analyzing National Innovation Systems by highlighting the roles of universities, industries, and government research institutes. However, increasing heterogeneity within these actor groups limits the explanatory power of typological approaches. This study introduces a capability-based network methodology that maps the structural relationships among innovation actors based on the similarity of their research and development (R&D) c…

Laminar: A Scalable Asynchronous RL Post-Training Framework
Guangming Sheng, Yuxuan Tong, Borui Wan, Wang Zhang, Chaobo Jia, Xibin Wu, Yuqi Wu, Xiang Li, Chi Zhang, Yanghua Peng, Haibin Lin, Xin Liu, Chuan Wu
https://arxiv.org/abs/2510.12633

Laminar: A Scalable Asynchronous RL Post-Training Framework
Reinforcement learning (RL) post-training for Large Language Models (LLMs) is now scaling to large clusters and running for extended durations to enhance model reasoning performance. However, the scalability of existing RL frameworks is limited, as extreme long-tail skewness in RL trajectory generation causes severe GPU underutilization. Current asynchronous RL systems attempt to mitigate this, but they rely on global weight synchronization between the actor and all rollouts, which creates a ri…

Pulp Motion: Framing-aware multimodal camera and human motion generation
Robin Courant, Xi Wang, David Loiseaux, Marc Christie, Vicky Kalogeiton
https://arxiv.org/abs/2510.05097

Pulp Motion: Framing-aware multimodal camera and human motion generation
Treating human motion and camera trajectory generation separately overlooks a core principle of cinematography: the tight interplay between actor performance and camera work in the screen space. In this paper, we are the first to cast this task as a text-conditioned joint generation, aiming to maintain consistent on-screen framing while producing two heterogeneous, yet intrinsically linked, modalities: human motion and camera trajectories. We propose a simple, model-agnostic framework that enfo…