arXiv subjects

Renjie Zhao

Publications and source records attributed to Renjie Zhao.

10 recordsLinked to original sources

arXiv2026

Population-Scalable Multi-Agent World Modeling

World models have recently achieved impressive progress in visual prediction and interactive generation, but extending them to multi-agent environments introduces a fundamental scalability challenge. Existing methods generally assume a fixed number of agents during training and inference, which ties the model to a pre-determined agent population and limits inference-time scalability. Our key insight is that cross-view consistency should arise from a shared world state whose evolution does not assume a predefined number of agents, while agent-specific observations should be generated by querying this state through a unified rendering interface. Based on this insight, we propose Khora, a scalable multi-agent world model that supports inference-time expansion to arbitrary numbers of agents without retraining. Our framework decouples world-state evolution from visual rendering and introduces a population-agnostic rendering mechanism for incorporating other agent information. This design maintains cross-view consistency through the shared world state rather than through dense interactions among observation streams inside the expensive video generator, enabling approximately linear practical scaling with the number of queried views. Qualitative experiments demonstrate that our approach generalizes to unseen numbers of agents while maintaining visual quality and multi-agent consistency. We further implement a real-time interactive system to demonstrate scalable open-world simulation.

cs.CV↗

arXiv2026

RF-HOI: Recognize Human-Object Interaction with Radio Frequency Signals

Recognizing Human-Object Interactions (HOI) is essential for intelligent systems, underpinning applications in virtual and augmented reality, embodied AI, and assistive robotics. However, vision-based HOI methods face challenges in privacy concerns and poor light conditions. In this work, we introduce RF-HOI, the first framework that only uses radio frequency (RF) signals for HOI recognition. A key challenge of RF-HOI is that single-modality RF sensing is insufficient to recognize both actions and the objects being interacted with. RF-HOI addresses this through a novel modality fusion that combines mmWave radar and RFID, enabling simultaneous action recognition and target identification. Another challenge is limited training data across diverse setups, which impairs the generalizability of the recognition model. To overcome this, we develop a simulator that synthesizes multimodal RF data for diverse HOIs at scale, allowing us to fine-tune with only a small amount of real-world data. Experiment results show that RF-HOI outperforms all baselines, approaching vision model performance, and that our diverse synthetic training data can significantly boost our system's performance on real-world scenarios. These results highlight the potential of multimodal RF sensing for robust and privacy-preserving HOI recognition as well as the effectiveness of our RF data synthesis.

cs.AI↗

arXiv2026

GarmentZoom: Generating Zoomable Images from Garment Listings

Online product listings for garments often include an overview photo and a close-up to show garment details. However, each photo focuses on either field of view or garment detail, forcing users to alternate between views and breaking browsing continuity. We present GarmentZoom, a system that enhances the full-view photo to match the fidelity of its accompanying close-up, enabling seamless zoom-and-pan exploration. Unlike standard reference-based super-resolution, our setting involves close-up references that are spatially unaligned with the full view, and scale factors that vary substantially across garments 3-20$\times$. Prior work typically relies on alignment to transfer details or requires per-instance fine-tuning to memorize them. Instead, we train a single model that supports a continuous range of scales across diverse garments. Our approach synthesizes details without requiring spatial alignment and matches the quality of per-instance methods with a fraction of the training cost.

cs.CV↗

arXiv2026

IPR-1: Interactive Physical Reasoner

Humans learn by observing, interacting with environments, and internalizing physics and causality. Here, we aim to ask whether an agent can similarly acquire human-like reasoning from interaction and keep improving with more experience. To study this, we introduce a Game-to-Unseen (G2U) benchmark of 1,000+ heterogeneous games that exhibit significant visual domain gaps. Existing approaches, including VLMs and world models, struggle to capture underlying physics and causality since they are not focused on core mechanisms and overfit to visual details. VLM/VLA agents reason but lack look-ahead in interactive settings, while world models imagine but imitate visual patterns rather than analyze physics and causality. We therefore propose IPR (Interactive Physical Reasoner), using world-model rollouts to score and reinforce a VLM's policy, and introduce PhysCode, a physics-centric action code aligning semantic intent with dynamics to provide a shared action space for prediction and reasoning. Pretrained on 1,000+ games, our IPR performs robustly on levels from primitive intuition to goal-driven reasoning, and even surpasses GPT-5 overall. We find that performance improves with more training games and interaction steps, and that the model also zero-shot transfers to unseen games. These results support physics-centric interaction as a path to steadily improving physical reasoning. Further demos and project details can be found at https://mybearyzhang.github.io/ipr-1.

cs.AI↗

arXiv2026

Trust Your Memory: Verifiable Control of Smart Homes through Reinforcement Learning with Multi-dimensional Rewards

Large Language Models (LLMs) have become a key foundation for enabling personalized smart home experiences. While existing studies have explored how smart home assistants understand user queries to control devices in real time, their ability to perform memory-driven device control remains challenging from both evaluation and methodological perspectives. In terms of evaluation, existing benchmarks either focus on immediate device control or general open-domain memory retrieval tasks, and therefore cannot effectively evaluate a model's ability to perform memory-driven device control. Methodologically, while memory-driven device control can be approached using Reinforcement Learning, conventional RL methods generally rely on outcome-based supervision (i.e., whether the final task is achieved). This lack of intermediate feedback can lead to sub-optimal performance or local failures in fine-grained memory management tasks (adding, updating, deleting, and utilizing). To address these issues, we first release MemHomeLife, built from anonymized real-world long-term user interaction logs. To enable more fine-grained evaluation of different memory-related subtasks, we further construct MemHome, the first benchmark designed to systematically evaluate memory-driven device control in smart home scenarios.

cs.AI↗

arXiv2025

Thermally Activated Snap-through Transitions Controlled by Tunable Metastability

The effects of thermal fluctuations on the morphology of two-dimensional materials are hard to harness. We propose that a geometrically constrained graphene nanoribbon (GNR) can exhibit thermally activated snap-through transitions with a predictable and controllable transition rate constant. The energetics and kinetics of such transitions can be fully captured by combining enhanced sampling methods and generalized transition state theory. Using well-tempered metadynamics, we determine the free energy landscape and a pair of asymmetric transition pathways of the GNR system. Notably, generalized transition state theory accurately captures how the transition rate constant responds to temperature and the tunable free energy landscape of our system. This work offers a theoretical framework for elastic metastability, introduces rare event methods into thermalized nanomechanical systems, and provides potential applications in designing nanoscale thermal switches and thermal actuators.

cond-mat.soft↗

arXiv2025

Response Theory for Static and Dynamic Solvation of Ionic and Dipolar Solutes in Water

The response of polar solvents to ions and polar molecules dictates many fundamental molecular processes. To understand such electrostatically-driven solvation processes, one ideally would probe the dielectric response of a solvent to an idealized point test charge or dipole solute, as envisioned in classic continuum treatments of the problem. However, this is difficult in simulations using standard atomically-detailed solvent models with embedded point charges due to possible overlap with the test charge that lead to singular interaction energies. This problem is traditionally avoided for a realistic charged solute by introducing an excluded volume core that shields its embedded point charge or dipole from the charges in the solvent. However, this core introduces additional molecular-scale perturbations of the solvent density that complicate the interpretation of solvent dielectric response. In this work, we avoid these complications through the use of Gaussian-smoothed test charges and dipoles. Gaussian charges and dipoles can be readily inserted anywhere into an atomistic solvent model without encountering infinite energies. If the Gaussian-smoothing is on the scale of molecular correlations in the solvent, both the thermodynamic and dynamic solvation response is linear. Using this observation, we construct accurate predictive theories for solvation free energies and solvation dynamics for insertion of Gaussian charges and dipoles in polar solvents and demonstrate the accuracy of the theories for a widely-used model of water. Our results suggest that Gaussian test charge distributions can be used as an informative probe of dielectric response in molecular models, and our theories can be used to analytically predict the largest component of solvation free energies of charged and polar solutes.

physics.chem-ph↗

arXiv2023

Quantifying the relevance of long-range forces for crystal nucleation in water

Understanding nucleation from aqueous solutions is of fundamental importance in a multitude of fields, ranging from materials science to biophysics. The complex solvent-mediated interactions in aqueous solutions hamper the development of a simple physical picture elucidating the roles of different interactions in nucleation processes. In this work we make use of three complementary techniques to disentangle the role played by short and long-range interactions in solvent mediated nucleation. Specifically, the first approach we utilize is the local molecular field (LMF) theory to renormalize long-range Coulomb electrostatics. Secondly, we use well-tempered metadynamics to speed up rare events governed by short-range interactions. Thirdly, deep learning-based State Predictive Information Bottleneck approach is employed in analyzing the reaction coordinate of the nucleation processes obtained from LMF treatment coupled with well-tempered metadynamics. We find that the two-step nucleation mechanism can largely be captured by the short-range interactions, while the long-range interactions further contribute to the stability of the primary crystal state at ambient conditions. Furthermore, by analyzing the reaction coordinate obtained from combined LMF-metadynamics treatment, we discern the fluctuations on different time scales, highlighting the need for long-range interactions when accounting for metastability.

cond-mat.soft↗

arXiv2022

RF-CHORD: Towards Deployable RFID Localization System for Logistics Network

RFID localization is considered the key enabler of automating the process of inventory tracking and management for high-performance logistic network. A practical and deployable RFID localization system needs to meet reliability, throughput, and range requirements. This paper presents RF-Chord, the first RFID localization system that simultaneously meets all three requirements. RF-Chord features a one-shot multisine-constructed wideband design that can process RF signal with a 200 MHz bandwidth in real-time to facilitate one-shot localization at scale. In addition, multiple SINR enhancement techniques are designed for range extension. On top of that, a kernel-layer-based near-field localization framework and a multipath-suppression algorithm are proposed to reduce the 99% long-tail errors. Our empirical results show that RF-Chord can localize more than 180 tags 6 m away from a reader within 1 second and with 99% long-tail error of 0.786 m, achieving a 0% miss reading rate and ~0.01% cross-reading rate in the warehouse and fresh food delivery store deployment.

eess.SP↗

arXiv2021

Influence of long range forces on the transition states and dynamics of NaCl ion-pair dissociation in water

We study NaCl ion-pair dissociation in a dilute aqueous solution using computer simulations both for the full system with long range Coulomb interactions and for a well chosen reference system with short range intermolecular interactions. Analyzing results using concepts from Local Molecular Field (LMF) theory and the recently proposed AI-based analysis tool "State predictive information bottleneck" (SPIB) we show that the system with short range interactions can accurately reproduce the transition rate for the dissociation process, the dynamics for moving between the underlying metastable states, and the transition state ensemble. Contributions from long range interactions can be largely neglected for these processes because long range forces from the direct interionic Coulomb interactions are almost completely canceled ($>90\%$) by those from solvent interactions over the length scale where the transition takes place. Thus for this important monovalent ion-pair system, short range forces alone are able to capture detailed consequences of the collective solvent motion, allowing the use of physically suggestive and computationally efficient short range models for the disassociation event. We believe that the framework here should be applicable to disentangling mechanisms for more complex processes such as multivalent ion disassociation, where previous work has suggested that long range contributions may be more important.

physics.chem-ph↗