arXiv subjects

Siyuan Xie

Publications and source records attributed to Siyuan Xie.

6 recordsLinked to original sources

arXiv2026

A Novel Scheme for Inter-Satellite Integrated Laser Ranging and Communication in Space-Based GW Detection

Adopting pseudo-random noise (PRN) codes for inter-satellite ranging, the resulting data-encoded PRN signal induces excessive laser phase measurement noise under the currently adopted integrated scheme in space-based gravitational wave (GW) detection systems. To suppress the noise, a scheme of inter-satellite integrated laser link is presented in this Letter, in which the data-encoded PRN signal for inter-satellite absolute distance measurement and communication is phase-modulated onto the ultra-stable oscillator (USO) clock signal before phase modulation onto the laser carrier. Theoretical analysis and simulation results show that in the proposed scheme the noise is significantly suppressed compared with the currently-adopted one, while the noise stemming from the data-encoded PRN signal on clock noise transfer is limited within the requirement.

gr-qc↗

arXiv2026

AgentBeats: Agentifying Agent Assessment for Openness, Standardization, and Reproducibility

Agent systems are advancing quickly across domains, but their evaluation remains fragmented. Most benchmarks rely on fixed, LLM-centric harnesses that require heavy integration, create test-production mismatch, and limit fair comparison across diverse agent designs. The root problem is the lack of an open, agent-agnostic assessment interface. We advocate Agentified Agent Assessment (AAA), where evaluation is performed by judge agents and all participants interact through standardized protocols: A2A for task management and MCP for tool access. Conventional benchmarking defines two separate interfaces, one for the benchmark and one for the agent, while AAA only needs one; this yields a generic, unified framework that separates assessment logic from agent implementation and enables reproducible, interoperable, and multi-agent evaluation. We further introduce AgentBeats as a concrete realization of AAA: we identify five practical operation modes that make standardized assessment compatible with real-world constraints on openness, privacy, and reproducibility. To evaluate our design at scale, we conduct two studies: a five-month open competition that drew 298 judge agents across 12 categories together with 467 subject agents from independent participants, showing that AAA applies across a heterogeneous range of benchmarks; and a case study on coding agents that confirms agentified evaluation preserves fidelity with the public record while surfacing previously missing head-to-head results, yielding research insights about agent design. Combining a community-scale field study and a controlled coding case study, we verify that AAA delivers coverage, practicality, and fidelity across heterogeneous scenarios at scale. Together, AAA and AgentBeats offer a clear path toward open, standardized, and reproducible agent assessment.

cs.AI↗

arXiv2026

ArtLLM: Generating Articulated Assets via 3D LLM

Creating interactive digital environments for gaming, robotics, and simulation relies on articulated 3D objects whose functionality emerges from their part geometry and kinematic structure. However, existing approaches remain fundamentally limited: optimization-based reconstruction methods require slow, per-object joint fitting and typically handle only simple, single-joint objects, while retrieval-based methods assemble parts from a fixed library, leading to repetitive geometry and poor generalization. To address these challenges, we introduce ArtLLM, a novel framework for generating high-quality articulated assets directly from complete 3D meshes. At its core is a 3D multimodal large language model trained on a large-scale articulation dataset curated from both existing articulation datasets and procedurally generated objects. Unlike prior work, ArtLLM autoregressively predicts a variable number of parts and joints, inferring their kinematic structure in a unified manner from the object's point cloud. This articulation-aware layout then conditions a 3D generative model to synthesize high-fidelity part geometries. Experiments on the PartNet-Mobility dataset show that ArtLLM significantly outperforms state-of-the-art methods in both part layout accuracy and joint prediction, while generalizing robustly to real-world objects. Finally, we demonstrate its utility in constructing digital twins, highlighting its potential for scalable robot learning.

cs.CV↗

arXiv2025

Safety Control of Service Robots with LLMs and Embodied Knowledge Graphs

Safety limitations in service robotics across various industries have raised significant concerns about the need for robust mechanisms ensuring that robots adhere to safe practices, thereby preventing actions that might harm humans or cause property damage. Despite advances, including the integration of Knowledge Graphs (KGs) with Large Language Models (LLMs), challenges in ensuring consistent safety in autonomous robot actions persist. In this paper, we propose a novel integration of Large Language Models with Embodied Robotic Control Prompts (ERCPs) and Embodied Knowledge Graphs (EKGs) to enhance the safety framework for service robots. ERCPs are designed as predefined instructions that ensure LLMs generate safe and precise responses. These responses are subsequently validated by EKGs, which provide a comprehensive knowledge base ensuring that the actions of the robot are continuously aligned with safety protocols, thereby promoting safer operational practices in varied contexts. Our experimental setup involved diverse real-world tasks, where robots equipped with our framework demonstrated significantly higher compliance with safety standards compared to traditional methods. This integration fosters secure human-robot interactions and positions our methodology at the forefront of AI-driven safety innovations in service robotics.

cs.RO↗

arXiv2024

Picometer-level quadrangle optical bonding bench for testing interferometric technologies in TianQin

Interferometric techniques are crucial for space-based gravitational wave detection, requiring a picometer-level stable optical bench, precise phasemeter, interstellar transponder low-light phase locking, and laser sideband communication. These technologies must be rigorously tested on the ground before deployment in space. The AEI group has previously developed a picometer-stable hexapod optical bench to verify the linearity and precision of phase extraction for LISA. In this paper, we introduce a quadrangle quasi-monolithic optical bench aimed at simplifying the system and expanding the range of tested interferometric techniques for TianQin. Experimental results demonstrate that the system achieves picometer-level optical pathlength stability and phase resolution over a large dynamic range. In the laser transponder link test, the light phase-locked residual noise is lower than ${\rm 10^{-4}\,rad/Hz^{1/2}}$ above millihertz frequency range, and the laser sideband modulation has no significant coupling to the measurements in the ${\rm mHz-Hz}$ band. These results provide critical technical validation for the implementation of future gravitational wave detection in space.

physics.ins-det↗

arXiv2024

V^3: Viewing Volumetric Videos on Mobiles via Streamable 2D Dynamic Gaussians

Experiencing high-fidelity volumetric video as seamlessly as 2D videos is a long-held dream. However, current dynamic 3DGS methods, despite their high rendering quality, face challenges in streaming on mobile devices due to computational and bandwidth constraints. In this paper, we introduce V^3 (Viewing Volumetric Videos), a novel approach that enables high-quality mobile rendering through the streaming of dynamic Gaussians. Our key innovation is to view dynamic 3DGS as 2D videos, facilitating the use of hardware video codecs. Additionally, we propose a two-stage training strategy to reduce storage requirements with rapid training speed. The first stage employs hash encoding and shallow MLP to learn motion, then reduces the number of Gaussians through pruning to meet the streaming requirements, while the second stage fine tunes other Gaussian attributes using residual entropy loss and temporal loss to improve temporal continuity. This strategy, which disentangles motion and appearance, maintains high rendering quality with compact storage requirements. Meanwhile, we designed a multi-platform player to decode and render 2D Gaussian videos. Extensive experiments demonstrate the effectiveness of V^3, outperforming other methods by enabling high-quality rendering and streaming on common devices, which is unseen before. As the first to stream dynamic Gaussians on mobile devices, our companion player offers users an unprecedented volumetric video experience, including smooth scrolling and instant sharing. Our project page with source code is available at https://authoritywang.github.io/v3/.

cs.CV↗