Publications (40)
MCP-Persona: Benchmarking LLM Agents on Real-World Personal Applications via Environment Simulation
Wenhao Wang, Peizhi Niu, Gongyi Zou +9
The Model Context Protocol (MCP) has emerged as a transformative standard for connecting large language models (LLMs) with external data sources and tools, and has been rapidly ado…
Decentralized and Lifelong-Adaptive Multi-Agent Collaborative Learning
Shuo Tang, Rui Ye, Chenxin Xu +3
Decentralized and lifelong-adaptive multi-agent collaborative learning aims to enhance collaboration among multiple agents without a central server, with each agent solving varied…
Advances in Anti-Deception Jamming Strategies for Radar Systems: A Survey
Helena Calatrava, Shuo Tang, Pau Closas
Deception jamming has long been a significant threat to radar systems, interfering with search, acquisition, and tracking by introducing false information that diverts attention fr…
Continuously Optimizing Radar Placement with Model Predictive Path Integrals
Michael Potter, Shuo Tang, Paul Ghanem +8
Continuously optimizing sensor placement is essential for precise target localization in various military and civilian applications. While information theory has shown promise in o…
HyperTool: Beyond Step-Wise Tool Calls for Tool-Augmented Agents
Yaxin Du, Yifan Zhou, Yujie Ge +7
Tool-augmented LLM agents commonly rely on step-wise atomic tool calls, where each invocation, observation, and value transfer is exposed in the main reasoning trace. This creates…
Electron form factors in Basis Light-front Quantization
Lingdi Meng, Shuo Tang, Zhi Hu +4
In this paper, we evaluate the electromagnetic and gravitational form factors as well as the corresponding generalized parton distributions of the electron using the Basis Light-fr…
Parton Distribution Functions of Heavy Mesons on the Light Front
Jiangshan Lan, Chandan Mondal, Meijian Li +4
The parton distribution functions (PDFs) of heavy mesons are evaluated from their light-front wave functions, which are obtained from a basis light-front quantization in the leadin…
BrowseMaster: Towards Scalable Web Browsing via Tool-Augmented Programmatic Agent Pair
Xianghe Pang, Shuo Tang, Rui Ye +3
Effective information seeking in the vast and ever-growing digital landscape requires balancing expansive search with strategic reasoning. Current large language model (LLM)-based…
Performance Evaluations of Signed and Unsigned Noisy Approximate Quantum Fourier Arithmetic
Robert A. M. Basili, Wenyang Qian, Shiplu Sarker +6
The Quantum Fourier Transform (QFT) grants competitive advantages, especially in resource usage and circuit approximation, for performing arithmetic operations on quantum computers…
Semileptonic Decay of to and on the Light Front
Shuo Tang, Shaoyang Jia, Pieter Maris +1
We study the semileptonic decay of the to charmonia through the bottom-to-charm-quark electroweak current in the framework of basis light-front quantization. Explicitly…
Self-Alignment of Large Language Models via Monopolylogue-based Social Scene Simulation
Xianghe Pang, Shuo Tang, Rui Ye +4
Aligning large language models (LLMs) with human values is imperative to mitigate potential adverse effects resulting from their misuse. Drawing from the sociological insight that…
An LLM-powered Agentic Recommendation System for Connected TV Content Discovery
Lei Shi, Di Wang, Harry Tran +22
Recommendation systems, from traditional multi-stage to recent unified generative architectures, face challenges in incorporating diverse contextual signals, such as trending topic…
Scaling Reinforcement Learning for Content Moderation with Large Language Models
Hamed Firooz, Rui Liu, Yuchen Lu +15
Content moderation at scale remains one of the most pressing challenges in today's digital ecosystem, where billions of user- and AI-generated artifacts must be continuously evalua…
InfoMosaic-Bench: Evaluating Multi-Source Information Seeking in Tool-Augmented Agents
Yaxin Du, Yuanshuo Zhang, Xiyuan Yang +10
Information seeking is a fundamental requirement for humans. However, existing LLM agents rely heavily on open-web search, which exposes two fundamental weaknesses: online content…
SciMaster: Towards General-Purpose Scientific AI Agents, Part I. X-Master as Foundation: Can We Lead on Humanity's Last Exam?
Jingyi Chai, Shuo Tang, Rui Ye +8
The rapid advancements of AI agents have ignited the long-held ambition of leveraging them to accelerate scientific discovery. Achieving this goal requires a deep understanding of…
Trends and Progress in Nuclear and Hadron Physics: a straight or winding road
James P. Vary, Lekha Adhikari, Guangyao Chen +8
Quantitative calculations of the properties of hadrons and nuclei, with assessed uncertainties, have emerged as competitive with experimental measurements in a number of major case…
AutoRefine: From Trajectories to Reusable Expertise for Continual LLM Agent Refinement
Libin Qiu, Zhirong Gao, Junfu Chen +5
Large language model agents often fail to accumulate knowledge from experience, treating each task as an independent challenge. Recent methods extract experience as flattened textu…
OpenSeeker-v2: Pushing the Limits of Search Agents with Informative and High-Difficulty Trajectories
Yuwen Du, Rui Ye, Shuo Tang +4
Deep search capabilities have become an indispensable competency for frontier Large Language Model (LLM) agents, yet their development remains dominated by industrial giants. The t…
EvoMaster: A Foundational Evolving Agent Framework for Agentic Science at Scale
Xinyu Zhu, Yuzhu Cai, Zexi Liu +20
The convergence of large language models and agents is catalyzing a new era of scientific discovery: Agentic Science. While the scientific method is inherently iterative, existing…
Robust Collaborative Perception without External Localization and Clock Devices
Zixing Lei, Zhenyang Ni, Ruize Han +5
A consistent spatial-temporal coordination across multiple agents is fundamental for collaborative perception, which seeks to improve perception abilities through information excha…
Unrolled Graph Learning for Multi-Agent Collaboration
Enpei Zhang, Shuo Tang, Xiaowen Dong +2
Multi-agent learning has gained increasing attention to tackle distributed machine learning scenarios under constrictions of data exchanging. However, existing multi-agent learning…
ML-Agent: Reinforcing LLM Agents for Autonomous Machine Learning Engineering
Zexi Liu, Jingyi Chai, Xinyu Zhu +5
The emergence of large language model (LLM)-based agents has significantly advanced the development of autonomous machine learning (ML) engineering. However, the dominant prompt-ba…
OpenSeeker: Democratizing Frontier Search Agents by Fully Open-Sourcing Training Data
Yuwen Du, Rui Ye, Shuo Tang +4
Deep search capabilities have become an indispensable competency for frontier Large Language Model (LLM) agents, yet the development of high-performance search agents remains domin…
Label Hijacking in Track Consensus-Based Distributed Multi-Target Tracking
Helena Calatrava, Shuo Tang, Pau Closas
Distributed multi-target tracking (DMTT) in limited field-of-view (FoV) sensor networks commonly suffers from label inconsistency, whereby different nodes disagree on the identity…
MNO: Multiscale Neural Operator for 3D Computational Fluid Dynamics
Qinxuan Wang, Chuang Wang, Mingyu Zhang +4
Neural operators have emerged as a powerful data-driven paradigm for solving partial differential equations (PDEs), while their accuracy and scalability are still limited, particul…
MeteorPred: A Meteorological Multimodal Large Model and Dataset for Severe Weather Event Prediction
Shuo Tang, Jian Xu, Jiadong Zhang +5
Timely and accurate forecasts of severe weather events are essential for early warning and for constraining downstream analysis and decision-making. Since severe weather events pre…
On the light-front wave functions of quarkonia
Pieter Maris, Shaoyang Jia, Meijian Li +3
The light-front wave functions of hadrons allow us to calculate a wide range of physical observables; however, the wave functions themselves cannot be measured. We discuss recent r…
Heavy-Light Mesons on the Light Front
Shuo Tang, Yang Li, Pieter Maris +1
We study the heavy-light mesons within basis light-front quantization. The resulting mass spectra of , , , and agree reasonably well with experiments. We also pred…
FORT-Searcher: Synthesizing Shortcut-Resistant Search Tasks for Training Deep Search Agents
Jia Deng, Yimeng Chen, Xiaoqing Xiang +9
Training deep search agents requires verifiable questions whose answers remain unavailable until sufficient evidence has been acquired through search. Existing synthesis methods of…
Self-Evolving Multi-Agent Collaboration Networks for Software Development
Yue Hu, Yuzhu Cai, Yaxin Du +6
LLM-driven multi-agent collaboration (MAC) systems have demonstrated impressive capabilities in automatic software development at the function level. However, their heavy reliance…
On Parametric Misspecified Bayesian Cramér-Rao bound: An application to linear Gaussian systems
Shuo Tang, Gerald LaMountain, Tales Imbiriba +1
A lower bound is an important tool for predicting the performance that an estimator can achieve under a particular statistical model. Bayesian bounds are a kind of such bounds whic…
HVR-Met: A Hypothesis-Verification-Replanning Agentic System for Extreme Weather Diagnosis
Shuo Tang, Jiadong Zhang, Gengxian Zhou +11
While deep learning-based weather forecasting paradigms have made significant strides, addressing extreme weather diagnostics remains a formidable challenge. This gap exists primar…
AI-Aided Kalman Filters
Nir Shlezinger, Guy Revach, Anubhab Ghosh +7
The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing. These methods are used for state estimation of dynamic systems by relying on…
MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systems
Rui Ye, Shuo Tang, Rui Ge +4
LLM-based multi-agent systems (MAS) have shown significant potential in tackling diverse tasks. However, to design effective MAS, existing approaches heavily rely on manual configu…
Robust Interference Mitigation techniques for Direct Position Estimation
Haoqing Li, Shuo Tang, Peng Wu +1
Global Navigation Satellite System (GNSS) is pervasive in navigation and positioning applications, where precise position and time referencing estimations are required. Conventiona…
mesons and their properties on the light front
Shuo Tang, Yang Li, Pieter Maris +1
We investigate the unequal mass relativistic bound state system, the mesons, in a light-front Hamiltonian formalism. We adopt an effective Hamiltonian based on soft-wall ligh…
Hadron Spectra, Decays and Scattering Properties within Basis Light Front Quantization
James P. Vary, Lekha Adhikari, Guangyao Chen +10
We survey recent progress in calculating properties of the electron and hadrons within the Basis Light Front Quantization (BLFQ) approach. We include applications to electromagneti…
Synthesizing Post-Training Data for LLMs through Multi-Agent Simulation
Shuo Tang, Xianghe Pang, Zexi Liu +6
Post-training is essential for enabling large language models (LLMs) to follow human instructions. However, its effectiveness depends on high-quality instruction data, which is cha…
Memory Layer: Train the In-Model Cache for Recommendation Models
Liangyuan Na, Gufan Yin, Yixin Bao +19
Early ranking stages in recommendation systems precompute item embeddings and cache them in-model for scoring within strict latency constraints. Because this cache exists only at s…
Mining Useful General Data for Low-Resource Domain Adaptation
Pingjie Wang, Hongcheng Liu, Yusheng Liao +5
Adapting large language models (LLMs) to low-resource domains remains challenging due to the scarcity of domain-specific data. While in-domain data is limited, there exists a vast…