1 citations · 2 across the 16 of their papers we have counts for
17 papers
LemonHarness Technical Report
Kailong Ren, Fubo Sun, Jiachen Liu +18
As large language model (LLM) agents are applied to longer tasks, they increasingly modify workspace state across multiple rounds of iteration. However, agents typically observe on…
Lemon Agent Technical Report
Haipeng Jiang, Kailong Ren, Zimo Yin +17
Recent advanced LLM-powered agent systems have exhibited their remarkable capabilities in tackling complex, long-horizon tasks. Nevertheless, they still suffer from inherent limita…
Optical diffraction neural networks assisted computational ghost imaging through dynamic scattering media
Yue-Gang Li, Ze Zheng, Jun-jie Wang +4
Ghost imaging leverages a single-pixel detector with no spatial resolution to acquire object echo intensity signals, which are correlated with illumination patterns to reconstruct…
Discovering autonomous quantum error correction via deep reinforcement learning
Yue Yin, Tailong Xiao, Xiaoyang Deng +3
Quantum error correction is essential for fault-tolerant quantum computing. However, standard methods relying on active measurements may introduce additional errors. Autonomous qua…
Real-time imaging through dynamic scattering media enabled by fixed optical modulations
Yuegang Li, Junjie Wang, Tailong Xiao +5
Dynamic scattering remains a significant challenge to the practical deployment of anti-scattering imaging. Existing methods, such as transmission matrix measurements, iterative wav…
Bridging Search and Recommendation through Latent Cross Reasoning
Teng Shi, Weicong Qin, Weijie Yu +4
Search and recommendation (S&R) are fundamental components of modern online platforms, yet effectively leveraging search behaviors to improve recommendation remains a challenging p…
Benefit from Rich: Tackling Search Interaction Sparsity in Search Enhanced Recommendation
Teng Shi, Weijie Yu, Xiao Zhang +3
In modern online platforms, search and recommendation (S&R) often coexist, offering opportunities for performance improvement through search-enhanced approaches. Existing studies s…
Similarity = Value? Consultation Value Assessment and Alignment for Personalized Search
Weicong Qin, Yi Xu, Weijie Yu +6
Personalized search systems in e-commerce platforms increasingly involve user interactions with AI assistants, where users consult about products, usage scenarios, and more. Levera…
ORMind: A Cognitive-Inspired End-to-End Reasoning Framework for Operations Research
Zhiyuan Wang, Bokui Chen, Yinya Huang +4
Operations research (OR) is widely deployed to solve critical decision-making problems with complex objectives and constraints, impacting manufacturing, logistics, finance, and hea…
Decoding Recommendation Behaviors of In-Context Learning LLMs Through Gradient Descent
Yi Xu, Weicong Qin, Weijie Yu +3
Recently, there has been a growing trend in utilizing large language models (LLMs) for recommender systems, referred to as LLMRec. A notable approach within this trend is not to fi…
Towards Heisenberg limit without critical slowing down via quantum reinforcement learning
Hang Xu, Tailong Xiao, Jingzheng Huang +3
Critical ground states of quantum many-body systems have emerged as vital resources for quantum-enhanced sensing. Traditional methods to prepare these states often rely on adiabati…
Instruct-of-Reflection: Enhancing Large Language Models Iterative Reflection Capabilities via Dynamic-Meta Instruction
Liping Liu, Chunhong Zhang, Likang Wu +4
Self-reflection for Large Language Models (LLMs) has gained significant attention. Existing approaches involve models iterating and improving their previous responses based on LLMs…
MAPS: Motivation-Aware Personalized Search via LLM-Driven Consultation Alignment
Weicong Qin, Yi Xu, Weijie Yu +5
Personalized product search aims to retrieve and rank items that match users' preferences and search intent. Despite their effectiveness, existing approaches typically assume that…
Paragon: Parameter Generation for Controllable Multi-Task Recommendation
Chenglei Shen, Jiahao Zhao, Xiao Zhang +3
Commercial recommender systems face the challenge that task requirements from platforms or users often change dynamically (e.g., varying preferences for accuracy or diversity). Ide…
MoRE: A Mixture of Reflectors Framework for Large Language Model-Based Sequential Recommendation
Weicong Qin, Yi Xu, Weijie Yu +5
Large language models (LLMs) have emerged as a cutting-edge approach in sequential recommendation, leveraging historical interactions to model dynamic user preferences. Current met…
MobileExperts: A Dynamic Tool-Enabled Agent Team in Mobile Devices
Jiayi Zhang, Chuang Zhao, Yihan Zhao +3
The attainment of autonomous operations in mobile computing devices has consistently been a goal of human pursuit. With the development of Large Language Models (LLMs) and Visual L…
A Survey of Controllable Learning: Methods and Applications in Information Retrieval
Chenglei Shen, Xiao Zhang, Teng Shi +3
Controllability has become a crucial aspect of trustworthy machine learning, enabling learners to meet predefined targets and adapt dynamically at test time without requiring retra…