:: Library Catalog

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Xehetasun bibliografikoak
Egile Nagusiak:	Zheng, Congmin, Zhu, Jiachen, Ou, Zhuoying, Chen, Yuxiang, Zhang, Kangning, Shan, Rong, Zheng, Zeyu, Yang, Mengyue, Lin, Jianghao, Yu, Yong, Zhang, Weinan
Formatua:	Preprint
Argitaratua:	2025
Gaiak:	Computation and Language Artificial Intelligence
Sarrera elektronikoa:	https://arxiv.org/abs/2510.08049
Etiketak:	Etiketa erantsi Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!

Antzeko izenburuak

CoLD: Counterfactually-Guided Length Debiasing for Process Reward Models in Mathematical Reasoning
nork: Zheng, Congmin, et al.
Argitaratua: (2025)

Retrieval-Augmented Process Reward Model for Generalizable Mathematical Reasoning
nork: Zhu, Jiachen, et al.
Argitaratua: (2025)

Contexting as Recommendation: Evolutionary Collaborative Filtering for Context Engineering
nork: Zhu, Jiachen, et al.
Argitaratua: (2026)

Turing Test on Screen: A Benchmark for Mobile GUI Agent Humanization
nork: Zhu, Jiachen, et al.
Argitaratua: (2026)

Stop DDoS Attacking the Research Community with AI-Generated Survey Papers
nork: Lin, Jianghao, et al.
Argitaratua: (2025)

Evolutionary Perspectives on the Evaluation of LLM-Based AI Agents: A Comprehensive Survey
nork: Zhu, Jiachen, et al.
Argitaratua: (2025)

Position: Academic Conferences are Potentially Facing Denominator Gaming Caused by Fully Automated Scientific Agents
nork: Shan, Rong, et al.
Argitaratua: (2026)

Full-Stack Optimized Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation
nork: Shan, Rong, et al.
Argitaratua: (2025)

An Automatic Graph Construction Framework based on Large Language Models for Recommendation
nork: Shan, Rong, et al.
Argitaratua: (2024)

Lifelong Personalized Low-Rank Adaptation of Large Language Models for Recommendation
nork: Zhu, Jiachen, et al.
Argitaratua: (2024)

A Survey on Diffusion Models for Recommender Systems
nork: Lin, Jianghao, et al.
Argitaratua: (2024)

Linking Process to Outcome: Conditional Reward Modeling for LLM Reasoning
nork: Zhang, Zheng, et al.
Argitaratua: (2025)

Large Language Models Make Sample-Efficient Recommender Systems
nork: Lin, Jianghao, et al.
Argitaratua: (2024)

Adaptive Milestone Reward for GUI Agents
nork: Zheng, Congmin, et al.
Argitaratua: (2026)

A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challenges
nork: Xi, Yunjia, et al.
Argitaratua: (2025)

Better Process Supervision with Bi-directional Rewarding Signals
nork: Chen, Wenxiang, et al.
Argitaratua: (2025)

ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation
nork: Lin, Jianghao, et al.
Argitaratua: (2023)

Learning ID-free Item Representation with Token Crossing for Multimodal Recommendation
nork: Zhang, Kangning, et al.
Argitaratua: (2024)

ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction
nork: Lin, Jianghao, et al.
Argitaratua: (2023)

Superplatforms Have to Attack AI Agents
nork: Lin, Jianghao, et al.
Argitaratua: (2025)

M-scan: A Multi-Scenario Causal-driven Adaptive Network for Recommendation
nork: Zhu, Jiachen, et al.
Argitaratua: (2024)

Outcome Accuracy is Not Enough: Aligning the Reasoning Process of Reward Models
nork: Wang, Binghai, et al.
Argitaratua: (2026)

Towards Efficient and Effective Unlearning of Large Language Models for Recommendation
nork: Wang, Hangyu, et al.
Argitaratua: (2024)

Entropy-Regularized Process Reward Model
nork: Zhang, Hanning, et al.
Argitaratua: (2024)

How Can Recommender Systems Benefit from Large Language Models: A Survey
nork: Lin, Jianghao, et al.
Argitaratua: (2023)

MemoCRS: Memory-enhanced Sequential Conversational Recommender Systems with Large Language Models
nork: Xi, Yunjia, et al.
Argitaratua: (2024)

MassTool: A Multi-Task Search-Based Tool Retrieval Framework for Large Language Models
nork: Lin, Jianghao, et al.
Argitaratua: (2025)

Generative Representational Learning of Foundation Models for Recommendation
nork: Zhou, Zheli, et al.
Argitaratua: (2025)

Play to Your Strengths: Collaborative Intelligence of Conventional Recommender Models and Large Language Models
nork: Xi, Yunjia, et al.
Argitaratua: (2024)

Rewarding the Scientific Process: Process-Level Reward Modeling for Agentic Data Analysis
nork: Qiu, Zhisong, et al.
Argitaratua: (2026)

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation
nork: Xi, Yunjia, et al.
Argitaratua: (2025)

The Lessons of Developing Process Reward Models in Mathematical Reasoning
nork: Zhang, Zhenru, et al.
Argitaratua: (2025)

StepORLM: A Self-Evolving Framework With Generative Process Supervision For Operations Research Language Models
nork: Zhou, Chenyu, et al.
Argitaratua: (2025)

A Comprehensive Survey on Retrieval Methods in Recommender Systems
nork: Huang, Junjie, et al.
Argitaratua: (2024)

Hierarchical Process Reward Models are Symbolic Vision Learners
nork: Zhang, Shan, et al.
Argitaratua: (2025)

FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Prediction
nork: Wang, Hangyu, et al.
Argitaratua: (2023)

The Bidirectional Process Reward Model
nork: Zhang, Lingyin, et al.
Argitaratua: (2025)

SINKT: A Structure-Aware Inductive Knowledge Tracing Model with Large Language Model
nork: Fu, Lingyue, et al.
Argitaratua: (2024)

DREAM: A Dual Representation Learning Model for Multimodal Recommendation
nork: Zhang, Kangning, et al.
Argitaratua: (2024)

WebArbiter: A Principle-Guided Reasoning Process Reward Model for Web Agents
nork: Zhang, Yao, et al.
Argitaratua: (2026)