180 citations · 286 across the 91 of their papers we have counts for
96 papers
The User Asks, Platforms Compete: How Agentic Recommendation Markets Take Shape
Deyao Hong, Kehan Zheng, Qian Li +3
Online recommendation has traditionally taken place after a user enters a platform, which determines the candidate pool and the ranking shown to the user. LLM-based user agents ena…
RUBAS: Rubric-Based Reinforcement Learning for Agent Safety
Xian Qi Loye, Qinglin Su, Zhexin Zhang +5
The evolution of LLMs into tool-enabled agents creates a new class of safety challenges associated with real-world execution rather than simple text generation. Existing alignment…
SynCred-Bench: Benchmarking Synthetic Credibility in AI-Generated Visual Misinformation
Junxiao Yang, Minghao Zhang, Xiaoce Wang +4
Recent generative models can now produce visual artifacts with realistic embedded text and layouts, creating a new misinformation threat: synthetic credibility. We introduce SYNCRE…
SkillEvolver: Skill Learning as a Meta-Skill
Genrui Zhang, Erle Zhu, Jinfeng Zhou +2
Agent skills today are static artifact: authored once -- by human curation or one-shot generation from parametric knowledge -- and then consumed unchanged, with no mechanism to imp…
You Live More Than Once: Towards Hierarchical Skill Meta-Evolving
Xujun Li, Kehan Zheng, Mingyuan Zhao +7
Test-time skill evolving is regarded as a new paradigm for enhancing deployed agentic systems. Existing works mainly focus on hard-coded skill evolving strategies or parametric lea…
LASA: Language-Agnostic Semantic Alignment at the Semantic Bottleneck for LLM Safety
Junxiao Yang, Haoran Liu, Jinzhe Tu +9
Large language models (LLMs) often demonstrate strong safety performance in high-resource languages, yet exhibit severe vulnerabilities when queried in low-resource languages. We a…
HoWToBench: Holistic Evaluation for LLM's Capability in Human-level Writing using Tree of Writing
Andrew Zhuoer Feng, Cunxiang Wang, Yu Luo +7
Evaluating the writing capabilities of large language models (LLMs) remains a significant challenge due to the multidimensional nature of writing skills and the limitations of exis…
IF-RewardBench: Benchmarking Judge Models for Instruction-Following Evaluation
Bosi Wen, Yilin Niu, Cunxiang Wang +5
Instruction-following is a foundational capability of large language models (LLMs), with its improvement hinging on scalable and accurate feedback from judge models. However, the r…
RLAR: An Agentic Reward System for Multi-task Reinforcement Learning on Large Language Models
Andrew Zhuoer Feng, Cunxiang Wang, Bosi Wen +4
Large language model alignment via reinforcement learning depends critically on reward function quality. However, static, domain-specific reward models are often costly to train an…
RAVEL: Reasoning Agents for Validating and Evaluating LLM Text Synthesis
Andrew Zhuoer Feng, Cunxiang Wang, Yu Luo +9
Large Language Models have evolved from single-round generators into long-horizon agents, capable of complex text synthesis scenarios. However, current evaluation frameworks lack t…
Grounding LLMs in Scientific Discovery via Embodied Actions
Bo Zhang, Jinfeng Zhou, Yuxuan Chen +3
Large Language Models (LLMs) have shown significant potential in scientific discovery but struggle to bridge the gap between theoretical reasoning and verifiable physical simulatio…
GLM-5: from Vibe Coding to Agentic Engineering
GLM-5-Team, :, Aohan Zeng +184
We present GLM-5, a next-generation foundation model designed to transition the paradigm of vibe coding to agentic engineering. Building upon the agentic, reasoning, and coding (AR…
Reasoning to Rank: An End-to-End Solution for Exploiting Large Language Models for Recommendation
Kehan Zheng, Deyao Hong, Qian Li +4
Recommender systems are tasked to infer users' evolving preferences and rank items aligned with their intents, which calls for in-depth reasoning beyond pattern-based scoring. Rece…
The Missing Half: Unveiling Training-time Implicit Safety Risks Beyond Deployment
Zhexin Zhang, Yida Lu, Junfeng Fang +8
Safety risks of AI models have been widely studied at deployment time, such as jailbreak attacks that elicit harmful outputs. In contrast, safety risks emerging during training rem…
Trust-Region Adaptive Policy Optimization
Mingyu Su, Jian Guan, Yuxian Gu +2
Post-training methods, especially Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), play an important role in improving large language models' (LLMs) complex reasoning…
IF-CRITIC: Towards a Fine-Grained LLM Critic for Instruction-Following Evaluation
Bosi Wen, Yilin Niu, Cunxiang Wang +6
Instruction-following is a fundamental ability of Large Language Models (LLMs), requiring their generated outputs to follow multiple constraints imposed in input instructions. Nume…
Glyph: Scaling Context Windows via Visual-Text Compression
Jiale Cheng, Yusen Liu, Xinyu Zhang +11
Large language models (LLMs) increasingly rely on long-context modeling for tasks such as document understanding, code analysis, and multi-step reasoning. However, scaling context…
AgentRL: Scaling Agentic Reinforcement Learning with a Multi-Turn, Multi-Task Framework
Hanchen Zhang, Xiao Liu, Bowen Lv +11
Recent advances in large language models (LLMs) have sparked growing interest in building generalist agents that can learn through online interactions. However, applying reinforcem…
Data-Efficient RLVR via Off-Policy Influence Guidance
Erle Zhu, Dazhi Jiang, Yuan Wang +8
Data selection is a critical aspect of Reinforcement Learning with Verifiable Rewards (RLVR) for enhancing the reasoning capabilities of large language models (LLMs). Current data…
Think Socially via Cognitive Reasoning
Jinfeng Zhou, Zheyu Chen, Shuai Wang +4
LLMs trained for logical reasoning excel at step-by-step deduction to reach verifiable answers. However, this paradigm is ill-suited for navigating social situations, which induce…
When Smiley Turns Hostile: Interpreting How Emojis Trigger LLMs' Toxicity
Shiyao Cui, Xijia Feng, Yingkang Wang +6
Emojis are globally used non-verbal cues in digital communication, and extensive research has examined how large language models (LLMs) understand and utilize emojis across context…
GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models
5 Team, Aohan Zeng, Xin Lv +167
We present GLM-4.5, an open-source Mixture-of-Experts (MoE) large language model with 355B total parameters and 32B activated parameters, featuring a hybrid reasoning method that s…
JPS: Jailbreak Multimodal Large Language Models with Collaborative Visual Perturbation and Textual Steering
Renmiao Chen, Shiyao Cui, Xuancheng Huang +7
Jailbreak attacks against multimodal large language Models (MLLMs) are a significant research focus. Current research predominantly focuses on maximizing attack success rate (ASR),…
InvestAlign: Overcoming Data Scarcity in Aligning Large Language Models with Investor Decision-Making Processes under Herd Behavior
Huisheng Wang, Zhuoshi Pan, Hangjing Zhang +3
Aligning Large Language Models (LLMs) with investor decision-making processes under herd behavior is a critical challenge in behavioral finance, which grapples with a fundamental l…