180 citations · 286 across the 91 of their papers we have counts for
15 papers · 1 filter
AOS-Bench: Can LLM Agents Manage Dynamic Workloads?
Erle Zhu, Genrui Zhang, Keyang Hu +2
Coding agents are moving from handling one request at a time to serving as persistent assistants to which users hand a stream of work: a build, a data-processing job and a bug fix…
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…