1 citations · 2 across the 16 of their papers we have counts for
17 papers
Understanding and Evaluating Claw-like Agent Security Through a Computer-Systems Lens
Peizhi Niu, Wenjie Qu, Shangding Gu +14
Claw-like AI agents (e.g., OpenClaw) are always-on processes with persistent access to credentials, files, tools, and external services. They take on system-level responsibilities…
Adaptive Latent Agentic Reasoning
Dongwon Jung, Peng Shi, Yi Zhang +2
Large reasoning models improve performance by generating extended chain-of-thought (CoT) reasoning, but this behavior becomes inefficient when applied to LLM agents. Current LLM ag…
GTA: Generating Long-Horizon Tasks for Web Agents at Scale
Tenghao Huang, Kung-Hsiang Huang, Prafulla Kumar Choubey +4
Web agents, which couple language models with browsing and tool-use capabilities, show promise as open web assistants. Yet progress is increasingly limited by the lack of scalable,…
Taming Extreme Tokens: Covariance-Aware GRPO with Gaussian-Kernel Advantage Reweighting
Cheng Wang, Qin Liu, Wenxuan Zhou +1
Group Relative Policy Optimization (GRPO) has emerged as a promising approach for improving the reasoning capabilities of large language models. However, it struggles to effectivel…
ModelLens: Finding the Best for Your Task from Myriads of Models
Rui Cai, Weijie Jacky Mo, Xiaofei Wen +5
The open-source model ecosystem now contains hundreds of thousands of pretrained models, yet picking the best model for a new dataset is increasingly infeasible: new models and unb…
Robust and Efficient Guardrails with Latent Reasoning
Siddharth Sai, Xiaofei Wen, Muhao Chen
Maintaining the safety of large language models (LLMs) is crucial as they are increasingly deployed in real-world applications. Existing safety guardrails typically rely on single-…
DebugLM: Learning Traceable Training Data Provenance for LLMs
Wenjie Jacky Mo, Qin Liu, Xiaofei Wen +3
Large language models (LLMs) are trained through multi-stage pipelines over heterogeneous data sources, yet developers lack a principled way to pinpoint the specific data responsib…
OmniGuard: Unified Omni-Modal Guardrails with Deliberate Reasoning
Boyu Zhu, Xiaofei Wen, Wenjie Jacky Mo +4
Omni-modal Large Language Models (OLLMs) that process text, images, videos, and audio introduce new challenges for safety and value guardrails in human-AI interaction. Prior guardr…
FRIEDA: Benchmarking Multi-Step Cartographic Reasoning in Vision-Language Models
Jiyoon Pyo, Yuankun Jiao, Dongwon Jung +11
Cartographic reasoning is the skill of interpreting geographic relationships by aligning legends, map scales, compass directions, map texts, and geometries across one or more map i…
Be My Eyes: Extending Large Language Models to New Modalities Through Multi-Agent Collaboration
James Y. Huang, Sheng Zhang, Qianchu Liu +5
Large Language Models (LLMs) have demonstrated remarkable capabilities in challenging, knowledge-intensive reasoning tasks. However, extending LLMs to perceive and reason over a ne…
OmniStruct: Universal Text-to-Structure Generation across Diverse Schemas
James Y. Huang, Wenxuan Zhou, Nan Xu +5
The ability of Large Language Models (LLMs) to generate structured outputs that follow arbitrary schemas is crucial to a wide range of downstream tasks that require diverse structu…
Optimizing Diversity and Quality through Base-Aligned Model Collaboration
Yichen Wang, Chenghao Yang, Tenghao Huang +3
Alignment has greatly improved large language models (LLMs)' output quality at the cost of diversity, yielding highly similar outputs across generations, especially in open-ended g…
ArenaBencher: Automatic Benchmark Evolution via Multi-Model Competitive Evaluation
Qin Liu, Jacob Dineen, Yuxi Huang +4
Benchmarks are central to measuring the capabilities of large language models and guiding model development, yet widespread data leakage from pretraining corpora undermines their v…
Less Diverse, Less Safe: The Indirect But Pervasive Risk of Test-Time Scaling in Large Language Models
Shahriar Kabir Nahin, Hadi Askari, Muhao Chen +1
Test-Time Scaling (TTS) improves LLM reasoning by exploring multiple candidate responses and then operating over this set to find the best output. A tacit premise behind TTS is tha…
Learning Efficient Guardrails for Compliance
Xiaofei Wen, Wenjie Jacky Mo, Yanan Xie +2
Autonomous web agents are increasingly deployed for long-horizon tasks, yet their ability to adhere to real-world policies remains critically underexplored compared to standard saf…
False Sense of Security: Why Probing-based Malicious Input Detection Fails to Generalize
Cheng Wang, Zeming Wei, Qin Liu +1
Large Language Models (LLMs) can comply with harmful instructions, raising serious safety concerns despite their impressive capabilities. Recent work has leveraged probing-based ap…
RedCoder: Automated Multi-Turn Red Teaming for Code LLMs
Wenjie Jacky Mo, Qin Liu, Xiaofei Wen +5
Large Language Models (LLMs) for code generation (i.e., Code LLMs) have demonstrated impressive capabilities in AI-assisted software development and testing. However, recent studie…