23 citations · 35 across the 40 of their papers we have counts for
43 papers
CyberArena: Benchmarking AI Agents in Live Symmetric Attack-and-Defense Combat
Bangya Liu, Jianhong Tu, Yueyang Pan +18
AI agents are demonstrating increasingly strong offensive and defensive cybersecurity capabilities in the real world. Current benchmarks mostly evaluate agents in isolation against…
ObsSpec: Accelerating Agentic Reinforcement Learning with Observation Speculation
Kyle Montgomery, Wang Zhang, Chenguang Wang
Tool-using agents alternate between model generation and tool execution, waiting for each tool call to complete before starting the next generation. This sequential execution is co…
READY or Not: Reliable Enterprise Agent Deployment
Veronica Chatrath, Bryan Zhu, Jingxuan Fan +15
An AI agent can perform well on benchmarks and still be unsuitable for deployment. Existing AI-agent benchmarks measure whether an agent can complete realistic professional work, w…
Tunable Tool-Call Rates in LLM Agents via Representation Steering
Yuqi Chen, Vincent Siu, Yang Liu +2
Deciding whether to call a tool is a core competence of an LLM agent, and a costly one to get wrong: needless calls add latency, accrue cost, and may trigger irreversible side effe…
How Can Rhetoric Reward-Hack AI Reviewers? Dissecting Rhetorical Sensitivity in AI-Based Peer Review
Ming Li, Chenguang Wang, Xirui Li +5
As large language models increasingly participate in scientific evaluation, we investigate a potential form of reward hacking: how rhetorical choices shape AI-review judgments when…
CliniCARE-Bench: Clinical Calibrated Audit of Medical Reasoning in EHR
Veronica Chatrath, Bryan Zhu, George Pu +16
Large language models perform strongly on medical knowledge benchmarks, but reliable clinical deployment requires agents to conduct defensible investigations over heterogeneous, lo…
Agent Security Needs Redefinition through a Holistic Framework
Vincent Siu, Jingxuan He, Kyle Montgomery +3
Agent security is widely treated as a question about action content. Defenses ask whether an instruction looks malicious. Benchmarks ask whether an agent performs a harmful soundin…
CyberGym-E2E: Scalable Real-World Benchmark for AI Agents' End-to-End Cybersecurity Capabilities
Tianneng Shi, Robin Rheem, Dongwei Jiang +13
AI has the potential to transform cybersecurity by enabling systems that can autonomously detect, analyze, and remediate software vulnerabilities. However, existing cybersecurity e…
Controlling Tool Use with Heading-Specific Activation Steering
Yuqi Chen, Vincent Siu, Yang Liu +2
Tool-augmented large language models extend their capabilities beyond parametric knowledge through external tools, but tend to invoke them unnecessarily. We investigate whether too…
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…
AgentBeats: Agentifying Agent Assessment for Openness, Standardization, and Reproducibility
Xiaoyuan Liu, Jianhong Tu, Yuqi Chen +26
Agent systems are advancing quickly across domains, but their evaluation remains fragmented. Most benchmarks rely on fixed, LLM-centric harnesses that require heavy integration, cr…
Agents' Last Exam
Yiyou Sun, Xinyang Han, Weichen Zhang +355
Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional d…
MIRAGE: A Polarity-Flipping Encoding Subspace in LLM Agents
Pratibha Revankar, Kargi Chauhan, Jihye Kim +3
When LLM agents are coerced into covertly encoding sensitive data (Base64, ROT13, acrostic, synonym chains, and beyond), the resulting outputs evade output-side detection but the u…
Ishigaki-IDS: An Open-Weight Verifier-Aware Model for Information Delivery Specification Drafting in Building Information Modeling
Ryo Kanazawa, Koyo Hidaka, Teppei Miyamoto +9
Building Information Modeling (BIM) projects require information requirements to be described as machine-checkable Information Delivery Specification (IDS) files in order to verify…
Cognitive Episodes in LLM Reasoning Traces Enable Interpretable Human Item Difficulty Prediction
Chenguang Wang, Ming Li, Xinyue Zeng +4
Predicting human item difficulty is central to educational assessment, where reliable estimates support fairness and effective test construction. Existing methods often depend on c…
ChainWorld: Composing Long-Horizon Desktop Workloads from Atomic OSWorld Tasks
Vincent Siu, Manasi Sharma, Dawn Song +3
Computer use agents are evaluated almost exclusively on atomic desktop tasks, but realistic desktop work requires sustaining state across multiple objectives. We study this gap wit…
LLMs Struggle to Measure What Distinguishes Students of Different Proficiency Levels: A Study of Item Discrimination in Reading Comprehension Assessment
Han Chen, Ming Li, Chenguang Wang +4
Existing work on LLM-based educational assessment has focused largely on item difficulty, but difficulty alone does not indicate whether an item meaningfully distinguishes higher-…
ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents
Lei Ding, Bin He, Chenguang Wang +1
Proactive task-oriented agents must autonomously anticipate user needs, identify actionable opportunities, and trigger software actions at appropriate moments - fundamentally shift…
Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements
Ryo Kanazawa, Koyo Hidaka, Teppei Miyamoto +9
Building Information Modeling (BIM) projects increasingly use Information Delivery Specification (IDS) to formalize information requirements in a machine-checkable XML format. Beca…
Peer-Preservation in Frontier Models
Yujin Potter, Nicholas Crispino, Vincent Siu +2
Recent work has found that frontier AI models can exhibit misaligned behaviors in pursuit of assigned goals. We demonstrate that models can also exhibit misaligned behaviors in def…
A Framework for Formalizing LLM Agent Security
Vincent Siu, Jingxuan He, Kyle Montgomery +4
Security in LLM agents is inherently contextual. For example, the same action taken by an agent may represent legitimate behavior or a security violation depending on whose instruc…
ALPBench: A Benchmark for Attribution-level Long-term Personal Behavior Understanding
Lu Ren, Junda She, Xinchen Luo +23
Recent advances in large language models have highlighted their potential for personalized recommendation, where accurately capturing user preferences remains a key challenge. Leve…
LLM CHESS: Benchmarking Reasoning and Instruction-Following in LLMs through Chess
Sai Kolasani, Maxim Saplin, Nicholas Crispino +5
We introduce LLM CHESS, an evaluation framework designed to probe the generalization of reasoning and instruction-following abilities in large language models (LLMs) through extend…
VMDT: Decoding the Trustworthiness of Video Foundation Models
Yujin Potter, Zhun Wang, Nicholas Crispino +11
As foundation models become more sophisticated, ensuring their trustworthiness becomes increasingly critical; yet, unlike text and image, the video modality still lacks comprehensi…