4.3k citations · 8.6k across the 159 of their papers we have counts for
21 papers · 1 filter
Comparing AI Agents to Cybersecurity Professionals in Real-World Penetration Testing
Justin W. Lin, Eliot Krzysztof Jones, Donovan Julian Jasper +10
We present the first comprehensive evaluation of AI agents against human cybersecurity professionals in a live enterprise environment. We evaluate ten cybersecurity professionals a…
Joint Distillation for Fast Likelihood Evaluation and Sampling in Flow-based Models
Xinyue Ai, Yutong He, Albert Gu +4
Log-likelihood evaluation enables important capabilities in generative models, including model comparison, certain fine-tuning objectives, and many downstream applications. Yet par…
Improved Mean Flows: On the Challenges of Fastforward Generative Models
Zhengyang Geng, Yiyang Lu, Zongze Wu +3
MeanFlow (MF) has recently been established as a framework for one-step generative modeling. However, its ``fastforward'' nature introduces key challenges in both the training obje…
Scalable Decision Making for Games of Imperfect Information
Samuel Sokota, Eugene Vinitsky, Hengyuan Hu +3
Real-world decision-making generally involves hidden information, that is, information that is unknown to one agent but possessed by another. Unfortunately, the presence of large a…
D-REX: A Benchmark for Detecting Deceptive Reasoning in Large Language Models
Satyapriya Krishna, Andy Zou, Rahul Gupta +6
The safety and alignment of Large Language Models (LLMs) are critical for their responsible deployment. Current evaluation methods predominantly focus on identifying and preventing…
Evaluating Language Model Reasoning about Confidential Information
Dylan Sam, Alexander Robey, Andy Zou +2
As language models are increasingly deployed as autonomous agents in high-stakes settings, ensuring that they reliably follow user-defined rules has become a critical safety concer…
Security Challenges in AI Agent Deployment: Insights from a Large Scale Public Competition
Andy Zou, Maxwell Lin, Eliot Jones +14
Recent advances have enabled LLM-powered AI agents to autonomously execute complex tasks by combining language model reasoning with tools, memory, and web access. But can these sys…
Adversarial Attacks on Robotic Vision Language Action Models
Eliot Krzysztof Jones, Alexander Robey, Andy Zou +5
The emergence of vision-language-action models (VLAs) for end-to-end control is reshaping the field of robotics by enabling the fusion of multimodal sensory inputs at the billion-p…
OpenUnlearning: Accelerating LLM Unlearning via Unified Benchmarking of Methods and Metrics
Vineeth Dorna, Anmol Mekala, Wenlong Zhao +4
Robust unlearning is crucial for safely deploying large language models (LLMs) in environments where data privacy, model safety, and regulatory compliance must be ensured. Yet the…
Accelerating Diffusion Planners in Offline RL via Reward-Aware Consistency Trajectory Distillation
Xintong Duan, Yutong He, Fahim Tajwar +3
Although diffusion models have achieved strong results in decision-making tasks, their slow inference speed remains a key limitation. While consistency models offer a potential sol…
Existing Large Language Model Unlearning Evaluations Are Inconclusive
Zhili Feng, Yixuan Even Xu, Alexander Robey +5
Machine unlearning aims to remove sensitive or undesired data from large language models. However, recent studies suggest that unlearning is often shallow, claiming that removed kn…
Looking beyond the next token
Abitha Thankaraj, Yiding Jiang, J. Zico Kolter +1
The structure of causal language model training assumes that each token can be accurately predicted from the previous context. This contrasts with humans' natural writing and reaso…
Compute-Optimal LLMs Provably Generalize Better With Scale
Marc Finzi, Sanyam Kapoor, Diego Granziol +4
Why do larger language models generalize better? To investigate this question, we develop generalization bounds on the pretraining objective of large language models (LLMs) in the…
Safety Pretraining: Toward the Next Generation of Safe AI
Pratyush Maini, Sachin Goyal, Dylan Sam +7
As large language models (LLMs) are increasingly deployed in high-stakes settings, the risk of generating harmful or toxic content remains a central challenge. Post-hoc alignment m…
Antidistillation Sampling
Yash Savani, Asher Trockman, Zhili Feng +5
Frontier models that generate extended reasoning traces inadvertently produce rich token sequences that can facilitate model distillation. Recognizing this vulnerability, model own…
Unnatural Languages Are Not Bugs but Features for LLMs
Keyu Duan, Yiran Zhao, Zhili Feng +9
Large Language Models (LLMs) have been observed to process non-human-readable text sequences, such as jailbreak prompts, often viewed as a bug for aligned LLMs. In this work, we pr…
Thinking Slow, Fast: Scaling Inference Compute with Distilled Reasoners
Daniele Paliotta, Junxiong Wang, Matteo Pagliardini +6
Recent advancements have demonstrated that the performance of large language models (LLMs) can be significantly enhanced by scaling computational resources at test time. A common s…
Training a Generally Curious Agent
Fahim Tajwar, Yiding Jiang, Abitha Thankaraj +4
Efficient exploration is essential for intelligent systems interacting with their environment, but existing language models often fall short in scenarios that require strategic inf…
Idiosyncrasies in Large Language Models
Mingjie Sun, Yida Yin, Zhiqiu Xu +2
In this work, we unveil and study idiosyncrasies in Large Language Models (LLMs) -- unique patterns in their outputs that can be used to distinguish the models. To do so, we consid…
Reevaluating Policy Gradient Methods for Imperfect-Information Games
Max Rudolph, Nathan Lichtle, Sobhan Mohammadpour +6
In the past decade, motivated by the putative failure of naive self-play deep reinforcement learning (DRL) in adversarial imperfect-information games, researchers have developed nu…
Predicting the Performance of Black-box LLMs through Follow-up Queries
Dylan Sam, Marc Finzi, J. Zico Kolter
Reliably predicting the behavior of language models -- such as whether their outputs are correct or have been adversarially manipulated -- is a fundamentally challenging task. This…