9 papers
A Threshold Exceedance Framework for CBRN Uplift Evaluation in Frontier Language Models
Rahul Gupta, Abhinav Mohanty, Payal Motwani +8
As frontier language models advance, policymakers and model developers need methods for assessing whether model access materially increases a non-expert actor's ability to plan hig…
When Actions Go Off-Task: Detecting and Correcting Misaligned Actions in Computer-Use Agents
Yuting Ning, Jaylen Jones, Zhehao Zhang +5
Computer-use agents (CUAs) have made tremendous progress in the past year, yet they still frequently produce misaligned actions that deviate from the user's original intent. Such m…
Evaluating Nova 2.0 Lite model under Amazon's Frontier Model Safety Framework
Satyapriya Krishna, Matteo Memelli, Tong Wang +5
Amazon published its Frontier Model Safety Framework (FMSF) as part of the Paris AI summit, following which we presented a report on Amazon's Premier model. In this report, we pres…
Embedded AI Companion System on Edge Devices
Rahul Gupta, Stephen D. H. Hsu
Computational resource constraints on edge devices make it difficult to develop a fully embedded AI companion system with a satisfactory user experience. AI companion and memory sy…
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…
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…
Diagnosing Memorization in Chain-of-Thought Reasoning, One Token at a Time
Huihan Li, You Chen, Siyuan Wang +4
Large Language Models (LLMs) perform well on reasoning benchmarks but often fail when inputs alter slightly, raising concerns about the extent to which their success relies on memo…
Evaluating the Critical Risks of Amazon's Nova Premier under the Frontier Model Safety Framework
Satyapriya Krishna, Ninareh Mehrabi, Abhinav Mohanty +4
Nova Premier is Amazon's most capable multimodal foundation model and teacher for model distillation. It processes text, images, and video with a one-million-token context window,…
An End-to-End Assurance Framework for AI/ML Workloads in Datacenters
Jit Gupta, Tarun Banka, Rahul Gupta +2
Modern machine learning workloads such as large language model training, fine-tuning jobs are highly distributed and span across hundreds of systems with multiple GPUs. Job complet…