activity
20222026
most citedDetecting LLM Hallucination Through Layer-wise Information Deficiency: Analysis of Ambiguous Prompts and Unanswerable Questions

2 citations · 3 across the 15 of their papers we have counts for

collaborators
Showing 2026Show all

9 papers · 1 filter

cs.LG2026

Interpreting Model Reasoning through Behavioral Counterfactuals

Shawn Im, James Oldfield, Adel Bibi +1

Faithfully understanding the internal process that occurs as models form their chain of thought is an important problem with the rise in model capabilities. However, this remains c…

cs.LG2026

How Much Do Activation Monitors Benefit from Original Model Computations?

James Oldfield, Shawn Im, Jay J Culligan +3

One powerful way to safeguard AI systems is to monitor their internal computations--where signs of undesirable behavior might be more easily detectable than from models' inputs and…

cs.MA2026

BazaarBench: Delegation Safety in Decentralized C2C Marketplaces Run by LLM Agents

Ziyan Wang, Shuqing Shi, James Oldfield +5

In decentralized consumer-to-consumer (C2C) marketplaces, people list goods, negotiate with strangers, and rate one another, so trust rests on reputation. Large language model (LLM…

cs.AI2026

Stealth Apart, Harm Together: Skill Cascading Attacks on Skill-Based Agent Systems

Zihao Zhu, Siwei Lyu, Adel Bibi +1

A skill is a modular package of natural-language instructions, executable scripts, and reference resources that an agent can load at runtime to extend its capabilities for a specif…

cs.CR2026

The Authorization-Execution Gap Is a Major Safety and Security Problem in Open-World Agents

Baoyuan Wu, Qingshan Liu, Adel Bibi +2

This position paper argues that the Authorization-Execution Gap (AEG) is a major safety and security problem in open-world agents. The AEG is the divergence between what a principa…

cs.LG2026

Safer by Diffusion, Broken by Context: Diffusion LLM's Safety Blessing and Its Failure Mode

Zeyuan He, Yupeng Chen, Lang Lin +7

Diffusion large language models (D-LLMs) offer an alternative to autoregressive LLMs (AR-LLMs) and have demonstrated advantages in generation efficiency. Beyond the utility benefit…

cs.AI2026

OMNI-LEAK: Orchestrator Multi-Agent Network Induced Data Leakage

Akshat Naik, Jay Culligan, Yarin Gal +4

As Large Language Model (LLM) agents become more capable, their coordinated use in the form of multi-agent systems is anticipated to emerge as a practical paradigm. Prior work has…

cs.LG2026

Reliable and Responsible Foundation Models: A Comprehensive Survey

Xinyu Yang, Junlin Han, Rishi Bommasani +49

Foundation models, including Large Language Models (LLMs), Multimodal Large Language Models (MLLMs), Image Generative Models (i.e, Text-to-Image Models and Image-Editing Models), a…

cs.LG2026

The Alignment Curse: Modality Alignment Supercharges Audio Attacks via Text Transfer

Yupeng Chen, Junchi Yu, Aoxi Liu +3

Recent advances in end-to-end trained omni-models have substantially improved audio capabilities by strengthening text-audio modality alignment. However, whether such alignment ina…