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Boyi Wei

7 accepted papers

2026

Holistic Agent Leaderboard: The Missing Infrastructure for AI Agent Evaluation

ICLR 2026poster

AI agents have been developed for complex real-world tasks from coding to customer service. But AI agent evaluations suffer from many challenges that undermine our understanding of how well agents really work (Figure 1). We introduce the Holistic Agent Leaderboard (HAL) to address these challenges.…

Cited by 0SourcecodeScholar
2026

Your Agent May Misevolve: Emergent Risks in Self-evolving LLM Agents

ICLR 2026poster

Advances in Large Language Models (LLMs) have enabled a new class of \textbf{\textit{self-evolving agents}} that autonomously improve through interaction with the environment, demonstrating strong capabilities. However, self-evolution also introduces novel risks overlooked by current safety research…

Cited by 0SourceScholar
2025

Dynamic Risk Assessments for Offensive Cybersecurity Agents

NeurIPS 2025poster

Foundation models are increasingly becoming better autonomous programmers, raising the prospect that they could also automate dangerous offensive cyber‑operations. Current frontier model audits probe the cybersecurity risks of such agents, but most fail to account for the degrees of freedom availabl…

Cited by 0SourcecodeScholar
2025

On Evaluating the Durability of Safeguards for Open-Weight LLMs

ICLR 2025poster

Many stakeholders---from model developers to policymakers---seek to minimize the risks of large language models (LLMs). Key to this goal is whether technical safeguards can impede the misuse of LLMs, even when models are customizable via fine-tuning or when model weights are openly available. Severa…

2025

SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal

ICLR 2025poster

Evaluating aligned large language models' (LLMs) ability to recognize and reject unsafe user requests is crucial for safe, policy-compliant deployments. Existing evaluation efforts, however, face three limitations that we address with **SORRY-Bench**, our proposed benchmark. **First**, existing meth…

2024

Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications

ICML 2024poster

Large language models (LLMs) show inherent brittleness in their safety mechanisms, as evidenced by their susceptibility to jailbreaking and even non-malicious fine-tuning. This study explores this brittleness of safety alignment by leveraging pruning and low-rank modifications. We develop methods to…

Cited by 98SourcePDFScholar
2024

Evaluating Copyright Takedown Methods for Language Models

NeurIPS 2024poster

Language models (LMs) derive their capabilities from extensive training on diverse data, including copyrighted material. These models can memorize and generate content similar to their training data, potentially risking legal issues like copyright infringement. Therefore, model creators are motivat…

Cited by 21SourcePDFScholar