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Benedikt Stroebl

ICLR 2026×2ACL 2026评测与基准可解释性智能体与工具多模态效率与系统
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1 一作 · 3 篇 · ICLR 2026 / ACL 2026

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2026 顶会论文

3
作者位次 #2/31ICLR 2026Poster· Social Aspects / Accountability, Transparency and Interpretability

Holistic Agent Leaderboard: The Missing Infrastructure for AI Agent Evaluation

Sayash Kapoor · Benedikt Stroebl · Peter Kirgis · Nitya Nadgir · Zachary Siegel · Boyi Wei · Tianci Xue · Ziru Chen · Felix Chen · Saiteja Utpala · Franck Ndzomga · Dheeraj Oruganty · Sophie Luskin · Kangheng Liu · Botao Yu · Amit Arora · Dongyoon Hahm · Harsh Trivedi · Huan Sun · Juyong Lee · Tengjun Jin · Yifan Mai · Yifei Zhou · Yuxuan Zhu · Rishi Bommasani · Daniel Kang · Dawn Song · Peter Henderson · Yu Su · Percy Liang · Arvind Narayanan

评测与基准智能体与工具可解释性OpenReview
一作ICLR 2026Poster· Applications / Language, Speech and Dialog

The Limits of Inference Scaling Through Resampling

Benedikt Stroebl · Sayash Kapoor · Arvind Narayanan

多模态效率与系统OpenReview
作者位次 #3/8ACL 2026Findings· Findings of ACL

Localized Cultural Knowledge is Conserved and Controllable in Large Language Models

Veniamin Veselovsky · Berke Argın · Benedikt Stroebl · Chris Wendler · Robert West · James Evans · Thomas L. Griffiths · Arvind Narayanan

摘要

Large language models (LLMs), like human language learners, show patterns influenced by their dominant training language. Just as humans display language patterns influenced by their native tongue (semantic accents) when learning new languages, LLMs often default to English-centric responses even...

可解释性社会与人本评测与基准OpenReview 项目页

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