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JZ

James Zou

NeurIPS 2026×6ACL 2026×3智能体与工具推理评测与基准安全多模态
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0 一作 · 9 篇 · NeurIPS 2026 / ACL 2026

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2026 顶会论文

9
作者位次 #6/6ACL 2026Poster· Natural Language Processing

O cto T ools: A Multi-Agent Framework with Extensible Tools for Complex Reasoning

Pan Lu · Bowen Chen · Sheng Liu · Rahul Thapa · Joseph Boen · James Zou

摘要

Solving complex reasoning tasks may involve visual understanding, domain knowledge retrieval, numerical calculation, and multi-step reasoning. Existing methods augment large language models (LLMs) with external tools but are restricted to specialized domains, limited tool types, or require additi...

推理智能体与工具检索与知识OpenReview 项目页
作者位次 #4/5ACL 2026Poster· Natural Language Processing

Impatient Users Confuse AI Agents: High-fidelity Simulations of Human Traits for Testing Agents

Muyu He · Anand Kumar · Soumyadeep Bakshi · James Zou · Nazneen Rajani

摘要

Despite rapid progress in building conversational AI agents, robustness is still largely untested. Small shifts in user behavior, such as being more impatient, incoherent, or skeptical, can cause sharp drops in agent performance, revealing how brittle current AI agents are. Today’s benchmarks fai...

社会与人本效率与系统评测与基准OpenReview 项目页
作者位次 #5/5ACL 2026Findings· Findings of ACL

R eason IF : Large Reasoning Models Fail to Follow Instructions During Reasoning

Yongchan Kwon · Shang Zhu · Federico Bianchi · Kaitlyn Zhou · James Zou

摘要

The ability of large language models (LLMs) to follow user instructions is central to their reliability, safety, and usefulness. While prior studies assess instruction adherence in the model’s main responses, we argue that it is also critical for large reasoning models (LRMs) to follow user instr...

评测与基准安全推理OpenReview 项目页

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