8 papers
On Safety Risks in Experience-Driven Self-Evolving Agents
Weixiang Zhao, Yichen Zhang, Yingshuo Wang +8
Experience-driven self-evolution has emerged as a promising paradigm for improving the autonomy of large language model agents, yet its reliance on self-curated experience introduc…
Agent Data Protocol: Unifying Datasets for Diverse, Effective Fine-tuning of LLM Agents
Yueqi Song, Ketan Ramaneti, Zaid Sheikh +18
Public research results on large-scale supervised finetuning of AI agents remain relatively rare, since the collection of agent training data presents unique challenges. In this wo…
Analyzing Information Sharing and Coordination in Multi-Agent Planning
Tianyue Ou, Saujas Vaduguru, Daniel Fried
Multi-agent systems (MASs) have pushed the boundaries of large language model (LLM) agents in domains such as web research and software engineering. However, long-horizon, multi-co…
VisualPuzzles: Decoupling Multimodal Reasoning Evaluation from Domain Knowledge
Yueqi Song, Tianyue Ou, Yibo Kong +3
Current multimodal benchmarks often conflate reasoning with domain-specific knowledge, making it difficult to isolate and evaluate general reasoning abilities in non-expert setting…
CowPilot: A Framework for Autonomous and Human-Agent Collaborative Web Navigation
Faria Huq, Zora Zhiruo Wang, Frank F. Xu +4
While much work on web agents emphasizes the promise of autonomously performing tasks on behalf of users, in reality, agents often fall short on complex tasks in real-world context…
Towards Global AI Inclusivity: A Large-Scale Multilingual Terminology Dataset (GIST)
Jiarui Liu, Iman Ouzzani, Wenkai Li +5
The field of machine translation has achieved significant advancements, yet domain-specific terminology translation, particularly in AI, remains challenging. We introduce GIST, a l…