6 papers · 1 filter
Long-Term Simulation Exposes Cognitive-Developmental Risks in AI Companions
Kaicheng Shen, Lingyu Li, Wen Wu +3
AI companions powered by large language models increasingly interact with cognition-developing users, including children and adolescents, creating risks that may accumulate over ti…
PseudoBench: Measuring How Agentic Auto-Research Fuels Pseudoscience
Xinyang Liao, Lingyu Li, Huacan Liu +5
As Large Language Model based agents enter autonomous scientific research, their ability to resist pseudoscience becomes increasingly important. Otherwise, such systems may rapidly…
Dr. Bench: A Multidimensional Evaluation for Deep Research Agents, from Answers to Reports
Yang Yao, Yixu Wang, Yuxuan Zhang +9
As an embodiment of intelligence evolution toward interconnected architectures, Deep Research Agents (DRAs) systematically exhibit the capabilities in task decomposition, cross-sou…
SafeWork-R1: Coevolving Safety and Intelligence under the AI-45 Law
Shanghai AI Lab, :, Yicheng Bao +115
We introduce SafeWork-R1, a cutting-edge multimodal reasoning model that demonstrates the coevolution of capabilities and safety. It is developed by our proposed SafeLadder framewo…
The Other Mind: How Language Models Exhibit Human Temporal Cognition
Lingyu Li, Yang Yao, Yixu Wang +3
As Large Language Models (LLMs) continue to advance, they exhibit certain cognitive patterns similar to those of humans that are not directly specified in training data. This study…
Reflection-Bench: Evaluating Epistemic Agency in Large Language Models
Lingyu Li, Yixu Wang, Haiquan Zhao +4
With large language models (LLMs) increasingly deployed as cognitive engines for AI agents, the reliability and effectiveness critically hinge on their intrinsic epistemic agency,…