activity
20232026
most citedThe Generative AI Paradox: "What It Can Create, It May Not Understand"

10 citations · 15 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2026

Toward Generalist Autonomous Research via Hypothesis-Tree Refinement

Jiajie Jin, Yuyang Hu, Kai Qiu +15

Scientific progress depends on a repeated loop of exploration, experimentation, and abstraction. Researchers test candidate directions, interpret the evidence, and carry the result…

cs.CV2025

Synthetic Visual Genome

Jae Sung Park, Zixian Ma, Linjie Li +9

Reasoning over visual relationships-spatial, functional, interactional, social, etc.-is considered to be a fundamental component of human cognition. Yet, despite the major advances…

cs.CV2025

Unfolding Spatial Cognition: Evaluating Multimodal Models on Visual Simulations

Linjie Li, Mahtab Bigverdi, Jiawei Gu +5

Spatial cognition is essential for human intelligence, enabling problem-solving through visual simulations rather than solely relying on verbal reasoning. However, existing AI benc…

cs.LG2025★ 3 cited

RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning

Zihan Wang, Kangrui Wang, Qineng Wang +15

Training large language models (LLMs) as interactive agents presents unique challenges including long-horizon decision making and interacting with stochastic environment feedback.…

cs.AI2024★ 2 cited

Certainly Uncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awareness

Khyathi Raghavi Chandu, Linjie Li, Anas Awadalla +5

The ability to acknowledge the inevitable uncertainty in their knowledge and reasoning is a prerequisite for AI systems to be truly truthful and reliable. In this paper, we present…

cs.AI2023★ 10 cited

The Generative AI Paradox: "What It Can Create, It May Not Understand"

Peter West, Ximing Lu, Nouha Dziri +11

The recent wave of generative AI has sparked unprecedented global attention, with both excitement and concern over potentially superhuman levels of artificial intelligence: models…