News & features
Flint: A visualization language for the AI era
| Chenglong Wang, Alper Sarikaya, Scott Tsukamaki, Michel Galley, and Jianfeng Gao
Short chart specifications are easy to write, but often produce uninspiring results. Flint is an open-source visualization language that offers a middle path, letting AI agents create expressive charts from compact, human-editable specifications.
Understanding the brain with AI-driven explanations and experiments
| Chandan Singh and Jianfeng Gao
Researchers introduce generative causal testing, which translates black box models into clear hypotheses and verifies them in the scanner, revealing what specific brain regions respond to in language.
Data Formulator 0.7: AI-powered data analytics for enterprise data
| Chenglong Wang, Scott Tsukamaki, Michel Galley, and Jianfeng Gao
Data Formulator introduces AI-powered analytics for enterprise data workflows. Data teams can easily bring enterprise data into an AI-ready workspace where users can explore, analyze, and visualize data with AI agents to turn raw data into actionable insights.
AsgardBench: A benchmark for visually grounded interactive planning
| Andrea Tupini, Lars Liden, Reuben Tan, Yu Wang, and Jianfeng Gao
Imagine a robot tasked with cleaning a kitchen. It needs to observe its environment, decide what to do, and adjust when things don't go as expected, for example, when the mug it was tasked to wash is already clean, or…
GroundedPlanBench: Spatially grounded long-horizon task planning for robot manipulation
| Sehun Jung, HyunJee Song, Dong-Hee Kim, Reuben Tan, Jianfeng Gao, Yong Jae Lee, and Donghyun Kim
Vision-language models (VLMs) use images and text to plan robot actions, but they still struggle to decide what actions to take and where to take them. Most systems split these decisions into two steps: a VLM generates a plan in…
PlugMem: Transforming raw agent interactions into reusable knowledge
| Ke Yang, Michel Galley, Chenglong Wang, Jianfeng Gao, Jiawei Han, and ChengXiang Zhai
It seems counterintuitive: giving AI agents more memory can make them less effective. As interaction logs accumulate, they grow large, fill with irrelevant content, and become increasingly difficult to use. More memory means that agents must search through larger volumes of…
Argos: Multimodal reinforcement learning with agentic verifier for AI agents
| Reuben Tan, Baolin Peng, Zhengyuan Yang, Oier Mees, and Jianfeng Gao
Argos improves multimodal RL by evaluating whether an agent’s reasoning aligns with what it observes over time. The approach reduces visual hallucinations and produces more reliable, data-efficient agents for real-world applications.
MindJourney enables AI to explore simulated 3D worlds to improve spatial interpretation
| Yuncong Yang, Reuben Tan, Swadheen Shukla, and Jianfeng Gao
MindJourney can enable AI to navigate and interpret 3D environments from limited visual input, potentially improving performance in navigation, planning, and safety-critical tasks.
CollabLLM: Teaching LLMs to collaborate with users
| Shirley Wu, Michel Galley, Baolin Peng, Swadheen Shukla, and Jianfeng Gao
Recipient of an ICML 2025 Outstanding Paper Award, CollabLLM improves how LLMs collaborate with users, including knowing when to ask questions and how to adapt tone and communication style to different situations. This approach helps move AI toward more user-centric…