4 citations · 6 across the 11 of their papers we have counts for
6 papers · 1 filter
PanoVLN: Towards Effective Panoramic Vision-and-Language Navigation
Zhen Wang, Changpeng Wang, Zhe Liu +5
Recent vision-language models (VLMs) have advanced vision-and-language navigation (VLN), enabling models to predict navigation actions from visual observations and language instruc…
Groundbench: Multi-Resolution Polygon Grounding Exposes the Geometry Gap in Vision-Language Models
Zhonghan Bian, Zhenran Wang, Jinsong Li +1
Bounding-box scores on RefCOCO-family grounding leave little room to distinguish frontier vision-language systems, yet boxes discard object shape. We introduce GroundingBench, a ma…
SocietyBench: Forecasting Counterfactual Social-World Evolution
Zhenran Wang, Zhonghan Bian, Jinsong Li +1
Large language models (LLMs), and the agents built on top of them, are now benchmarked heavily on whether they can finish a task -- fix a bug, drive a browser, operate a GUI. A com…
Kimi K3: Open Frontier Intelligence
Kimi Team, Tongtong Bai, Yifan Bai +398
We introduce Kimi K3, a 2.8T parameter Mixture-of-Experts model with 104 billion activated parameters, native vision capabilities, and a 1-million-token context window. Kimi K3 is…
WorldCup Arena: Prospective, Leakage-Free Evaluation of Frontier LLMs on a Live Tournament
Zhenran Wang, Zhonghan Bian, Jinsong Li +1
Benchmarks that measure the forecasting ability of large language models are almost always retrospective: the event has happened, the answer is somewhere on the Web, and the evalua…
PerceptionBench: Evaluating Atomic Visual Perception in Multimodal Large Language Models
Zichao Lin, Yifeng Xie, Bowen Qu +30
We introduce PerceptionBench, a benchmark specifically designed to evaluate the atomic visual perception capabilities of Multimodal Large Language Models (MLLMs). Existing benchmar…