7 papers
Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale
Ang Li, Ben Liu, Bin Han +215
Efficient and scalable agentic intelligence requires models that can deliver both low-latency responses and strong reasoning capabilities while remaining practical to train, serve,…
GraphThinker: Reinforcing Temporally Grounded Video Reasoning with Event Graph Thinking
Zixu Cheng, Da Li, Jian Hu +4
Video reasoning requires a fine-grained understanding of the temporal dependencies and event-level relations between objects and events in videos. Current Multimodal Large Language…
Grounding Video Reasoning in Physical Signals
Alibay Osmanli, Zixu Cheng, Shaogang Gong
Physical video understanding requires more than naming an event correctly. A model can answer a question about pouring, sliding, or collision from textual regularities while still…
Uncertainty-quantified Rollout Policy Adaptation for Unlabelled Cross-domain Temporal Grounding
Jian Hu, Zixu Cheng, Shaogang Gong +4
Video Temporal Grounding (TG) aims to temporally locate video segments matching a natural language description (a query) in a long video. While Vision-Language Models (VLMs) are ef…
V-STaR: Benchmarking Video-LLMs on Video Spatio-Temporal Reasoning
Zixu Cheng, Jian Hu, Ziquan Liu +3
Human processes video reasoning in a sequential spatio-temporal reasoning logic, we first identify the relevant frames ("when") and then analyse the spatial relationships ("where")…
CoS: Chain-of-Shot Prompting for Long Video Understanding
Jian Hu, Zixu Cheng, Chenyang Si +2
Multi-modal Large Language Models (MLLMs) struggle with long videos due to the need for excessive visual tokens. These tokens exceed massively the context length of MLLMs, resultin…