The article analyzes SenseTime’s recent SenseNova U1 Pro and SenseNova‑Vision releases, detailing their NEO‑unify architecture, unified multimodal training, extensive SN‑VC‑50M dataset, benchmark breakthroughs, and the broader shift toward a single, long‑term, agentic AI base model.
In a WAIC interview, SenseTime’s chief scientist Lin Dahua explains why multimodal AI, embodied in the native‑unified NEO‑unify architecture and the commercial‑grade SenseNova U1 Pro, is poised to surpass coding as the next competitive frontier, highlighting technical challenges, data efficiency, design‑focused benchmarks, and a 70% delivery‑rate claim.
In an interview at the 2026 WAIC conference, SenseTime chief scientist Lin Dahua explains why multimodal AI—driven by the native unified NEO‑unify architecture and embodied in the commercial‑grade SenseNova U1 Pro with a 70% delivery rate—represents the next decisive frontier beyond AI coding, highlighting technical challenges, market trends, and future research directions.
The SenseNova-U1-8B-MoT-Infographic model dramatically improves AI‑generated infographics by enhancing dense‑text rendering, layout stability, and chart accuracy through targeted data, extended mid‑training, and reinforcement‑learning fine‑tuning, achieving top scores on BizGenEval and IGenBench and surpassing many commercial rivals.
Amid the surge of token‑driven revenue models, AI firms face rising costs and price hikes; the article analyzes how companies like DeepSeek and SenseNova lower token consumption through technical innovation, adopt productivity‑focused strategies, and build anti‑fragile business models to sustain growth despite market volatility.
The 9th Tsinghua Big Data Intelligent Lecture gathered leading scholars and industry experts to showcase cutting‑edge research on multimodal perception, embodied intelligence, spatial AI, large‑model multimodal systems, and industrial time‑series databases, emphasizing their technical depth and real‑world impact.
