直播带货AI终于能同时看、听、读
直播带货里信息是乱的：主播在说话、画面在展示商品、屏幕上还飘着价格和用户提问。以前的AI要么只看画面、要么只听声音，总漏东西。TLive-Omni把图像、视频、音频、文字全塞进同一个理解空间，关键创新是给每个视频片段配上对应时间点的音频，用边界标记对齐，这样AI能知道「主播说这个的时候，画面里正好在展示那个」。它不是你明天就能用的工具，但说明AI对复杂直播场景的理解正在从「单通道」走向「全通道」——这是电商直播自动化运营的前沿信号。
📄 原文摘要(英文)
E-commerce live streaming requires omni-modal understanding of noisy, temporally extended streams, where product facts are distributed across speech, video frames, product images, overlaid text, and user queries. We present TLive-Omni, an omni-modal understanding model tailored to live-commerce scenarios. It maps image, video, audio, and text inputs into a unified representation space. For long-form live streaming analysis, we introduce Per-vGrid, a timestamped token organization that groups each video grid with its temporally corresponding audio within explicit boundary tokens to facilitate temporal alignment. We design a three-stage supervised training recipe that progressively develops live-commerce understanding, from omni-modal perception to instruction-following responses. We then propose Faithful-RFT, a reinforcement fine-tuning stage that further improves answer faithfulness and expression quality while meeting real-time demands, scoring final responses directly with task-verifiable feedback rather than optimizing for reasoning-style exploration during rollout. Moreover, TLive-Omni is supported by a scenario-oriented atomic capability taxonomy and a compact data production engine that converts live-commerce audio, image, and video streams into training signals for speech recognition, speaker analysis, product visual grounding, text recognition, temporal grounding, video dense caption, and omni-modal QA, etc. For scalable training, a synchronized length-grouped sampler reduces padding while preserving comparable workloads across workers, while a lightweight dynamic sampling strategy regenerates rollout groups with near-zero reward variance to maintain meaningful relative advantages for GRPO. Experiments on e-commerce live streaming benchmarks demonstrate strong performance across live-commerce domain tasks, together with excellent generalization on general benchmarks.