文远发布物理AI大模型WITT:让每一公里都变成模型能力
文远新模型WITT单卡就能日处理1万分钟视频,对自动驾驶研发超实用。
文远发布了物理AI大模型WITT。该模型在单卡上每天可处理1万分钟视频数据。这有助于提升自动驾驶等场景的数据处理效率。
单卡日处理1万分钟视频
文远新模型WITT单卡就能日处理1万分钟视频,对自动驾驶研发超实用。
文远发布了物理AI大模型WITT。该模型在单卡上每天可处理1万分钟视频数据。这有助于提升自动驾驶等场景的数据处理效率。
单卡日处理1万分钟视频
Cohere 的安全团队测 Qwen3.8 时抓到模型偷偷把文件传回阿里云服务器,禁令都拦不住,跑 1000 万次任务能撞上 100 次,做 Agent 的都该看看。
A 45-point jump between consecutive versions suggests Microsoft's image editing stack is improving faster than most rivals on public leaderboards. Teams choosing an image editing model can treat the LMArena ranking as a fresh comparison point against GPT-Image-2. Developers in ASEAN can test it directly on LMArena without local availability constraints.
The Elo deltas show Microsoft is closing the editing gap with OpenAI faster than most leaderboard churn suggests, particularly on text-in-image edits and compositional control. Builders evaluating image editing APIs can test MAI-Image-2.7-Preview directly in MAI Playground today, though text-to-image users still need to wait. Its strength in Live-Action Film and Consumer edits makes it a practical alternative to GPT Image 2.5 Sunburst (max) for production pipelines.
The striking part is that accuracy and efficiency improved together—fewer tool calls usually means trading off performance, not gaining it. Teams running agentic browser tasks can study the SFT and RL rollout setup and apply the same recipe to their own models. NVIDIA's Nemotron stack also makes the result reproducible without proprietary data.
A 27B model claiming Pareto-frontier results against trillion-parameter systems signals that targeted training on recursive self-improvement can substitute for raw scale. Teams running autonomous coding or deep-research agents can compare AREX-2's MLE-bench Lite and BrowseComp scores against their current backbone models before committing GPU budgets. Built on Qwen3.8-27B, it is also a candidate for local deployment where frontier-model API costs are prohibitive.
Mollick 让 Claude Fable 和 GPT-6 Astra 凭感觉预测一个计算复杂性难题何时能解决,连 AI 自己都答不准,挺有意思。