Thinking Machines

Thinking Machines released TML-Interaction-Small 276B-A12B, a 276-billion-parameter MoE model with only 12 billion active parameters, achieving sub-200ms end-to-end latency, surpassing GPT-4o and Gemini 3.1-Flash in real-time voice interaction, time-aligned microturns, and visual proactivity, effectively eliminating standard VAD.

Interaction models enable native real-time multimodal interaction, overcoming the limitations of traditional turn-based interfaces and significantly enhancing human-AI collaboration efficiency.

中国开源模型正成为西方AI公司的重要依赖，但这也带来结构性风险。Qwen市场份额激增，蒸馏技术加剧中美模型差距。

Thinking Machines发布的新AI模型Inkling通过混合专家架构和独特设计实现36%的token节省，支持用户自定义训练。

Inkling-Small在Agent Arena中排名#12，性能接近Inkling但成本降低50%，参数量276B且开放权重。

Agent Arena平台通过真实任务评估模型性能，Inkling-Small在性价比上表现突出。

开源模型正成为AI行业核心价值载体，Thinking Machines、腾讯等企业通过开放模型参数和许可协议推动技术普惠。

中国开源模型已占据西方AI公司训练栈核心位置，蒸馏技术加剧中美模型能力差距，西方需独立构建或接受滞后学习。

Thinking Machines发布Inkling模型，采用9700亿参数混合专家架构，仅使用410亿参数处理每个token，预训练数据达45万亿token。

Thinking Machines发布名为Inkling的开源模型，参数量达41B，支持多模态，可与GPT-5.6竞争。

Inkling模型现已在AI Gateway上提供，支持多任务处理，AI Gateway提供统一API和性能优化。

This episode highlights OpenAI's real-time voice API, Thinking Machines' interactive model architecture, Anthropic's legal product, and market dynamics, revealing advancements in real-time interaction, vertical applications, and security compliance.

Thinking Machines' latest launch approach is impressive, showcasing through platform-native content, which is more effective than traditional methods.

Still early days, but the naturalness of interactions with multimodal AI models is rapidly improving; real-time, low-latency capabilities are unlocking applications unimaginable today, reshaping human-AI collaboration.

Thinking Machines introduces a native multimodal Interaction Model enabling 200ms-level real-time collaboration, breaking traditional Agent framework limitations by integrating all modalities into a single architecture. The model supports instant user interruptions through frontend-backend model coordination, significantly improving conversational naturalness.

Thinking Machines推出开源模型Inkling，基于NVIDIA硬件训练，支持多模态推理。

AI领域人才流动频繁，多位关键人物在不同机构间转移，影响技术发展。

The article discusses advances in real-time multimodal interaction models, emphasizing their closeness to real human interaction.

Lilian Weng advocates for increased collaboration in human-AI interactivity and announces a $100,000 grant program by Thinking Machines, but the post lacks technical depth and serves primarily as a promotional announcement.

Human-to-human collaboration plays a key role in improving human-AI collaboration, as learned from maintaining a 137-page training log across 12 iterations.