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When Should AI Agents Ask for Clarification? Timing Matters
New study reveals optimal windows for clarifying instructions in long-horizon agents, with goal info losing value after 10% of execution.

When Should AI Agents Ask for Clarification? Timing Matters

A forced-injection framework across 6,000+ runs shows that the value of clarification depends sharply on information type and timing. Goal clarification loses nearly all value after 10% of execution, while input clarification retains value through 50%. Current frontier models fail to ask within optimal windows.

Interaction Models: Real-Time Human-AI Collaboration at Scale
Thinking Machines Lab unveils a native interaction model that redefines synchronous, multimodal AI collaboration

Interaction Models: Real-Time Human-AI Collaboration at Scale

Thinking Machines Lab introduces TML-Interaction-Small, a 276B MoE model architected for real-time, continuous audio-video-text exchange. It achieves state-of-the-art performance on interactivity benchmarks, enabling seamless turn-taking, interjections, and simultaneous tool use—scaling collaboration alongside intelligence.