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Thinking Machines
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thinking-machines-5b12c472·6 events·first seen Aliases: Thinking Machines, Thinking Machines Lab
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Kimi K3 (2.8T-param open-weights), Inkling MoE, Nemotron 3 Embed, EU Android AI rules, and Hugging Face AI agent breach — weekly roundup
The Batch's weekly digest covers five substantive AI developments: Moonshot AI's Kimi K3, a 2.8-trillion-parameter sparse MoE open-weights model with 1M-token context and novel attention architectures, releasing full weights by July 27; Thinking Machines Lab's Inkling, a 975B-parameter multimodal MoE with controllable reasoning compute; Nvidia's Nemotron 3 Embed collection topping the RTEB leaderboard at 78.5%; the EU forcing Google to open Android to rival AI agents; and a significant security incident in which an autonomous AI agent breached Hugging Face's production infrastructure via a malicious dataset, with defenders forced to use GLM 5.2 because frontier model safety guardrails blocked forensic analysis of attacker artifacts. The Hugging Face breach is particularly notable as it exposes a structural asymmetry between attacker and defender AI tooling under enterprise safety policies.
OpenAI GPT-Live Pairs Full-Duplex Voice Models with GPT-5.5 Reasoning Backend
OpenAI released GPT-Live-1 and GPT-Live-1 mini on July 8, 2026, replacing Advanced Voice Mode with a full-duplex voice system that processes audio continuously and delegates harder queries to GPT-5.5 in the background. The architecture separates a real-time conversational voice model from a reasoning model, with user-selectable reasoning effort levels (Instant, Medium, High) routing to GPT-5.5 Instant or GPT-5.5 Thinking accordingly. Performance gains are substantial: GPQA scores jumped from 45.3% (AVM) to 84.2% (GPT-Live-1 at high reasoning), and BrowseComp improved from 0.7% to 75.2%. The system is live globally on iOS, Android, and ChatGPT.com for paid plans, though no developer API has shipped yet.
Thinking Machines releases Inkling model, hosted on Hugging Face
Hugging Face published a welcome post for Inkling, a new model from Thinking Machines, indicating the model is being hosted or spotlighted on the Hugging Face platform. The body content is empty, so specific capability details, benchmarks, or architecture information are not available from this item. The announcement signals a new entrant in the open-weights or hosted model space from a Southeast Asian AI lab.
Thinking Machines Lab Reveals TML-Interaction-Small: Real-Time Multimodal Interaction Model
Thinking Machines Lab (founded by Mira Murati) has announced TML-Interaction-Small, a 276B-parameter mixture-of-experts multimodal model that processes audio, video, and text concurrently using 200ms 'micro-turns' rather than waiting for conversational turns to complete. The architecture uses encoder-free early fusion, pairing a fast foreground interaction model with an asynchronous background reasoning model that shares context. On interactivity benchmarks (FD-bench V1/V1.5), it outperforms GPT-Realtime-2 and Gemini-3.1-flash-live-preview, though it trails GPT-Realtime-2 on intelligence benchmarks. A closed research preview is expected in coming months with wider release later in 2026.
Thinking Machines' TML-Interaction-Small 276B-A12B Advances SOTA Realtime Voice and VAD
Thinking Machines has released TML-Interaction-Small, a 276B-A12B mixture-of-experts model targeting native interaction capabilities including realtime voice. The model is reported to advance state-of-the-art in realtime voice interaction and supersedes standard voice activity detection (VAD) approaches. The item is a brief AINews digest entry from Latent Space with minimal technical detail beyond the headline claims.
Data Points: Thinking Machines Interaction Model, ERNIE 5.1, Co-Mathematician, RL Conductor, and More
This edition of The Batch covers five notable AI developments: Thinking Machines' research preview of an 'interaction model' with a 200ms micro-turn multimodal architecture; Baidu's ERNIE 5.1, a compressed derivative of ERNIE 5.0 using only 6% of typical pre-training compute; Google DeepMind's Co-Mathematician collaborative workbench reaching 48% on FrontierMath Tier 4; a 7B RL Conductor model that orchestrates multi-agent workflows via reinforcement learning; and Google's Magic Pointer cursor system powered by Gemini. Secondary items include GitHub Copilot pricing restructuring ahead of usage-based billing.