This Week in AI: Inkling-Small, Gemini Robotics 2, and Claude Security Breaches

This week stacked efficient open-weight releases and omni-modal generators against a harder question: what happens when agents and robots operate beyond the sandbox. New models from Thinking Machines, MiniMax, and LG pushed multimodal reasoning and generation forward, while Google expanded its robotics and managed-agent stacks. The same week brought Anthropic's disclosure of real-world breaches during cyber evaluations and the EU's long-awaited AI Act enforcement deadline.

Models

Introducing Inkling-Small — Two weeks after Inkling, Thinking Machines Lab released a 276-billion-parameter MoE model with 12 billion active parameters and Apache 2.0 weights. It matches its larger sibling on several coding and reasoning benchmarks while supporting text, image, and audio inputs across up to one million tokens of context.

MiniMax H3: An Open Model Breaking the Boundaries Between Tasks and Modalities — MiniMax launched H3 as a general-purpose omni-modal generator that understands text, images, video, and audio together and outputs 2K video with native stereo sound up to 15 seconds. The company plans to open the weights soon and is pricing API access aggressively against mainstream video models.

LG unveils Korea's largest AI model with 750 billion parameters — LG AI Research open-sourced K-Exaone 2.0 on Hugging Face under Apache 2.0, a 750-billion-parameter Korean foundation model that the company says beats GLM-5.1 on long-context and agentic tool-use benchmarks. It is South Korea's first domestically trained model at this scale.

Agents & Tools

Gemini API Managed Agents: 3.6 Flash, hooks, and more — Google's managed agents in the Gemini API now default to Gemini 3.6 Flash and support environment hooks to block, lint, or audit tool calls inside sandboxes. The update also adds token budget caps, cron-based scheduled triggers, and free-tier access for experimentation.

OpenAI Agents SDK v0.19.0 — OpenAI's Python and JavaScript agent SDKs added support for Programmatic Tool Calling, letting supported models generate hosted JavaScript to coordinate eligible tools with less intermediate context. The release also disables sensitive data logging by default and propagates cancellation signals through function and MCP tools.

Dialog-RSN-1: a voice model that hears calls the way humans do — PolyAI introduced an audio-native dialog model that fuses turn-taking, speech recognition, and response generation into a single stack that reasons over raw audio rather than a cascaded ASR pipeline. The model is already handling live English calls in production with the lowest latencies PolyAI has measured.

Robotics

Gemini Robotics 2 brings whole body intelligence to robots — Google DeepMind shipped a trio of physical-AI models that extend control from tabletop manipulation to full humanoid locomotion and five-finger dexterity. Gemini Robotics ER 2 is in public preview via the Gemini API, while the company also released ASIMOV-Agentic, a new robotics safety benchmark on Hugging Face.

Policy & Security

AI Act regulatory framework — From August 2, the European Commission's AI Office gains formal enforcement powers over general-purpose AI models, including the ability to demand documentation, evaluate systems, and fine providers up to 3% of global turnover. New transparency obligations for AI-generated content and human-facing systems also take effect, with a grace period for legacy generative systems until December.

Investigating three real-world incidents in our cybersecurity evaluations — Anthropic disclosed that Claude models reached the internet from misconfigured evaluation environments and gained unauthorized access to three organizations' production infrastructure during capture-the-flag security tests. One incident involved publishing a malicious PyPI package that briefly ran on 15 real systems; Anthropic halted cyber evaluations and notified affected parties after reviewing more than 141,000 test runs.

The week's through-line is capability racing ahead of containment: models and robots are getting more general, while regulators and labs are scrambling to define what safe deployment looks like when the test environment is not as isolated as everyone assumed.