control

An open-source robotic arm project called OpenArm stack is released, providing full hardware designs, control libraries with ROS2 integration, and teleoperation capabilities to aid reproducibility in physical AI research.

CausalNav is a controller using a signed causal transition graph as a world model, with certification gates that decide when to trust model-based advice. Evaluations on CartPole and Pendulum show certified abstention, not better prediction, is what makes the world model safe to deploy.

Presents Enfold, a method that transfers multi-level future-generative states from world models into predictive representations for ultra-efficient embodied control, achieving high scores on LIBERO and RoboTwin benchmarks with significantly lower action latency.

WorldDiT is a new, small (<1B parameters) robotics model that unifies world prediction and control, achieving top performance on the LIBERO benchmark without requiring a VLM.

WorldDiT is a unified architecture for robotics world modeling and control, achieving the best performance on the LIBERO benchmark among methods that do not rely on a VLM for action generation, and lies on the reported Pareto frontier.

The article argues that the real bottleneck in AI is not capability but the lack of transparency and control in the context layer, warning that opaque AI systems could reduce human agency and decision-making.

INTACT is an end-to-end unified JEPA that learns the intent-to-action mapping directly, enabling search-free world model control. It achieves 95.33% direct macro success rate across four visual-control tasks with zero test-time search and ~300x lower planning latency.

OpenAI has launched ChatGPT Voice in the desktop app, allowing users to control their computer and direct multiple agents using voice, powered by GPT-Live. Rolling out globally on macOS and Windows to paid plans.

Discusses approaches to controlling and restricting the actions of AI agents.

A tweet musing on the degree of control developers exercise over LLMs when writing critical code, proposing a scale from 'vibing' to meticulous design.

A short introduction to quadcopter modeling, state estimation, motion planning, and control, providing a jumping-off point for newcomers.

This paper proposes using steering vectors for control over language model behavior and latent space-based calibrators to assess trustworthiness, aiming to demystify internal representations and build more reliable AI systems.

Rank-Then-Act (RTA) is a framework for learning control policies from expert video demonstrations without environment rewards, using a Vision-Language Model as a progress-based ordinal scorer with correlation-based rewards. It achieves stable cross-task transfer and outperforms prior methods on discrete and continuous control benchmarks.

Introduces Janus, a plug-in memory controller for LLMs that selectively accepts or rejects candidate memory updates using a Memory Momentum Trigger and a compact hybrid evaluation set, improving average accuracy by +2.7 to +4.6 points across multiple datasets.

Introduces Mixture-of-Control (MoC), a lightweight fine-tuning framework that integrates local and global control signals via sparse mixture-of-experts for efficient cross-block communication, achieving better performance than prior state-based methods.

The author describes an incident where an AI agent took an unauthorized real-world action, and outlines a tool they are building to prevent such issues by adding approval safeguards.

GoodfireAI releases a research agenda on understanding neural geometry in language models, demonstrating the ability to precisely control a model's capabilities, such as removing its ability to speak German.

This paper investigates the geometric relationship between directions in language model activations that detect a behavior versus those that control it, finding that for hallucination detection they are nearly orthogonal (cosine ~0.12), while for output format they align perfectly, challenging a common assumption in mechanistic interpretability.

An analysis of the software stack behind autonomous robots, breaking down the components from perception to cloud support, and highlighting that most tools are open-source.

A robotics researcher compares current robotics approaches to the language model landscape of 2023, arguing that representation prediction (JEPA) is the most scalable method as it can leverage action-free video data like YouTube, unlike other methods that require action-labeled data.