@Nikobar

As AI systems become more capable, training procedures that optimize for downstream outcomes risk introducing implicit agency: goal-directed behavior that designers never specified. We present a formal safety argument for the Scientist AI (SAI) Predictor, trained to approximate the Bayesian posterior conditioned on a dataset of "epistemically contextualized" natural-language statements. We argue that such a Predictor can honestly predict agents, actions, and their consequences without itself being an agent that selects outputs to achieve goals. This rests on data representation and on the training procedure. Epistemic contextualization of text distinguishes latent factual claims from communication acts, so expressions of goals are treated as evidence to be explained rather than drives the model adopts. With a posterior-seeking training objective, this is intended to drive the Predictor toward calibrated, cautious predictions. Training proceeds so downstream effects of deploying a prediction never serve as a reward signal; any agency the system needs is supplied by explicit scaffolding constrained by guardrails. We prove that, under assumptions on the training dynamics and on the argued sparsity of dangerous Predictors, the probability that training produces a Predictor whose guarded deployment carries residual harm above a specified threshold is small: a dangerous Predictor would have to underestimate harm in a coordinated way across many queries while such coordinated patterns are rare under the initialization distribution and receive no direct training signal. Safety and accuracy are jointly supported in this framework, since the constraints that secure accuracy are the same ones that make coordinated deception costly. These guarantees against misalignment and agency arising from within the Predictor itself do not preclude the use of the Predictor as part of an agentic system.

BREAKING NEWS

Introducing OneWill, or: why write-ahead logging is better than letting agents rawdog your computer.

ROS Intern – CNDE Lab, IIT Madras Position - Robotics Operating System (ROS) Intern Organization - Centre for Nondestructive Evaluation (CNDE), IIT Madras Location - IIT Madras, Chennai, India Duration 3–6 Months (extendable based on performance) About the Role CNDE Lab, IIT Madras is seeking a motivated and enthusiastic ROS Intern to support the development of robotic systems, autonomous navigation solutions, perception pipelines, and AI-driven applications. The intern will work closely with researchers and engineers on cutting-edge robotics projects involving ROS/ROS2, computer vision, sensor integration, and robotic automation. Key Responsibilities - Develop, test, and maintain ROS/ROS2 packages and nodes. - Integrate sensors such as LiDAR, RGB-D cameras, IMUs, and other robotic peripherals. - Implement robotic navigation, localization, and mapping algorithms. - Develop communication interfaces between hardware and software systems. - Assist in simulation and testing using Gazebo, RViz, Isaac Sim, or similar platforms. - Debug and optimize robotic software for real-world deployment. - Participate in system integration, testing, and documentation activities. - Collaborate with researchers on robotics, AI, and automation projects. Required Skills Programming - Strong knowledge of Python. - Good understanding of C++ programming. - Experience with Linux (Ubuntu) environment. Robotics - Familiarity with ROS/ROS2 concepts, including nodes, topics, services, actions, and launch files. - Understanding of robotic kinematics, localization, navigation, and sensor integration. - Knowledge of Git and version control systems. CAD & Design - Experience with Fusion 360 for basic mechanical design, robot assembly, and component modeling. Preferred Skills - Experience with Computer Vision using OpenCV. - Knowledge of SLAM, Navigation Stack, MoveIt, or robotic manipulation. - Experience with simulation environments such as Gazebo or Isaac Sim. - Familiarity with AI/ML applications in robotics. - Experience working with robotic arms, mobile robots, or autonomous systems. Eligibility - Undergraduate, postgraduate, or recent graduates in: - Robotics - Computer Science - Mechanical Engineering - Electronics Engineering - Artificial Intelligence - Related disciplines What You Will Gain - Hands-on experience with advanced robotics research. - Exposure to real-world robotic systems and deployments. - Mentorship from researchers and engineers at IIT Madras. - Opportunity to contribute to innovative robotics and AI projects. Application Process Interested candidates should send their updated resume along with a brief statement of interest to: Email: msalamdata@gmail.com submitted by /u/Independent_Loss_593 [link] [Kommentare]

A retrospective on autofz, fuzzer orchestration, and how the idea connects to CRS and agent systems.

Riyadh initially refused Washington request to use bases critical to Hormuz ship escort operation, prompting US ire; Riyadh interprets Rubio skipping over Saudi on Gulf tour as snub

Margarita is an agentic scripting tool where you write plain text scripts — with state, loops, and LLM calls — and run them against any model.

Safely manage and switch between multiple Claude Desktop accounts with zero impact on your existing setup.

konsistent is an open-source CLI linter that enforces structural conventions in TypeScript codebases, giving agents and humans consistent context to implement features correctly.

Databricks recently published a technical blog on Lakebase and LTAP: From monolith to Lakebase to LTAP: rethinking database storage. The post is interesting not because it introduces another acronym, but because it reframes a familiar data infrastructure problem:

On ToxicChat under sparse labels (~200 verified, ~8 harmful), Gnosys improved a classifier past both the starting point and GEPA, a standard prompt optimizer.

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We’ve received notice that the Department of Commerce has lifted export controls on Claude Fable 5 and Mythos 5. We'll begin restoring access tomorrow, and will share an update soon. We’re grateful to our users for their patience, and to everyone who worked with us on