Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
Digital advertising is an important means for platforms to reach potential users and drive business growth. Advertising delivery is the core link between marketing objectives and actual returns. Exist...
Configuring distributed LLM training across hundreds of GPUs requires jointly optimizing tensor, pipeline, expert, context, and data parallelism alongside micro-batch size, recomputation, sharding, an...
Simulated environments enable scalable training data generation for embodied agents, but more data does not necessarily provide what a particular agent needs to learn. Existing environment-generation...
Large language models (LLMs) are increasingly used for multi-turn agentic tasks that require interactions with users and environments over extended sequences of actions. A common approach to improving...
The article discusses how the adoption of agentic AI is increasing among EU enterprises and small businesses, based on new analysis. It highlights the current growth trends and identifies areas where deeper use of agentic AI could unlock additional value for these organizations.
Model routing can reduce the cost of LLM applications by assigning simpler requests to cheaper models without sacrificing the quality of the response. However, most routers treat requests as independe...
In so-called "Industry 4.0," integrated sensors, analytics tools, and other digital technologies supplement industrial workflows, potentiating enhanced visibility and automation. Prognostics and healt...
Anthropic redesigned its source control system, creating a custom alternative called Scion to replace Git. The change was driven by a dramatic rise in code changes—from about 2,500 per day in May 2026 to over 30,000 per day by September—requiring a system that could handle a 100 GB main‑branch history and 25 million daily checkouts. Scion was built by a seven‑person team, migrated from Git in five months, and includes a new code‑review system named Antique, while offline support was deemed unnecessary due to Claude’s constant online use.
The article announces the release of llm‑gemini 0.35, adding support for the new Gemini 4 Argon model available through the Gemini API and for customers on the $100/month subscription plan. It highlights Argon’s pricing—$2/million input and $10/million output at launch, with a planned price increase on December 1st—and compares its cost-effectiveness to other models, noting it remains cheaper than many competitors even after the increase. The post also showcases example SVG outputs and creative illustrations generated by Argon, such as a pelican riding a bicycle and an armadillo in fishnet tights on Mars.
Operational reporting for high-performance computing (HPC) facilities requires combining information from monitoring services, schedulers, incident and maintenance records, storage measurements, perio...
Coordinators that act on what they infer about teammates are usually certified by the accuracy of those inferences. We argue that accuracy is the wrong certificate, for two reasons. First, the accurac...
The article titled "Your Agent Doesn't Need More Freedom" discusses how agents have become proficient, but the real challenge lies in the surrounding environment. It highlights that the focus should shift from granting agents additional autonomy to addressing the complexities that exist around them. The piece underscores the importance of improving the context in which agents operate rather than simply increasing their freedom.
Agentic robotics places a large language model (LLM) agent in the robot control loop, where it composes perception, motion, and vision-language-action (VLA) tools under the guidance of a memory and th...
Long-horizon agents have drawn growing attention for their ability to work autonomously -- from hours to days -- on economically valuable tasks, a capability essential to automated discovery and recur...
The paper demonstrates that Earth observation (EO) models can be automatically constructed and refined by a general-purpose coding agent when provided with a harness (defining permissible tools) and a verifier (defining success criteria). By casting any verifiable EO task—using labeled benchmarks, independent sensors, physical models, or repeated observations—as such a harness-verifier pair, the agent recursively improves its own outputs. On four EO benchmarks covering optical, SAR, and multispectral time-series imagery, the agent-built models outperform state‑of‑the‑art methods while retaining most accuracy with only about 1 % of the training labels.
RoboSimStudio (RSS) is an interactive toolkit designed to simplify the creation of realistic robot manipulation simulations. It allows users to explicitly choose and edit solvers, contact models, and physical parameters, enabling iterative refinement of simulations based on observed behavior. Experiments demonstrate human-guided real-to-sim approaches using visual evaluation, force-feedback, and agentic real-to-sim-to-real methods.
The paper introduces the Self‑Evolving Agent Harness, a framework that allows an agent’s software scaffolding—prompts, memory, workflows, and executable components—to evolve while keeping the underlying model weights unchanged. It formalizes a feedback‑driven process where accepted revisions persist across runs, separating update targets from proposal and admission mechanisms, and distinguishes changes to interpreted state from revisions to tools and code. The authors analyze evolutionary search, provide generalization and retention bounds under PAC‑learning assumptions, and discuss the impact of persistent harness revisions on knowledge sharing, organizational workflows, and broader implications for scientific reliability, safety, and accountability.
Agentic reinforcement learning (RL) enables large language models (LLMs) to learn from multi-turn interactions with verifiable outcomes. Textual skills can improve exploration by distilling prior inte...
Reinforcement learning (RL) has emerged as the primary paradigm for optimizing Large Language Model (LLM) agents, yet efficient critic-free algorithms (e.g., GRPO) suffer from a fundamental uniform cr...