arXiv:2603.23613v1 Announce Type: new Abstract: Large Language Models (LLMs) are showing remarkable performance in generating source code, yet the generated code often has issues like compilation errors or incorrect code. Researchers and developers often face wasted effort in implementing checks and refining LLM-generated code, frequently duplicating their efforts. This paper presents LLMLOOP, a framework that automates the refinement of both source code and test cases produced by LLMs. LLMLOOP employs five iterative loops: resolving compilation errors, addressing static […]

Ver mais

Key Highlights – Google released a native Gemini app for Mac today. It free to download, requires macOS 15 or later and is available at gemini.google/mac. The app was built in native Swift by a small team. According to Google, over 100 features were shipped in under 100 days. Sundar Pichai announced it on X this morning. What It Does Pressing Option + Space from anywhere on the Mac opens a floating chat overlay. If required, users can […]

Ver mais

arXiv:2601.20016v1 Announce Type: new Abstract: Wikipedia administrators are vital to the platform’s success, performing over a million administrative actions annually. This multi-method study systematically analyzes adminship across 284 Wikipedia languages since 2018, revealing a critical two-sided trend: while over half of all Wikipedias show a net increase in administrators, almost two-thirds of highly active Wikipedias face decline. Our analysis, drawing from large-scale adminship log analysis, over 3000 surveys, and 12 interviews, reveals this decline is primarily driven by […]

Ver mais

:::info Authors: Alhussein Fawzi Matej Balog Aja Huang Thomas Hubert Bernardino Romera-Paredes Mohammadamin Barekatain Alexander Novikov Francisco J. R. Ruiz Julian Schrittwieser Grzegorz Swirszcz David Silver Demis Hassabis Pushmeet Kohli ::: Abstract Improving the efficiency of algorithms for fundamental computations can have a widespread impact, as it can affect the overall speed of a large amount of computations. Matrix multiplication is one such primitive task, occurring in many systems—from neural networks to scientific computing routines. The automatic discovery […]

Ver mais

arXiv:2603.20295v1 Announce Type: new Abstract: Uncovering causal structures from observational data is crucial for understanding complex systems and making informed decisions. While reinforcement learning (RL) has shown promise in identifying these structures in the form of a directed acyclic graph (DAG), existing methods often lack efficiency, making them unsuitable for online applications. In this paper, we propose MARLIN, an efficient multi agent RL based approach for incremental DAG learning. MARLIN uses a DAG generation policy that maps a […]

Ver mais

Distilling humanoid locomotion control from offline datasets into deployable policies remains a challenge, as existing methods rely on privileged full-body states that require complex and often unreliable state estimation. We present Sensor-Conditioned Diffusion Policies (SCDP) that enables humanoid locomotion using only onboard sensors, eliminating the need for explicit state estimation. SCDP decouples sensing from supervision through mixed-observation training: diffusion model conditions on sensor histories while being supervised to predict privileged future state-action trajectories, enforcing the model to infer […]

Ver mais

The evaluation of fairness in machine learning systems has become a central concern in high-stakes applications, including biometric recognition, healthcare decision-making, and automated risk assessment. Existing approaches typically rely on a small number of fairness metrics to assess model behaviour across group partitions, implicitly assuming that these metrics provide consistent and reliable conclusions. However, different fairness metrics capture distinct statistical properties of model performance and may therefore produce conflicting assessments when applied to the same system. In this […]

Ver mais

arXiv:2601.18813v1 Announce Type: new Abstract: Fog of War chess is a popular variant of classical chess, in which both players have only partial information about the position of the opponent’s pieces. This study provides the first theoretical analysis of endgames in Fog of War chess. In particular, we analyze the setups king and queen versus king, king and rook versus king, and king and two rooks versus king. We show that a king and queen can always guarantee […]

Ver mais

arXiv:2601.02390v1 Announce Type: new Abstract: We make modifications to the unscented Kalman filter (UKF) which bestow almost complete practical identifiability upon a lumped-parameter cardiovascular model with 10 parameters and 4 output observables – a highly non-linear, stiff problem of clinical significance. The modifications overcome the challenging problems of rank deficiency when applying the UKF to parameter estimation. Rank deficiency usually means only a small subset of parameters can be estimated. Traditionally, pragmatic compromises are made, such as selecting […]

Ver mais