Anthropic invests $1.5 million in the Python Software Foundation and open source security This is outstanding news, especially given our decision to withdraw from that NSF grant application back in October. We are thrilled to announce that Anthropic has entered into a two-year partnership with the Python Software Foundation (PSF) to contribute a landmark total of $1.5 million to support the foundation’s work, with an emphasis on Python ecosystem security. This investment will enable the PSF to make […]

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arXiv:2602.20225v1 Announce Type: new Abstract: This paper introduces Function-space Adaptive Constrained Trajectory Optimization (FACTO), a new trajectory optimization algorithm for both single- and multi-arm manipulators. Trajectory representations are parameterized as linear combinations of orthogonal basis functions, and optimization is performed directly in the coefficient space. The constrained problem formulation consists of both an objective functional and a finite-dimensional objective defined over truncated coefficients. To address nonlinearity, FACTO uses a Gauss-Newton approximation with exponential moving averaging, yielding a smoothed […]

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arXiv:2603.00694v1 Announce Type: new Abstract: Explainability and transparent decision-making are essential for the safe deployment of autonomous driving systems. Scene captioning summarizes environmental conditions and risk factors in natural language, improving transparency, safety, and human–robot interaction. However, most existing approaches target structured urban scenarios; in off-road environments, they are vulnerable to single-modality degradations caused by rain, fog, snow, and darkness, and they lack a unified framework that jointly models structured scene captioning and path planning. To bridge this […]

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arXiv:2602.18468v1 Announce Type: new Abstract: This article introduces the concept of the algorithmic unconscious to designate the set of structural determinations that operate within large language models (LLMs) without being accessible either to the model’s own reflexivity or to that of its users. In contrast to approaches that reduce AI bias solely to dataset composition or to the projection of human intentionality, we argue that a significant class of biases emerges directly from the technical mechanisms of the […]

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arXiv:2306.10987v2 Announce Type: replace Abstract: Generating accurate extremes from an observational data set is crucial when seeking to estimate risks associated with the occurrence of future extremes which could be larger than those already observed. Applications range from the occurrence of natural disasters to financial crashes. Generative approaches from the machine learning community do not apply to extreme samples without careful adaptation. Besides, asymptotic results from extreme value theory (EVT) give a theoretical framework to model multivariate extreme […]

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arXiv:2602.21320v1 Announce Type: new Abstract: Large language models (LLMs) are becoming the foundation for autonomous agents that can use tools to solve complex tasks. Reinforcement learning (RL) has emerged as a common approach for injecting such agentic capabilities, but typically under tightly controlled training setups. It often depends on carefully constructed task-solution pairs and substantial human supervision, which creates a fundamental obstacle to open-ended self-evolution toward superintelligent systems. In this paper, we propose Tool-R0 framework for training general […]

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arXiv:2603.22299v1 Announce Type: new Abstract: Large language models (LLMs) are often confidently wrong, making reliable uncertainty estimation (UE) essential. Output-based heuristics are cheap but brittle, while probing internal representations is effective yet high-dimensional and hard to transfer. We propose a compact, per-instance UE method that scores cross-layer agreement patterns in internal representations using a single forward pass. Across three models, our method matches probing in-distribution, with mean diagonal differences of at most $-1.8$ AUPRC percentage points and $+4.9$ […]

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This paper presents the first demonstration of a viable, ultra-fast, radiation-hard machine learning (ML) application on FPGAs, which could be used in future high-energy physics experiments. We present a three-fold contribution, with the PicoCal calorimeter, planned for the LHCb Upgrade II experiment, used as a test case. First, we develop a lightweight autoencoder to compress a 32-sample timing readout, representative of that of the PicoCal, into a two-dimensional latent space. Second, we introduce a systematic, hardware-aware quantization strategy […]

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Traditional data platforms rely on centralized monoliths and complex engineering pipelines that often create bottlenecks for business decision-makers. This article introduces Da2a, an open-source prototype that proposes a decentralized, agentic architecture where specialized Domain Agents (e.g., Marketing, E-commerce) collaborate through a Root Orchestrator using the Agent-to-Agent (A2A) protocol. By treating data sources as autonomous experts that communicate via standardized “agent cards” rather than static tables, Da2a demonstrates how multi-agent systems can intuitively solve complex cross-domain problems, offering a […]

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