arXiv:2601.08991v1 Announce Type: new Abstract: The ubiquity of machine learning (ML) and the demand for ever-larger models bring an increase in energy consumption and environmental impact. However, little is known about the energy scaling laws in ML, and existing research focuses on training cost — ignoring the larger cost of inference. Furthermore, tools for measuring the energy consumption of ML do not provide actionable feedback. To address these gaps, we developed Energy Consumption Optimiser (ECOpt): a hyperparameter tuner […]

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arXiv:2601.03389v1 Announce Type: new Abstract: Understanding how artificial agents model internal mental states is central to advancing Theory of Mind in AI. Evidence points to a unified system for self- and other-awareness. We explore this self-awareness by having reinforcement learning agents infer their own internal states in gridworld environments. Specifically, we introduce an introspective exploration component that is inspired by biological pain as a learning signal by utilizing a hidden Markov model to infer “pain-belief” from online observations. […]

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arXiv:2505.00356v3 Announce Type: replace-cross Abstract: In an era of increasing computational capabilities and growing environmental consciousness, organizations face a critical challenge in balancing the accuracy of forecasting models with computational efficiency and sustainability. Global forecasting models, lowering the computational time, have gained significant attention over the years. However, the common practice of retraining these models with new observations raises important questions about the costs of forecasting. Using ten different machine learning and deep learning models, we analyzed various […]

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In Robotics, one of the hardest problems is how to make your model transfer to the real world. Due to the sample inefficiency of deep RL algorithms and the cost of data collection on real robots, we often need to train models in a simulator which theoretically provides an infinite amount of data. However, the reality gap between the simulator and the physical world often leads to failure when working with physical robots. The gap is triggered by […]

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Current multimodal large language model (MLLM)-based autonomous driving systems struggle with deep contextual understanding, fine-grained personalization, and transparent risk assessment in complex real-world scenarios. This paper introduces ConsciousDriver, a novel context-aware multimodal personalized autonomous driving system designed to address these limitations. Our system integrates a Context-Awareness Module for richer environmental understanding and a Dynamic Risk Adaptation Mechanism to flexibly modulate driving behaviors based on real-time user prompts and situational risks. Built upon an extended MLLM architecture, ConsciousDriver processes […]

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arXiv:2601.09898v1 Announce Type: new Abstract: Politeness is a core dimension of human communication, yet its role in human-AI information seeking remains underexplored. We investigate how user politeness behaviour shapes conversational outcomes in a cooking-assistance setting. First, we annotated 30 dialogues, identifying four distinct user clusters ranging from Hyperpolite to Hyperefficient. We then scaled up to 18,000 simulated conversations across five politeness profiles (including impolite) and three open-weight models. Results show that politeness is not only cosmetic: it systematically […]

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Group Relative Policy Optimization (GRPO) significantly enhances the reasoning performance of Large Language Models (LLMs). However, this success heavily relies on expensive external verifiers or human rules. Such dependency not only leads to significant computational costs and training latency, but also yields sparse rewards that hinder optimization efficiency. To address these challenges, we propose Latent-GRPO, a framework that derives intrinsic rewards directly from latent space geometry. Crucially, our empirical analysis reveals a compelling geometric property: terminal token representations […]

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Let 1 < a1 < a2 < · · · be integers with ( sum_{k=1}^infty a_k^{-1}<infty ), and set ( F(s)=1+sum_{k=1}^infty a_k^{-s}, qquad Re s>1. ) A question of Erdős and Ingham, recorded as Erdős Problem #967 in a compilation by T. F. Bloom (accessed 2025–12–01), asks whether one always has ( F(1+it)neq 0 ) for all real t. This paper does not resolve the problem; instead, it develops a modern dynamical-systems framework for its study. Using the […]

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Not everything is about saving time. It’s about solving a real problem and things that matter. Everywhere you look, someone’s building an AI tool to save you time. There’s an app to plan your meals, one to write your emails, another to manage your money automatically. It’s all supposed to make life effortless. But lately, I’ve started to wonder if effort itself is the point. A few years ago, I set up a budgeting app that tracked everything. […]

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