We live in a world obsessed with speed. Faster apps, quicker deliveries, instant updates, and endless streams of information define our daily lives. In this race to move faster, topics that require patience and long-term thinking—like the environment—often feel slow, inconvenient, or easy to ignore. Yet, the environmental challenges we face today are deeply connected to the technology-driven world we are building. Writing about the environment in such a fast-paced, tech-addicted era feels both challenging and necessary. The […]

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arXiv:2602.17778v1 Announce Type: new Abstract: Multi-turn interaction length is a dominant factor in the operational costs of conversational LLMs. In this work, we present a new failure mode in conversational LLMs: turn amplification, in which a model consistently prolongs multi-turn interactions without completing the underlying task. We show that an adversary can systematically exploit clarification-seeking behavior$-$commonly encouraged in multi-turn conversation settings$-$to scalably prolong interactions. Moving beyond prompt-level behaviors, we take a mechanistic perspective and identify a query-independent, universal […]

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We study reinforcement learning in infinite-horizon discounted Markov decision processes with continuous state spaces, where data are generated online from a single trajectory under a Markovian behavior policy. To avoid maintaining an infinite-dimensional, function-valued estimate, we propose the novel Q-Measure-Learning, which learns a signed empirical measure supported on visited state-action pairs and reconstructs an action-value estimate via kernel integration. The method jointly estimates the stationary distribution of the behavior chain and the Q-measure through coupled stochastic approximation, leading […]

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arXiv:2601.16393v1 Announce Type: new Abstract: This paper presents a terrestrial GNSS-based orbit and clock estimation framework for lunar navigation satellites. To enable high-precision estimation under the low-observability conditions encountered at lunar distances, we develop a stochastic-cloning UD-factorized filter and delayed-state smoother that provide enhanced numerical stability when processing precise time-differenced carrier phase (TDCP) measurements. A comprehensive dynamics and measurement model is formulated, explicitly accounting for relativistic coupling between orbital and clock states, lunar time-scale transformations, and signal propagation […]

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arXiv:2604.08609v1 Announce Type: new Abstract: Digital forensic investigations increasingly rely on heterogeneous evidence such as images, scanned documents, and contextual reports. These artifacts may contain explicit or implicit expressions of harm, hate, threat, violence, or intimidation, yet existing automated approaches often assume clean text input or apply vision models without forensic justification. This paper presents a case-driven multimodal approach for hate and threat detection in forensic analysis. The proposed framework explicitly determines the presence and source of textual […]

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arXiv:2604.01328v1 Announce Type: new Abstract: Traditional scientific discovery relies on an iterative hypothesise-experiment-refine cycle that has driven progress for centuries, but its intuitive, ad-hoc implementation often wastes resources, yields inefficient designs, and misses critical insights. This tutorial presents Bayesian Optimisation (BO), a principled probability-driven framework that formalises and automates this core scientific cycle. BO uses surrogate models (e.g., Gaussian processes) to model empirical observations as evolving hypotheses, and acquisition functions to guide experiment selection, balancing exploitation of known […]

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The Cloudflare developer platform is excellent. The tooling gap is real. Cloudflare’s developer platform has matured significantly. D1 gives you a SQLite-compatible serverless database running at the edge. R2 offers S3-compatible object storage with zero egress fees. Workers KV delivers globally replicated key-value reads with single-digit millisecond latency. Cloudflare Images handles upload, optimization, transformation, and delivery via a CDN; all through a single API. The platform is genuinely capable. What it lacks is a decent local development experience […]

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arXiv:2603.25946v1 Announce Type: new Abstract: High infraction rates remain the primary bottleneck for end-to-end (E2E) autonomous driving, as evidenced by the low driving scores on the CARLA Leaderboard. Despite collision-related infractions being the dominant failure mode in closed-loop evaluations, collision-aware representation learning has received limited attention. To address this gap, we first develop a Video-Language-Augmented Anomaly Detector (VLAAD), leveraging a Multiple Instance Learning (MIL) formulation to obtain stable, temporally localized collision signals for proactive prediction. To transition these […]

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Continual learning in Large Language Models (LLMs) is hindered by the plasticity-stability dilemma, where acquiring new capabilities often leads to catastrophic forgetting of previous knowledge. Existing methods typically treat parameters uniformly, failing to distinguish between specific task knowledge and shared capabilities. We introduce Mixture of Sparse Experts for Task Agnostic Continual Learning (SETA), a framework that resolves the plasticity-stability conflict through adaptive sparse subspace decomposition into task-specific expert modules. Unlike standard updates, where tasks compete for the same […]

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