arXiv:2602.05053v1 Announce Type: new Abstract: Inclement weather conditions can significantly impact driver visibility and tire-road surface friction, requiring adjusted safe driving speeds to reduce crash risk. This study proposes a hybrid predictive framework that recommends real-time safe speed intervals for freeway travel under diverse weather conditions. Leveraging high-resolution Connected Vehicle (CV) data and Road Weather Information System (RWIS) data collected in Buffalo, NY, from 2022 to 2023, we construct a spatiotemporally aligned dataset containing over 6.6 million records […]

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arXiv:2602.23370v1 Announce Type: new Abstract: Long-document topic segmentation plays an important role in information retrieval and document understanding, yet existing methods still show clear shortcomings in ultra-long text settings. Traditional discriminative models are constrained by fixed windows and cannot model document-level semantics; generative large language models can output paragraph boundaries, but inference is expensive and long inputs are difficult to support. To address these issues, we propose a discriminative segmentation model based on Qwen3-0.6B. On top of the […]

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We study reinforcement learning with delayed state observation, where the agent observes the current state after some random number of time steps. We propose an algorithm that combines the augmentation method and the upper confidence bound approach. For tabular Markov decision processes (MDPs), we derive a regret bound of $tilde{mathcal{O}}(H sqrt{D_{max} SAK})$, where $S$ and $A$ are the cardinalities of the state and action spaces, $H$ is the time horizon, $K$ is the number of episodes, and $D_{max}$ […]

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arXiv:2602.05031v1 Announce Type: new Abstract: Planning with a learned model remains a key challenge in model-based reinforcement learning (RL). In decision-time planning, state representations are critical as they must support local cost computation while preserving long-horizon structure. In this paper, we show that the Laplacian representation provides an effective latent space for planning by capturing state-space distances at multiple time scales. This representation preserves meaningful distances and naturally decomposes long-horizon problems into subgoals, also mitigating the compounding errors […]

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arXiv:2602.02544v1 Announce Type: new Abstract: While Diffusion Language Models (DLMs) offer a flexible, arbitrary-order alternative to the autoregressive paradigm, their non-causal nature precludes standard KV caching, forcing costly hidden state recomputation at every decoding step. Existing DLM caching approaches reduce this cost by selective hidden state updates; however, they are still limited by (i) costly token-wise update identification heuristics and (ii) rigid, uniform budget allocation that fails to account for heterogeneous hidden state dynamics. To address these challenges, […]

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arXiv:2603.00968v1 Announce Type: new Abstract: The Nash-Sutcliffe efficiency ($text{NSE}$) is a widely used, positively oriented relative measure for evaluating forecasts across multiple time series. However, it lacks a decision-theoretic foundation for this purpose. To address this, we examine its negatively oriented counterpart, which we refer to as Nash-Sutcliffe loss, defined as $L_{text{NS}} = 1 – text{NSE}$. We prove that $L_{text{NS}}$ is strictly consistent for an elicitable and identifiable multi-dimensional functional, which we name the Nash-Sutcliffe functional. This functional […]

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If you are active on X (aka Twitter), you must be aware that Elon Musk keeps posting things from his personal X handle. He is the richest person on the planet, so what he thinks about the future, technology, and AI immediately becomes a headline. Besides courtroom rivalry with OpenAI and Microsoft, Musk’s favorite topic these days appears to be robotaxis. Or maybe he is talking about them because his automotive company, Tesla, plans to roll out a […]

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Getting locked out of your digital wallet can feel like watching your set of house keys drop into the ocean as you stand on the shore. You may not expect it, but it’s over before you realize it and leaves a sting forever—not to mention the financial losses. In most cases with crypto, you’re the only person who has control of your private keys. No one else can get access to your funds, so no one else can […]

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:::info Astounding Stories of Super-Science October, 1994, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. The Picture of Dorian Gray – Chapter XV Astounding Stories of Super-Science October 1994: The Picture of Dorian Gray – Chapter XV By Oscar Wilde ::: That evening, at eight-thirty, exquisitely dressed and wearing a large button-hole of Parma violets, Dorian Gray was ushered into Lady Narborough’s drawing-room by bowing servants. […]

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arXiv:2407.04117v3 Announce Type: replace-cross Abstract: Recent years have witnessed a growing call for renewed emphasis on neuroscience-inspired approaches in artificial intelligence research, under the banner of NeuroAI. A prime example of this is predictive coding networks (PCNs), based on the neuroscientific framework of predictive coding. This framework views the brain as a hierarchical Bayesian inference model that minimizes prediction errors through feedback connections. Unlike traditional neural networks trained with backpropagation (BP), PCNs utilize inference learning (IL), a more […]

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