When we optimize responses using a reward model as a proxy for “goodness” in reinforcement learning, models sometimes learn to “hack” this proxy and output an answer that only “looks good” to it (because coming up with an answer that is actually good can be hard). The philosophy behind confessions is that we can train models to produce a second output — aka a “confession” — that is rewarded solely for honesty, which we will argue is less […]

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arXiv:2601.19951v1 Announce Type: new Abstract: Symbolic music representation is a fundamental challenge in computational musicology. While grid-based representations effectively preserve pitch-time spatial correspondence, their inherent data sparsity leads to low encoding efficiency. Discrete-event representations achieve compact encoding but fail to adequately capture structural invariance and spatial locality. To address these complementary limitations, we propose Pianoroll-Event, a novel encoding scheme that describes pianoroll representations through events, combining structural properties with encoding efficiency while maintaining temporal dependencies and local spatial […]

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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:2604.16921v1 Announce Type: new Abstract: In this paper, we study the many-to-many matching problem on planar point sets with integer coordinates: Given two disjoint sets $R,B subset [Delta]^2$ with $|R|+|B|=n$, the goal is to select a set of edges between $R$ and $B$ so that every point is incident to at least one edge and the total Euclidean length is minimized. In the general case that $R$ and $B$ are point sets in the plane, the best-known algorithm […]

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Cultural historian Thomas Berry issues a profound summons to reimagine humanity’s place within the living fabric of the Earth. He envisions a radical transformation—from a species that wounds its home to one that nurtures it—calling for the “resacralization of nature” and a renewal of our ecological consciousness. More than a philosophical reflection, Berry’s work is a moral and spiritual manifesto, demanding an overhaul of our ethics, politics, economics, and education. Only through this sweeping reorientation, he insists, can […]

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Human mobility trajectories are widely studied in public health and social science, where different demographic groups exhibit significantly different mobility patterns. However, existing trajectory generation models rarely capture this heterogeneity because most trajectory datasets lack demographic labels. To address this gap in data, we propose ATLAS, a weakly supervised approach for demographic-conditioned trajectory generation using only (i) individual trajectories without demographic labels, (ii) region-level aggregated mobility features, and (iii) region-level demographic compositions from census data. ATLAS trains a […]

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## What I’ve learned building an agent for Renovate config (as a cautious skeptic of AI) By @mend [ 9 Min read ] As an opportunity to “kick the tyres” of what agents are and how they work, I set aside a couple of hours […]

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Hi I’m currently looking to use DQN to implement an ai that plays yugioh (a two player card game), but have had basically no experience with Ml. I don’t know if I am underestimating the complexity of this, given how complex yugioh is, but with how big the size of the state that needs to be fed in is, along with the number of actions that need to be mapped (possibly around 120 total possible moves, though obviously […]

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arXiv:2603.15771v1 Announce Type: new Abstract: Autonomous driving requires safe planning, but most learning-based planners lack explicit self-correction ability: once an unsafe action is proposed, there is no mechanism to correct it. Thus, we propose CorrectionPlanner, an autoregressive planner with self-correction that models planning as motion-token generation within a propose, evaluate, and correct loop. At each planning step, the policy proposes an action, namely a motion token, and a learned collision critic predicts whether it will induce a collision […]

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