arXiv:2602.15236v1 Announce Type: new Abstract: Virtual screening aims to efficiently identify active ligands from massive chemical libraries for a given target pocket. Recent CLIP-style models such as DrugCLIP enable scalable virtual screening by embedding pockets and ligands into a shared space. However, our analyses indicate that such representations can be insensitive to fine-grained binding interactions and may rely on shortcut correlations in training data, limiting their ability to rank ligands by true binding compatibility. To address these issues, […]

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Hi everyone, I’m working on a university project and could really use some advice from people with more experience in autonomous navigation / RL / simulation. Task: I need to design a deep learning model that directly controls a drone (x, y, z, pitch, yaw — roll probably doesn’t make much sense here 😅). The drone should autonomously patrol and map indoor and outdoor environments. Example use case: A warehouse where the drone automatically flies through all aisles […]

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arXiv:2604.14243v1 Announce Type: new Abstract: Real-world decision-making systems operate in environments where state transitions depend not only on the agent’s actions, but also on textbf{exogenous factors outside its control}–competing agents, environmental disturbances, or strategic adversaries–formally, $s_{h+1} = f(s_h, a_h, bar{a}_h)+omega_h$ where $bar{a}_h$ is the adversary/external action, $a_h$ is the agent’s action, and $omega_h$ is an additive noise. Ignoring such factors can yield policies that are optimal in isolation but textbf{fail catastrophically in deployment}, particularly when safety constraints must […]

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arXiv:2504.21787v3 Announce Type: replace-cross Abstract: We study the problem of estimating a distribution over a finite alphabet from an i.i.d. sample, with accuracy measured in relative entropy (Kullback-Leibler divergence). While optimal bounds on the expected risk are known, high-probability guarantees remain less well-understood. First, we analyze the classical Laplace (add-one) estimator, obtaining matching upper and lower bounds on its performance and establishing its optimality among confidence-independent estimators. We then characterize the minimax-optimal high-probability risk and show that it […]

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arXiv:2602.05790v1 Announce Type: cross Abstract: Fast computation of a matrix product $W^top X$ is a workhorse of modern LLMs. To make their deployment more efficient, a popular approach is that of using a low-precision approximation $widehat W$ in place of true $W$ (“weight-only quantization”). Information theory demonstrates that an optimal algorithm for reducing precision of $W$ depends on the (second order) statistics of $X$ and requires a careful alignment of vector quantization codebook with PCA directions of $X$ […]

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Combining Large Language Models (LLMs) with Reinforcement Learning (RL) enables agents to interpret language instructions more effectively for task execution. However, LLMs typically lack direct perception of the physical environment, which limits their understanding of environmental dynamics and their ability to generalize to unseen tasks. To address this limitation, we propose Visual-Language Knowledge-Guided Offline Reinforcement Learning (VLGOR), a framework that integrates visual and language knowledge to generate imaginary rollouts, thereby enriching the interaction data. The core premise of […]

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Machine learning (ML)-based models have demonstrated high skill and computational efficiency, often outperforming conventional physics-based models in weather and subseasonal predictions. While prior studies have assessed their fidelity in capturing synoptic-scale atmospheric dynamics, their performance across timescales and under out-of-distribution forcing, such as +3K or +4K uniform-warming forcings, and the sources of biases remain elusive, to establish the model reliability for Earth science. Here, we design three sets of experiments targeting synoptic-scale phenomena, interannual variability, and out-of-distribution uniform-warming […]

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arXiv:2603.10026v1 Announce Type: new Abstract: Operator fusion, as a key performance optimization technique in the deployment of AI models, significantly improves execution efficiency and has been widely adopted in modern AI compilers. However, for cascaded reduction operations involving multiple loops with inter-loop data dependencies, such as the safe softmax followed by GEMM within attention mechanisms, existing compilers lack effective automated fusion and kernel generation capabilities. Although some works have addressed specific instances through hand-crafted fusion strategies, their solutions […]

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