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The deployment of agentic artificial intelligence systems in clinical environments is accelerating rapidly, with autonomous agents increasingly applied across radiology, clinical decision support, intensive care monitoring, drug discovery, and patient facing care. Unlike conventional single turn AI tools, agentic systems autonomously plan multistep tasks, invoke external tools, retain memory across interactions, and pursue clinical goals with minimal human intervention, introducing a qualitatively distinct and poorly characterised safety profile that existing literature has not comprehensively addressed. This paper addresses […]

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Here are two beliefs that are sort of haunting me right now: Folk who try to push people to uphold principles (whether established ones or novel ones), are kinda an important bedrock of civilization. Also, those people are really annoying and often, like, a little bit crazy And these both feel fairly important. I’ve learned a lot from people who have some kind of hobbyhorse about how society is treating something as okay/fine, when it’s not okay/fine. When […]

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I keep coming back to this idea that reinforcement learning isn’t really being used the way people think it is. A lot of systems right now feel like they’re really good at pattern recognition but not actually getting better from outcomes. They can predict what should work, but they don’t really remember whether something did work in a good way that changes future decisions. Does anyone have thoughts on this? submitted by /u/TaleAccurate793 [link] [comments]

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Scaling critic capacity is a promising direction for enhancing off-policy reinforcement learning (RL). However, larger critics are prone to overfitting and unstable in replay-buffer-based bootstrap training. This paper leverages Low-Rank Adaptation (LoRA) as a structural-sparsity regularizer for off-policy critics. Our approach freezes randomly initialized base matrices and solely optimizes low-rank adapters, thereby constraining critic updates to a low-dimensional subspace. Built on top of SimbaV2, we further develop a LoRA formulation, compatible with SimbaV2, that preserves its hyperspherical normalization […]

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Interventions implemented in geographic space (geo-interventions), have had success in reducing preventable deaths across the world. However, many studies supporting geo-interventions have focused on where to implement them rather than what they are. In this paper, we answer how to model and generate geo-interventions using spatial data, providing what these geo-interventions are and where to apply them. We defined geo-intervention modelling as a problem of optimizing actions and their locations, given the objective of maximizing predicted outcomes. To […]

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Real-time video object detection on unmanned aerial vehicles (UAVs) is essential for urban inspection and autonomous perception, yet its deployment on edge devices is severely constrained by the high computational cost of accurate detectors, the quantization sensitivity of hybrid convolution-attention networks, and the system-level latency of full video processing pipelines. To address these challenges, we present DUST-YOLO, a deployment-oriented algorithm-hardware co-design framework for lightweight and efficient UAV small-object detection on edge platforms. First, we introduce a multi-dimensional structured […]

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Recognizing emotions from written text is a very important part of Natural Language Processing (NLP) and is commonly used for feeling or sentiment analysis or keeping track of someone’s mental health status. This study uses a readable emotion-detecting framework with a RoBERTa-base model that has been modified and trained specifically for the Emotions for NLP dataset and provides an accuracy of 0.924% and f1 score of 0.925%. The main contributions of this study are the use of four […]

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Whole-slide imaging has transformed histopathology into a data-intensive domain, with current approaches dominated by end-to-end deep learning that encode morphology implicitly within latent representations. This limits interpretability, reproducibility, and cross-dataset generalization. This review positions histomics as an intermediate phenotype representation layer that maps histological images to structured, multi-scale descriptors of tissue morphology, spatial organization, and architectural context. A unified taxonomy of histomic features across biological scales is presented, along with an analysis of artificial intelligence frameworks spanning classical […]

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While Large Language Models (LLMs) have shown great promise in transforming credit risk assess-ment, existing evaluation frameworks are almost exclusively concerned with general financial NLP tasks and neglect the specific reasoning needed by practitioners. To address this, we develop the Credit Context Log Understanding and Prediction Evaluation (CCLUPE) benchmark. Unlike the previous benchmarks, CCLUPE aims to capture and evaluate the intricate reasoning unique to each constituent of the Chinese credit market, where evaluations are heavily based on the […]

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