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Crime pattern analysis is critical for law enforcement and predictive policing, yet the surge in criminal activities from rapid urbanization creates high-dimensional, imbalanced datasets that challenge traditional classification methods. This study presents a quantum-classical comparison framework for crime analytics, evaluating four computational paradigms: quantum models, classical baseline machine learning models, and two hybrid quantum-classical architectures. Using 16-year Bangladesh crime statistics, we systematically assess classification performance and computational efficiency under rigorous cross-validation methods. Experimental results show that quantum-inspired approaches, […]

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Replacing gaze annotations with language-driven attention masking makes robot perception annotation-free and up to 5x faster at inference. Picture a robot arm sitting across a table from you. You say: “Put the black bowl in the drawer.” The arm moves. But not toward the bowl. It hovers. It hesitates. Then it grabs the wrong thing. From the outside this looks like a minor coordination failure. From the inside, it is a fundamental problem with how the robot perceives […]

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When OpenAI pulled the plug on its AI video app Sora in March, a landmark deal with Disney evaporated with it.  Under the three-year agreement, Sora would have generated short fan-created videos drawing on more than 200 characters from Disney, Marvel, Pixar, and Star Wars, with a curated selection streaming on Disney+. Disney had also committed to a $1 billion equity investment in OpenAI and was set to deploy ChatGPT enterprise-wide for its employees.  The agreement was supposed to […]

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I use AI tools every day. I think they’re genuinely brilliant. I hand Claude a disorganized pile of research notes and it finds connections I missed. I give it a rough thesis and it stress-tests it from angles I hadn’t considered. I’ve built entire websites, course curricula, and client deliverables with AI assistance. The technology is real, it’s powerful, and it has already changed how I work. But unless there is a serious breakthrough, AI is not ready […]

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Clinical Reasoning on Electronic Health Records (EHRs) is a fundamental yet challenging task in modern healthcare. While in-context learning (ICL) offers a promising inference-time adaptation paradigm for large language models (LLMs) in EHR reasoning, existing methods face three fundamental challenges: (1) Perspective Limitation, where data-driven similarity fails to align with LLM reasoning needs and model-driven signals are constrained by limited clinical competence; (2) Cohort Awareness, as demonstrations are selected independently without modeling population-level structure; and (3) Information Aggregation, […]

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One of the hardest parts of reinforcement learning isn’t the algorithm — it’s the reward function. You combine multiple objectives into a scalar reward, run training for hours, and the agent learns to optimize only one of them. Not because the others don’t matter, but because their gradients were too weak to compete. I built a tool to help catch this before training: Reward Design Workbench You define your reward components, set realistic state ranges, and the tool […]

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i know it might be a stupid question but what are your thoughts on specialised post-training becoming a narrower wedge over time? If the base models are already able to 80% agentic tasks out of box and ~15% can be covered by system prompt + few shot engineering, is specialised RL post-training worth the investment there? do companies like Prime-Intellect exist in that world? submitted by /u/Sharp_Variation7003 [link] [comments]

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