Reinforcement Learning (RL) offers a powerful framework for optimizing dynamic treatment regimes (DTRs). However, clinical RL is fundamentally bottlenecked by reward engineering: the challenge of defining signals that safely and effectively guide policy learning in complex, sparse offline environments. Existing approaches often rely on manual heuristics that fail to generalize across diverse pathologies. To address this, we propose an automated pipeline leveraging Large Language Models (LLMs) for offline reward design and verification. We formulate the reward function using […]

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I used to think that when AI initiatives stalled, it was because the models weren’t good enough. Over time, what I’ve learned is that this is rarely the case. More often, AI exposes something else entirely: programs that were never designed to support systems that learn. In many organizations, AI is treated as the most complex part of the stack. In practice, it’s often the surrounding program design – how work is structured, governed, and measured – that […]

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arXiv:2601.21789v1 Announce Type: cross Abstract: We introduce ECSEL, an explainable classification method that learns formal expressions in the form of signomial equations, motivated by the observation that many symbolic regression benchmarks admit compact signomial structure. ECSEL directly constructs a structural, closed-form expression that serves as both a classifier and an explanation. On standard symbolic regression benchmarks, our method recovers a larger fraction of target equations than competing state-of-the-art approaches while requiring substantially less computation. Leveraging this efficiency, ECSEL […]

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[Crossposted on lesswrong, see here for prior posts] The following statements seem to be both important for AI safety and are not widely agreed upon. These are my opinions, not those of my employer or colleagues. As is true for anything involving AI, there is significant uncertainty about everything written below. However, for readability, I present these points in their strongest form, without hedges and caveats. That said, it is essential not to be dogmatic, and I am […]

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arXiv:2601.04193v1 Announce Type: cross Abstract: We propose a discrete transport equation on graphs which connects distributions on both vertices and edges. We then derive a discrete analogue of the Benamou-Brenier formulation for Wasserstein-$1$ distance on a graph and as a result classify all $W_1$ geodesics on graphs.

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arXiv:2601.18833v1 Announce Type: new Abstract: Since the early 90s, the evolution of the Business Process Management (BPM) discipline has been punctuated by successive waves of automation technologies. Some of these technologies enable the automation of individual tasks, while others focus on orchestrating the execution of end-to-end processes. The rise of Generative and Agentic Artificial Intelligence (AI) is opening the way for another such wave. However, this wave is poised to be different because it shifts the focus from […]

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arXiv:2601.05336v1 Announce Type: new Abstract: Designing intuitive interfaces for robotic control remains a central challenge in enabling effective human-robot interaction, particularly in assistive care settings. Eye gaze offers a fast, non-intrusive, and intent-rich input modality, making it an attractive channel for conveying user goals. In this work, we present GAMMA (Gaze Assisted Manipulation for Modular Autonomy), a system that leverages ego-centric gaze tracking and a vision-language model to infer user intent and autonomously execute robotic manipulation tasks. By […]

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arXiv:2602.21247v1 Announce Type: new Abstract: The fastest indexes for Approximate Nearest Neighbor Search today are also the slowest to build: graph-based methods like HNSW and Vamana achieve state-of-the-art query performance but have large construction times due to relying on random-access-heavy beam searches. We introduce PiPNN (Pick-in-Partitions Nearest Neighbors), an ultra-scalable graph construction algorithm that avoids this “search bottleneck” that existing graph-based methods suffer from. PiPNN’s core innovation is HashPrune, a novel online pruning algorithm which dynamically maintains sparse […]

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How are you, hacker? 🪐 What’s happening in tech today, February 20, 2026? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, we present you with these top quality stories. From I Migrated My Blog From Jekyll to Hugo – Or At Least, I Almost Did to Living With the Lethal Trifecta: A Guide to Personal AI Agent Security, let’s dive right in. Developer Hackathon by {{Company}} HackerNoon? By @hackmarketing [ 2 Min […]

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