January 2026

In-context learning (ICL) has proven highly effective across diverse large language model (LLM) tasks. However, its potential for enhancing tasks that demand step-by-step logical deduction, such as mathematical reasoning, remains underexplored. A core limitation of existing ICL approaches is their static use of demonstrations: examples are pre-selected before inference and remain fixed, failing to adapt to the dynamic confusion points that often arise during multi-step reasoning such as ambiguous calculations or logical gaps. These unresolved confusion points can […]

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Constrained Reinforcement Learning (CRL) aims to maximize cumulative rewards while satisfying constraints. However, existing CRL algorithms often encounter significant constraint violations during training, limiting their applicability in safety-critical scenarios. In this paper, we identify the underestimation of the cost value function as a key factor contributing to these violations. To address this issue, we propose the Memory-driven Intrinsic Cost Estimation (MICE) method, which introduces intrinsic costs to mitigate underestimation and control bias to promote safer exploration. Inspired by […]

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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. ## Governing and Scaling AI Agents: Operational Excellence and the Road Ahead By @denisp [ 23 Min read ] Success isn’t building the agent; it’s managing it. From “AgentOps” to ROI dashboards, here is the operational playbook for scaling Enterprise AI. Read More. The Seven […]

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I’m new to RL and was wondering what the current literature says on the accuracy of training an LLM (Qwen3 8B for example) using RL on the task of predicting the number of hours a software engineering PR took to make. Basically the inputs for the agent would be a pr with metadata and the full patch and the output would be a float value from 0.0 to theoretically infinity, although for the purpose of creating a good […]

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The rapid proliferation of airborne platforms, including commercial aircraft, drones, and UAVs, has intensified the need for real-time, automated threat assessment systems. Current approaches depend heavily on manual monitoring, resulting in limited scalability and operational inefficiencies. This work introduces a dual-task model based on EfficientNetB4 capable of performing airborne object classification and threat-level prediction simultaneously. To address the scarcity of clean, balanced training data, we constructed the AODTA Dataset by aggregating and refining multiple public sources. We benchmarked […]

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When using Crossplane to provision Azure resources from Kubernetes, authentication becomes a critical challenge. Traditional approaches using service principal secrets are insecure and operationally complex. This blog post shares how we solved Azure authentication using Workload Identity Federation across three distinct deployment scenarios: Local Development: Kind cluster with Crossplane on developer laptops CI/CD Pipeline: GitHub Actions running Kind cluster with Crossplane for automated testing Production: EKS cluster with Crossplane managing Azure infrastructure Each scenario presented unique challenges, and […]

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Fairness-aware machine learning has recently attracted various communities to mitigate discrimination against certain societal groups in data-driven tasks. For fair supervised learning, particularly in pre-processing, there have been two main categories: data fairness and task-tailored fairness. The former directly finds an intermediate distribution among the groups, independent of the type of the downstream model, so a learned downstream classification/regression model returns similar predictive scores to individuals inputting the same covariates irrespective of their sensitive attributes. The latter explicitly […]

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To develop a deep-learning method for achieving fast high-resolution MR elastography from highly undersampled data without the need of high-quality training dataset. We first framed the deep neural network representation as a nonlinear extension of the linear subspace model, then used it to represent and reconstruct MRE image repetitions from undersampled k-space data. The network weights were learned using a multi-level k-space consistent loss in a self-supervised manner. To further enhance reconstruction quality, phase-contrast specific magnitude and phase […]

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Hey there. I’m currently writing an assignment paper comparing the performance of various deep RL algorithms for continuous control. All was going pretty smoothly, until I hit a wall with finding publicly available data for MuJoCo v4/v5 environments. I searched the most common sources, such as algorithm implementation papers or StableBaselines / Tianshou repositories, but almost all reported results are based on older MuJoCo versions (v1/v2/v3), which are not really comparable to the modern environments. If anyone knows […]

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