March 2026

The Context Reliability Framework (CRF): A Production Architecture for Trustworthy AI Systems

The Context Reliability Framework (CRF): A Production Architecture for Trustworthy AI Systems

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Introducing the Context Reliability Framework (CRF) for Production-Grade AI Systems Introduction Most engineers blame the model when an AI system fails. If an output is incorrect, hallucinated, or inconsistent, the immediate assumption is that the model is unreliable. Engineers start tweaking prompts, adjusting temperature settings, or swapping models entirely. But in real production environments, that is rarely the root cause. Failures usually happen before the model is invoked — inside the context pipeline that feeds the model. This is the hidden reliability problem in […]

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Disney cancels $1 billion OpenAI partnership amid Sora shutdown plans

Disney cancels $1 billion OpenAI partnership amid Sora shutdown plans

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OpenAI’s recently announced plans to shutter its Sora video-generating app have also scuttled the company’s planned $1 billion licensing partnership with Disney, according to multiple press reports. “As the nascent AI field advances rapidly, we respect OpenAI’s decision to exit the video generation business and to shift its priorities elsewhere,” Disney said in a statement provided to media outlets. “We appreciate the constructive collaboration between our teams and what we learned from it, and we will continue to […]

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CGRL: Causal-Guided Representation Learning for Graph Out-of-Distribution Generalization

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Graph Neural Networks (GNNs) have achieved impressive performance in graph-related tasks. However, they suffer from poor generalization on out-of-distribution (OOD) data, as they tend to learn spurious correlations. Such correlations present a phenomenon that GNNs fail to stably learn the mutual information between prediction representations and ground-truth labels under OOD settings. To address these challenges, we formulate a causal graph starting from the essence of node classification, adopt backdoor adjustment to block non-causal paths, and theoretically derive a […]

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Honda cancels the two electric vehicles it was developing with Sony

Honda cancels the two electric vehicles it was developing with Sony

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Earlier this month Honda decided to cancel a trio of electric vehicles it was planning to build in the US. And those cancellations are having a ripple effect. Today Sony Honda Mobility—the automaker’s joint venture with the electronics and entertainment company—announced that it won’t bring its EVs to market either. Although Honda was an early adopter of hybrid technology, it has been left badly lagging when it comes to developing battery-electric cars. The diminutive Honda e might look like […]

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Greedy Algorithms in Practice: Building an FMCG Route Optimization Solver

Greedy Algorithms in Practice: Building an FMCG Route Optimization Solver

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As a product analyst, how I turned a supply chain constraint into a step by step optimization engine. AI Agent for FMCG Route Optimization Introduction: The Real Problem When on-time delivery in a major FMCG (Fast-Moving Consumer Goods) territory sat at 72%, the immediate instinct of management was to blame the trucks, the drivers, or the dispatch process. But the real culprit was invisible to the naked eye: no one knew what was sitting in a retailer’s stockroom until […]

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Why My PyTorch Diffusion Model Was Slow — and How I Made It 3× Faster

Why My PyTorch Diffusion Model Was Slow — and How I Made It 3× Faster

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Training a Diffusion Model — even on a “simple” dataset like MNIST — is a trial by fire for your hardware. You expect the GPU to do the heavy lifting, but more often than not, your expensive silicon is sitting idle, waiting for a sluggish pipeline to throw it a bone. I recently took a DDPM (Denoising Diffusion Probabilistic Model) U-Net and put it through a rigorous optimization gauntlet. What started as a sluggish 3 minute and 10 second epoch ended in a […]

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C-STEP: Continuous Space-Time Empowerment for Physics-informed Safe Reinforcement Learning of Mobile Agents

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Safe navigation in complex environments remains a central challenge for reinforcement learning (RL) in robotics. This paper introduces Continuous Space-Time Empowerment for Physics-informed (C-STEP) safe RL, a novel measure of agent-centric safety tailored to deterministic, continuous domains. This measure can be used to design physics-informed intrinsic rewards by augmenting positive navigation reward functions. The reward incorporates the agents internal states (e.g., initial velocity) and forward dynamics to differentiate safe from risky behavior. By integrating C-STEP with navigation rewards, […]

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Learning Mesh-Free Discrete Differential Operators with Self-Supervised Graph Neural Networks

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Mesh-free numerical methods provide flexible discretisations for complex geometries; however, classical meshless discrete differential operators typically trade low computational cost for limited accuracy or high accuracy for substantial per-stencil computation. We introduce a parametrised framework for learning mesh-free discrete differential operators using a graph neural network trained via polynomial moment constraints derived from truncated Taylor expansions. The model maps local stencils relative positions directly to discrete operator weights. The current work demonstrates that neural networks can learn classical […]

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Experiential Reflective Learning for Self-Improving LLM Agents

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Recent advances in large language models (LLMs) have enabled the development of autonomous agents capable of complex reasoning and multi-step problem solving. However, these agents struggle to adapt to specialized environments and do not leverage past interactions, approaching each new task from scratch regardless of their accumulated experience. We introduce Experiential Reflective Learning (ERL), a simple self-improvement framework that enables rapid environment adaptation through experiential learning. ERL reflects on task trajectories and outcomes to generate heuristics, capturing actionable […]

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How unconstrained machine-learning models learn physical symmetries

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The requirement of generating predictions that exactly fulfill the fundamental symmetry of the corresponding physical quantities has profoundly shaped the development of machine-learning models for physical simulations. In many cases, models are built using constrained mathematical forms that ensure that symmetries are enforced exactly. However, unconstrained models that do not obey rotational symmetries are often found to have competitive performance, and to be able to emph{learn} to a high level of accuracy an approximate equivariant behavior with a […]

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