The Silicon Wars 2025: NVIDIA vs AMD vs Intel vs Huawei
From NVIDIA Blackwell to AMD MI350 and Intel Gaudi 3, discover the specs, strategies, and geopolitics shaping AI’s hardware future. The race to power…
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Entropy-Aware Training: Building Self-Regulating AI
Entropy-aware training leverages information theory to regulate uncertainty in AI. By monitoring and minimizing entropy, models become more stable, generalizable, and trustworthy. Verified use…
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Cognitive Black Holes: Preventing AI Collapse
Artificial intelligence faces a hidden threat: cognitive black holes. When models are trained on their own outputs, they spiral into collapse—producing distorted, self-referential, and…
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How Agentic Reflex Loops Might Make AI Scalable, Faster, and Cheaper
Agentic Reflex Loops are lightweight AI subsystems that handle routine tasks instantly, reducing LLM load and latency. Inspired by human reflexes, they enable fast,…
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Training Deep Reasoning Architectures for HRM–MoE AI Systems
This article introduces a structured curriculum to train Hierarchical Reasoning Models(HRM)–Mixture of Experts(MOE) hybrid systems — combining reasoning depth with scalable specialization. It’s a…
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Hierarchical Reasoning Meets Mixture of Experts: Pathways to Scalable, Deep-Thinking AI
This article delves into the potential of combining Hierarchical Reasoning Models (HRMs) and Mixture of Experts (MoE) to create AI systems that excel in…
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