Constantine Goltsev is the Co-founder & CTO of Apolo. With 20+ years of experience in leading tech teams and building AI-driven solutions, he brings deep expertise in machine learning, cloud infrastructure, and digital publishing. He holds a BA in Applied Mathematics from UC Berkeley.
The Hierarchical Reasoning Model (HRM) is a brain-inspired architecture that overcomes the depth limitations of current LLMs by reasoning in layers. Using two nested recurrent modules - fast, low-level processing and slower, high-level guidance, it achieves state-of-the-art results on complex reasoning benchmarks with only 27M parameters. HRM’s design enables adaptive computation, interpretable problem-solving steps, and emergent dimensional hierarchies similar to those seen in the brain. While tested mainly on structured puzzles, its efficiency and architectural innovation hint at a promising alternative to brute-force scaling.
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At the 2025 International Mathematical Olympiad, AI models from OpenAI and DeepMind achieved gold-medal level performance, solving 5 out of 6 challenging math problems. This historic milestone marks a turning point in AI’s ability to reason creatively, not just compute. The article traces the evolution from formal theorem provers to language-based models, exploring benchmark controversies and how extended deliberation unlocked new capabilities. It also examines the broader implications for math education, research, and the future of machine reasoning.
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In 2025, AI is quietly entering the "plumbing phase" of AGI, where integration, infrastructure, and iteration are driving real progress. Instead of flashy model releases, we’re seeing AI agents write production code, optimize their own training, and even discover new algorithms. But significant gaps remain in robustness, continual learning, and contextual understanding. The next breakthroughs may come not from larger models, but from smarter engineering, better protocols, and systems that can learn and adapt over time.
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Modern AI dazzles with feats like theorem-proving yet still bungles grade-school logic, creating a “jagged frontier” of uneven skills. This article unpacks new evidence—from Salesforce’s SIMPLE puzzle benchmark, IBM-led Enterprise Bench, and Apple’s controversial “Illusion of Thinking” study—to show why LLM brilliance can hide catastrophic blind spots and what that means for anyone betting their business on AI.
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AI systems like Robin and Zochi are no longer just tools - they’re emerging as autonomous researchers. From proposing drug treatments to publishing peer-reviewed papers, these multi-agent AI scientists signal a radical shift in how scientific discovery is conducted, accelerating breakthroughs and challenging the role of human researchers.
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Large language model hallucinations—when AI generates false but convincing information—have become a serious real-world problem, impacting fields like law and academia. New research shows these hallucinations stem from specific, traceable neural mechanisms rather than random errors, opening the door to better understanding, prediction, and potential control.
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Modern LLMs use reward models—trained to reflect human preferences—to align their behavior through RLHF. While effective, this approach faces challenges like reward hacking and Goodhart's law. New research offers solutions such as verifiable feedback, constrained optimization, and self-critiquing models to improve alignment and reliability in complex tasks.
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Transformers have powered today’s AI revolution—but limitations around speed, memory, and scalability are becoming clear. This article explores three promising alternatives: diffusion-based LLMs that generate text in parallel for faster, more controllable outputs; Mamba’s state space models, which scale to million-token contexts without quadratic costs; and Titans, a memory-augmented architecture that can learn new information at inference time. Each approach tackles core challenges in latency, context handling, and long-term reasoning—opening new opportunities for businesses to reduce compute costs and deploy smarter, more adaptable AI systems.
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As AI evolves toward reasoning models and near-AGI, enterprises need secure, scalable, and compliant infrastructure. Apolo offers an on-prem, future-ready AI stack—built with data centers—that supports model deployment, fine-tuning, and inference at scale. Designed for privacy, agility, and rapid AI growth, Apolo empowers organizations to stay in control as the AI revolution accelerates.
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AI is transforming data centers, enabling businesses across industries to drive real revenue through faster, smarter infrastructure. Apolo’s multi-tenant MLOps platform supports these advancements, allowing companies to unlock the full potential of AI for tangible business outcomes.
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Apolo turns your Data Center into an AI Powerhouse.
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