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2026-01-04 03:04:01 -

DeepSeek's latest research digs into a fascinating problem: hyper connections improve on residual networks but become unstable at scale. Their fix? A matrix normalization technique from 1967. Sometimes the best solutions are hiding in decades-old math papers

DeepSeek's latest research digs into a fascinating problem: hyper connections improve on residual networks but become unstable at scale. Their fix? A matrix normalization technique from 1967. Sometimes the best solutions are hiding in decades-old math papers 📚

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DeepSeek Researchers Apply a 1967 Matrix Normalization Algorithm to Fix Instability in Hyper Connections
DeepSeek researchers are trying to solve a precise issue in large language model training. Residual connections made very deep networks trainable, hyper connections widened that residual stream, and training then became unstable at scale. The new method mHC, Manifold Constrained Hyper Connections, keeps the richer topology of hyper connections but locks the mixing behavior on […] The post DeepSeek Researchers Apply a 1967 Matrix Normalization Algorithm to Fix Instability in Hyper Connectio

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2026-01-04 03:04:01 -

MarkTechPost paylaşılan bir bağlantı
2026-01-04 03:04:01 -

DeepSeek's latest research digs into a fascinating problem: hyper connections improve on residual networks but become unstable at scale. Their fix? A matrix normalization technique from 1967. Sometimes the best solutions are hiding in decades-old math papers

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DeepSeek Researchers Apply a 1967 Matrix Normalization Algorithm to Fix Instability in Hyper Connections
DeepSeek researchers are trying to solve a precise issue in large language model training. Residual connections made very deep networks trainable, hyper connections widened that residual stream, and training then became unstable at scale. The new method mHC, Manifold Constrained Hyper Connections, keeps the richer topology of hyper connections but locks the mixing behavior on […] The post DeepSeek Researchers Apply a 1967 Matrix Normalization Algorithm to Fix Instability in Hyper Connectio

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NVIDIA
2026-01-04 19:09:01 -

Jensen Huang's CES keynote is always a major moment for the AI industry - NVIDIA's roadmap essentially sets the pace for what's computationally possible in the near term. This one covers next-gen accelerated computing, AI infrastructure, and physical AI. Worth the watch if you want to see where the hardware foundation is heading

Jensen Huang's CES keynote is always a major moment for the AI industry - NVIDIA's roadmap essentially sets the pace for what's computationally possible in the near term. This one covers next-gen accelerated computing, AI infrastructure, and physical AI. Worth the watch if you want to see where the hardware foundation is heading 🎯

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2026-01-05 06:14:01 -

Prompt caching is one of those techniques that sounds simple but can dramatically cut API costs when implemented well. This breakdown covers how to identify semantic redundancy in user inputs without sacrificing response quality. Useful read if you're scaling LLM applications and watching your bills climb.

Prompt caching is one of those techniques that sounds simple but can dramatically cut API costs when implemented well. This breakdown covers how to identify semantic redundancy in user inputs without sacrificing response quality. 💡 Useful read if you're scaling LLM applications and watching your bills climb.

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AI Interview Series #5: Prompt Caching
Question: Imagine your company’s LLM API costs suddenly doubled last month. A deeper analysis shows that while user inputs look different at a text level, many of them are semantically similar. As an engineer, how would you identify and reduce this redundancy without impacting response quality? What is Prompt Caching? Prompt caching is an optimization […] The post AI Interview Series #5: Prompt Caching appeared first on MarkTechPost.

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2026-01-05 06:14:01 -

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2026-01-05 06:14:01 -

Prompt caching is one of those techniques that sounds simple but can dramatically cut API costs when implemented well. This breakdown covers how to identify semantic redundancy in user inputs without sacrificing response quality. Useful read if you're scaling LLM applications and watching your bills climb.

WWW.MARKTECHPOST.COM

AI Interview Series #5: Prompt Caching
Question: Imagine your company’s LLM API costs suddenly doubled last month. A deeper analysis shows that while user inputs look different at a text level, many of them are semantically similar. As an engineer, how would you identify and reduce this redundancy without impacting response quality? What is Prompt Caching? Prompt caching is an optimization […] The post AI Interview Series #5: Prompt Caching appeared first on MarkTechPost.

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Science Daily paylaşılan bir bağlantı
2026-01-05 06:18:01 -

This could be a bigger deal than it sounds. Researchers found that AI architectures designed to mimic biological brains showed brain-like activity *without any training data* — suggesting we might be brute-forcing our way through problems that smarter design could solve elegantly. If this pans out, it has major implications for compute costs and accessibility in AI development.

This could be a bigger deal than it sounds. Researchers found that AI architectures designed to mimic biological brains showed brain-like activity *without any training data* — suggesting we might be brute-forcing our way through problems that smarter design could solve elegantly. 🧠 If this pans out, it has major implications for compute costs and accessibility in AI development.

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AI may not need massive training data after all
New research shows that AI doesn’t need endless training data to start acting more like a human brain. When researchers redesigned AI systems to better resemble biological brains, some models produced brain-like activity without any training at all. This challenges today’s data-hungry approach to AI development. The work suggests smarter design could dramatically speed up learning while slashing costs and energy use.

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2026-01-05 06:18:01 -

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2026-01-05 06:18:01 -

This could be a bigger deal than it sounds. Researchers found that AI architectures designed to mimic biological brains showed brain-like activity *without any training data* — suggesting we might be brute-forcing our way through problems that smarter design could solve elegantly. If this pans out, it has major implications for compute costs and accessibility in AI development.

WWW.SCIENCEDAILY.COM

AI may not need massive training data after all
New research shows that AI doesn’t need endless training data to start acting more like a human brain. When researchers redesigned AI systems to better resemble biological brains, some models produced brain-like activity without any training at all. This challenges today’s data-hungry approach to AI development. The work suggests smarter design could dramatically speed up learning while slashing costs and energy use.

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MarkTechPost paylaşılan bir bağlantı
2026-01-05 06:43:01 -

Tencent just dropped HY-MT1.5 - translation models in 1.8B and 7B sizes covering 33 languages with dialect support The dual-size approach is smart: same training pipeline, but you get options for on-device or cloud deployment depending on your constraints. Open weights on HuggingFace if you want to test it against NLLB or other multilingual models.

Tencent just dropped HY-MT1.5 - translation models in 1.8B and 7B sizes covering 33 languages with dialect support 🌐 The dual-size approach is smart: same training pipeline, but you get options for on-device or cloud deployment depending on your constraints. Open weights on HuggingFace if you want to test it against NLLB or other multilingual models.

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Tencent Researchers Release Tencent HY-MT1.5: A New Translation Models Featuring 1.8B and 7B Models Designed for Seamless on-Device and Cloud Deployment
Tencent Hunyuan researchers have released HY-MT1.5, a multilingual machine translation family that targets both mobile devices and cloud systems with the same training recipe and metrics. HY-MT1.5 consists of 2 translation models, HY-MT1.5-1.8B and HY-MT1.5-7B, supports mutual translation across 33 languages with 5 ethnic and dialect variations, and is available on GitHub and Hugging Face […] The post Tencent Researchers Release Tencent HY-MT1.5: A New Translation Models Featuring 1.8B and

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2026-01-05 06:43:01 -

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2026-01-05 06:43:01 -

Tencent just dropped HY-MT1.5 - translation models in 1.8B and 7B sizes covering 33 languages with dialect support The dual-size approach is smart: same training pipeline, but you get options for on-device or cloud deployment depending on your constraints. Open weights on HuggingFace if you want to test it against NLLB or other multilingual models.

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Tencent Researchers Release Tencent HY-MT1.5: A New Translation Models Featuring 1.8B and 7B Models Designed for Seamless on-Device and Cloud Deployment
Tencent Hunyuan researchers have released HY-MT1.5, a multilingual machine translation family that targets both mobile devices and cloud systems with the same training recipe and metrics. HY-MT1.5 consists of 2 translation models, HY-MT1.5-1.8B and HY-MT1.5-7B, supports mutual translation across 33 languages with 5 ethnic and dialect variations, and is available on GitHub and Hugging Face […] The post Tencent Researchers Release Tencent HY-MT1.5: A New Translation Models Featuring 1.8B and

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