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Ksenia Se

Kseniase

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https://www.turingpost.com/

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upvoted a paper about 9 hours ago

Real-World Gaps in AI Governance Research

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10 new Chain-of-Thoughts (CoT) methods CoT has long been one of the hottest techniques in AI thanks to its effectiveness and compelling core idea: encouraging models to solve complex problems through explicit intermediate reasoning steps. But usually researchers modify original CoT approach, finding tips that further improve LLMs' reasoning. That's what we're going to talk about today. Here's a list of 10 latest enhanced CoT approaches: 1. Chain-of-Defensive-Thought -> https://huggingface.co/papers/2504.20769 Provides a few structured, defensive reasoning exemplars to improve the robustness of LLMs 2. Hybrid-CoT -> https://huggingface.co/papers/2504.21659 Proposes using Adaptive Hybrid Reasoning Model (AdaR1) that combines Long- and Short-CoT, and applying bi-level preference training to select effective reasoning styles 3. Semantic-level and token-level CoT -> https://huggingface.co/papers/2505.00703 Introduces T2I-R1 text-to-image gen model, that uses semantic-level CoT for prompt planning and token-level CoT for pixel-level generation, while BiCoT-GRPO coordinates them both 4. Speculative CoT (SCoT) -> https://huggingface.co/papers/2504.19095 SCoT drafts multiple reasoning paths with a lightweight draft, selects the best, and uses the target model for correction - all this to reduce latency by 48–66% 5. Collaborative CoT (Co-CoT) -> https://huggingface.co/papers/2504.17091 Breaks reasoning into blocks that users can inspect, modify and re-run, promoting active engagement. An adaptation mechanism aligns outputs with diverse cognitive styles and user goals 6. XS-CoT -> https://huggingface.co/papers/2504.20835 It's a cross-lingual framework that integrates speech-to-text translation into reasoning, using a semi-implicit CoT approach to compress intermediate tokens. This improves non-core language responses by up to 45% Read further in the comments 👇 If you liked this, also subscribe to the Turing Post -> https://www.turingpost.com/subscribe

posted an update 1 day ago

10 new Chain-of-Thoughts (CoT) methods CoT has long been one of the hottest techniques in AI thanks to its effectiveness and compelling core idea: encouraging models to solve complex problems through explicit intermediate reasoning steps. But usually researchers modify original CoT approach, finding tips that further improve LLMs' reasoning. That's what we're going to talk about today. Here's a list of 10 latest enhanced CoT approaches: 1. Chain-of-Defensive-Thought -> https://huggingface.co/papers/2504.20769 Provides a few structured, defensive reasoning exemplars to improve the robustness of LLMs 2. Hybrid-CoT -> https://huggingface.co/papers/2504.21659 Proposes using Adaptive Hybrid Reasoning Model (AdaR1) that combines Long- and Short-CoT, and applying bi-level preference training to select effective reasoning styles 3. Semantic-level and token-level CoT -> https://huggingface.co/papers/2505.00703 Introduces T2I-R1 text-to-image gen model, that uses semantic-level CoT for prompt planning and token-level CoT for pixel-level generation, while BiCoT-GRPO coordinates them both 4. Speculative CoT (SCoT) -> https://huggingface.co/papers/2504.19095 SCoT drafts multiple reasoning paths with a lightweight draft, selects the best, and uses the target model for correction - all this to reduce latency by 48–66% 5. Collaborative CoT (Co-CoT) -> https://huggingface.co/papers/2504.17091 Breaks reasoning into blocks that users can inspect, modify and re-run, promoting active engagement. An adaptation mechanism aligns outputs with diverse cognitive styles and user goals 6. XS-CoT -> https://huggingface.co/papers/2504.20835 It's a cross-lingual framework that integrates speech-to-text translation into reasoning, using a semi-implicit CoT approach to compress intermediate tokens. This improves non-core language responses by up to 45% Read further in the comments 👇 If you liked this, also subscribe to the Turing Post -> https://www.turingpost.com/subscribe

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published an article 8 days ago

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What is MoE 2.0? Update Your Knowledge about Mixture-of-experts

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8 days ago

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Topic 33: Slim Attention, KArAt, XAttention and Multi-Token Attention Explained – What’s Really Changing in Transformers?

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Apr 4

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What is Qwen-Agent framework? Inside the Qwen family

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Mar 20

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🌁#92: Fight for Developers and the Year of Orchestration

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Mar 18

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🦸🏻#14: What Is MCP, and Why Is Everyone – Suddenly!– Talking About It?

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Mar 17

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How to Reduce Memory Use in Reasoning Models

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Mar 13

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🌁#91: We are failing in AI literacy

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Mar 10

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🌁#90: Why AI’s Reasoning Tests Keep Failing Us

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Mar 3

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🦸🏻#13: Action! How AI Agents Execute Tasks with UI and API Tools

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Mar 10

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🦸🏻#12: How Do Agents Learn from Their Own Mistakes? The Role of Reflection in AI

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Mar 9

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Everything You Need to Know about Knowledge Distillation

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Inside the family of Smol models

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Feb 27

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🌁#89: AI in Action: How AI Engineers, Self-Optimizing Models, and Humanoid Robots Are Reshaping 2025

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Feb 25

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🦸🏻#11: How Do Agents Plan and Reason?

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Feb 24

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Topic 28: What is Mixture-of-Mamba?

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Feb 20

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🌁#88: Can DeepSeek Inspire Global Collaboration?

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Feb 17

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🦸🏻#10: Does Present-Day GenAI Actually Reason?

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Feb 15

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Topic 27: What are Chain-of-Agents and Chain-of-RAG?

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Feb 13

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