FinSentinel
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raahul thakur
Raahulthakur
AI & ML interests
Data enthusiast pursuing a Master's in Astrophysics. Passionate about deep learning models and their applications in solving complex problems.
Recent Activity
updated a Space about 1 month ago
Raahulthakur/FinSentinel published a Space about 1 month ago
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updated a
Space about 1 month ago
published a
Space about 1 month ago
reacted to Kseniase's post with 🔥 about 1 year ago
Post
4313
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 -> Chain-of-Defensive-Thought: Structured Reasoning Elicits Robustness in Large Language Models against Reference Corruption (2504.20769)
Provides a few structured, defensive reasoning exemplars to improve the robustness of LLMs
2. Hybrid-CoT -> AdaR1: From Long-CoT to Hybrid-CoT via Bi-Level Adaptive Reasoning Optimization (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 -> T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoT (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) -> Efficient Reasoning for LLMs through Speculative Chain-of-Thought (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) -> Co-CoT: A Prompt-Based Framework for Collaborative Chain-of-Thought Reasoning (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 -> Enhancing Non-Core Language Instruction-Following in Speech LLMs via Semi-Implicit Cross-Lingual CoT Reasoning (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
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 -> Chain-of-Defensive-Thought: Structured Reasoning Elicits Robustness in Large Language Models against Reference Corruption (2504.20769)
Provides a few structured, defensive reasoning exemplars to improve the robustness of LLMs
2. Hybrid-CoT -> AdaR1: From Long-CoT to Hybrid-CoT via Bi-Level Adaptive Reasoning Optimization (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 -> T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoT (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) -> Efficient Reasoning for LLMs through Speculative Chain-of-Thought (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) -> Co-CoT: A Prompt-Based Framework for Collaborative Chain-of-Thought Reasoning (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 -> Enhancing Non-Core Language Instruction-Following in Speech LLMs via Semi-Implicit Cross-Lingual CoT Reasoning (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
- 1 reply
reacted to nyuuzyou's post with 🔥 about 1 year ago
Post
3850
🖼️ PublicDomainFiles.com Collection - nyuuzyou/publicdomainfiles
Collection of 206,204 Public Domain multimedia files featuring:
- Comprehensive metadata: title, description, creator name, keywords, original page URL, and more.
- Contains various media types including images, clip art, artwork, fonts, videos, and TV shows.
- All content explicitly released into the public domain under the CC0 license.
- Organized in a single
Collection of 206,204 Public Domain multimedia files featuring:
- Comprehensive metadata: title, description, creator name, keywords, original page URL, and more.
- Contains various media types including images, clip art, artwork, fonts, videos, and TV shows.
- All content explicitly released into the public domain under the CC0 license.
- Organized in a single
train split with 206,204 entries. reacted to samihalawa's post with 🔥 about 1 year ago
Post
3461
HELLO GUYS 🚀 Just released my first MCP: VUDA – Visual UI Debug Agent
Ever been stuck debugging buttons that don’t work? Broken flows? Inconsistent UI behavior?
VUDA sees it, clicks it, fixes it.
An automated visual debug agent that inspects, validates, and repairs your UI — like magic 🧠✨ Better that any other playwright / puppeteer.
🔧 Install now via Smithery:
npx -y @smithery /cli@latest install @samihalawa /visual-ui-debug-agent-mcp --client cursor
Ever been stuck debugging buttons that don’t work? Broken flows? Inconsistent UI behavior?
VUDA sees it, clicks it, fixes it.
An automated visual debug agent that inspects, validates, and repairs your UI — like magic 🧠✨ Better that any other playwright / puppeteer.
🔧 Install now via Smithery:
npx -y @smithery /cli@latest install @samihalawa /visual-ui-debug-agent-mcp --client cursor
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