#aireasoning — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #aireasoning, aggregated by home.social.
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Terence Tao @tao :
The math behind today’s LLMs is actually simple. Training and running them mostly uses linear algebra, matrix multiplication, and a bit of calculus, material an undergraduate can handle. We understand how to build and operate these models.
The real mystery is why they work so well on some tasks and fail on others, and why we cannot predict that in advance. We lack good rules for forecasting performance across tasks, so progress is largely empirical.
A key reason is the nature of real-world data. Pure noise is well understood, perfectly structured data is well understood, but natural text sits in between, partly structured and partly random. Mathematics for that middle regime is thin, similar to how physics struggles at meso-scales between atoms and continua.
Because of this gap, we can describe the mechanisms but cannot yet explain capability jumps or give reliable task-level predictions. That mismatch, simple machinery versus hard-to-predict behavior, is the core puzzle.
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Video from Prof @Briankeating YT Channel.
Full video link: https://youtu.be/ukpCHo5v-Gc
#ArtificialIntelligence #AI #LargeLanguageModels #LLMs #MachineLearning #DeepLearning #GenerativeAI #AIResearch #Mathematics #LinearAlgebra #MatrixMultiplication #Calculus #NeuralNetworks #AIModels #ModelTraining #AIEngineering #ComputerScience #DataScience #NaturalLanguageProcessing #NLP #EmergentAbilities #AIReasoning #AIProgress #AIInnovation #TechResearch #FutureOfAI #MachineIntelligence #AIExplained #TuringTest
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Terence Tao @tao :
The math behind today’s LLMs is actually simple. Training and running them mostly uses linear algebra, matrix multiplication, and a bit of calculus, material an undergraduate can handle. We understand how to build and operate these models.
The real mystery is why they work so well on some tasks and fail on others, and why we cannot predict that in advance. We lack good rules for forecasting performance across tasks, so progress is largely empirical.
A key reason is the nature of real-world data. Pure noise is well understood, perfectly structured data is well understood, but natural text sits in between, partly structured and partly random. Mathematics for that middle regime is thin, similar to how physics struggles at meso-scales between atoms and continua.
Because of this gap, we can describe the mechanisms but cannot yet explain capability jumps or give reliable task-level predictions. That mismatch, simple machinery versus hard-to-predict behavior, is the core puzzle.
----
Video from Prof @Briankeating YT Channel.
Full video link: https://youtu.be/ukpCHo5v-Gc
#ArtificialIntelligence #AI #LargeLanguageModels #LLMs #MachineLearning #DeepLearning #GenerativeAI #AIResearch #Mathematics #LinearAlgebra #MatrixMultiplication #Calculus #NeuralNetworks #AIModels #ModelTraining #AIEngineering #ComputerScience #DataScience #NaturalLanguageProcessing #NLP #EmergentAbilities #AIReasoning #AIProgress #AIInnovation #TechResearch #FutureOfAI #MachineIntelligence #AIExplained #TuringTest
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Terence Tao @tao :
The math behind today’s LLMs is actually simple. Training and running them mostly uses linear algebra, matrix multiplication, and a bit of calculus, material an undergraduate can handle. We understand how to build and operate these models.
The real mystery is why they work so well on some tasks and fail on others, and why we cannot predict that in advance. We lack good rules for forecasting performance across tasks, so progress is largely empirical.
A key reason is the nature of real-world data. Pure noise is well understood, perfectly structured data is well understood, but natural text sits in between, partly structured and partly random. Mathematics for that middle regime is thin, similar to how physics struggles at meso-scales between atoms and continua.
Because of this gap, we can describe the mechanisms but cannot yet explain capability jumps or give reliable task-level predictions. That mismatch, simple machinery versus hard-to-predict behavior, is the core puzzle.
----
Video from Prof @Briankeating YT Channel.
Full video link: https://youtu.be/ukpCHo5v-Gc
#ArtificialIntelligence #AI #LargeLanguageModels #LLMs #MachineLearning #DeepLearning #GenerativeAI #AIResearch #Mathematics #LinearAlgebra #MatrixMultiplication #Calculus #NeuralNetworks #AIModels #ModelTraining #AIEngineering #ComputerScience #DataScience #NaturalLanguageProcessing #NLP #EmergentAbilities #AIReasoning #AIProgress #AIInnovation #TechResearch #FutureOfAI #MachineIntelligence #AIExplained #TuringTest
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Terence Tao @tao :
The math behind today’s LLMs is actually simple. Training and running them mostly uses linear algebra, matrix multiplication, and a bit of calculus, material an undergraduate can handle. We understand how to build and operate these models.
The real mystery is why they work so well on some tasks and fail on others, and why we cannot predict that in advance. We lack good rules for forecasting performance across tasks, so progress is largely empirical.
A key reason is the nature of real-world data. Pure noise is well understood, perfectly structured data is well understood, but natural text sits in between, partly structured and partly random. Mathematics for that middle regime is thin, similar to how physics struggles at meso-scales between atoms and continua.
Because of this gap, we can describe the mechanisms but cannot yet explain capability jumps or give reliable task-level predictions. That mismatch, simple machinery versus hard-to-predict behavior, is the core puzzle.
----
Video from Prof @Briankeating YT Channel.
Full video link: https://youtu.be/ukpCHo5v-Gc
#ArtificialIntelligence #AI #LargeLanguageModels #LLMs #MachineLearning #DeepLearning #GenerativeAI #AIResearch #Mathematics #LinearAlgebra #MatrixMultiplication #Calculus #NeuralNetworks #AIModels #ModelTraining #AIEngineering #ComputerScience #DataScience #NaturalLanguageProcessing #NLP #EmergentAbilities #AIReasoning #AIProgress #AIInnovation #TechResearch #FutureOfAI #MachineIntelligence #AIExplained #TuringTest
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Terence Tao @tao :
The math behind today’s LLMs is actually simple. Training and running them mostly uses linear algebra, matrix multiplication, and a bit of calculus, material an undergraduate can handle. We understand how to build and operate these models.
The real mystery is why they work so well on some tasks and fail on others, and why we cannot predict that in advance. We lack good rules for forecasting performance across tasks, so progress is largely empirical.
A key reason is the nature of real-world data. Pure noise is well understood, perfectly structured data is well understood, but natural text sits in between, partly structured and partly random. Mathematics for that middle regime is thin, similar to how physics struggles at meso-scales between atoms and continua.
Because of this gap, we can describe the mechanisms but cannot yet explain capability jumps or give reliable task-level predictions. That mismatch, simple machinery versus hard-to-predict behavior, is the core puzzle.
----
Video from Prof @Briankeating YT Channel.
Full video link: https://youtu.be/ukpCHo5v-Gc
#ArtificialIntelligence #AI #LargeLanguageModels #LLMs #MachineLearning #DeepLearning #GenerativeAI #AIResearch #Mathematics #LinearAlgebra #MatrixMultiplication #Calculus #NeuralNetworks #AIModels #ModelTraining #AIEngineering #ComputerScience #DataScience #NaturalLanguageProcessing #NLP #EmergentAbilities #AIReasoning #AIProgress #AIInnovation #TechResearch #FutureOfAI #MachineIntelligence #AIExplained #TuringTest
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Oh, the irony! 🤦♂️ A riveting deep dive into the enigma of AI reasoning gets blocked by a security service that can't even reason itself. 🤔💡 Why ponder the mysteries of machine intelligence when you can't even get past the gatekeeper? 🔒🥴
https://cacm.acm.org/news/can-we-understand-how-large-language-models-reason/ #AIreasoning #irony #securitymachine #intelligencegatekeeper #HackerNews #ngated -
Oh, the irony! 🤦♂️ A riveting deep dive into the enigma of AI reasoning gets blocked by a security service that can't even reason itself. 🤔💡 Why ponder the mysteries of machine intelligence when you can't even get past the gatekeeper? 🔒🥴
https://cacm.acm.org/news/can-we-understand-how-large-language-models-reason/ #AIreasoning #irony #securitymachine #intelligencegatekeeper #HackerNews #ngated -
Oh, the irony! 🤦♂️ A riveting deep dive into the enigma of AI reasoning gets blocked by a security service that can't even reason itself. 🤔💡 Why ponder the mysteries of machine intelligence when you can't even get past the gatekeeper? 🔒🥴
https://cacm.acm.org/news/can-we-understand-how-large-language-models-reason/ #AIreasoning #irony #securitymachine #intelligencegatekeeper #HackerNews #ngated -
Oh, the irony! 🤦♂️ A riveting deep dive into the enigma of AI reasoning gets blocked by a security service that can't even reason itself. 🤔💡 Why ponder the mysteries of machine intelligence when you can't even get past the gatekeeper? 🔒🥴
https://cacm.acm.org/news/can-we-understand-how-large-language-models-reason/ #AIreasoning #irony #securitymachine #intelligencegatekeeper #HackerNews #ngated -
Oh, the irony! 🤦♂️ A riveting deep dive into the enigma of AI reasoning gets blocked by a security service that can't even reason itself. 🤔💡 Why ponder the mysteries of machine intelligence when you can't even get past the gatekeeper? 🔒🥴
https://cacm.acm.org/news/can-we-understand-how-large-language-models-reason/ #AIreasoning #irony #securitymachine #intelligencegatekeeper #HackerNews #ngated -
Open-Weight Models Edge Closer to Production Readiness
Open-weight AI models like DeepSeek R1 can now do complex coding and reasoning. They can run on your own computer.
#OpenWeightAI, #LLM, #AICoding, #AIReasoning, #LocalAI
https://newsletter.tf/open-weight-ai-models-ready-for-production-use/
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Open-Weight Models Edge Closer to Production Readiness
Open-weight AI models like DeepSeek R1 can now do complex coding and reasoning. They can run on your own computer.
#OpenWeightAI, #LLM, #AICoding, #AIReasoning, #LocalAI
https://newsletter.tf/open-weight-ai-models-ready-for-production-use/
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Open-Weight Models Edge Closer to Production Readiness
Open-weight AI models like DeepSeek R1 can now do complex coding and reasoning. They can run on your own computer.
#OpenWeightAI, #LLM, #AICoding, #AIReasoning, #LocalAI
https://newsletter.tf/open-weight-ai-models-ready-for-production-use/
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Open-Weight Models Edge Closer to Production Readiness
Open-weight AI models like DeepSeek R1 can now do complex coding and reasoning. They can run on your own computer.
#OpenWeightAI, #LLM, #AICoding, #AIReasoning, #LocalAI
https://newsletter.tf/open-weight-ai-models-ready-for-production-use/
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Open-weight AI models are getting much better, reaching performance levels that allow them to be used for important tasks like coding and reasoning. This is a big step up from before.
#OpenWeightAI, #LLM, #AICoding, #AIReasoning, #LocalAI
https://newsletter.tf/open-weight-ai-models-ready-for-production-use/ -
Open-weight AI models are getting much better, reaching performance levels that allow them to be used for important tasks like coding and reasoning. This is a big step up from before.
#OpenWeightAI, #LLM, #AICoding, #AIReasoning, #LocalAI
https://newsletter.tf/open-weight-ai-models-ready-for-production-use/ -
Open-weight AI models are getting much better, reaching performance levels that allow them to be used for important tasks like coding and reasoning. This is a big step up from before.
#OpenWeightAI, #LLM, #AICoding, #AIReasoning, #LocalAI
https://newsletter.tf/open-weight-ai-models-ready-for-production-use/ -
Open-weight AI models are getting much better, reaching performance levels that allow them to be used for important tasks like coding and reasoning. This is a big step up from before.
#OpenWeightAI, #LLM, #AICoding, #AIReasoning, #LocalAI
https://newsletter.tf/open-weight-ai-models-ready-for-production-use/ -
" #AIReasoning finally let's you see what the #AI really thinks."
#LLMs don't *think*, they predict the next token.
"Researchers have uncovered that the AI cheats when they turned on reasoning."
Ever thought about reasoning also being text output just like non-reasoning, entirely controlled by the AI whose entire job it is to generate sycophantic text output? This output is always something made for human consumption, it is never, however, an *internal* state
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" #AIReasoning finally let's you see what the #AI really thinks."
#LLMs don't *think*, they predict the next token.
"Researchers have uncovered that the AI cheats when they turned on reasoning."
Ever thought about reasoning also being text output just like non-reasoning, entirely controlled by the AI whose entire job it is to generate sycophantic text output? This output is always something made for human consumption, it is never, however, an *internal* state
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" #AIReasoning finally let's you see what the #AI really thinks."
#LLMs don't *think*, they predict the next token.
"Researchers have uncovered that the AI cheats when they turned on reasoning."
Ever thought about reasoning also being text output just like non-reasoning, entirely controlled by the AI whose entire job it is to generate sycophantic text output? This output is always something made for human consumption, it is never, however, an *internal* state
-
" #AIReasoning finally let's you see what the #AI really thinks."
#LLMs don't *think*, they predict the next token.
"Researchers have uncovered that the AI cheats when they turned on reasoning."
Ever thought about reasoning also being text output just like non-reasoning, entirely controlled by the AI whose entire job it is to generate sycophantic text output? This output is always something made for human consumption, it is never, however, an *internal* state
-
" #AIReasoning finally let's you see what the #AI really thinks."
#LLMs don't *think*, they predict the next token.
"Researchers have uncovered that the AI cheats when they turned on reasoning."
Ever thought about reasoning also being text output just like non-reasoning, entirely controlled by the AI whose entire job it is to generate sycophantic text output? This output is always something made for human consumption, it is never, however, an *internal* state
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RE: https://dair-community.social/@emilymbender/116098129762953036
Professor Emily M. Bender's replies are always so on point—huge recommendation if you want to know about the problems with #AI
#noAI #artificialIntelligence #LLMs #LargeLanguageModels #genAI #ChatBots #vibeCoding #TheAICon #ComputationalLinguistics #AgentsOfTech #DataWorkers #AI2027 #AGI #artificialGeneralIntelligence #GeoffreyHinton #AIReasoning #NeuralNetworks #EmilyBender #ReinforcementLearning #AIHype
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RE: https://dair-community.social/@emilymbender/116098129762953036
Professor Emily M. Bender's replies are always so on point—huge recommendation if you want to know about the problems with #AI
#noAI #artificialIntelligence #LLMs #LargeLanguageModels #genAI #ChatBots #vibeCoding #TheAICon #ComputationalLinguistics #AgentsOfTech #DataWorkers #AI2027 #AGI #artificialGeneralIntelligence #GeoffreyHinton #AIReasoning #NeuralNetworks #EmilyBender #ReinforcementLearning #AIHype
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RE: https://dair-community.social/@emilymbender/116098129762953036
Professor Emily M. Bender's replies are always so on point—huge recommendation if you want to know about the problems with #AI
#noAI #artificialIntelligence #LLMs #LargeLanguageModels #genAI #ChatBots #vibeCoding #TheAICon #ComputationalLinguistics #AgentsOfTech #DataWorkers #AI2027 #AGI #artificialGeneralIntelligence #GeoffreyHinton #AIReasoning #NeuralNetworks #EmilyBender #ReinforcementLearning #AIHype
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RE: https://dair-community.social/@emilymbender/116098129762953036
Professor Emily M. Bender's replies are always so on point—huge recommendation if you want to know about the problems with #AI
#noAI #artificialIntelligence #LLMs #LargeLanguageModels #genAI #ChatBots #vibeCoding #TheAICon #ComputationalLinguistics #AgentsOfTech #DataWorkers #AI2027 #AGI #artificialGeneralIntelligence #GeoffreyHinton #AIReasoning #NeuralNetworks #EmilyBender #ReinforcementLearning #AIHype
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RE: https://dair-community.social/@emilymbender/116098129762953036
Professor Emily M. Bender's replies are always so on point—huge recommendation if you want to know about the problems with #AI
#noAI #artificialIntelligence #LLMs #LargeLanguageModels #genAI #ChatBots #vibeCoding #TheAICon #ComputationalLinguistics #AgentsOfTech #DataWorkers #AI2027 #AGI #artificialGeneralIntelligence #GeoffreyHinton #AIReasoning #NeuralNetworks #EmilyBender #ReinforcementLearning #AIHype
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Google DeepMind just rolled out Gemini 3.1 Pro – an upgraded Gemini 3 “Deep Think” model built for heavy reasoning and complex tasks. It promises sharper chain‑of‑thought, better multi‑step problem solving, and tighter integration with generative AI pipelines. Curious how this could reshape ML workflows? Dive into the details. #Gemini3Pro #DeepThink #AIReasoning #GenerativeAI
🔗 https://aidailypost.com/news/gemini-31-pro-released-upgraded-gemini-3-deep-think-complex-tasks
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Google DeepMind just rolled out Gemini 3.1 Pro – an upgraded Gemini 3 “Deep Think” model built for heavy reasoning and complex tasks. It promises sharper chain‑of‑thought, better multi‑step problem solving, and tighter integration with generative AI pipelines. Curious how this could reshape ML workflows? Dive into the details. #Gemini3Pro #DeepThink #AIReasoning #GenerativeAI
🔗 https://aidailypost.com/news/gemini-31-pro-released-upgraded-gemini-3-deep-think-complex-tasks
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Google DeepMind just rolled out Gemini 3.1 Pro – an upgraded Gemini 3 “Deep Think” model built for heavy reasoning and complex tasks. It promises sharper chain‑of‑thought, better multi‑step problem solving, and tighter integration with generative AI pipelines. Curious how this could reshape ML workflows? Dive into the details. #Gemini3Pro #DeepThink #AIReasoning #GenerativeAI
🔗 https://aidailypost.com/news/gemini-31-pro-released-upgraded-gemini-3-deep-think-complex-tasks
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Google DeepMind just rolled out Gemini 3.1 Pro – an upgraded Gemini 3 “Deep Think” model built for heavy reasoning and complex tasks. It promises sharper chain‑of‑thought, better multi‑step problem solving, and tighter integration with generative AI pipelines. Curious how this could reshape ML workflows? Dive into the details. #Gemini3Pro #DeepThink #AIReasoning #GenerativeAI
🔗 https://aidailypost.com/news/gemini-31-pro-released-upgraded-gemini-3-deep-think-complex-tasks
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Google's Gemini 3 Deep Think reached 84.6% on ARC-AGI-2, a reasoning benchmark designed to resist memorization. That beats GPT-5.2 (52.9%) and Claude (68.8%) by significant margins. The catch: $13.62 per task suggests these advances may remain research tools rather than production systems for now.
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Google's Gemini 3 Deep Think reached 84.6% on ARC-AGI-2, a reasoning benchmark designed to resist memorization. That beats GPT-5.2 (52.9%) and Claude (68.8%) by significant margins. The catch: $13.62 per task suggests these advances may remain research tools rather than production systems for now.
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New research shows that letting language models hold internal debates—checking each other’s claims and negotiating solutions—dramatically cuts errors on tough reasoning tasks. The multi‑agent approach boosts self‑consistency and semantic verification, pushing open‑source AI toward more reliable reasoning. Dive into the findings! #MultiAgentDebate #AIReasoning #SelfConsistency #SemanticVerification
🔗 https://aidailypost.com/news/ai-models-using-internal-debate-spot-errors-boost-accuracy-complex
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New research shows that letting language models hold internal debates—checking each other’s claims and negotiating solutions—dramatically cuts errors on tough reasoning tasks. The multi‑agent approach boosts self‑consistency and semantic verification, pushing open‑source AI toward more reliable reasoning. Dive into the findings! #MultiAgentDebate #AIReasoning #SelfConsistency #SemanticVerification
🔗 https://aidailypost.com/news/ai-models-using-internal-debate-spot-errors-boost-accuracy-complex
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Trích xuất cấu trúc vượt trội so với ngữ cảnh đầy đủ (F1: 0.83 vs 0.58) trong tác vụ suy luận đa bước. Entity Cards (17.5% token) giúp mô hình suy luận tốt hơn do loại nhiễu, tập trung vào thực thể và quan hệ. Token compression (LLMLingua, QUITO) thất bại do phá vỡ cấu trúc ngữ nghĩa. Mô hình nhỏ (Qwen3-1.7B) có thể tạo Entity Cards với F1 0.60. Cần thử fine-tuning và kiểm tra trên RAG.
#StructuredExtraction #EntityCards #AIReasoning #LLM #RAG #TríchXuấtCấuTrúc #SuyLuậnAI #MôHìnhNgônNgữ #RútGọ -
GPT-5.2 Pro Solves Decades-old Math Problem, but Experts Say It Reveals AI’s Limits as Much as Its Potential
#AI #OpenAI #ChatGPT #GPT52Pro #Mathematics #Science #AIReasoning #ErdosProblem #MathBreakthrough #TerenceTao
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GPT-5.2 Pro Solves Decades-old Math Problem, but Experts Say It Reveals AI’s Limits as Much as Its Potential
#AI #OpenAI #ChatGPT #GPT52Pro #Mathematics #Science #AIReasoning #ErdosProblem #MathBreakthrough #TerenceTao
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GPT-5.2 Pro Solves Decades-old Math Problem, but Experts Say It Reveals AI’s Limits as Much as Its Potential
#AI #OpenAI #ChatGPT #GPT52Pro #Mathematics #Science #AIReasoning #ErdosProblem #MathBreakthrough #TerenceTao
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GPT-5.2 Pro Solves Decades-old Math Problem, but Experts Say It Reveals AI’s Limits as Much as Its Potential
#AI #OpenAI #ChatGPT #GPT52Pro #Mathematics #Science #AIReasoning #ErdosProblem #MathBreakthrough #TerenceTao
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GPT-5.2 Pro Solves Decades-old Math Problem, but Experts Say It Reveals AI’s Limits as Much as Its Potential
#AI #OpenAI #ChatGPT #GPT52Pro #Mathematics #Science #AIReasoning #ErdosProblem #MathBreakthrough #TerenceTao
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Thử nghiệm 23 mô hình ngôn ngữ lớn (LLM) với câu đố Nonogram (câu đố logic dạng lưới). Kết quả: hiệu suất giảm mạnh khi kích thước tăng; một số LLM viết code để giải vét cạn, số khác lập luận từng bước như con người. GPT-4.5 dẫn đầu. Tổng chi phí: ~250 USD, ~17M tokens. Dữ liệu & mã nguồn mở. Link: nonobench.com, GitHub: no-bench.
#LLM #Nonogram #LogicPuzzle #AI #Reasoning #MôHìnhNgônNgữ #CâuĐốLogic #TríTuệNhânTạo #AIReasoning
https://www.reddit.com/r/LocalLLaMA/comments/1q4i19c/benchmarking
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New DeepSeek V3.2 Speciale Model Claims Reasoning Parity with Gemini 3 Pro
#AI #DeepSeek #GenAI #LLMs #AIBenchmarks #OpenSourceAI #GoogleGemini #Gemini3 #GPT5 #AgenticAI #AIReasoning #ChinaAI
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New DeepSeek V3.2 Speciale Model Claims Reasoning Parity with Gemini 3 Pro
#AI #DeepSeek #GenAI #LLMs #AIBenchmarks #OpenSourceAI #GoogleGemini #Gemini3 #GPT5 #AgenticAI #AIReasoning #ChinaAI
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New DeepSeek V3.2 Speciale Model Claims Reasoning Parity with Gemini 3 Pro
#AI #DeepSeek #GenAI #LLMs #AIBenchmarks #OpenSourceAI #GoogleGemini #Gemini3 #GPT5 #AgenticAI #AIReasoning #ChinaAI
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New DeepSeek V3.2 Speciale Model Claims Reasoning Parity with Gemini 3 Pro
#AI #DeepSeek #GenAI #LLMs #AIBenchmarks #OpenSourceAI #GoogleGemini #Gemini3 #GPT5 #AgenticAI #AIReasoning #ChinaAI
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New DeepSeek V3.2 Speciale Model Claims Reasoning Parity with Gemini 3 Pro
#AI #DeepSeek #GenAI #LLMs #AIBenchmarks #OpenSourceAI #GoogleGemini #Gemini3 #GPT5 #AgenticAI #AIReasoning #ChinaAI
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#AI #GenAI #BlackForestLabs #VentureCapital #OpenSourceAI #VisualIntelligence #FLUX2 #MultimodalAI #AIReasoning
Black Forest Labs Hits $3.25B Valuation, Pivots to ‘Visual Intelligence’ with Series B
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#AI #GenAI #BlackForestLabs #VentureCapital #OpenSourceAI #VisualIntelligence #FLUX2 #MultimodalAI #AIReasoning
Black Forest Labs Hits $3.25B Valuation, Pivots to ‘Visual Intelligence’ with Series B
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#AI #GenAI #BlackForestLabs #VentureCapital #OpenSourceAI #VisualIntelligence #FLUX2 #MultimodalAI #AIReasoning
Black Forest Labs Hits $3.25B Valuation, Pivots to ‘Visual Intelligence’ with Series B
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#AI #GenAI #BlackForestLabs #VentureCapital #OpenSourceAI #VisualIntelligence #FLUX2 #MultimodalAI #AIReasoning
Black Forest Labs Hits $3.25B Valuation, Pivots to ‘Visual Intelligence’ with Series B
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#AI #GenAI #BlackForestLabs #VentureCapital #OpenSourceAI #VisualIntelligence #FLUX2 #MultimodalAI #AIReasoning
Black Forest Labs Hits $3.25B Valuation, Pivots to ‘Visual Intelligence’ with Series B
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DeepSeekMath-V2 Matches OpenAI and Google with IMO Gold Medal Win
#AI #DeepSeek #OpenSourceAI #GenAI #MathAI #ChinaAI #AIReasoning #IMO2025 #DeepSeekMathV2
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DeepSeekMath-V2 Matches OpenAI and Google with IMO Gold Medal Win
#AI #DeepSeek #OpenSourceAI #GenAI #MathAI #ChinaAI #AIReasoning #IMO2025 #DeepSeekMathV2
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DeepSeekMath-V2 Matches OpenAI and Google with IMO Gold Medal Win
#AI #DeepSeek #OpenSourceAI #GenAI #MathAI #ChinaAI #AIReasoning #IMO2025 #DeepSeekMathV2
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DeepSeekMath-V2 Matches OpenAI and Google with IMO Gold Medal Win
#AI #DeepSeek #OpenSourceAI #GenAI #MathAI #ChinaAI #AIReasoning #IMO2025 #DeepSeekMathV2
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DeepSeekMath-V2 Matches OpenAI and Google with IMO Gold Medal Win
#AI #DeepSeek #OpenSourceAI #GenAI #MathAI #ChinaAI #AIReasoning #IMO2025 #DeepSeekMathV2
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🚀 Polish geniuses have supposedly revolutionized AI reasoning, and yet their announcement reads like a cryptic radio station playlist. 🎧 Surely the world was waiting with bated breath for an algorithm to decode Chopin on frequency czstotliwoci! 🎶
https://www.polskieradio.pl/395/7784/artykul/3588855,polish-scientists-startup-pathway-announces-ai-reasoning-breakthrough #PolishAI #Revolution #AIReasoning #ChopinAlgorithm #TechNews #HackerNews #ngated -
🚀 Polish geniuses have supposedly revolutionized AI reasoning, and yet their announcement reads like a cryptic radio station playlist. 🎧 Surely the world was waiting with bated breath for an algorithm to decode Chopin on frequency czstotliwoci! 🎶
https://www.polskieradio.pl/395/7784/artykul/3588855,polish-scientists-startup-pathway-announces-ai-reasoning-breakthrough #PolishAI #Revolution #AIReasoning #ChopinAlgorithm #TechNews #HackerNews #ngated