You could train text-to-image models without expensive human feedback! Alibaba Group and Zhejiang University researchers present PromptEcho—a reward method that uses a frozen vision-language model to measure image-prompt alignment directly, with zero annotations or extra
RESEARCH
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Thinking Machines launches native interaction model for realtime voice
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[AINews] TML-Interaction-Small 276B-A12Bhttps://t.co/Z8FHf217oU
— Latent.Space (@latentspacepod) 12 mai 2026
Thinking Machines' Native Interaction Models – TML-Interaction-Small 276B-A12B – advances SOTA Realtime Voice and kills standard VAD
all our highlights! https://t.co/QagGmERPPw[AINews] TML-Interaction-Small 276B-A12B https://
latent.space/p/ainews-think
ing-machines-native-interaction
… Thinking Machines' Native Interaction Models – TML-Interaction-Small 276B-A12B – advances SOTA Realtime Voice and kills standard VAD all our highlights! -
True Superintelligence: Infer or Bypass Tacit Knowledge?
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A true superintelligence should be able to infer or elicit tacit/contextual knowledge? Or route around it? Otherwise AI installations are still held back by Hayek, Coase, Weber, etc.: human constraints navigated by humans. Mediated adoption, not the "all jobs transformed" pitch
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Can AI determine personality traits from ChatGPT interaction history?
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Can #AI ascertain our personality traits from our ChatGPT history?
by Ingrid Fadelli @TechXplore_com Learn more: https://
bit.ly/4f8EiqI #LLM #GenerativeAI #ArtificialIntelligence #MachineLearning #ML -
The Shift from Deep Learning to Neurosymbolic AI
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Deep learning hit a wall. Neurosymbolic AI rescued it.
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Historical Research on LSTMs for Music Improvisation
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The agent also reimplemented the “Blues Improvisation” experiment by @douglas_eck and @SchmidhuberAI in 2002 which show that LSTMs can learn temporal structure in music.
— hardmaru (@hardmaru) 12 mai 2026
Finding temporal structure in music: Blues improvisation with LSTM recurrent networkshttps://t.co/2oU4iIcJEr pic.twitter.com/o4Eq4KqSGqThe agent also reimplemented the “Blues Improvisation” experiment by @douglas_eck and @SchmidhuberAI in 2002 which show that LSTMs can learn temporal structure in music. Finding temporal structure in music: Blues improvisation with LSTM recurrent networks https://
sferics.idsia.ch/pub/juergen/20
02_ieee.pdf
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The necessity of world models in AI systems
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might be the other way around. and it’s not totally clear what the term means. but you need a world model for any of these things.
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Recursion as the Next Scaling Law in AI
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Recursion Is The Next Scaling Law In AI! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
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Essential Research Papers for Mastering Transformers and LLMs
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Top 26 Essential Papers for Mastering LLMs and Transformers Implement those and you’ve captured ~90% of the alpha behind modern LLMs. Everything else is garnish. This list bridges the Transformer foundations with the reasoning, MoE, and agentic shift Recommended Reading
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Agentic Context Engineering: Evolving AI Context Over Time
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Instead of treating prompts as static, Agentic Context Engineering (ACE) explores how context can evolve over time through generation, reflection, and curation. Read the paper