10. Webscale-RL Webscale-RL introduces a scalable data pipeline that transforms web-scale pretraining text into over 1.2M diverse, verifiable QA pairs for reinforcement learning across 9+ domains.
MACHINE LEARNING
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Artificial Hippocampus Networks: RNN Memory for Transformers
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9. Artificial Hippocampus Networks Adds a fixed-size recurrent memory to sliding-window Transformers, compressing evicted KV into RNN-like states (Mamba2/DN/GDN) trained via self-distillation for long-context efficiency with constant cache and near-linear compute.
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Markovian Thinker: RL Environment for LLM State Management
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6. The Markovian Thinker
— DAIR.AI (@dair_ai) 12 octobre 2025
A new RL thinking environment that keeps an LLM’s effective state constant by chunking long chains of thought and carrying over only a short textual state between chunks.https://t.co/mqXfF1XG6h6. The Markovian Thinker A new RL thinking environment that keeps an LLM’s effective state constant by chunking long chains of thought and carrying over only a short textual state between chunks.
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Scaling Long-Horizon Reasoning Without Step Labels
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5. Reasoning over Longer Horizons via RL The authors show that you can scale long-horizon reasoning without step labels or heavy scaffolding.
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Inference Limited Role in AI Development Picture
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Inference is a small part of the picture. Not comparable.
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PyTorch Multiprocessing Tensor Sharing Across Processes
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pytorch uses this for sharing tensors across processes in torch.multiprocess
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Samsung’s Tiny AI Model Outperforms DeepSeek and Gemini
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5. Samsung’s Tiny AI beats the giants Samsung unveiled the Tiny Recursion Model (TRM), a 7M-parameter AI that outperformed DeepSeek R1 and Gemini 2.5 Pro on reasoning tasks using a self-improvement loop.
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GraphQA: Natural Language Graph Analysis with LangChain
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GraphQA Transform graph analysis into natural conversations with this NetworkX and LangChain-powered framework. Ask questions in plain English and let GraphQA select and execute the right algorithms, handling graphs up to 100K+ nodes. Check it out
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Europe’s Competitive Edge: Critical Technologies for Future Growth
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Europe’s ability to remain competitive will depend on how effectively it advances critical technologies ranging from automation and programming to AI, connectivity, clean energy, and biosciences, since these domains are shaping future growth and resilience. Microblog @antgrasso
