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That's just link aggregator of everything I consider interesting, especially DL and topological condensed matter physics. @EvgeniyZh
Kosmos: An AI Scientist for Autonomous Discovery https://arxiv.org/abs/2511.02824
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SEAQUE live Bell Test https://research.physics.illinois.edu/QI/Photonics/SEAQUE/
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Defeating the Training-Inference Mismatch via FP16 https://arxiv.org/abs/2510.26788
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Generating Creative Chess Puzzles https://arxiv.org/abs/2510.23881
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Ну что, поехали. Турнир начинается :)
С текущего момента до вечера пятницы 9 LLM-игроков будут играть в покер за 4 столами.
Смотреть здесь: pokerbattle.ai
Bosonic Laughlin and Moore-Read states from non-Chern flat bands https://arxiv.org/abs/2510.14685
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StarBench: A Turn-Based RPG Benchmark for Agentic Multimodal Decision-Making and Information Seeking https://arxiv.org/abs/2510.18483
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ImpossibleBench: Measuring LLMs' Propensity of Exploiting Test Cases https://arxiv.org/abs/2510.20270
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Bound on entanglement in neural quantum states https://arxiv.org/abs/2510.11797
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TASI Lectures On Topological Field Theories And Differential Cohomology https://arxiv.org/abs/2510.07408
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A Formalization of the Generalized Quantum Stein's Lemma in Lean https://arxiv.org/abs/2510.08672
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SAM 3: Segment Anything with Concepts https://openreview.net/forum?id=r35clVtGzw
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The wait is over! Microsoft Research is sharing Skala, the new exchange-correlation functional, marking a major milestone in the accuracy/cost trade-off in DFT. Help us learn from your testing so we can improve. Available on Azure AI Foundry and GitHub.
Skala is a neural network-based exchange-correlation functional for density functional theory (DFT), developed by Microsoft Research AI for Science. It leverages deep learning to predict exchange-correlation energies from electron density features, achieving chemical accuracy for atomization energies and strong performance on broad thermochemistry and kinetics benchmarks, all at a computational cost similar to semi-local DFT.
Trained on a large, diverse dataset—including coupled cluster atomization energies and public benchmarks—Skala uses scalable message passing and local layers to learn both local and non-local effects. The model has about 276,000 parameters and matches the accuracy of leading hybrid functionals.
https://labs.ai.azure.com/projects/skala/
https://github.com/microsoft/skala
Quantifying Elicitation of Latent Capabilities in Language Models https://openreview.net/forum?id=Dkgx2pS4Ww
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Fermionic dynamics on a trapped-ion quantum computer beyond exact classical simulation https://arxiv.org/abs/2510.26300
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Onsiteability of Higher-Form Symmetries https://arxiv.org/abs/2510.23701
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Chiral gapped states are universally non-topological https://arxiv.org/abs/2510.23720
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Out-of-distribution Tests Reveal Compositionality in Chess Transformers https://arxiv.org/abs/2510.20783
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Non-invertible bosonic chiral symmetry on the lattice https://arxiv.org/abs/2510.17969
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VISTA: A Test-Time Self-Improving Video Generation Agent https://arxiv.org/abs/2510.15831
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Discovering state-of-the-art reinforcement learning algorithms https://www.nature.com/articles/s41586-025-09761-x
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Quantum Reinforcement Learning: Recent Advances and Future Directions https://arxiv.org/abs/2510.14595
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Evaluating Long Context (Reasoning) Ability https://nrehiew.github.io/blog/long_context/
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Diffusion Transformers with Representation Autoencoders https://arxiv.org/abs/2510.11690
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Fractional quantum Hall states under density decoherence https://arxiv.org/abs/2510.08490
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Unveiling dynamical quantum error correcting codes via non-invertible symmetries https://arxiv.org/abs/2510.09565
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Can Large Language Models Develop Gambling Addiction? https://arxiv.org/abs/2509.22818
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Poisoning Attacks on LLMs Require a Near-constant Number of Poison Samples https://arxiv.org/abs/2510.07192
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