Tootfinder

Neanderthal DNA is largely missing from the human X chromosome.
“We found a pattern indicating a sex bias: gene flow occurred predominantly between Neanderthal males and anatomically modern human females,”
said Dr Alexander Platt, a senior research scientist at the University of Pennsylvania and first author of the research.
The ancestors of modern humans and the closest related species, the Neanderthals, diverged, forming two distinct groups, about 600,000 years ago.

Regret-Guided Search Control for Efficient Learning in AlphaZero
Yun-Jui Tsai, Wei-Yu Chen, Yan-Ru Ju, Yu-Hung Chang, Ti-Rong Wu
https://arxiv.org/abs/2602.20809 https://arxiv.org/pdf/2602.20809 https://arxiv.org/html/2602.20809
arXiv:2602.20809v1 Announce Type: new
Abstract: Reinforcement learning (RL) agents achieve remarkable performance but remain far less learning-efficient than humans. While RL agents require extensive self-play games to extract useful signals, humans often need only a few games, improving rapidly by repeatedly revisiting states where mistakes occurred. This idea, known as search control, aims to restart from valuable states rather than always from the initial state. In AlphaZero, prior work Go-Exploit applies this idea by sampling past states from self-play or search trees, but it treats all states equally, regardless of their learning potential. We propose Regret-Guided Search Control (RGSC), which extends AlphaZero with a regret network that learns to identify high-regret states, where the agent's evaluation diverges most from the actual outcome. These states are collected from both self-play trajectories and MCTS nodes, stored in a prioritized regret buffer, and reused as new starting positions. Across 9x9 Go, 10x10 Othello, and 11x11 Hex, RGSC outperforms AlphaZero and Go-Exploit by an average of 77 and 89 Elo, respectively. When training on a well-trained 9x9 Go model, RGSC further improves the win rate against KataGo from 69.3% to 78.2%, while both baselines show no improvement. These results demonstrate that RGSC provides an effective mechanism for search control, improving both efficiency and robustness of AlphaZero training. Our code is available at https://rlg.iis.sinica.edu.tw/papers/rgsc.
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X-ray astronomers often look at
X-ray binaries,
which consist of a black hole or a neutron star that is gravitationally bound to a normal star.
Material from that star is accreted onto the black hole or neutron star, producing X-rays.
The companion star is quite often a massive star with very strong stellar winds, and X-ray astronomers looking at these winds tend to think:
“Oh my god, it’s so complicated: there’s all this additional absorption, it’s super annoyi…

Victoria Grinberg: the astrophysicist sharing her love for science – Physics World
X-ray astronomer Victoria Grinberg talks about spreading her passion for science

The outcome of a bunch of shader tuning last night: the upsample filter (4x sin(x/x) from 20M to 80M points in this test) went from 6.55 ms to 1.5 ms.
Original: 8% of peak DRAM read BW, 31% write, 14% L2$ hit rate.
New (just changed memory access patterns to be more coalesce/cache friendly): 9% read, 37% write, 73% L2$ hit
A similar memory ordering optimization cut the PAM edge detector from about 14 to 10 ms but my SM occupancy is still crap (around 12% of warp slots used)…

from my link log —
Faster practical modular inversion.
https://purplesyringa.moe/blog/faster-practical-modular-inversion/
saved 2025-12-21

Faster practical modular inversion
Last year, Lemire wrote about an optimized variation of the Euclidean algorithm for computing the greatest common divisor of two numbers, called binary Euclidean algorithm or Stein’s algorithm. It’s a best-of-class implementation, though it’s currently only used by libc++. The post also briefly mentions the extended Euclidean algorithm, a related algorithm most often used to compute the modular multiplicative inverse (given a remainder a and a modulus m , find x such that a ⋅ x mod m = …

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/6]:
- Towards Scalable Oversight via Partitioned Human Supervision
Ren Yin, Takashi Ishida, Masashi Sugiyama
https://arxiv.org/abs/2510.22500 https://mastoxiv.page/@arXiv_csLG_bot/115451787490434401
- ContextPilot: Fast Long-Context Inference via Context Reuse
Yinsicheng Jiang, Yeqi Huang, Liang Cheng, Cheng Deng, Xuan Sun, Luo Mai
https://arxiv.org/abs/2511.03475 https://mastoxiv.page/@arXiv_csLG_bot/115502245581974540
- Metabolomic Biomarker Discovery for ADHD Diagnosis Using Interpretable Machine Learning
Nabil Belacel, Mohamed Rachid Boulassel
https://arxiv.org/abs/2601.11283 https://mastoxiv.page/@arXiv_csLG_bot/115921183182326799
- PhysE-Inv: A Physics-Encoded Inverse Modeling approach for Arctic Snow Depth Prediction
Akila Sampath, Vandana Janeja, Jianwu Wang
https://arxiv.org/abs/2601.17074
- SAGE-5GC: Security-Aware Guidelines for Evaluating Anomaly Detection in the 5G Core Network
Cristian Manca, Christian Scano, Giorgio Piras, Fabio Brau, Maura Pintor, Battista Biggio
https://arxiv.org/abs/2602.03596
- LORE: Jointly Learning the Intrinsic Dimensionality and Relative Similarity Structure From Ordina...
Anand, Helbling, Davenport, Berman, Alagapan, Rozell
https://arxiv.org/abs/2602.04192
- Towards Robust Scaling Laws for Optimizers
Alexandra Volkova, Mher Safaryan, Christoph H. Lampert, Dan Alistarh
https://arxiv.org/abs/2602.07712 https://mastoxiv.page/@arXiv_csLG_bot/116046369672796465
- Do We Need Adam? Surprisingly Strong and Sparse Reinforcement Learning with SGD in LLMs
Sagnik Mukherjee, Lifan Yuan, Pavan Jayasinha, Dilek Hakkani-T\"ur, Hao Peng
https://arxiv.org/abs/2602.07729 https://mastoxiv.page/@arXiv_csLG_bot/116046377539155485
- AceGRPO: Adaptive Curriculum Enhanced Group Relative Policy Optimization for Autonomous Machine L...
Yuzhu Cai, Zexi Liu, Xinyu Zhu, Cheng Wang, Siheng Chen
https://arxiv.org/abs/2602.07906 https://mastoxiv.page/@arXiv_csLG_bot/116046423413650658
- VESPO: Variational Sequence-Level Soft Policy Optimization for Stable Off-Policy LLM Training
Guobin Shen, Chenxiao Zhao, Xiang Cheng, Lei Huang, Xing Yu
https://arxiv.org/abs/2602.10693 https://mastoxiv.page/@arXiv_csLG_bot/116057229834947730
- KBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models
Zukang Xu, Zhixiong Zhao, Xing Hu, Zhixuan Chen, Dawei Yang
https://arxiv.org/abs/2602.11184 https://mastoxiv.page/@arXiv_csLG_bot/116062537528208461
- MUSE: Multi-Tenant Model Serving With Seamless Model Updates
Correia, Ferreira, Martins, Bento, Guerreiro, Pereira, Gomes, Bono, Ferreira, Bizarro
https://arxiv.org/abs/2602.11776 https://mastoxiv.page/@arXiv_csLG_bot/116062952355379801
- Pawsterior: Variational Flow Matching for Structured Simulation-Based Inference
Jorge Carrasco-Pollo, Floor Eijkelboom, Jan-Willem van de Meent
https://arxiv.org/abs/2602.13813 https://mastoxiv.page/@arXiv_csLG_bot/116085828112928218
- Silent Inconsistency in Data-Parallel Full Fine-Tuning: Diagnosing Worker-Level Optimization Misa...
Hong Li, Zhen Zhou, Honggang Zhang, Yuping Luo, Xinyue Wang, Han Gong, Zhiyuan Liu
https://arxiv.org/abs/2602.14462 https://mastoxiv.page/@arXiv_csLG_bot/116085997857526328
- Divine Benevolence is an $x^2$: GLUs scale asymptotically faster than MLPs
Alejandro Francisco Queiruga
https://arxiv.org/abs/2602.14495 https://mastoxiv.page/@arXiv_csLG_bot/116086011618741857
- \"UberWeb: Insights from Multilingual Curation for a 20-Trillion-Token Dataset
DatologyAI, et al.
https://arxiv.org/abs/2602.15210 https://mastoxiv.page/@arXiv_csLG_bot/116090912256712568
- GLM-5: from Vibe Coding to Agentic Engineering
GLM-5-Team, et al.
https://arxiv.org/abs/2602.15763 https://mastoxiv.page/@arXiv_csLG_bot/116091080686771018
- Anatomy of Capability Emergence: Scale-Invariant Representation Collapse and Top-Down Reorganizat...
Jayadev Billa
https://arxiv.org/abs/2602.15997 https://mastoxiv.page/@arXiv_csLG_bot/116096541546306333
- AI-CARE: Carbon-Aware Reporting Evaluation Metric for AI Models
KC Santosh, Srikanth Baride, Rodrigue Rizk
https://arxiv.org/abs/2602.16042 https://mastoxiv.page/@arXiv_csLG_bot/116096581524696028
- Beyond Message Passing: A Symbolic Alternative for Expressive and Interpretable Graph Learning
Chuqin Geng, Li Zhang, Haolin Ye, Ziyu Zhao, Yuhe Jiang, Tara Saba, Xinyu Wang, Xujie Si
https://arxiv.org/abs/2602.16947 https://mastoxiv.page/@arXiv_csLG_bot/116102426238903124
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Docs: xAI had a net loss of $1.46B in Q3, up from $1B in Q1; sources: xAI told investors it plans to build AI that will eventually power Optimus humanoid robots (Carmen Arroyo/Bloomberg)
https://www.bloomberg.com/news/articles/20

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/3]:
- SMaRT: Online Reusable Resource Assignment and an Application to Mediation in the Kenyan Judiciary
Farabi, Pinto, Lu, Ramos-Maqueda, Das, Deeb, Sautmann
https://arxiv.org/abs/2602.18431 https://mastoxiv.page/@arXiv_csCY_bot/116119352329590193
- Benchmarking Distilled Language Models: Performance and Efficiency in Resource-Constrained Settings
Sachin Gopal Wani, Eric Page, Ajay Dholakia, David Ellison
https://arxiv.org/abs/2602.20164 https://mastoxiv.page/@arXiv_csCL_bot/116130101399805837
- VISION-ICE: Video-based Interpretation and Spatial Identification of Arrhythmia Origins via Neura...
Dorsa EPMoghaddam, Feng Gao, Drew Bernard, Kavya Sinha, Mehdi Razavi, Behnaam Aazhang
https://arxiv.org/abs/2602.20165 https://mastoxiv.page/@arXiv_csCV_bot/116130222034322594
- Benchmarking Early Deterioration Prediction Across Hospital-Rich and MCI-Like Emergency Triage Un...
KMA Solaiman, Joshua Sebastian, Karma Tobden
https://arxiv.org/abs/2602.20168 https://mastoxiv.page/@arXiv_csCY_bot/116130239074411770
- Cross-Chirality Generalization by Axial Vectors for Hetero-Chiral Protein-Peptide Interaction Design
Yang, Tian, Jia, Zhang, Zheng, Wang, Su, He, Liu, Lan
https://arxiv.org/abs/2602.20176 https://mastoxiv.page/@arXiv_qbioBM_bot/116130281674122586
- Enhancing Heat Sink Efficiency in MOSFETs using Physics Informed Neural Networks: A Systematic St...
Aniruddha Bora, Isabel K. Alvarez, Julie Chalfant, Chryssostomos Chryssostomidis
https://arxiv.org/abs/2602.20177 https://mastoxiv.page/@arXiv_csNE_bot/116130397676559696
- Data-Driven Deep MIMO Detection:Network Architectures and Generalization Analysis
Yongwei Yi, Xinping Yi, Wenjin Wang, Xiao Li, Shi Jin
https://arxiv.org/abs/2602.20178 https://mastoxiv.page/@arXiv_eessSP_bot/116130257424413457
- OrgFlow: Generative Modeling of Organic Crystal Structures from Molecular Graphs
Mohammadmahdi Vahediahmar, Matthew A. McDonald, Feng Liu
https://arxiv.org/abs/2602.20195 https://mastoxiv.page/@arXiv_condmatmtrlsci_bot/116130271189617558
- KEMP-PIP: A Feature-Fusion Based Approach for Pro-inflammatory Peptide Prediction
Soumik Deb Niloy, Md. Fahmid-Ul-Alam Juboraj, Swakkhar Shatabda
https://arxiv.org/abs/2602.20198 https://mastoxiv.page/@arXiv_qbioQM_bot/116130341315320687
- Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control
Shaorong Chen, Jingbo Zhou, Jun Xia
https://arxiv.org/abs/2602.20209 https://mastoxiv.page/@arXiv_qbioQM_bot/116130374083646541
- The Sim-to-Real Gap in MRS Quantification: A Systematic Deep Learning Validation for GABA
Zien Ma, S. M. Shermer, Oktay Karaku\c{s}, Frank C. Langbein
https://arxiv.org/abs/2602.20289 https://mastoxiv.page/@arXiv_eessSP_bot/116130267228834775
- Gap-Dependent Bounds for Nearly Minimax Optimal Reinforcement Learning with Linear Function Appro...
Haochen Zhang, Zhong Zheng, Lingzhou Xue
https://arxiv.org/abs/2602.20297 https://mastoxiv.page/@arXiv_statML_bot/116130255458256497
- Multilevel Determinants of Overweight and Obesity Among U.S. Children Aged 10-17: Comparative Eva...
Joyanta Jyoti Mondal
https://arxiv.org/abs/2602.20303 https://mastoxiv.page/@arXiv_csAI_bot/116130097466859145
- An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes ...
Shyr, Hu, Tinker, Cassini, Byram, Hamid, Fabbri, Wright, Peterson, Bastarache, Xu
https://arxiv.org/abs/2602.20324 https://mastoxiv.page/@arXiv_csAI_bot/116130100089848459
- Circuit Tracing in Vision-Language Models: Understanding the Internal Mechanisms of Multimodal Th...
Jingcheng Yang, Tianhu Xiong, Shengyi Qian, Klara Nahrstedt, Mingyuan Wu
https://arxiv.org/abs/2602.20330 https://mastoxiv.page/@arXiv_csCV_bot/116130463214879334
- No One Size Fits All: QueryBandits for Hallucination Mitigation
Nicole Cho, William Watson, Alec Koppel, Sumitra Ganesh, Manuela Veloso
https://arxiv.org/abs/2602.20332 https://mastoxiv.page/@arXiv_csCL_bot/116130370809116915
- Learning During Detection: Continual Learning for Neural OFDM Receivers via DMRS
Mohanad Obeed, Ming Jian
https://arxiv.org/abs/2602.20361 https://mastoxiv.page/@arXiv_csIT_bot/116130289537785136
- Detecting and Mitigating Group Bias in Heterogeneous Treatment Effects
Joel Persson, Jurri\"en Bakker, Dennis Bohle, Stefan Feuerriegel, Florian von Wangenheim
https://arxiv.org/abs/2602.20383 https://mastoxiv.page/@arXiv_statME_bot/116130509065601748
- Selecting Optimal Variable Order in Autoregressive Ising Models
Shiba Biswal, Marc Vuffray, Andrey Y. Lokhov
https://arxiv.org/abs/2602.20394 https://mastoxiv.page/@arXiv_statML_bot/116130299369541741
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Tesla will end production of its two flagship models, the Model S and Model X, which have long carried the company's prestige.
According to statements by Tesla CEO Elon Musk, the fundamental reason behind this decision is the company's desire to shift its resources and production infrastructure toward autonomous driving and robotics.
Planned to take effect from the next quarter, this production halt has generated significant reverberations throughout the automotive indus…

Tesla Halts Model S and Model X Production, Shifts Focus to Autonomous Future
Tesla has decided to discontinue production of its flagship Model S and Model X vehicles. The company will convert production lines at its Fremont factory to focus on the humanoid robot Optimus and autonomous vehicle technologies. This move is considered a significant milestone in Tesla's long-term strategic transformation.

Fuz-RL: A Fuzzy-Guided Robust Framework for Safe Reinforcement Learning under Uncertainty
Xu Wan, Chao Yang, Cheng Yang, Jie Song, Mingyang Sun
https://arxiv.org/abs/2602.20729 https://arxiv.org/pdf/2602.20729 https://arxiv.org/html/2602.20729
arXiv:2602.20729v1 Announce Type: new
Abstract: Safe Reinforcement Learning (RL) is crucial for achieving high performance while ensuring safety in real-world applications. However, the complex interplay of multiple uncertainty sources in real environments poses significant challenges for interpretable risk assessment and robust decision-making. To address these challenges, we propose Fuz-RL, a fuzzy measure-guided robust framework for safe RL. Specifically, our framework develops a novel fuzzy Bellman operator for estimating robust value functions using Choquet integrals. Theoretically, we prove that solving the Fuz-RL problem (in Constrained Markov Decision Process (CMDP) form) is equivalent to solving distributionally robust safe RL problems (in robust CMDP form), effectively avoiding min-max optimization. Empirical analyses on safe-control-gym and safety-gymnasium scenarios demonstrate that Fuz-RL effectively integrates with existing safe RL baselines in a model-free manner, significantly improving both safety and control performance under various types of uncertainties in observation, action, and dynamics.
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